It’s easy to get lost in a tsunami of AI hype. Between vendors promising the moon and consultants positioning themselves as AI prophets, the noise level is deafening. It can feel like everyone is an expert except you.
Here’s the thing: Most of us in the corporate world are actual professionals with actual jobs. We’re not AI influencers or evangelists. We’re scrambling to figure out how AI fits into our individual roles and organizations, and we’re hoping (praying, pretending) that we’ve got a viable strategy. The disconnect between what we read online and what is really going on in the workplace is vast–totally different universes, really.
A recent experience crystallized this disconnect for me. I hosted a private event called “Practical AI for Professionals” (the antithesis of hype, I know), bringing together clients, industry colleagues, entrepreneurs, and friends. The format was simple: brief presentations, interactive discussions, and cocktails. Once everyone loosened up (whether from the spirits or just the relaxed atmosphere), I witnessed a collective exhale.
Nobody has AI figured out—and that’s actually reassuring. What we discovered is that people are seeing real wins, but not the transformational ones promised in vendor pitches. Many are getting small productivity gains from personal experimentation. Others are tackling bigger projects like AI-driven bots, interfaces, and makeshift applications. What struck me most? What the people at the after-work event appreciated the most wasn’t the learning (though that was valuable), it was the relief of realizing we’re all stumbling forward together.
What we in the corporate and professional worlds need is to get on board with AI and find strategies that work for our specific situations. What we don’t need is to waste energy chasing misconceptions or pursuing misguided efforts. So here are some reality checks, with practical advice mixed in.
My AI Journey: From Christmas Panic to Reality
A year and a half ago, I had one of those “OMG” moments about AI. After trying Claude.ai (a game-changer) and engaging in a memorable discussion with a friend and fellow business owner who said something like, Adrienne, you don’t have time to be the good girl who does AI right, you just need to jump in and do something.
With my friend’s call to action and eggnog in hand over Christmas break, the panic set in. I couldn’t plan based on the consulting market of the past. I had to look at where the industry and world were heading and how my business could be part of that. It clicked—the internal audit, internal control and consulting landscape would change rapidly. Knowledge work was shifting. As cheesy as it sounds, the phrase I’d seen on LinkedIn finally clicked: “AI won’t replace you, someone who uses AI will.”
Over the past year, I’ve approached AI learning through sprints and experimental goals. Learning AI is like “I really should go to the gym more.” It takes time and consistency. Here’s how I’ve approached the journey (with good intentions, even if inconsistent):
- Online research to get my bearings, including learning from influencers. There are incredible influencers in this space. They’re intimidating to a certain degree, but they teach brilliantly and give you free content.
- Implementation and experiments—learning by doing. Client projects, conference presentations, and team planning all become commitments that force me to clarify and crystallize what I know and my point of view. I recently created an investor deck for a client and experimented with the incredible tool Gamma, which was a smash success. Last week, I redesigned an org chart for a client, and my experience with AI-based diagramming tools was less impressive. It’s hard when you’re under deadline pressure, but I’m trying to incorporate this into everyday work.
- Peer discussions, sharing, and interviewing. Peer-to-peer learning has been hugely beneficial–lessons friends glean at conferences, brainstorming and updating with my husband (who I also work with) and shared notes about tools that work (and don’t). My “Practical AI for Professionals” event was a hit precisely because of peer-to-peer sharing. Interviewing (formally or informally) expands my learning and perspectives to different areas of business and different personality types, too.
From where I sit, both as a consultant and practitioner, the time savings, creativity boosts, and idea generation are real—but I’m acutely aware of what’s NOT happening too. Here are four revelations I’ve reached since my Christmas week panic, and what they might mean for your journey.
4 Revelations About AI Adoption (In the Real World) and What They Mean
Whether you need a kick-in-the-pants like I did or more grounding and direction, here’s what I’ve gleaned about how AI is actually being used in the workplace. This should give you comfort and relief that there’s a path forward and that you’re not as behind as you think.
- I am NOT seeing mass transformation of processes, workflows, and business operations.
- I AM seeing productivity gains at individual levels. But people aren’t really 10X’ing productivity. (My professional advice: aim for 20% gains, not 80%.)
1. Companies Are All Over the Map
Truth be told, the companies I work with are like kids at a school dance—some are confidently showing off their moves, others are awkwardly standing by the punch bowl, and a few are hiding in the bathroom.
But before we go further, let’s clarify what we mean by “AI.” Companies have been using “classic AI” for information management, invoice processing, and expense reports for years. This is old news for most mid-sized to larger clients. Generative AI—like Copilot and ChatGPT—is where the corporate world splits into different universes.
The Corporate AI Adoption Spectrum:
- Eager adopters: Some have strategies and teams in place. They’re investing serious money, rolling out big implementations, doing legitimately cool things like building custom bots and using AI to streamline invoices (which, let’s be honest, is about as exciting as invoice processing can get).
- Adopter-resisters: Some are doing zippo. In fact, some of my clients are blocking ChatGPT and many other powerful AI apps entirely (yes, really, though not on my recommendation).
Adoption patterns vary wildly:
- By company size: I know small companies doing tons with AI, and others doing nothing. I know big companies innovating like crazy, and others that think “AI strategy” means buying a Copilot license for the intern.
- By industry: The stereotypes are predictable. Marketing and consumer-based companies are all over AI. Walmart is doing more than Shell—which makes sense since Walmart is direct-to-consumer, where knowledge and data are everything. Shell sells oil, which, last time I checked, wasn’t exactly a knowledge-based product. My smaller oil and gas clients are doing less, probably because their biggest AI question is, “Will this help us find more oil?” and the answer is usually “Not yet.”
What the Numbers Show:
While the stats for corporate AI adoption change faster than a teenager’s mood, here’s what I’ve pulled (but keep in mind these could be outdated by next week):
- 42% of large North American enterprises have actively implemented AI solutions.
- 72% of organizations worldwide have adopted AI in at least one business function.
- 65% report regular use of generative AI in at least one function.
Forget the stats. What you need to know: Some companies are holding back and others are iterating rapidly. What a company does today might differ completely from next quarter.
Predictions:
- Knowledge-based companies have and will continue to adopt AI faster to gain productivity gains to stay relevant—because that’s literally what they do for a living.
- AI won’t be a major strategic advantage for every company, but it will be a partial one for most, and essential for specific functions.
2. Professionals and Knowledge Workers Are Even More Scattered
When you see stats claiming 60-70% of companies are using AI for at least one process, that’s about as meaningful as saying “60% of people have used a computer.” In a big company, having one AI process is like having one employee who uses Excel—technically true, but hardly transformational. Through all my conversations and research, I’ve discovered something fascinating: individual professionals are even more scattered than their companies. It’s like a corporate version of “choose your own adventure.”
The Professional AI Adoption Spectrum:
Within the same companies, you’ll find wildly different approaches. It’s either “AI will destroy us all” or “AI will save us all,” with some middle ground for “AI might help us do some things slightly better.” Here are a few examples by profession:
- Accountants: Some embrace AI and others remain suspicious. I’ve met spreadsheet wizards integrating ChatGPT into workflows, others using Copilot to make their emails less dry, and many sitting it out entirely.
- Engineers and geologists: Like accountants, some are diving in headfirst, others do little more than occasionally ask ChatGPT why their code doesn’t work (spoiler: missing semicolon). But they’re professionally trained to ask, “What if this kills someone?” about every new technology, so cautious skepticism comes with the territory.
- Lawyers: They have the most to lose and know it. Some use AI to speed up tasks (ironic for hourly billing) and replacing paralegal work. Smaller law firms move faster because they can’t afford to bill a client for 47 hours for research that AI can do in 47 seconds.
Predictions:
- Non-adopters will be left behind—not because AI is magic, but because productivity and high-demand AI skills remain a solid career strategy.
- Professionals with real AI skills (not just a bunch of courses to their name) will be sought after in the workforce.
- The AI skills of professionals will converge as best practices, industry-specific tools, training, and individual expertise grow. Non-adopters will be left in the dust.
3. Entrepreneurs Are Moving Faster Than the Corporate World – Way Faster
It’s not even close. Entrepreneurs are learning, testing, and gaining first-mover advantage using AI to generate marketing materials, proposals, research, and design workflows. I’m uniquely positioned to see this because I live in both worlds—half entrepreneur, half corporate. I own a small business and belong to entrepreneur groups, but my day job keeps me firmly planted in the corporate universe. Think of me as a double agent, but with more spreadsheets.
The Entrepreneurial AI Love Affair: Over the past two years, I’ve watched my entrepreneurial circle jump headfirst into AI tools. They get their hands dirty and talk about AI without waiting for corporate training or committee approvals. My entrepreneurial friends pulled me forward when I was behind. Yes, even colleagues in their 60s who own dry cleaners, divorce practices, law firms, and accounting firms. They love their AI.
Why Entrepreneurs Are Natural AI Adopters:
- Time obsession: They’re fanatical about saving time because more efficiency equals more money or freedom or time for strategy-level work. It’s a beautiful, simple equation that corporate workers rarely experience.
- Idea addiction: They’re naturally creative and obsessed with new concepts. Sure, “shiny object syndrome” can be a weakness, but it makes them perfect early adopters.
- Risk comfort: They’re comfortable with uncertainty and unknowns—essential traits for AI adoption.
The Corporate Constraint:
Corporate environments operate differently. Efficiency gains don’t directly hit your paycheck, so the motivation isn’t visceral. You’re rewarded for following policy, not taking risks that might revolutionize your workflow. When AI adoption means pushing boundaries and risking being wrong, corporate culture creates natural resistance.
Predictions:
- Entrepreneurial thinkers inside companies will become change agents, pushing adoption from within.
- The corporate world will be forced to accelerate as competitive pressure mounts.
- Entrepreneurial companies will continue driving innovation, forcing traditional corporations to keep up or get left behind.
4. “Little ai” Adoption Is Running Circles Around “Big AI” Implementations
My final revelation hits close to home. Let me explain this framework that I’ve borrowed from my book, The 24-Hour Rule, about documentation practices that fuel momentum and growth. There’s a critical difference between “Big D” and “little d” documentation approaches:
- Big D: Traditional enterprise approach—teams of IT experts, formal systems, comprehensive policies, massive implementations.
- Little d: Everyday practices that actually get work done—notes, emails, reports, presentations. The skills that make everything else possible.
This same dynamic is playing out perfectly in corporate AI adoption:
- Little ai is exploding: People are revolutionizing how they take notes, write emails, organize information, and generate ideas.
- Big AI is still catching up: Sure, classic AI handles invoice matching and expense audits. But when I walk into client offices and examine core processes, procurement, sales order processing, and accounting workflows haven’t fundamentally changed….yet.
The Ripple Effect:
When your team starts using ChatGPT for planning, they inevitably ask, “Why can’t our project management system do this?” When marketing professionals create content with AI assistance, they start pushing for integrated workflows. The tail is wagging the dog, and that’s exactly how real transformation happens.
Predictions:
- The tail will continue wagging the dog—Little ai will drive Big AI transformation at an accelerating pace as experimentation at the individual level creates pressure for systematic adoption.
- Individual AI literacy will become the foundation for organizational AI strategy, not the other way around.
- Teams using GenAI tools like Copilot and ChatGPT regularly for daily tasks will naturally evolve toward integrated workflows, but it will require deliberate effort to bridge the gap.
The Bottom Line: It’s Time to Get Real About AI
The companies and professionals who are winning aren’t the ones with the biggest budgets or fanciest tools—they’re the ones who’ve embraced the messy reality of learning by doing. They’re figuring it out as they go, making mistakes, iterating, and gradually building AI into daily workflows.
But here’s the thing—you don’t have to figure this out alone.
Want Help to Move Beyond the Hype?
If you’re ready for practical, realistic AI adoption approaches that actually work in the real world, we’d love to help. No grandiose promises. Just practical, tested approaches that help knowledge workers and companies become genuinely more productive—without the snake oil.
What Risk Oversight offers:
- AI and Documentation Training that focuses on real-world application, not theoretical frameworks.
- Process and Workflow Consulting to bridge the gap between individual AI wins and organizational transformation.
- Peer Learning Groups for facilitated discussions and hands-on learning (because the best insights come from sharing war stories, not vendor presentations).
Ready to start building an AI strategy that works? Contact me at adrienne@riskoversight.ca to discuss how we can bring realistic AI adoption to your team—minus the hype, plus the results.