AI As Fuel For Organizational Change: The Biggest Market Of All
Across all the HR 2030 AI opportunities, change, training and enablement could be the highest ROI of all
Now that AI is expensive (even our i-Phones are paying for it), we need to focus our AI efforts on the highest ROI investments. Across all of HR and business, I believe one of the biggest opportunities is AI as a tool for Organizational Change.
Let me explain.
The Big Picture: Where We Are Going
First, we have many distractions going on, with AI ornaments appearing everywhere. This includes agents for payroll reconciliation, candidate interviewing, personal coaching, and hundreds more.
The HR profession directs trillions of dollars in payroll and benefits, recruiting and job advertising, many forms of HR compliance and regulatory processing, and hundreds of types of training and enablement, so this is an opportunity rich environment.
Most of these new “agents” are cost-reduction or productivity hacks. In other words, they take work we’re doing today and make it easier, faster, and more data driven. This is good, but the ROI may not be high.
I often liken the productivity agents to tools like Grammerly or the inline spell checker. They may improve quality or accuracy in writing, but they also inject themselves into your productivity flow, so it’s not clear how well they pay off.
Well as we sort through hundreds of use-cases through our HR 2030 research (blueprint here), I concluded that all this amazing tech, added together, is NOT here to cut costs or reduce headcount in HR. HR itself is typically 1-2% of payroll, so even if you cut it in half, the cost reduction is minor. The big value is “how do we make HR more relevant and impactful for the business itself!”
So in our vision we see AI accelerating a shift in HR’s role from a compliance and services organization to an integrated business function focused on what we call “Dynamic Enablement for Growth.”
This essentially means that all the 95 disciplines of HR (recruiting, interviewing, sourcing, training, performance management, leadership development, compliance processing, employee services, safety, scheduling, etc.) all become “agentified” over time, with Superagents coordinating the work. And as this agent architecture takes place, we don’t “eliminate HR” but rather “transform HR” into a team of people who help us quickly and accurately hire, move, train, and otherwise “enable” people to grow the business and grow their careers.
What Dynamic Enablement For Growth Means
Let’s talk about what this means. Every company I’ve worked in, including my own, faces the “from - to” problem. We are doing X and it’s working well, but the market or competitive landscape is pushing us to do Y. How do we get there from here?
The most visible one of these epic shifts is the adoption of AI into tech companies themselves. Meta, a company that has redefined itself many times, is morphing into an AI-powered eyeglasses, commerce (they call it “conversational commerce”) and next-gen advertising company. Yet how they’re getting there from here is quite messy.
The recent fiasco at Meta where more than 8,000 people were terminated and the company forced its engineers to focus on “AI training,” caused the and then completely flipped back to engineering, is a good example of “not dynamically enabling the business.” And this included installing monitoring software on people’s PCs, and a variety of other odd decisions.
(This is a company with a market cap of $1.3 Trillion.)
This is an enormously common issue. I talk with CHROs almost every day and I often hear details about seemingly sound organizational decisions (reorgs, layoffs, mergers, etc.) that run into a series of mistakes, thus slowing their growth or possibly losing a market opportunity.
Here’s another fascinating one. I just listened to Ford CEO Jim Farley discuss how the company is completely reorganizing its EV business to build an “all new, all digital” team. This is after a billion or more invested in the F150 Lightning and the Mustang Mach-E, which he describes as “learning experiences.” And he admits that Ford’s engineering, IT, supply chain, and digital teams are 25 years behind, thus he decided to start from scratch.
Well I don’t know about you, but most companies don’t like to spend a billion or more on “learning experiences” (the story of the Apple car will eventually be told), yet it happens all the time.
Why are these huge transformations so difficult?
I would venture to say there is no lack of “strategic thinking” in these situations. You don’t need McKinsey to tell you that EVs are different (in every way) from Gas cars and you don’t need a Harvard professor to tell Apple that building cars is different from building phones. (Although in China the phone companies do build cars.)
The problem is all about people.
Every transformation I learn about (and we talk with big companies every day) is not about a big idea - it’s about reskilling, realigning, or redefining the role of people. In other words, even if Ford hired the top engineer from Tesla (apparently the head of engineering invented the Segway), he or she can’t build an EV business without focusing on human capital.
I won’t belabor all the research we’ve done on Dynamic Organizations (we kind of wrote the book on this) but it’s very clear that companies like NVIDIA, Microsoft, and even IBM do not “reinvent” themselves without an enormous focus on training, alignment, skills, and crystal clear cultural foundations.
One of the most amazing companies we work with is SharkNinja. (I’ll have the CHRO on our podcast soon.) This is a company that makes coffee machines, vacuum cleaners, and other consumer products. They compete with Dyson, Bissell, De Longhi, Whirlpool, and hundreds of chinese knockoff companies. Yet they’re a $5 billion company growing at double digits, constantly reinventing markets and moving into new segments.
Is this because they have better engineers?
Of course not. It’s all about their culture and how they go to market. I won’t disclose any secrets, but suffice it to say this is a company which learns very very fast.
Watch this: https://youtube.com/shorts/s4Hj_ew8jrs?si=gIKaiDx3EBj6kirD
Organizational Learning Is Fundamental to Business Success
So when we say the word “learning” it has multiple meanings.
There’s learning how to do your job; there’s learning a skill or technical process; and then there’s organizational learning as a whole. (ie. What we, as a business, now understand - about the market, our company, our competitors, our opportunity.) They are not the same thing.
That’s why I now refer it to “dynamic enablement,” not just “learning.”
In reality, if your company doesn’t learn and share information every day, you’re falling behind. Yes we have to “execute” on our sales, products, services, and marketing - but all that “doing” has to continuously improve by “learning.” No need to belabor the point, I’m sure you get it.
How do organizations really “learn?”
Well we’ve studied this for many years and we have a list of things that matter. What we found, and this is a very foundational body of research, is that “learning” or “enablement” is a combination of sharing information, pointing out mistakes, and clearly articulating “what we’ve learned” at all levels of leadership. It’s filled with culture, management, process, and rewards issues.
Here’s our research from years ago, and it’s still relevant today.
As we watch and work with companies over the years, we see that fast-growing companies are both “quick learners” but even more importantly “quick at adapting” to what they learned.
At SharkNinja, for example, any employee can point out a glitch in a product to any leader or product team, and they are then expected to “lead a project to address the issue.” This alone is a very difficult and rare business practice.
Most companies are filled with people who “find problems” but a lot of that work is pointing things out and complaining, because “it’s not my job” to fix something “I don’t own.”
If you can effectively lubricate this process, including having some form of “expertise management” so everyone isn’t randomly reinventing things they don’t understand, you’ve built a highly scalable company.
And I call this process “dynamic enablement,” for lack of a better word. (The word “learning” always connotes a classroom or individual experience.)
How Do We Create Dynamic Enablement?
Ok let’s get back to AI. How do we facilitate this continuous learning, problem-identification, and upskilling to take place?
It’s actually simpler (not easy, but simple) than you think. We have to create a rewards system and information system that lets people find and share information easily, curate the quality of information, and then reward people to act on it.
So Dynamic Enablement involves:
Making it easy to find, share, and validate information (all types of information, including customer feedback)
Quality checking or validating information (through subject-matter-experts, validated data, or other means)
Rewarding, encouraging, and empowering people to act on (ie. fix and adapt) to what we’re learning.
Management plays a huge role. If a manager (we call them Supermanagers) is trained and rewarded for continuous improvement (ie. Toyota), they will support and facilitate this process. But the information has to be there!
And as SharkNinja teaches us, once an opportunity or glitch is identified, we need a management culture that facilitates interrupting what we’re doing to talk about, discuss, design, and implement a fix. (Note how “talking about mistakes” is the #1 factor in our research.)
AI Is A Miracle To Help In This Process
I’ve spent 35 years working in organizational learning and HR and the problem we always run into is “what information should we act on?'“ And this problem of many distracting signals is everywhere, even in our personal lives.
In many ways the “skills based organization” (many of you know why I dislike this phrase) is an attempt to simplify and glorify this problem. Rather than figure out what your company needs to do, we create a massive library of “skills” and assume that training and assessing people will fix our problems. It’s not a terrible idea but it’s not central to the issue.
What if, just perhaps, we had a “system” that let any person, customer, or business process “tell us what they think is important?” And what if this “system” validated the quality of this information so we could see if the issue is an outlier or a massive problem we need to address?
This is precisely what AI is designed to do!
AI, through its miracle of language modeling and deep statistical analysis, can help us share, analyze, and find information to “dynamic enable” our companies like never before.
Let me give you some examples.
Polestar, the Swedish EV car company, created a sales training, enablement, and support system that lets any staff member publish new content about the car and it’s shared with individuals on a personalized basis. This is only possible because they use the Sana AI platform which dynamically creates content and personalizes each employee’s interface. No more “browsing for courses which may be out of date” - all employees and sales people can keep up to date on new features, promotions, software updates, pricing every single day. Listen to my convo with Rita at Sana for more.
Databricks, one of the fastest growing data management companies in the world, operates in one of the most brutally competitive markets in the world (Oracle, Microsoft, and Amazon compete). Their head of “learning” told the CEO that her job is not to “train employees and customers on Databricks” but rather to “enable employees and customers to help their company grow.” As a result she positioned the business function as a C-level operation, taking L&D out of HR.
Rolls Royce, a company that has been going through a massive turnaround (this is the UK company that builds jet engines, nuclear plants, and defense systems) has been on a massive turnaround, led by Tufan Erginbilgiç, a former BP executive.
As I’ve met with the company (this is a 100 year old company) the big story has been integrating engineering across all the business groups and implementing a new “dynamic enablement” technology and culture to share engineering expertise, product knowledge, testing data, and customer experience data. AI has been core to their DNA for years but much of this new energy comes from a reorganization that creates rapid sharing of technical, product, and research information.
The company has used AI to build domain knowledge systems that let new engineers quickly learn about 100+ years of experience, rapidly driving productivity, problem-solving, and time to market.
Dynamic Enablement Has Enormous Potential
Let me give you a sense of how big this is. The average company spends around $1,400 per employee per year on formal training. This money alone is spent on courses, books, instructors, tools, and a myriad of consultants to help.
We estimate that the L&D market alone, which includes vendors like Cornerstone, Docebo, Sana, Coursera, LinkedIn, Masterclass, Skillsoft, Harvard, and thousands of smaller players, generates around $400 Billion in spending. Then, if you add the markets for leadership development, coaching, sales training, and other revenue-generating enablement, it’s easy $550 billion or more.
Next look at tools for customer self-service, which make up a $20-30 billion market and some percentage of the tools for portals (Sharepoint), web distribution (Zoom, Teams, Google), and other formal tools for knowledge sharing, and it’s pretty clear to me that companies spend more than $600 Billion or more on the entire domain of “dynamic employee enablement,” and most of it is not dynamic at all.
Remember that the white collar workforce alone makes up almost 1.4 billion people in companies around the world. How much would you spend to make sure that each of your employees stays up to date and shares knowledge with others? This is a much larger opportunity than corporate learning alone.
Here Are Some Dynamic Enablement Use-Cases
The real miracle benefit of AI is speed. Rather than wait for an analyst, writer, or instructional designer to build something, AI gives us analyzed and relevant information in seconds.
This “dynamic publishing” nature of the technology (ie. take my podcast and turn it into a 500 word brief, or take this article and turn it into a 10 minute podcast!) has magnificent benefits at work.
In our company, we bring Galileo (our AI which is expert in HR and the labor market) into meetings as we discuss issues. Not only does it listen but we give it things to read and we ask it for advice. In our case it immediately connects new research or a client issue to our entire history of work, giving us advice and information that would typically take days to produce in the past.
Companies like Rolls Royce use this to quickly share technical findings, test results, and customer information. Companies like Polestar and Expedia now use AI content systems to share customer issues and new product or travel information. Companies like Asics (shoes) and Databricks use AI learning platforms to train and support leaders and sales teams.
We recently put together an integrated AI solution for a large technology company that involved connecting Galileo (the intelligent orchestrator) to SAP Successfactors for HR business partners. The HR team is now asking questions like “show me a list of top potential successors for this VP role and produce development advice for the top candidate.” This is work, if done by hand, that would take weeks or months. Now the HRBP and leadership team can evaluate candidates in real-time during a talent review.
And this goes much further. We’re now working with an exciting new AI team who has built a whole “change management” system designed to diagnose and fix change issues at the individual or team level. Here’s how it works:
You “talk” with the AI to explain the program, system, or process you’re implementing and what skills or training or information is needed (ie. a new pricing structure or new product launch).
You “import” into the AI your team or company organization structure, job titles, and geography. It then determines, through job title, what role and schedule each person has and how best to reach them. It now has their mobile phone number, email, location, and manager.
You “ask and consult” with the AI to reach out and have a “conversation” with each person impacted to get their feedback and understanding on the new process or launch.
The AI emails, sends Teams or Slack messages, or text messages each person in their time zone to ask them some questions. It offers to “call them” for a brief conversation if they’re interested.
The AI then summarizes its findings and either offers personalized advice or goes back to the program manager and presents results. The change leader can now query the AI for specific issues (Ie. what did line supervisors in manufacturing understand or think about our new leave policy) and the system answers in detail.
Finally this system could build dynamic training or answer questions to fill the gap.
I consider this AI agent an “employee or company change Superagent,” to tell you the truth. It could easily be implemented as an ambient “always on” agent that periodically looks for employee issues and then talks with the L&D Superagent to build training, videos, or nudges to help.
Why This Market Is So Enormous
Not only is this a rather spectacular set of use-cases, this type of solution obsoletes or replaces billions of dollars of survey software, consulting, employee experience tools, portals, and traditional training systems.
We explain this in detail in our research The Definitive Guide to Corporate Learning: From Static Training to Dynamic Enablement. You can read this or take courses on it or “ask questions” or read and listen to case studies if you use Galileo.
The fascinating part of “Dynamic Enablement for Growth” is that it integrates and cuts across the traditional silos in HR and business. Training, employee experience, knowledge management, and enablement are suddenly integrated.
I really think vendors like Workday (Sana), Glean, Microsoft (Copilot can do a lot of this) are going to see a massive new market here.
Additional Information
The World of Corporate Training Lurches Toward Dynamic Enablement
The Enterprise Learning Tech Market Quickly Transforms Around AI
The Definitive Guide to Corporate Learning: Reinvention in the Age of AI
2026 Imperatives for Enterprise AI: The Road Ahead
The Great Reinvention of Human Resources Has Begun
This hits right at why large-scale transformations crumble. The "from-to" problem isn't a failure of strategic intent or a lack of expensive consulting decks; it’s almost always an architectural failure of human incentives and false alignment.
We love to "agentify" the easy stuff because it looks good on a quarterly report. But the real friction in change management happens when a company tries to rewrite its cultural operating system overnight without fixing the underlying plumbing. When Ford or Meta stumbles through a $1B "learning experience," it’s usually because the senior leadership mistook silent compliance in a conference room for actual organizational capacity to adapt. AI can be a massive unlock, but only if leaders are willing to look at the uncomfortably true data it surfaces. If your culture rewards people for hiding glitches, a superagent is just going to amplify those.
We need to dissect the gap between the clean, official version of strategy and the messy reality of what actually happens in the trenches. Instead, nobody pushes back in the deck review, but three months later the project tilts because nobody actually owned the trade-offs together.
Fascinating read. Thank you.