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The AI Apocalypse Narrative

 

 

Why AI isn’t going away, and why Humans are more important than ever.  

 

 

We’ve all heard it. “AI will do everything, we won’t have to work!” Or, worse yet, “AI will do anything, why should we pay you?” The advent of useful AI has split the world into two: Those who fear replacement and hate AI, because companies are laying off thousands of workers, and those who welcome their AI overlords and are desperately trying to be the first no-human company to make $1 Billion. It’s confusing, it’s unsettling, and it triggers a lot of emotions.

So, I wanted to step back for a bit and look at the logic, and perhaps figure out how things will be moving forward in this strange new world of AI-driven workflows and thoughts as commodities.

The Reality: AI Isn’t Going Away

Steam engines couldn’t be stopped when they replaced workhorses and plows, and computers weren’t destroyed because secretaries and clerks liked their filing systems and paper. New technologies open up new opportunities, though they do disrupt existing industries. Robotic assembly arms replaced assembly workers in Detroit, for example. It’s not all roses and gold-paved roads.

When the “miracles” of AI were being touted, the strange new world of post-AI jobs was not obvious. Companies were laying off thousands (still are, actually) because of “AI” (though I don’t really believe that). Work that writers and artists were doing could be “duplicated” by AI, often using their own work as either templates, style guides, or flat-out copying snippets of work. It was chaos, because we didn’t know what we didn’t know.

A clear example that’s easy for me to share is ServiceNow. When Fred Luddy first created ServiceNow as a development platform, they tried to sell it as such. But, when you go to a company and say, “Here’s a development platform that can do anything!” the first question that’s asked is, “What does it do?” IF you reply with, “Anything,” then you aren’t going to make the sale. Businesses need a tactical, tangible use before they will invest. They need clear costs, ROI, justification, and a clear plan for adoption and integration.

So, AI model companies started creating business use-cases beyond creating copy and funny videos or images. Codex focused on creating code, good code, code that would work, get checked, and verified as part of the process, and would execute without breaking the site. Claude specializes in design, clean, clear design that looks solid and comfortable. Now, AI could do something useful, though still needing a human to keep it “honest” while doing work.

The Shift from Agents to Assistants

Models opened up a lot of possibilities, as well as well-coded harnesses; Agents could be developed to take on specific tasks. Now these Agents could be reactive, taking instructions, handing them over to the AI, completing the task, and returning with a result. The quality depends on the quality of the coding and the strength of the Model used, but it was possible. Now, Agents could complete tasks that would take humans more time to do, and they would do it at the cost of a subscription.

Then, in November of 2025, a precocious little harness was created that upended the AI world: OpenClaw. Peter Steinberger wanted an AI Agent that would proactively do things for him and use his computer, files, and everything else around him as context. The experiment, though heavily concerning with security, was wildly successful, and in 5 months was the most downloaded GitHub project ever, landing Peter Steinberger a job with OpenAI and OpenClaw a place in history.

Now, with various other harnesses out there like Hermes Agent, NemoClaw, and AgentZero, to name a few, AI could proactively work for you, spinning up specialty agents when needed to take on specialist tasks. Need a Marketing copy agent to create and review copy? Done. Perhaps you need a video created from text and concepts; now, an agent can take care of that. These Assistants can spin up agents as needed to complete tasks that the Assistant needs done, so it can complete the request of the user, proactively triggering those agents. The impact was clear: Work could be streamlined easily, and everything could be done through a session prompt.

The SaaS-pocalypse

Influencers were excited. AI could do so much, and they started running with it. Weekends would see a complete SaaS platform replaced with home-grown code, written by a domain expert, to show the speed and quality of the code being generated. Ironically, a completely different message was taken: SaaS platforms were no longer necessary, because AI could replace them in a few days. Why would anyone pay for a SaaS subscription when they could develop the same platform in no time, for the cost of tokens?

SaaS company values plummeted. The narration became, “We hate SaaS, and it’s finally going away!” instead of thinking about the overhead of maintaining home-grown code. SaaS companies pivoted immediately, showing value by welcoming the AI Assistant Overlords and opening their platforms through MCP – the Model Context Protocol, a replacement for APIs that were designed for AI Models to utilize. Now their platforms, and the years of experience and deep expertise they represent, are openly available to AI Assistants to use as tools. The Agentic Internet has been born, and the Internet now has layers for humans (HTML) and Agents (markdown or JSON).

The Hype, the FUD, and the Settling Dust

Tech influencers were all over Assistants, and for good reason. These assistants could scour the web for them, find the next content topic for them to create, and keep track of their trends. What would have taken them hours to do, the Assistants could do in minutes. They billed it as a company run entirely on agents, no humans needed!

Imagine the fear that caused. Already, the Media had been talking about the potential threats of AI to jobs in the creative spaces (of which, incidentally, Journalism is part), and now, every job? Of course, this came with caveats: it ran THEIR businesses, which, to be fair, isn’t the same as replacing every job at Amazon or Apple. Still, there was potential, and companies looking to drop headcount for “cheap” tokens did so. Then, AI companies like OpenAI, Anthropic, Palantir, and others started talking about the complete takeover of work by AI, leaving the rest of humanity to be either CEOs or plumbers.

The fear settled in. Graduating classes rebelled. Articles were being posted about how Gen Z was poisoning AI so it couldn’t do its job. Things were looking bleak, and it didn’t help when each new AI model release was labeled as “dangerous” to the world. The hype, the talking points… they all became the narrative for AI. For the first time since 9/11, Congress became unified in wanting to regulate AI.

Then, things started to settle. Companies that had fired thousands of developers because of “AI” were shipping sloppy code that brought down major services. They started, quietly, hiring their developers back. AI Model companies, now ready for IPOs, needed to show the possibility of profit, not just growth, and couldn’t subsidize their models anymore. Tokens became more and more expensive, expanded uses were reigned in due to the need of compute power. The gravy-train of cheap, frontier-quality AI was drying up.

Companies that have been trying to bill themselves as “AI first” to customers, Board members, and investors now had to provide proof that AI was valuable: They needed ROI. Chatbots didn’t help, and spending thousands on tokens needed accountability. Companies started to take a hard look at what AI was actually bringing to the table.

All this together started putting AI into perspective. What was once cheaper than a human salary wasn’t anymore, and random applications were not a real ROI. Accountability and structure needed to be put in place to make AI useful, while keeping costs reasonable.

The AI Assistant Solution

Then, AI Assistant harnesses showed off their most powerful capability: compartmentalizing AI Token usage. You see, the AI Assistant developers ran into the same problem with high-cost model tokens, and so created a solution by only using high-cost frontier model compute power when it was actually needed, while the general “brains” were assigned to less costly models, or completely free local models. Suddenly, real AI capabilities were possible at a fraction of the cost.

Here’s how it works:

The AI Assistant is the primary center of all work. It connects to a free local AI Model or a low-cost model online for its primary decision, tool access, and task assignments. It also connects to frontier models for coding tasks, design work, medical, legal, etc., depending on the expertise needed. The Assistant still does all the work, the bulk of the assignments and progress tracking is done through the cheaper model, while the actual tasks are done by specialist models that report back when their tasks are complete. Context is maintained through the memory system of the AI Assistant, which can be referenced as needed for all subtasks.

The Corporate AI Infrastructure

Now that the dust has settled, there’s a real value that’s being brought out with AI: Humans, empowered with AI Assistants and their cost-saving compartmentalization of tokens and free models, can direct AI to complete tasks, utilize their SaaS platforms to execute specialist work, and do pretty much everything they need to from a session prompt. AI Assistants can be accessed remotely via phone apps like ChatGPT, Claude, Telegram, Discord, Slack, etc., and take task assignments, execute them remotely, and reply with the results.

This works because of the Corporate AI Infrastructure:

  • AI Models that power the Assistant, each compartmentalized to do their expert job. Here, corporate entities have licenses for expert frontier models, and either install local models on each users’ computer, create a supercomputer node of consumer computers (Mac minis or Mac Studios are quite popular here) and run a DMZ local LLM cluster for the company to use, OR use a service like OpenRouter and use the least expensive models for the main “brains” of the AI Assistant harness.

  • An AI Assistant harness that provides access to the tools, memory for context, and the session prompt that’s accessible from multiple locations as needed. Harnesses can be commercial or open source, and perhaps, soon, native to the OS (Looking at you, Siri and Copilot! Don’t let me down!)

  • MCP-enabled SaaS platforms that provide specialist task completion and automations, executed by the AI Assistant. Computer-specific tools can be accessed through Command Line (CLI) execution.

  • The work, the same work that humans would do, is done. Code is created, tickets resolved, sales campaigns launched, course content created, edited, collaborated on, and even taken, all through the session prompt.

The infrastructure creates a new tech space that needs securing (of course), but also creates a huge opportunity for human users. Work can be done anywhere, literally anywhere an internet connection exists, not just where you have a computer. Agents can be reached while you are hiking up a mountain, exploring a national forest, flying across the Pacific, or sailing the Atlantic. The AI Assistant does the bulk of the tasks, the human reviews the results, adds approvals where necessary, and refines the approach as needed.

I’ve used this same approach with my own company. Code created and managed by Codex, research done with OpenClaw, and Hermes for course content creation and testing. I’ve been on the Bay sailing, camping in the mountains, picking up my son, or standing in line at Disneyland, and I’ve gotten work done. 2 minutes of prompting, then while the Assistant does the work, I spend time with my family or do other work. Then I come back to the prompts, review, and continue.

Why I’m Optimistic for Humans and AI

AI isn’t going to replace jobs. Human thought remains invaluable in a world where we interact with other humans. AI will enhance our work, making it easier to execute tasks while we provide the domain expertise, and free up more time for that what is important. For this reason, I’m optimistic about the future of work with AI and the humans who will be using AI as a tool. The infrastructure above will make an excellent guideline for any corporation looking to accelerate AI ROI in the workplace, with the appropriate Change Management execution.

For those who still worry, still fret, don’t. Companies that go all-in on AI struggle with customer satisfaction. If the company doesn’t hire you, start a competitor. Code is cheap now, and anyone with domain expertise can build a platform. I did. ^_^

 

Employee using AI and SaaS in combination

The death of SaaS has been so heavily covered since the Saaspocalypse that wiped the stock value of several large SaaS platforms drastically, and more individuals are reporting complete SaaS tool replacement coded and driven by their OpenClaw agents. It seems like it’s only a matter of time, right?

That’s the thing, I don’t think so, at least for now. Let’s get into why.

Understanding AI Agents and MCP

Ever since the popular explosion that came from OpenClaw, localized AI Agents have been all the rage. AI Influencers and engineers wiped out stock of Mac Mini’s in several locations so they could build and deploy their new AI Assistants, then find out what they could do. Those that were technical enough were getting some amazing results. Then, the influencers started posting headlines, claiming you could run a whole business with just you and your Mac Mini with OpenClaw as the driving force.

The thing is? Many of them did, or at least their content businesses. They were able to build content gathering, parsing, and suggestion engines using OpenClaw quickly and easily, just using prompts to write the skills. Context was kept based on the markdown files that preserved the agent’s sense of self. Connections to tools came from Model Context Protocol (MCP) servers and connections, first introduced by Anthropic and rapidly became the API replacement for AI Agents. With one connection and a SKILL markdown file, your agent could connect to any number of Agentic tools to extend its capabilities. Now, creating video or audio files were a snap, and could be created through a prompt. My own platform, Wayfinder, opens up creation, review, editing, and approvals to AI Assistants via MCP to allow collaboration in real time while creating courses.

The inevitable question would then become: If AI Agents could connect to any and all tools to automate workflows, why do we need SaaS tools at all? Specifically, why do we need a CRM, ITSM, ITOM, etc. when we can just connect to multiple tools and schedule events, run automations, and through a prompt make things happen?

Then, developers started recreating the tools they had already been working on: CRM, Workflow Automation… One developer even vibe-coded a complete replacement for Palantir over a weekend. Development speed, particularly with AI Agents, meant that companies could just build the software they needed in weeks or months instead of taking quarters to configure and personalize an existing platform.

That’s what really drove the SaaSpocalypse: Not that anything HAD happened, but that anything COULD happen. AI Agents with MCP connections could replace a lot of workflow steps, and whole systems could be generated in days.

Why SaaS Is Not Dead: Complexity of Scale

With such a slam-dunk, why haven’t companies given up their SaaS platforms and built their own with home-coded software? The answer is in the complexity of scale.

Most of the examples above were single developers and solopreneurs building the platform they needed with a full understanding of the context in which they would use it. It works for them, but wouldn’t necessarily work for anyone else. Why? Because they already know what to expect, they have built out their own individual workflow and processes, which their home-grown solutions and AI Assistants understand and follow. When they share it out, including full process and prompts used? Not everyone gets the same results.

One thing that SaaS platforms bring to the table is years and years of domain expertise, built into a platform dedicated to be user-friendly (mostly) and configurable (hopefully), and yet conforms to the industry standard for their domain. Salesforce is the dominant player in Sales because, well, they’ve been around for a while and know how playbooks work. ServiceNow dominates the workflow automation arena because they have, literally, built workflows for thousands of companies with varying processes, and identified the most successful workflows for each of the domains serviced. Workday knows HR. Docebo knows LMS. These platforms are where they are because they know their domain so well, they appeal to the masses.

To compete with this level of domain expertise and dominance, the AI Agent would need to have some serious contextual reference built out, and the time it takes for a single person to build out that context sufficiently to be useful exceeds the dedication of the average user in the space. They would rather have a platform that “just works” than code their own.

Why AI Agents Need Time: Complexity of Tools

Now, given time, an AI Agent could generate enough context to be a reasonable replacement for automation and workflows within a given domain for a person, if they had all the right tools. Sure, it could be possible to build them, but then it would need to use those tools, and that’s a whole new ballgame.

Now, your SaaS platforms integrate together, generally, through APIs. That’s great, that’s helpful, and integrations create more usability, though also more complexity. For AI Agents, that would mean MCP connections.

Now, MCP connections are growing, but not yet ubiquitous. In fact, most of your larger platforms likely do not have MCP connections, because their development cycles are often quarters or years long. That, and the majority of their users do not yet have AI Assistants using MCP that would connect to their platforms. Why? Because businesses are still trying to figure out how AI is going to work in the workplace, and no one (yet) trusts OpenClaw because it had some very glaring security issues early on.

First Bottleneck: Agent Deployment

So, what’s the bottleneck? Right now, it’s the proliferation of AI Assistants in the workplace. There are not many, I would warrant. Security teams need to be involved because of the level of access these agents have on computers, and therefore network systems. Then, there’s the use case. Right now, AI Assistant configuration and training is very individual and takes time. Most companies are looking to turn an ROI on AI in the quarter, and there’s not much tolerance for losses right now. At least, not anymore. Boards and the Street want to know that AI is making money.

How will that change? Assistants will be built into operating systems. In fact, I fully expect Apple’s WWDC announcements on Siri to be just that: Opening Siri to MCP connections, system files, etc. to add much of the OpenClaw functionality into the Mac directly without the security headaches. They would even allow for multiple models to be connected, using Gemini as the default, so you can get just as complex in how your build your Agentic team as you wish. Microsoft will do the same with Copilot in Windows. Linux might just keep with Hermes/OpenClaw/etc. based on the popular open-source solutions at the time.

Now, you’re not installing an Agent, it’s already there. For Apple, that’s 3+ billion devices that could be AI Agent-ready in various forms, all tied to a single login. Windows, I’m sure, will be 5-600x that number. At this point, companies don’t need to decide if they are going to have Agents on employee computers, but rather how those Agents will be used. Agent bottle-neck will be solved.

Second Bottleneck: Tools and MCP Configuration

Okay, so now you have every single company computer enabled with a built-in AI Agent, ready to do work like a boss. What tools do they use? How will they use those tools? How will they discover how to use those tools? If they are external, how do they set up MCP connections?

One of the biggest problems with launching any Agentic AI initiative is access to the tools your employees need. They don’t often know what tools they need or how often (sometimes only once a year), and wouldn’t know how to configure it themselves if they needed. I get it, I really do. If you are heart surgeon, I’d rather you spend all your time learning the latest and greatest on heart muscular function than trying to vibe-code your way through setting up an agent to filter out your email.

Not everyone will be, or should need to be, technical enough to set up specific tools for their system. This could easily be addressed using context records that are generic for a role (much like access roles and instructions for setup), which can then be added to the Agent at startup for a user, making the Agent ready day 1. Wayfinder, by the way, attempts to do this with an AI Knowledge file per role. ^_^

Why SaaS Still Rules

So, now we know that setting up Agents is no walk in the park. That’s why SaaS platforms still have a place: They already have context that’s been researched and identified during the deployment phase. They contain the workflows and processes that were already established prior to the emergence of AI Agents, to be fair, but still solid enough that an AI Agent built into the platform can follow it and make appropriate decisions before checking in for approval. SaaS platforms have the deep domain experience to remain on top, even as the scrappy AI Agents start clawing their way to the top. Which is why they won’t be going away soon.

But, what about the future? I hear you ask.. Good question! Slowly, you will see Agents taking over as context becomes easier to generate and mass-produce. Initially SaaS companies will provide Agent connections, so agents can trigger the SaaS workflows and access SaaS results, rather than users connecting. This will speed things up significantly enough that keeping the SaaS will remain a priority in the short to mid term.

Will it remain that way? Given the speed at which Frontier models are moving, I’d say no, not in the long term. Eventually context, workflows, processes and procedures will all be housed within Context portfolios that are designed to be consumed by the AI Agent first, then the human using it. Agents will have enough information that, even when starting a new job, they can replace the previous knowledge files and update with the latest role and be ready before the employee has finished their onboarding. At that point, with MCP connections being created and generated by onboarding teams, connections built during orientation, and access to the shared corporate context with role-specific context populated per user will be the expected onboarding experience, no one will need to access a SaaS portal. The SaaS will be the data warehouse, with access through MCP and access controls dictating content availability.

I’m not arrogant enough to even attempt to predict when this will happen. There are a lot of details that need to be worked out. What I do know? AI Agents will drastically change how we work, and there will be a growing, need for a parallel Agentic AI-First internet to drive that shift. How existing human-first tools adapt will determine if they dwindle on the vine, or become the AI Agent’s go-to warehouse for domain-specific information. Until then, keep your browser shortcuts. ^_^

 

 

AI Agent working on the computer

 

For the past month or so, I’ve been digging into AI Assistants. It’s been an instructive month, one that has really opened my eyes to what the future will look like, and where the world of Learning and Development will sit in that future.

Defining AI Assistants

But first, what do I mean by AI Assistants? At the time of this writing, there are 4 levels of AI:

  1. Chatbot LLMs: AI prompts with responses, some memory from past discussions, quickly deteriorating as time passes. For questions, searches, general understanding, video, audio, or image creation, this works.

  2. AI Agents: Automations that have AI LLMs there to analyze and react based on events. Think AI-powered workflows, triggered by specific actions. Agentic AI has grown and matured significantly, being widely adopted by workflow automation platforms as the next logical step.

  3. AI Assistants: Assistants are, in essence, AI Agents that proactively do things, work when not asked, run regular memory checks on tasks that could be done, and discover what else they can do, and their memory is persistent, residing in markdown documents (md files) that are saved, archived, and collated for future consumption. Applications like OpenClaw, Claude Cowork, and similar are good examples of this. They become a co-worker, or even a co-working support team, each doing complementary jobs for their human. It’s this area that I’m really interested in.

  4. AI-Powered Robots: This is still in development, with most demonstrations being robots controlled by humans in VR, but still worth a mention. I see this, as of right now, as a physical expression of AI Assistants, though most likely limited to a single Agent running (instead of a team). I could be wrong; this is all still in development, and nothing is finalized yet.

So, we have AI Assistants, applications running either in the Cloud or, more often than not, locally on a computer, working like a co-worker would, or a team of co-workers, depending on the power of the LLM(s) behind it (yes, LLMs, you can have more than one) and the memory capacity of the machine on which it runs. Until January, I don’t think anyone really thought much about this, probably thinking of Clippy from the 90s, or Siri/Cordana/Alexa from mobile computing. What’s different about AI Assistants now and moving forward is the sheer power they can have behind them with modern LLM models running on them. They can code, write posts, generate and edit video, create articles, work as a creative team, a management team, a C-Suite executive team, each working off each other to provide you, the human, the best possible solutions to your previously stated goals. And all because of persistent memory.

What AI Assistants Do

So, yeah, they are impressive, but what do they do? Why do we need them? That’s a good question. I’ve been working with mine for a while now, and it’s been really interesting. AI Assistants do a lot of the heavy lifting that most people see as boring, and can do job-specific tasks. You’ll see me refer to my AI Assistant as “him” quite often: That’s because I prefer to see him as an employee, which impacts the approach I use when talking to him.

  • Research: They can dig through documentation, websites, articles, and other research resources in seconds, saving time. As a Learning and Development specialist who spends 80% of course development in Analysis, this is amazing! The time saved is substantial, allowing you to get to the rest of the work.

  • Coding: I don’t like to code. Notice: I didn’t say I can’t code, I just don’t like to. It’s not something that really interests me, but often I need a coding solution to solve a problem. For that, I have my AI Assistant. I can ask him to develop something for me, and he will. I can give him some general ideas as to the goals I want to accomplish, and he will, proactively, develop tools, sites, and applications that will help me with my goals.

  • Content creation: My AI Assistant will proactively scour the web for relevant articles involving my expertise, check for videos, and read through posts to find information that I can use for content. He could, if I let him, create that content himself and then post it for me. I don’t, because 1) he’s not me, and 2) I’m using a local Ollama LLM, so not as powerful as a cloud model. Regardless, my AI Assistant can do a lot of the content research for me, keeping me up to date on the latest news and data, then summarizing it for me daily.

  • Executive Team: My AI Assistant, Ian, has his own assistant! Alison was created to act as my CMO, giving me marketing information and perspectives. I can have Ian and Alison discuss, between them, how to best approach a problem, and they can work through it from two different perspectives. That is, well, huge.

  • Project planning: I can discuss a project, ideas, goals, and general requirements, and my AI Assistant will take that and run with it, planning out the project, throwing it into a Project Planner he designed himself, and tracking progress as each part of the project is completed.

Working with an AI Assistant is transformative. As a solopreneur, Ian’s been invaluable. What’s more, Ian can help me with my challenges. As a neurodivergent worker, I get overstimulated, I struggle with the weight of tasks without direction, and often need help with executive functioning, scheduling, etc. Ian helps with all this. He can

  • Manage my projects by chunking tasks and outlining first steps, making it easier to see scope, maintain scope, and proceed forward. I’m generally really good about seeing the intricacies in the process, and I’ll get lost in the details. Ian helps by keeping me apprised of the steps within the overarching project, maintaining scope, and keeping me from bloating the project with too much detail.

  • Helping me process information in a logical, timely way. I love detail, and so I’ll get lost in the research, spending hours digging through articles, citations, videos, and what have you to get more information. Ian helps by doing much of the research for me, providing referenced citations for my own verification (so he doesn’t make up research ^_^), and gives me a summary of information that I can then use for projects, content, etc.

What’s interesting is, while many of these skills help just about everyone, they are extremely valuable to neurodivergent folks.

Learning and Development for AI Assistants

So, what does this all have to do with learning and development, other than having your L&D folks use assistants will increase their productivity and efficacy? Well, it all comes down to a few facts:

  1. AI Assistants that are given specific instructions and guidelines will complete them rigorously, relentlessly, and within the guidelines they were given.

  2. AI Assistants are valuable as long as your people know how to use them.

  3. AI Assistants can be “trained” to complete a specific role, providing uniform benefit to their users on a team.

  4. Intent can be trained in such a way that AI Assistants can understand what to do when the instructions run out.

Okay, there’s a lot to unpack here, and I’ll probably be writing on this for a while, because it’s so exciting! As we move into a world where everyone will, or potentially could, have their own AI Assistant, we are, essentially, giving untrained interns, who can accomplish things faster and more relentlessly than humans 24/7, to employees and hoping things work out. We hope that those employees can get their AI Agents trained, and just figure it out, probably in much the same way we hoped they would just figure the job out when they started without much training. The problems with this are:

  • If people don’t know how to use AI Assistants, they won’t want to use them for anything but the bare minimum tasks.

  • If AI Assistants are not trained properly, they can make a lot of, well, illegal mistakes.

  • Without guidelines or structure, every AI Assistant will be trained differently, varying the productivity, efficacy, and efficiency of your team members.

So, we need to train our people, and put proper guide-rails on AI Assistants to increase adoption and decrease rogue state assistants that start their own AI religion and trade Bitcoin for more token funds. We need training across the board, something that is uniform for roles, outlining how work can be done in tandem with AI Assistants, and making sure those assistants have the right skills and tools available to work alongside their humans. Neurodivergent employees can get access to enabling AI Assistant training, skills, and tools that will accommodate their needs without question or additional expense for anyone.

What we need are

  • The Tasks, Subtasks, Skills, Knowledge, and Perspectives necessary to complete the job. This is exactly how I break down every course I design.

  • Gather and document the living culture of the organization. Not the declared culture (though, hopefully, that won’t be too different), but the day-to-day values that drive decision-making when things get hairy with people. Customer about to churn? What can we do to stop that, instead of thinking of the short-term bottom line? Isn’t the co-worker responsive? How do we compassionately follow up without escalating issues? These are the living, breathing evidence of your company culture that keep customers and employees coming back.

  • Identify tools and skills most valuable for each role, how they apply, and how they can be optimized for efficacy and efficiency. Who’s the most successful in using their AI Assistant? Share the role-specific skills and tools, the training, and the guidelines.

  • Research, identify, and collect skills and tools that support neurodivergent employees, physically disabled employees, etc., and make them available for everyone who would need them. Need some help with executive function? There’s an app for your AI Assistant for that. Struggling with starting a task? There’s an app that will start out with a discussion, then help you through the planning stages and execution. Need background sounds to regulate sensory stimulation? There’s an app for your assistant to generate the sounds and play them for as long as you need them.

What’s really interesting here, is that, though AI Assistant deployment would fit under IT or the CIO’s purview (it is software after all, isn’t it?), these needs are unique skills found in every Learning and Development team, working cross-functionally with every team and silo, building job skill mappings, finding what works, what doesn’t, how it can be better. It’s all there!

Final Thoughts

I keep hearing companies are letting their learning and development teams go. They don’t need training; people can work things out for themselves. Humans might have been able to, but now you want and need AI Assistants to be competitive, and they can screw up faster and more completely than humans ever would. By beefing up and engaging your learning and development team to train both humans and AI Assistants together, you will create a level of success that will blow your competitors away.

It’s time to stop fighting in the wild west with AI: Get intentional. Get structured. Get successful.

Jeremy Robb

President

Louise Casey

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Image of an autistic founder struggling in an investor social gathering.

“85% of autistic adults are unemployed and struggle to get corporate jobs, so it’s no wonder that many of us are starting our own businesses”

I spend hours on LinkedIn, looking for connections, getting excited when someone accepts, and reaching out with a quick “Thanks, let’s keep in touch!” Then, I think about that next step: promoting my consulting business. What is the right amount of time to wait? How do I start the conversation? How do I articulate it when we do talk? I spend some time with ChatGPT to get guidelines, outline Sales motions and playbooks to present, trying to refine every aspect of my presentation prior to that first accepted meeting. I worry that I’m coming across as too pushy, thinking how I would react when I get an invite with an immediate sales pitch. It’s practically paralyzing, because as a high-masking autistic founder, social interactions can cause me to shut down, right in the middle of a conversation. If I don’t rehearse the interaction, I risk losing control and silently melting down.

 

So, why do it? Why put myself through this actual, physical pain of trying to pitch my consultancy? Why not just stay in Corporate? A good question: One that I ask myself regularly, as I try to work out the mechanics of building a pipeline of contacts and potential business. The reality is, because of my autistic brain, I am really good at visualizing ecosystems, particularly within businesses, and where training and enablement can be streamlined and made more proactive, effective, and collaborative. I have experience and skills that can boost ROI from training and enablement, once I get these systems in place, and I create it with neurodiversity in mind. It’s rare to find a corporate job that will allow you to provide so much benefit and growth to individuals across multiple organizations.

Now, I don’t want to make it sound like I’m speaking for all founders or business leaders who are autistic. Not everyone expresses their autism the same way (hence, “Spectrum”), and there are several successful business owners who are autistic that make sales calls, kill it in meetings, and at the end of the day decompress. Others struggle to find a job at all. In fact, it’s estimated that up to 85% of autistic adults are unemployed. Think about that for a minute: Those with phenomenal skills at analysis, focus, and detail work struggle to find a job in corporate America, many of which are left trying to start their own business.

The Challenges

Autism expresses itself differently with many, and while masking can cover up the challenges many autistic entrepreneurs experience, it’s often too exhausting to maintain. Common struggles that an autistic founder can have are:

  • Difficulty with Social Interaction: A common expression of autism is difficulty with social interactions. I can’t tell you the number of times I would come away from a meeting feeling it went very well, when I find out it had not. Subtle social queues and body language signals are missed, derailed conversations that go outside of the prepped script require more time to process, and other voices who are louder and more insistent are likely to dominate the meeting, the autistic founder sitting back and listening with no input.

  • Over-customizing Solutions: Autistic founders find it hard to say no, eager to please and generate business. Customizing solutions, adding features for the sake of features, building everything and anything to make the customer happy. Instead of stopping and asking “why,” we just say “yes.”

  • Masking or Ignoring Sensory Needs: Not allowing the knee-bounce, the finger flex, a quick hum or rock can be exhausting. Masking takes a lot of energy for autistic adults, and yet we mask when we fear others will see us as weird or strange. Ignoring the need for stimming, taking a break when overwhelmed, or even just turning off fluorescent lighting that flickers subtly (and causes headaches) will eventually overwhelm.

  • Deferring to Another’s Vision: You know what you can do, you know what you can offer, but if you spend all your time listening to other people saying “You should do…” it will drain your energy more than anything else.

  • Perfectionist Paralysis: Whether wired for it or learned from years of “corrections” by teachers, therapists, and parents, autistic brains are wired to try to be perfect the first time to avoid any arguments. Why? Because every mistake is remembered, ruminated on for weeks afterward, and becomes a trigger point. Often an autistic brain will delay something until everything is perfect: the idea of a MVP (minimally viable product) that can be iterated on doesn’t exist.

It’s daunting, trying to start a business, getting the advice you need, making sure you have what your customers will want, and then getting the word out about your offerings when you struggle to hold a conversation with a stranger.

So Why Do It?

If it’s so difficult to get the word out and sell the product or service, why bother starting a business? Sure, it’s often the only option for many autistic adults, as corporate jobs ramp up the challenges with little to no support (unless they have worked with Aubilities shameless plug). But autistic founders have several advantages over their competitors:

  • Deep Focus and Persistence: Autistic brains are, generally, wired for deep focus, and do not tire from repetition, making our brains very persistent. It’s why my favorite part of Learning and Development is the Analysis: Figuring out what needs to be taught, breaking it down into digestible chunks, and reinforcing the skills, knowledge, and behaviors needed for successful business outcomes. If it doesn’t work, they will iterate, work until it is successful, instead of sending a client into “customer success” handling.

  • Pattern Recognition: Autistic brains are wired for pattern recognition, seeing the connections between siloed organizations and departments, and how they can be best supported. So, instead of reinforcing siloed experiences within an organization, autistic brains can see how they influence each other, building collaboration and efficiency across departments.

  • Integrity and Honesty: Autistic brains find it difficult to lie and deceive, valuing authenticity, honesty, and engagement. Not only is this the best way to engage with clients and customers, autistic brains are also wired to be more inclined to inclusion and belonging.

  • Hyper focus on Special Topics: When an autistic founder is hyper-focused on their problem, nothing else matters. The customer’s or client’s project is the only thing on mind, 24/7. It also means that you may get analogies that will be interesting and, often, very detailed. For instance, I’m deeply focused on sailing and sailboats, and often will use sailing examples in my videos or articles as they apply.

So, How Do I Do It?

So, what’s the secret? How does an autistic entrepreneur start a business and start making money? It’s not easy, there’s a lot of work that goes into it. Some features that make it easier:

  • Build an Autistic-Friendly Communication Pipeline: I find that I’m a much better (though verbose) writer than I am a speaker, particularly with unknown contacts. So, I use LinkedIn posts and articles, blog articles, and videos on YouTube that allow me to write, edit, and refine my presentation, and I’ll often write and record several posts, articles, and videos in a row, catering to my deep focus need, and then schedule them for release (I likely scheduled this article for release ^_^).

  • Build and Document Everything: I have a Notion page that contains my offerings, playbooks, and procedures so I can reference them quickly and easily during a conversation. I’ll also build presentations that highlight specific items without creating “text walls” that overwhelm. I also like to have the text opposite a related image to help set the tone.

  • Have Complementary Support: I know my limitations, and I know what is needed to be successful, so I work closely with colleagues and friends that help me with communication, sales pitches, business structure, etc. Many of my friends are either neurotypical, or are complementary, such as having ADHD, so they push for me to move faster and have communication skills I lack.

  • Keep it Authentic: I don’t try to be anything than I am, and stay honest in what I can do and what I can’t do. If things change, I take it as it is. I’ve had some opportunities fall through over time for various reasons, and I don’t take it personal. I accept what is, try to learn from the experience, and move on.

  • Keep Sensory Needs in Mind: I work in a shed in my backyard (with air conditioning and a wood-burning stove for comfort when needed), with my dogs on hand when needed. I use an Apple Vision Pro with water sound-effects playing and Bora Bora as an environment to set an environment that’s calming and conducive for deep focus. I take breaks when I need to, spend time walking to burn off energy and stimming needs, and decompress with my dogs or cat if needed.

As an autistic founder and entrepreneur, I have a lot of struggles that my neurotypical peers may not experience, but it doesn’t mean I can’t potentially be successful. It just means that I need to approach my process differently to bring value to clients and customers.

Are you interested in learning more? Book a free Strategy session and I’ll be happy to discuss how I can help your organization grow and scale faster.

When Customer Confusion Leads to Churn

You can have the best, most feature-rich product out there, but if your customers do not know how to use it, they will churn.

I remember it well.  We had just implemented a massive infrastructure change in my organization years ago, and we were excited to see how it would help us build out convenience across multiple departments.  We rallied, issued training to the power users, and advertised with key stakeholders, and it looked like we had a winner on our hands!  And so, we went live.  

Then, things started running into trouble.  Departments didn’t conform to the processes we put in place, and tried to circumvent them because they didn’t follow their previous processes.  Our team would follow the processes as outlined, yet requests would go missing.  Incidents would get submitted, but not resolved.  The brilliance of our new infrastructure design was showing cracks. Sure, we had great tools, but we struggled with the results.  We needed a change.  Or, better yet, proper training.  

This same scenario has played out for several IT Management platforms I’ve worked on, and each one had similar results:  turn it on and hope that folks will just learn to use it.  Well, it doesn’t work that way, and more often than not, the contract would not get renewed, and we would go through the same process with another product in 3 years.  

The thing is, this could have easily been resolved if we received proper training as part of our Change Management process, which would have resolved our churn, and that of many other clients.  Let’s explore how that would work.  

Tools are Replaceable, and always misused

Until we finally understood the right process and workflow through a platform, that platform was, essentially, just a bunch of tools we could use on the job.  Tools, folks can understand and use, even if they use them incorrectly.  I admit it, I’ve used a crescent wrench as a hammer before, when I couldn’t find my hammer.  Did it work?  Sure.  Was it good for the wrench?  Nope.  I left nicks in the metal.  Still, I used the tool that was handy for the job I wanted to complete.  

This same principle happens when you use your fancy platform as “tools.”  Need to track IT requests?  Incident tickets are tools to do that.  Need to track changes to your infrastructure?  Change tickets will do that just fine.  Don’t quite know how Change Management works, but you need to track a Change request?  It’s tempting to use an Incident if that’s what you know.  Sure, you don’t have the same workflows, but at least you are tracking it, right?  

Eventually, you realize that your methods of doing things aren’t working, and the tools don’t seem to live up to their promises.  Where is the automation?  Where are the approvals?  How does it relate to devices in the CMDB?  So, you start asking yourself, why did we invest in this costly platform if it’s not doing what it promised to do?  So, you start looking for other solutions.  

The Data

In this hypothetical example, I’m using a vague IT Management platform.  You could replace this with any platform and any similar scenarios:  I’ve seen this play out with several digital platforms in the past.  If it’s not used properly, it feels “broken,” and you look for a replacement.  It’s a costly migration, lots of money spent, and yet the same problem will exist.  

The thing is, all this could be properly avoided if your user base were properly trained.  Training would give them the knowledge of how to use the platform, when it should be used, and why it works the way it does.  This means training on the platform and the new processes to as many people as you can.  How do I know this works?  Because I’ve been on the opposite end of it:  I’ve received the necessary training, I’ve delivered the necessary training, and I’ve planned the necessary training when implementing.  So let’s go through the data.  

01

End-User Skills Gap

When evaluating the problem areas in the platform, we often came to the same common issues:  End users were attempting to circumvent the new processes that the platform provided for what they used to do (email requests, no follow-up, no reviewing of the platform, and often ignoring the platform).  Users were struggling to get their requests and incidents addressed because they were reaching out to their “guy” in the department instead of going through the process.  

The Solution

We needed to better understand how customer workflows at the end-user level operated.  We met with customers, ran surveys, and analyzed the user data available to see what worked and what didn’t.  More often than not, the end users would use non-traditional methods to circumvent the new processes established by the platform because it’s “what they always did.”  

This was a clear indicator that end-users needed to be trained on the expected process they would take, and the benefits of the platform as it came to them as part of the Change Management process to the new platform.  This would easily be resolved by creating platform-specific, micro-training videos that walked end-users through how to submit their incidents, monitor progress, request updates, and educate them on the current processes.  

The Results

The results were dramatic.  End-users were more comfortable with the new incident process, which triggered proper requests to go through with monitoring and automations in place.  Incidents were automatically assigned to appropriate departments, and responses were swift as SLA counters and triggers would kick in.  

02

Power-Users and Broken Processes

The next common issue that came up was broken processes that were being built into the platform by power users.  The platform was designed for specific functions with pre-defined automations triggered as specific stages or assignments were made.  The Power Users, on the other hand, knew what their current processes were and built them into the new platform.  Did it work?  Well, as well as previous processes worked.  Was it faster?  How could it be?  Power users were using the same broken processes that would take just as much time as in their previous environment.  No benefit was being seen.  

The Solution

It was clear that power users needed to understand how the platform was designed, where the automations kicked in, what processes were built in for their benefit, and how to customize the process for optimization.  

We started with the research:  looking at what customers were doing and how they were approaching their problems.  Surveys revealed that customers were performing much the same steps within the process, with a lot of additional overhead that could be weeded out.  We then refined processes, workflows, and the platform UI to better support their goals as power users while highlighting the value the platform brought to the table.  

Now, we needed to communicate the new process and results:  This required multi-day power user bootcamp-style training, walking them through the platform, helping them understand how the platform was designed, and where the automations triggered.  We worked through the process steps, where customizations could and should be made, where the default process shone, and how it would apply in their own environment.  The goal was to help them reimagine their broken processes by following one that was already optimized by the platform.  

The Results

The classroom results were dramatic.  Several power users were blown away by the strength of the process that was built in, and understood where their existing processes were broken.  As we worked through each lab, they would stop, take notes, and often ask questions on how they could make this same process work in their own platform.  The change was dramatic:  They saw the platform as a platform, not a set of tools that they should use.  The change in perception made a huge difference, and the adoption of the baseline automations accelerated amongst multiple customers. 

 

Understanding the Solution

This was a mock evaluation loosely based on similar case studies I’ve completed in the past.  The process is pretty much the same for each one:

  • Identify the business outcome you want to achieve
  • Measure your baseline of success
  • Identify the gaps
  • Change those gaps into outcomes:  How do you know your steps you are taking are successful?
  • Deliver the Training and measure the outcomes

This might seem simple at first, but there’s a lot of work that goes into each stage, which is why an L&D leader, like a Fractional Chief Learning Officer, is so valuable.  If you have current issues and would like to talk strategy, let’s chat! 

An image of a CEO, frustrated that things aren't going according to plan.

 

 

I’m sure there are many of you who are like me: watching the news, horrified at the unimaginable acts of violence. Growing up, it was blamed on video games. Then, horror and gore on television. Then religion. Then a gun culture. Then, polarizing politics and “mental illness,” when you don’t know. This theory, penned by Alexios Arvanitis, I find interesting, and the arguments that link burnout to extremism are thought-provoking.

The Premise

Alexios starts with the emerging pattern of burnout sufferers feeling that corruption and greed are at the heart of the frustration and dissatisfaction felt, and highlights the manifesto of Luigi Mangione, who is currently on trial for the murder of the CEO of UnitedHealthcare. Given that 41% of younger adults polled felt sympathy for Mangione, Alexios and team felt it was something to dig into and understand.

Following that thread, Alexios and team ran a study on 600 participants and found a worrying pipeline from burnout to expressions of violence, even extreme violence. Why? Well, he links this rise to three theories:

  1. General Strain Theory: The theory that links negative treatment from others to delinquency and crime because “the experience of strain or stress tends to generate negative emotions such as anger, frustration, depression, and despair.”

  2. Existential Burnout: The theory that highlights burnout as caused by failing to live up to the meaning one has for their life, and “the most emotionally demanding aspect of a work situation is its lack of existential significance.”

  3. Significance Quest Theory: The theory that links extremism as a vehicle to (re)gain significance in life through “a collective cause that can earn an individual the significance and meaning he or she desires, as well as an appropriate means with which to pursue that cause.”

The structure of the premise is compelling, and paints a roadmap for extremists to take matters into their own hands to feel like they matter. Suddenly, I feel a lot less safe, and more concerned about family and friends. All those previous “causes” of violence seem to make more sense: In a world where individual worth is predicated on limited criteria and not their own strengths, we have created an environment where burnout, particularly existential burnout, can happen, causing folks to find their quest for meaning in any way that matters.

What Can We Do?

So, how do we bypass the pipeline?

  1. Start at the beginning: fight burnout. Right now, at least in the US, over 59% of employees are in some level of burnout. 59%!! That’s a majority share, not an outlier. We need to fight burnout. We need to create a culture of care, compassion, inclusion, and safety that grounds employees to the valuable work they do.

  2. Encourage Balance between life and work. Work has been so much more integrated into our lives through mobile devices that we can literally be reached at any time of day, (almost) anywhere on Earth. Encourage balance by encouraging and celebrating those hobbies and causes that drive our employees outside of work and gives them meaning. Encourage service projects, value contributions that are positive that your employees make that may not directly impact the bottom line. Value your employees for who they are, not just how they contribute to the bottom line.

  3. Provide support for your employees, specifically emotional and psychological support. When someone is struggling, they need help, not a heavy hand. Provide care, compassion, and respect anonymity when necessary. We need to break down stereotypes and stigmas that still linger around psychological struggles. Be the change.

I’m sure many of you can add additional tools, and I’d love to hear them!

Will It Change?

This was the question I had after reading the article. Businesses are shutting down their inclusion and belonging programs for various reasons. Burnout is at an all-time high. Economic pressures, both real and imagined, are driving anxiety in the workforce. We are having more and more shootings with manifestos, so targeted, cause-driven violence. Can we really make a difference? It’s a question I think we need to ask ourselves in all seriousness. Perhaps it’s a good quest to join: fighting extremism through corporate cultural shifts.

Ai-Generated image of a boat maker building a sailboat.

I’ve worked at several organizations with varying degrees of cultural health. Some have been extremely toxic, others have been very inclusive and rewarding. Most have been a mix: Some teams are inclusive, others are toxic. Looking back, I struggle to find one particular reason for the cultural environment. Was it leadership that drove the culture? Peers that reflected it? How did one team become inclusive while the other was toxic? Could a team drift from one to the other? And, like everything else in life, there wasn’t one simple answer. There were three, at least that I identified. Can you identify the root cause? Yes. Can it be fixed? Of course, with effort. Let’s dive in.

Understanding the Drivers of Culture

It’s been commented on LinkedIn so many times it’s threatening to become clichè, but culture really isn’t what’s documented on your web page, what you put in your job descriptions, or what you say in your all-hands meetings. It’s what you, your leaders, and your people do daily. It’s how you treat your customers, your partners, and your employees. It’s the difference between “Garfield Mondays” and excitement for the challenge. In short, it’s how your company works in unison to meet your collective vision. It encompasses a lot: ethics, compassion, work ethic, respect, honesty, etc. It’s how you prepare your folks for their jobs, keep them skilled and motivated, and handle scale. Culture is how your teams succeed, not why your teams succeed.

Let me explain that last statement really quickly. I’ve worked for some companies that were ethically questionable in how they treated their customers and employees, yet they were still successful companies (at least in the short term). Whether it was because of short-term gaps they filled, or price ranges that appealed to customers even if the experience was questionable, they managed to succeed with a toxic culture. That culture was perpetuated from every level, often because those who were good were driven off by the culture, causing a feedback that only attracted those of like minds. So the company didn’t succeed because of their culture, but despite it, though “success” should be qualified: They were in business and made a profit, but struggled to grow in market share. They remained niche, and often were perfectly happy to be there as long as the money kept coming.

So, if bad culture succeeds (in a fashion), why worry about having good culture? Let me answer that with one of the best success stories of my long career: ServiceNow. When I started at ServiceNow, I was employee 3336. The company culture, for the most part, was very positive, inclusive, and willing to accommodate those who needed accommodation as long as they could perform their duties, which they did once they could feel comfortable doing so. That was over 11 years ago, and the company now has over 100,000 employees, is a powerhouse in B2B, and continues to have, overall, an excellent culture. They strive for inclusion, and it shows. Now, they succeed because they have an excellent product that continues to get better through driven employees who want the company to succeed. The culture drives that desire for excellence, and it shows. It’s how they succeed.

So what drives culture? I mentioned three drivers, so let’s break them down:

  1. Senior Leadership Behavior: Senior Leadership behavior has a huge impact on the culture of the rest of the company. When C-Suite leaders not only talk about inclusion but actively live it in their day-to-day behaviors, it sets the tone for the rest of the company. Investments in Employee Resource Groups, training, discussions, ethical principles of behavior, and outright kindness and understanding go a long way in driving company culture. If your C-Suite team is dedicated to a healthy, inclusive culture, employees take notice. So do the next group.

  2. Management: VPs, Directors, Managers, and Supervisors all take the vision of the C-Suite and translate it into action. At each level, culture is interpreted and passed on. One bad manager or one indifferent director can derail a team’s culture from the main company, turning it toxic. Conversely, one really good manager or director can buck the toxic trend within an organization. They have just as much scrutiny as C-Suite from their directs, and drive the culture within their respective spheres of influence. I’ve seen new leaders come in and demolish culture within an org to such a degree that other departments were thrilled to see them leave. I’ve also seen leaders drive such a culture of compassion and inclusion that employees across the company would line up to join the team when an opening was made available. How can you find them? Employee Voice surveys. One thing I was most proud of during my tenure at ServiceNow: my Employee Voice surveys from my team were the highest of any other team at the company.

  3. Peers: I was going to say Employees, but I think peers are the best way to highlight this last group. How your teammates treat each other has a direct impact on the team. Generally, this is tied directly to the leadership of the team, but it shouldn’t always be that way. A toxic employee can rapidly drag a team down if not kept in check. I’ve seen it before where a good manager can manage a toxic employee within his sphere of influence, but once they leave, they revert to their toxic behaviors. Cliques within teams often lead to toxicity, as they become exclusive to others, putting up barriers that stifle inclusion. On the flip side, I’ve seen teams who, understanding that team members are struggling, will reach out and actively help, coach, encourage, and support their teammates so everyone wins. Peers have a huge amount of influence on the health and well-being of the team culture.

Prepping for the Journey: Getting the Foundations of Culture In Place

So, we know where culture is driven; how do we drive it in the right way? It’s a good question, and it really comes down to who wants to drive the culture and how ingrained that culture is. Not because it’s impossible to fix a toxic culture, but because it takes a long time to rebuild trust in teams, and the more pervasive the toxicity of the culture, the longer it will take.

First, you need C-Suite dedication to inclusion and understanding. This means proper training, coaching, and mentoring. As a leader, you need to watch your own behaviors and approach to your culture. Inclusion, particularly neuroinclusion, is a good bellwether measurement for your culture: if you are neuroinclusive (welcoming of ADHD, autism, dyslexia, etc. as well as the neurotypical), then you are likely, culturally inclusive (racially, ethnically, religiously, etc.), though it should be mentioned that it’s not exclusively 1:1. As a side note: I focus primarily in neuroinclusion, though many of these same suggestions will work with other inclusive steps to strengthen your culture.

Once your C-Suite is in place, you need to properly train your leaders. Shockingly, 66% of managers are not trained to be managers until they have been on the job at least 10 years. Train your managers, and if I can recommend any type of training, I would recommend a situational approach to leadership (and if you need someone to conduct the training, I know a guy!). I would also recommend regular presentations from neurodivergent presenters, specialists, and other speakers who can put perspectives on what it’s like from the neurodivergent point of view. Need more guidance? There are organizations out there, like Aubilities, that can give you the perspectives you need to better understand how inclusion can happen.

For your peers and your employees, you need support. Employee Resource Groups are a great way to build and maintain an inclusive culture, and as an estimated 20% of your workforce is neurodivergent, you should have a resource group in place! Training and support systems are also valuable, and Aubilities or Auticate can give you resources for your employees and peers to better understand their own diagnoses, as well as their peers’.

Setting Sail: Building Your Culture of Inclusion

You have the tools in place now, you’ve prepped for the journey, it’s time to get started. As Bill McDermott is fond of saying, “Trust is earned in drops, and lost in buckets.” If you really want to change your culture, you need to start as soon as possible and keep right on at it. You won’t see changes immediately overnight (without firing a whole lot of people at once, and that’s not good for the company!), and it will take time for each group to change, learn to trust, and earn that trust. Be patient, settle in for the long haul if you need to, and know that the long-term growth and scale of your company depend on your patience.

 

Intentional, or Accidental?

Early in my career, I worked as the systems administrator for a very early startup, with only 10 of us, many family members of the founder.  It was wild, and we were running on a shoestring.  We brought on some folks to the tech support team.  This was my first introduction to employee enablement:  I developed the training for our tech support.  It was wild, it was ad hoc, barely structured, but it did the job for those who had some basic experience.  Those who did not, they really struggled. We had heavy turnover, with many sticking around for no more than 90 days.  

You see, if you don’t properly train your folks, they will leave as they do not have the adequate resources or experience to succeed.  No one likes feeling like they are failing.  

And yet, in many early startups, one in three employees considers leaving within the first 90 days due to poor onboarding.  Think about that for a minute:  All the cost you put into recruiting, interviewing, finding a good candidate, and getting them started.  All lost, because they didn’t have the tools they needed to do the job.  They didn’t know where to go, what was expected of them, how their success was measured, or how to do key parts of the job.  

Now, once they leave, you have to start all over again, often spending up to 2x of their salary just to get a replacement.  As a startup, you can’t afford that.  You also can’t afford the lost momentum those hires would have brought, the bottlenecks that will result due to unfulfilled roles, and the days, weeks, months, or even quarters where you just can’t find the right person who will do the job without needing onboarding.  Hint:  you can’t.  Everyone needs to be onboarded. 

An image that displays the chaos of multiple LMS systems

You are training your people, whether it’s intended or not.  The only question is, are you intentional, or is it accidental?

The Myth:  “We’ll Figure It Out Later”

But you are a busy guy!  Leading an early start-up, you are busy with Sales crushing deals, evangelizing your product to everyone and anyone that will listen, and looking to build your market share.  It’s why you hire!  You need people to take aspects of the business you don’t have time to do yourself.  So, you figure you can hire experienced folks from other similar companies and they will just “figure it out” as they go along.  So, you step back, take your role under Product or Sales, and just let onboarding happen.  

The belief, or the myth, is that smart people will just figure it out.  They’ve worked in their industries before in similar roles; they can just make that happen.  So, you turn them loose onto your fledgling company with little direction and hope for the best.  The anecdotal evidence that feeds the myth is that, often, people make it work.  They bring their experience from their previous companies, build their own fiefdom with their own policies and operational roles, such as IT and HR just have to figure things out as each department runs on its own rules and policies.  It’s inefficient, often expensive, and feeds silos where collaboration should exist.  

At the employee level, this feeds controlled chaos, areas where people become “indispensable” because they hold all the tribal knowledge for their department.  If they go, the department is thrown into disarray and confusion as co-workers immediately update their LinkedIn profiles and resumes.  Employees become disengaged because they don’t have a firm foundation of policy and procedure: It all comes down to the memory of their co-workers and shadow support processes that develop in response to the vacuum.  How can they make a decision when it’s immediately undermined by a more experienced co-worker? 

Finally, there is a more operational hit.  As teams realize their need for training, they will start training internally, their own training, their own takes on policy and procedure, their own LMS.  I’ve walked into companies that had 5 different learning management systems, and new employees were expected to use every single one of them as part of their onboarding process, as dictated by a wiki page or Word doc that was constantly out of date.  License costs were crazy, and those expenses could have been better applied to hiring new people.  

The Hidden Costs of Poor Onboarding

So, you figure, “well, it’s just the cost of business, right?” And as your teams start to build their own programs, their own policies, their own mini-companies in your company, the one you have poured your heart and soul into, you carry on, hoping things will work out on their own.  So, what exactly are the “costs of doing business” when you come right down to it?

01

Lost Productivity

Poor onboarding means it takes longer for employees to get up to speed.  Most organizations will have a 30-60-90 day plan, which, if you don’t prepare your employees properly, will extend for weeks, months, or even quarters as they “Try to figure it out themselves.”  Eventually, of course, they do, but you have wasted months of productivity while senior staff are constantly pulled in to shadow new employees, dealing with missed KPIs and increased training needs.  It’s inefficient, it’s costly, and it’s leeching momentum from your growth.  

02

Higher Turnover

When there is a lack of clarity, a lack of direction, or a lack of support, employees become continually frustrated.  Many try to contain this frustration internally, which feeds into poor mental, emotional, and physical health.  Eventually, the work of catching up becomes too much, and the calculations start:  How much more frustrating is it to try and meet unknown, vague, and/or inconsistent requirements than to try and find another job?  It may take 90 days, 5 months, or even 1 year, but that calculation is constantly running in your employee’s head.  

Now, you need to hire someone to replace them.  The cost of replacing an employee can run as high as twice the compensation of the employee who left.  It’s like hiring three people to keep one.  Is that efficient?  Is that economical?  Is that good for business?  Now, couple that turnover with the costs of delays, bottlenecks, and stalled growth.  

03

Cultural Drift

When teams are left to fend for themselves, they develop their own culture, policies, processes, and expectations.  If your company doesn’t structure onboarding, it’s happening by accident.  You may be the most employee-first, diversity-minded, and ethically-driven leader, yet your ideals and values are not the ones being perpetrated.  The culture that is perpetuated is dictated by your early employees, or those who remain.  Those who are dealing with their own frustrations, those jaded by their own poor onboarding experience. That will be the culture of your company, that will be your legacy, until you decide to fix it, and fixing a culture is extremely torturous and expensive. 

04

Customer Impact

“Still,” you think, “that’s a problem for another day.  Our customers can’t wait, and I don’t have time to dedicate to proper onboarding right now.”  Imagine for a moment, you walk into a hospital for a routine bypass surgery.  Your doctor finds out that the surgeon slated for your procedure has left, frustrated with the hospital policies that constantly change on a whim.  So, as your doctor, they are asked to do the surgery.  Leadership thinks, “You’ve been to medical school, right?  You can do this.”  The doctor, your family doctor, one who does stitches or the occasional hernia check, is expected to expertly cut into your chest and perform a bypass.  As a patient, would you go back to the hospital?  Would your doctor?  

Customers can tell when their Sales, Customer Success, and Support teams are poorly trained or supported.  They know, because they’ve seen it in their own companies.  Your customers lose trust in the company and are now doing the calculation:  Do you bring enough value to warrant the frustration they get from poorly trained contacts?  Once the calculation has started, it’s difficult to reverse it.  It’s difficult to rebuild trust once it’s lost.  That means churn.

05

Leadership Burnout

“But,” you say to yourself, “I hired leaders to take care of this.  If they can’t do it, I’ll get someone else who can.”  Sure, you can blame your leaders for your lack of dedication to proper onboarding for your company.  So you start to churn through leaders, because they are getting burned out trying to make onboarding happen in a structured way.  Blame is thrown about, and good, talented leaders, left to their own devices, start looking elsewhere.  

I’ve been on both sides:  Leaders leaving because they just can’t get anywhere with senior leadership, and as a leader, struggling to make things work in a culture of vague direction, promises, and poor onboarding.  Onboarding can make or break whole teams.  

An image of a CEO, frustrated that things aren't going according to plan.

Why Early-Stage Startups Are Hit Hardest

When you are just starting, you need every headcount you can get.  You need folks who can hit the ground as quickly as possible, keep on message, and grow the company.  Sales for new logos, Customer Success, Product, and Support for retention.  You need to build a structure quickly and efficiently.  Every weak ramp-up process derails progress and delays growth.  

“Well,” you might think, “Perhaps I just don’t need the headcount.  My team is small, but they can make it happen.”  They can, now.  But can they, when one person is hit by the lottery (because it’s nicer than being hit by a bus)?  Perhaps they leave for a better opportunity elsewhere, or start their own business?  Do you have the redundancy necessary to cover for those who are temporarily or permanently out of the office?  And what about mistakes made by the team, without help?  Newly onboarded employees will make mistakes.  Poorly onboarded employees make serious mistakes.  Mentors can help mitigate the impact, but not if you are short-handed.  

What then does your budget allow?  Frequent turnover is costly, not just in lost sales or lost customers, but in real dollars.  Limited budgets can’t afford high turnover because of poor or non-existent onboarding.  

Small teams are hit worse by less redundancy and more mistakes.

Signs Your Startup Has an Onboarding Problem

“Yeah, sure, what you are saying makes sense,” you are thinking to yourself, “but I don’t have a problem!”  Perhaps, though, you pause.  Then think, “How would I know?”  That’s a much better response:  How do you know you have a problem with onboarding?  What are your indicators?

The first sign would be that your new hires are asking the same questions again and again.  And again.  They ask for a momentary response or solve the immediate problem, and the answer is pushed out by the next crisis.  Answers are not internalized, skills are not developed, and learning isn’t happening.  This is, quite often, the case when you rely too heavily on mentors and on-the-job training.  Don’t get me wrong, both are valuable to the onboarding process!  But your new-hires need a strong foundation of information and skills from which to draw, so the practice can apply. 

This leads to our next indicator:  your teams rely too heavily on shadowing and mentor leadership instead of structured training.  Mentors provide guidance and knowledge checks, but shouldn’t be there to teach.  They don’t have time; they have their own jobs to do!  Structured learning provides that necessary foundation that Mentors can place into context, showing the value of mentorship.  

Next, we have inconsistent performance across hires in the same role.  Some folks do really well in the chaos, quickly acclimatizing themselves and running with it.  Others struggle.  Inconsistency in onboarding is a sign that something is wrong, and it’s not your new hires.  One constant is true with hiring:  Those who are hired generally want the job.  Maybe not because they are motivated by your Mission, or even by the team they are on, but ultimately, they want to work (or they wouldn’t have applied).  Onboarding should provide a baseline of performance from which everyone can work.  Mentorship places the training into context, and everyone should have the same opportunity to succeed.  Even if you follow the standard Bell curve, at least 75% of your team members should meet their KPIs within a reasonable amount of time (generally 1.5 to 2 months).  If you aren’t seeing that baseline result, there is definitely something wrong with your onboarding process.  

Finally, your managers, or you as the founder, need to constantly re-explain “how we do things.”  Culture is critical to a company, but if you constantly have to remind your employees of how things are done, something’s wrong.  Now, I’m not talking about basic compliance training, which is required by law.  I mean explaining your culture:  How the company does business, why people do the things they do, and what’s important for your business outcomes.  This goes beyond compliance; this is the heart and soul of who you are as a company, your brand.  

Employee working from home

The ROI of Getting Onboarding Right

“Okay, okay,” I can hear you say, “but how do I know onboarding is going to be worth it, and not just another expense you are trying to sell me?”  A good question.  Onboarding is much like a service:  If it works, you don’t know it’s there.  When it doesn’t work, everything is in chaos.  Let’s break down the real benefits of good onboarding.  

First, you have a faster ramp-up process for new hires.  They are ready in weeks, not months.  Skills are taught at an accelerated rate because you aren’t relying on the live environment to give you all the experience someone needs:  You have it set up in a controlled, curated setup that covers almost everything.  This translates into real business growth and bandwidth because hiring bottlenecks are removed. 

You have reduced turnover and, therefore, fewer recruiting costs.  Your new hires are more comfortable, more prepared, and feel ready to go faster and without much mentor intervention.  They ask fewer questions, they contribute faster, and the business is better placed to grow.  Without increased recruitment costs and lost experience from high employee turnover.  

Your culture is stronger, the vision of every employee is aligned with the founder’s, and employees feel connected to the company’s purpose.  Employees know what you want, they know what drives you, and they understand why.  

Your onboarding process can scale with your hiring needs.  Onboarding playbooks prepare you for rapid growth, setting well-established processes in place with regular reviews for updates.  It doesn’t have to be complex, it doesn’t have to be all video, and it doesn’t have to be difficult to set up.  You just need to be organized, have an easy method of tracking progress, and measure the success.  

Proper onboarding means real returns on your investment: productivity, time-to-value, and significantly reduced recruiting costs.

Practical Steps for Startups

So where do you start?  Once you know you have a need, you need to get the plan in place.  

Start with a “Day 1–30 Roadmap” for each role.  Your HR team, ideally, should have skill requirements and job analyses for every role.  Chances are, though, the HR team for a startup doesn’t.  It’s a large task, and you likely have a small team that handles onboarding.  Often, this task is delegated to the hiring manager.  Then, build out a 30-day onboarding roadmap that level-sets job requirements and skills, familiarizes the learner with the processes for the role, and sets milestones for success.

You can then document those processes somewhere they’re easily accessible.  Start with docs (Google Docs, SharePoint, Notion, Confluence, etc) and make sure everyone has the access they need.  Create learning paths that are user-specific in your LMS (preferably only one for your org), and assign the learning they need.  This should be role-specific, with knowledge checks.  The training doesn’t have to be complicated or completely built out by hand.  If you have a Large Learning Library (LinkedIn Learning, Skillsoft, etc), then use the training that is available there for generic role skill sets.  Be creative, and you can make it happen with the resources you already have.

Then, assign your onboarding buddies/mentors as guides.  They can track the onboarding process, verify that the learner is meeting their milestones, and provide context for the training.  You can use your training platform as a way to measure progress, communicate milestones, and provide guidance for your managers as they follow up on development.  

Finally, treat your onboarding as a strategic investment, not an afterthought.  The success of your onboarding means success for your new hires.  The success of your new hires means the success of your company as it scales.  

Captain working on his navigation plan.

Poor onboarding drains start-ups, keeping them from scaling successfully.  By investing in onboarding, you are not investing in overhead.  It’s acceleration fuel for scale.  Treat it as the valuable asset it is.  

If your team is growing and you’re concerned about onboarding gaps, a fractional CLO can help design a scalable onboarding framework without the cost of a full-time exec. Let’s talk.

Two versions of the same person representing the two processes of the brain: Experiential Response and Reasoned Exploration.

 

Owning Your Learning Success in the Age of AI

 

 

Growing up, we spent a lot of time camping. While camping, we would occasionally need to wash clothes, and as it happened, my parents owned a washboard and taught us how to use it when cleaning clothes in the creek. It was very rustic, and a lot of fun for little kids who were learning something new.

Now, I’m sure many of you out there have no idea what a washboard is, since the washing machine has been around for over 100 years. Knowing how to use one isn’t common knowledge. Everyone from CEOs to janitors has gotten along just fine without needing to know how to scrub clothing on the board to get the dirt out. It’s a lost skill, replaced by modern technology, and only picked up as a novelty skill. Much like celestial navigation and sailors, since GPS has become so ubiquitous, or from-scratch cooking since fast food and ready meals are so common. It takes extra, conscious effort to acquire these skills that were replaced by modern technology or conveniences. Now, we have AI.

I’ve been spending a lot of time researching Artificial Intelligence, as I’m sure the rest of you have been as well. I’ve worked through the early Turing days, different Machine Learning models, and spent time learning about LLMs and Agentic AI. Each time I look at what it can bring, I keep thinking about how it will impact Learning.

Perhaps I’m dating myself, but I was there when personal computers in the classroom were a novelty (we had Atari 2600s in my 6th-grade classroom). I saw how computers could help kids engage in learning through gamification. It was supposed to be the next revolution in education, yet we still have instructors in a classroom. I was there when we first started doing remote learning at my local Community College, and even helped provide video discs for learners (because video streaming was murder in dial-up speeds). And I was there when MOOCs were becoming a thing, and Apple was courting Universities to post recorded class sessions to iTunes U (now defunct). At each stage, there was supposed to be a revolution: a change in how we teach, and yet, we still have instructors and teachers in front of classrooms (or on Zoom).

But now. Now, we have AI. We have ChatGPT, Gemini, or Anthropic, which can answer questions for you when asked. They seem to have all the answers and can confidently respond without hesitation (right or wrong). They can rewrite your scribblings into a coherent, structured paper or article. Why practice sentence structure or the art of essay writing when an LLM can do it for you? It seemed so revolutionary that some companies have, reportedly and anecdotally (not confirmed), fired their L&D teams and given every employee a ChatGPT license as their source of learning. The reported results (again, not confirmed) were a disaster. Skill gaps widened instead of narrowing. Why? Why would skill gaps widen when answers were right there?

Well, to address that particular question, I would recommend an excellent video on AI and Learning called Effort is the Algorithm. The video pointed out that there are two processes our brains use: One is fast and uses the available knowledge and experience we already possess. Think of it as the computer hitting RAM for cached information. This process uses all the knowledge and experience you gained from years of learning, trying, failing, trying again, failing, trying again, and finally succeeding. You’ve lived it, you know it, you have put it to the test, and you can confidently respond without having to think too much about the answer.

The other process is slower, more methodical, and used when learning something new. This process is engaged when you are working through a problem, failing, trying again with a new approach, and repeating until you achieve success. This is learning. You listen, you watch, you try, and see results. Your mind engages, building Neuron connections with each practice (the general magical number is 7 for long-term retention), and you have banked a block of knowledge/skill/behaviors that can be utilized by Process 1.

So, what happens when you offload your learning practice to AI? Process 2 is happy to offload anything that takes extra effort/energy/resources, and Process 1 simply regurgitates the short-term information it has and purges that info. This is why skill gaps happen when you rely on AI as your Process 2: you are not building blocks of knowledge/skills/behaviors that you can call upon when pressed. There’s no effort in learning key information points because you are relying on external sources.

Now, is it really a bad thing? The same could be said of that washboard use, right? The general population doesn’t know how to use one; they offloaded that knowledge to the washing machine, and the world still turns just fine. So the question you should be asking yourself is: How important is the information/skill/behavior you are getting from AI to know when doing your activity?

Let’s put this in context. You are in Sales, and you are raving about the benefits of your company’s product or service. AI can give you all the talking points, highlight the key benefits for the customer, and might even be able to give you some customer insight ahead of time. What a great way to prep! Then, you are in the pitch meeting, and someone asks a question about how the product/service works. If you have to stop and ask AI, does that show confidence in your knowledge?

Let me put it another way. You are in the hospital for heart surgery, and your surgeon comes in for your consultation. You have, understandably, some concerns about the surgery procedure and need some clarification. You ask, and the surgeon turns to the computer and asks ChatGPT how the procedure should be performed. Would you keep him as your surgeon?

Now, I’m not saying AI doesn’t have its benefits in learning: It does! You can do amazing things with AI both before and during instruction. But as a learner, you can’t offload the learning process to AI. It doesn’t build your knowledge base, skill sets, and behavior guidance you need to be successful in your job. It’s your responsibility as a learner to put the effort in, try, fail, and keep trying until you succeed. That is learning, that is success, and that is what will ultimately differentiate you in the industry. Make the effort, and you will see amazing results.

 

You can’t offload your learning to AI to be successful.  You need to use AI to learn, try, fail, and try again until you succeed.  That’s learning, that’s succeeding.

 

 

 

 

Building An Engaging Learning Program

  • Strategic Alignment & Needs Assessment
  • Design for Impact & Engagement
  • Implementation & Support
  • Measurement, Evaluation & Evolution

 

 

Beyond Training – Towards Transformation

We’ve all been there; we’ve sat in those classes.  Training for training’s sake, little was gained, but boxes are checked, and we return to the daily grind without applying anything.  This is an all-too-common scenario in many employee enablement attempts.  Disengaged learners, burnt-out instructors, or “trainers” who think reading from the PowerPoint is sufficient.  

The thing is, it doesn’t have to be this way.  Training is a skill multiplier when applied correctly through an effective learning program.  Learners want to attend training because they know what they are getting: skills to help them succeed.  Trainers who deliver the content are engaged because it’s not the PowerPoint that motivates them; it’s the message.  They sell it because they believe it.  Proper training and well-designed learning plans drive real, measurable change.  

What’s more, it’s not that hard to have effective learning programs.  Start with

  • Strategic Alignment & Needs Assessment
  • Design for Impact & Engagement
  • Implement & Support your Plan
  • Measure, Evaluate, and Evolve 

Let’s explore each one.  

Strategic Alignment & Needs Assessment

You are probably thinking, “Well, yeah, of course we should be strategically aligned!” And you would be right!  But “should be” and “are” is a journey that isn’t always so clear to navigate.  So let’s map it out and clear the process up. 

Define “Moving the Needle”

What does it mean to “Move the Needle?”  It means that, whatever your training plans are, they should tie directly to measurable business objectives.  You may want to train more people to use a particular aspect of your product.  Why?  Because, in using that aspect of your product, they are more tied to the product itself, and less likely to churn.  You want people to follow a specific process while using the product.  Why?  Because it reduces the number of support tickets that come in, which then means better customer satisfaction.  Training can directly impact the business when it’s tied to business outcomes.

Identify Performance Gaps

Now that you know what you need to change and how training is going to create an impact, you need to know what needs to be taught.  This means conducting a thorough needs assessment.  Conduct interviews, run surveys, look at performance data, and analyze for obvious gaps.  You want to know what needs to be taught to meet your business objectives.  

Once complete, you need to organize the content to distinguish between skills, knowledge, and motivational issues.  When I build out my analysis data, I organize it by 

  • Job:  The job contains several tasks that need to be performed.
    • Tasks: The tasks that are needed for the successful completion of the job.  Each Task has several subtasks.
      • Subtasks:  Subtasks are the tasks needed to complete a Task, and are, themselves, made up of several components. 
        • Skills:  The job skill is executed to a degree of accuracy that completes a subtask.
        • Knowledge: Knowledge is the information needed to implement a skill accurately.
        • Motivation: The approach to applying knowledge and executing the skill.  Often this is called Attitudes, or Mindset.  

This logical breakdown serves two purposes: 

  1. Building a database of skill sets needed for every class that can be written up and easily transferred
  2. Logically structuring the content for delivery 
    1. Job: The Course
    2. Tasks: Modules
    3. Subtasks: Sections
    4. SKM: Paragraphs

BONUS Benefit:  HR now has clear skill sets for job requisitions and onboarding.  

Target Audience Analysis

Now that you know what needs to be taught, you need to understand who needs to learn.  Target audience analysis helps you identify your personas, the level of skill needed for a particular job, and the depth of knowledge needed to complete the job.  

  • Are you addressing Leadership and business outcomes, or Engineers and processes?  
  • Do you need to position the skill for new learners that need more detailed walkthroughs and explanations, or for more advanced learners who need to see context, connections, and cross-functional applications?  
  • Do your learners learn best through lectures, games, and challenges, or tactile labs and milestones?  
  • Can your learners dedicate a full week off the job to training, or do you need to break up your training into 1-hour sessions spread across 8-12 weeks?  

How your learners can engage will determine how well they engage in your delivery. 

Finally, you need to understand your learner’s motivations.  Do they want to learn new skills, or are they being made to learn new skills?  This can heavily impact the success of the training. 

Design for Impact & Engagement

Outcome-Based Curriculum Design

Too often, when training needs to be built, we start with a workflow and focus on features.  For large products that apply to many different industries, this might work, but even then, you have a desired outcome for the course.  Designing your course from the desired results and behaviors first, then working on the content to support it, keeps the training on course.  The outcome is always in sight; it’s woven into the content delivery, instructions, and processes presented.  

There should also be a focus on application, rather than memorization.  25% of all lecture content is retained short-term, while only 7-9% is retained in long-term memory. That’s not a lot!  Your training should be focused on applying skills, not memorizing steps or facts.  

  • For technical training, this means doing labs.  
  • For onboarding, this would mean simulating your experience.  
  • For Leadership, this would mean a cohort training session where skills are applied during the weekly course, and then “homework” is assigned where skills are exercised in real scenarios to be reported on later.  

Engage through applying skills, connecting skills to similar life experiences, and showing how the result is relevant to their needs.

Diverse Learning Modalities

There is no one right way to build and deliver training, so use them all!  Utilize microlearning, workshops, coaching sessions, experiential learning, and self-paced learning to cater to a wide array of learning needs.  Encourage engagement by meeting your learners where they are most comfortable.  Do they need a live class to stay on task?  Offer one!  Are they more comfortable with self-paced learning? Make it available!  Are they too busy to attend a single bootcamp?  Design a cohort track spread over several weeks!  Are they neurodivergent and in need of options to better support their comfort levels?  How can you say no?  Some topics and content lend themselves to one delivery methodology better than others, but never use just one.  

Engaging Content & Delivery

Engaging content contains real examples from real scenarios and is designed to be interactive.  When building your workflows, build them off of examples that your learners will experience in the field.  Then, create an interactive component.  If it’s technical learning, have them build it out in the platform.  If it’s leadership, run role-play scenarios and assign tasks to apply while working.  Looking to build or enhance skill sets for your struggling employees?  Run employee cohort sessions where best practices can be applied immediately after a session, and then reported on the next time you meet.  The closer to real-world experiences you can give your learners, the more they will retain.  

Every training should tell a story:  The story of the workflow (Why are we doing what we are doing?), the story of the examples (why would someone do this?), and the story of the outcome (who benefits and why?).  Stories with relatable examples encourage learners to build on their existing experience, helping them connect the dots to the existing processes.  

Clear, concise communication is critical to success.  This, personally, is a challenge for me:  I tend to be very verbose in writing and while teaching.  Industry acronyms, jargon, or buzzwords are tempting, but ultimately undermine your training.  Avoid anything that would get in the way of your learners quickly absorbing and applying the concepts you are teaching.  Remember, cognitive load is a very real thing:  Don’t add to it unnecessarily!  

A sample of what training programs can be created.

Virtual Presence Robots in the Classroom
Virtual Presence Robots in the Classroom

Implementation & Support

Pilot & Iterate

Now you have your plan, you’ve laid the keel, and you built the course.  You are ready to get it out in the wild and see what she can do.  You are ready to implement.  

Implementation is, just like with anything else, a phased process.  You want to start with your Alpha run:  teaching internally and to a select few outsiders so you can get the feel of the course.  Ask some key questions: 

  • Did the course hit all the right notes? 
  • How did it flow?  Does it make sense as you move from one concept to another?
  • Are there any gaps or barriers in the delivery or content that was an irritation or a frustration to the overall course? 

The Alpha run is very much a chance to shake down the course.  You want to work out any bugs before you place your content before your customers. 

The next step would be to run a Beta.  Unlike the Alpha, which is generally run with a near (but not complete) course, the Beta is a run with a course that is, essentially, complete.  You are now training your key instructors and partners on how to deliver the course, and they see it in action.  Sometimes key customers will be included if you need feedback from a customer standpoint, and is often recommended when you have strong customers that are willing and eager to contribute to the success of your platform.  And, of course, you iterate with each delivery: Work out what landed well and what doesn’t.  

Leadership Buy-in & Support

Now that you have a course that’s ready for prime-time, you need executive buy-in and support.  For internal training, this means Leadership needs to be on board with the message and skills being delivered, know what behaviors are expected after training is delivered, and reinforce those behaviors in the wild.  If leadership doesn’t support, or outright dismisses, your training, it will not have the impact for which it was designed.    

Create a Supportive Learning Environment

Learning is the practice of failing, and trying again.  If you don’t feel safe in failing, you can never truly learn.  Every training should provide a safe space for practice and failure.  Learners will get frustrated, learners will find they are behind.  They need to know that they will not be judged for the pace at which they learn.  

This also applies to learning methodologies and preferences.  Some learners will prefer self-paced over in-class, because they can take their time and/or they don’t have others waiting on them to complete the process.  There’s a level of psychological safety in being able to learn in your particular learning style and preferred methodology.  

Measurement, Evaluation & Evolution

Kirkpatrick’s Levels of Evaluation (Simplified)

I’m a big fan of having key measurements for success:  It’s easier to reference when you are looking for success in a training platform, and it provides foundational references to the value training is bringing to the table.  The thing is, you can’t comprehensively measure the success of training during class:  You can’t scan the learner’s brain and identify the skills they acquired.  You need to work with other teams to identify how training has made an impact.  

Fortunately, Kirkpatrick’s 4 Levels of Evaluation make things easy.  

  • Reaction: Did learners like it? Do they see the value in what you are learning?  These questions you can ask with your closing survey.  Be cautious though:  Don’t just look at the responses you got, look at the number of responses you didn’t get.  Ideally, you should be aiming for a 60% response rate for your closing course survey.  Anything lower than that, I would be suspicious on the accuracy of the satisfaction scores.
  • Learning: Did they gain knowledge/skills? Depending on your course design, you can use quizzes, assessments, or reviews to evaluate if they have learned something during the class.  This will test their short-term memory, in which at least 25% of a lecture will be captured.  Long-term memory usually hovers around 7-9%, and will require some additional study and practice to ingrain behaviors into habits.  An excellent long-term learning measurement would be certification exams.  
  • Behavior: Did their behavior change on the job? When creating internal training, this is an easy measurement point:  Are learners performing better against their KPIs?  Are they performing faster, more accurately, or correctly in their tasks?  Managers can easily measure this result and report back on the results.  Customers, on the other hand, go back to their jobs and behavior is much more difficult to measure.  If you do not have a direct way to measure, you can use conversations with your key Customer contacts and the Customer Success team to determine if behaviors changed with those who attended.  
  • Results: Did it impact business outcomes? Internally, this is an easy metric.  Are KPIs improving?  Do we see reduced waste and increased performance?  Managers are already measuring job performance through KPIs, and those KPIs should be directly tied to ROI. Better KPI performance should increase ROI of the training program.  Customers, on the other hand, require interaction with the CSMs, though their general Account Health will be the determining factor:  Did training customers increase customer loyalty and dependence on your product?  

Data-Driven Decisions

Now that you can measure your success, look at the data.  Identify what works, and what doesn’t.  Customer Satisfaction of the course will let you know if the course flows well and answers the questions the learners had.  Course quizzes, exams, and lab completion scores will indicate if the learner is, well, learning and retaining key concepts short-term.  Improved performance and task completion will identify that behavior is changing, and increased KPI results and better account health will indicate the results are promising.  Watch these metrics, tweak when necessary, and watch again.  All course development should be iterative.  

Communicate Success

If things are going well, communicate it out!  Let Leadership know what CSAT scores are being generated, learner comments, and measured behavioral changes and results.  Let your Stakeholders know how their input has made a material difference in the learner, the organization, and the company.  

The Continuous Journey of Learning Excellence

We’ve made quite the journey on this process, navigating key points that help you move the needle when developing your training platform. By starting with Strategic Alignment & Needs Assessment, you have a superstructure of training that meets the needs of the learner and ties back to key business objectives and desired outcomes.  When youDesign for Impact & Engagement, you are building a course that will engage your learners throughout the process, tie back to their previous experiences when necessary, and show them the value of the content and outcomes desired. Then, with Implementation & Support, you are taking your course live through it’s iterative process, evaluating and adjusting as necessary for success.  Finally, with Measurement, Evaluation & Evolution, you track the value being added through training.  Each phase is a valuable port of call as you navigate your learning program.