AI Readiness: Objects in the Mirror Are Closer Than They Appear

We were bringing a new acquisition onto SAP – a big implementation, the kind that touches every process in the company – and I was the IT lead. First thing I did was refuse to let anyone call it “the SAP Project.” We called it the Business Process Optimization Project. Not exactly poetry, I know. But here’s what happens when you call something the SAP Project: people hear “SAP” and think “technology,” and when they think “technology,” they think “IT’s problem.” The operating departments who actually owned the processes we were redesigning would have been perfectly happy to sit back and let us drive. Which is, of course, exactly how implementations go sideways.

So we stuck with the clunky name. And it worked – for about three months, until people drifted back to calling it the SAP Project. Fewer syllables, easier to say, I get it. But by then it didn’t matter. The manufacturing folks, the finance team, the supply chain people – they all understood they owned the project. The name had done its job. It reframed who was responsible before anyone had a chance to assume otherwise.

I think about that story a lot these days, because I’m watching the same pattern play out with AI. A leadership team gets together, someone says “we need an AI strategy,” and every head in the room swivels toward the CIO or the most technical person at the table. Everyone nods. The meeting moves on. And just like that, AI readiness becomes somebody else’s job – the same way digital transformation became somebody else’s job a decade ago, with roughly the same results.

The problem isn’t that organizations lack AI talent or AI tools. The problem is that they’ve already decided – through language, through assumptions, through the way they frame the conversation – that AI is a technology initiative. It’s not. AI readiness is an organizational capability, and every functional area of the business has something specific and irreplaceable to contribute. Most of them are already contributing, whether anyone’s noticed or not.

The Language That Built the Wall

There’s a phrase that’s been rattling around corporate hallways for as long as I can remember: “IT and the business.” You’ve heard it. Demonstrating IT alignment with the business. IT’s relationship with the business. IT and the business need to work together. It sounds reasonable – collaborative, even. But think about what it actually says. IT is over here. The business is over there. And we need to build a bridge between them.

Do you hear people talk about “Finance and the business”? “HR and the business”? Of course not. Because nobody would suggest that Finance is somehow separate from the business it serves. But we’ve been doing exactly that with IT for decades – and now we’re doing it with AI. “The AI team and the business.” “Aligning AI with business objectives.” Same language, same wall, same problem.

There’s a concept in social psychology called the minimal group paradigm – the observation that it takes almost nothing to get people to self-organize into groups and start behaving like rivals. In the classic experiments, people sorted by something as meaningless as a colored cap would start favoring their own group within minutes. No history, no conflict, no real difference – just a label. And once those groups form, you get in-group favoritism and out-group suspicion almost immediately. It’s not malice. It’s just how people are wired.

That’s what “IT and the business” does. That’s what “the AI team” does. The language itself creates the minimal group. The moment you have an “AI team,” you have an “everyone else,” and everyone else assumes that AI readiness is something happening in a room they’re not in. I spent years falling into this trap myself – using “the business” as convenient shorthand – before I realized the shorthand was doing real damage.¹I eventually switched to “functional areas of the business” in meetings and presentations. Clunky? Sure. But it stopped reinforcing the wall I was supposedly trying to tear down.

The AI version of this is happening right now, faster than the IT version ever did. Companies are hiring “AI leads,” creating “AI centers of excellence,” building “AI strategy teams” – and every one of those moves, however well-intentioned, sends the same signal to the rest of the organization: this isn’t your thing. The finance team goes back to their spreadsheets. The operations team goes back to their metrics. And the AI team wonders why nobody is engaged.

Your Organization Is Already Using AI (You Just Don’t Know It)

Years ago, when cloud computing and SaaS were still relatively new ideas, IT leaders started noticing something uncomfortable: departments across the company were buying and deploying technology without asking permission. Marketing had signed up for a CRM platform. Finance was running consolidation tools on their own budget. Operations had brought in warehouse management handhelds. The phenomenon got a name – shadow IT – and most CIOs treated it as a problem to be managed. A governance failure. A risk.

But it wasn’t a governance failure. It was a canary in the coal mine. People across the organization were telling you, through their actions, that information and technology were fundamental to how they did their jobs. They weren’t going rogue for fun. They were solving real problems with the tools available to them, and they weren’t waiting for IT to get around to it.

The same thing is happening right now with AI – and it’s happening faster. Your finance team is using AI-enhanced forecasting features that came bundled in their planning tools. Your marketing group is generating content, testing campaign variations, running sentiment analysis through platforms they subscribed to last quarter. Your operations people are exploring predictive maintenance vendors whose pitch decks are full of machine learning. Your product development team is prototyping with AI-assisted design tools. And your HR team is screening resumes through a platform that’s been using AI for two years – they just didn’t call it that until recently.

None of this is going through your AI strategy team, because most of it doesn’t look like “AI” from the outside. It looks like a software upgrade. A new vendor feature. A tool someone found that made their Tuesday afternoon less painful. But add it all up and you’ve got AI adoption happening in every corner of the business, with no coordination, no shared learning, and no way to measure the total investment.

Remember the old “IT as a percent of revenue” metric? It was always misleading, because it only counted the dollars that ran through IT’s cost center. The real technology spend – Finance’s Hyperion systems, Marketing’s web platforms, Operations’ material handling investments – sat in departmental budgets where nobody aggregated it. We’ve recreated the same blind spot with AI. Whatever you think your organization is spending on AI, the real number is higher, spread across a dozen cost centers, and growing every month.

Every Department Is Already an AI Department

This isn’t just about shadow AI tools slipping through the cracks. The deeper reality is that every functional area of your business has been running on information and technology for years – in many cases, for decades. AI doesn’t change that equation. It accelerates it.

Think about what Finance actually does day to day. Their complex spreadsheets and financial models aren’t just “using Excel.” Those are genuine custom software developments – sophisticated, interconnected, business-critical systems that Finance built, maintains, and owns. The consolidation and reporting platforms, the forecasting tools, the budgeting systems – these live in Finance’s world, run by Finance’s people, funded by Finance’s budget. When machine learning features start showing up in those platforms (and they already have), it’s not a technology shift for Finance. It’s an incremental capability on top of systems they already understand deeply.

Or look at Sales and Marketing. CRM systems – on-premise or SaaS, selected, justified, developed, and administered by the sales organization. Web platforms and ecommerce – conceived, budgeted, and managed by Marketing. These aren’t IT projects that Sales and Marketing happen to use. They’re Sales and Marketing projects that happen to involve technology. When AI-powered personalization, lead scoring, or content generation gets layered into those platforms, the department that understands the customer context is the one best positioned to make it work.

Operations has its own version of the same story. Shop floor teams deploying warehouse management systems, implementing handhelds and scanners, connecting manufacturing machines to networks for monitoring and optimization. Product Development has been running the most complex and expensive workstations in the building for years, driving sophisticated design and simulation software that demands more computing power than anything else in the company. HR manages payroll – possibly the most critical system in the business – along with talent management, recruiting platforms, and training systems that have gone SaaS faster than almost any other category.

The point isn’t that these departments are dabbling in technology. The point is that they are, and have been, technology-intensive operations in their own right. The distinction between “IT” as a department and “information and technology” as a business capability matters, because once you see it clearly, the idea that AI readiness belongs to any single team starts to look absurd. Every department is already an AI department. They just need to start acting like it.

AI Readiness Is an Organizational Capability

So if every department is already running on information and technology – and increasingly on AI – what does that actually mean for how you approach AI readiness? It means you stop thinking about it as a technology initiative and start thinking about it as something your organization builds from the contributions of every functional area. Each one brings something specific, something the others can’t replicate.

Finance, for example, brings the discipline of speaking in facts. These are the people who have spent their careers turning ambiguity into numbers, building models that force clarity, insisting on data that can be trusted. When an AI initiative needs clean data governance, a credible ROI framework, or someone willing to kill an experiment that isn’t delivering – that’s Finance. They’ve been doing this kind of rigorous, evidence-based work for decades. AI just raises the stakes.

Sales and Marketing sit on what might be the richest unstructured data in the company – customer conversations, objections, buying patterns, competitive intelligence gathered one deal at a time. They also know how to communicate change and articulate value in terms that real people care about. When AI adoption stalls because nobody in the organization understands why it matters to them, that’s a Sales and Marketing problem, not a technology problem.

Product Development is where the conversation shifts from using AI internally to putting intelligence into what you sell. This is the “widgets to digits” transition – the recognition that your products and services are increasingly defined by the data, sensors, and software embedded in them. The product team brings design thinking, innovation discipline, and the ability to envision what AI makes possible for customers. And frankly, your own organization can be the first beta customer for those AI-enabled products, which is a tremendous advantage if you use it.

Operations brings something that doesn’t sound glamorous but is absolutely essential: discipline. Lean principles, waste elimination, management by metrics, the daily rigor of running processes that work. Here’s the thing about AI – it amplifies whatever it touches. If your process is solid, AI makes it better. If your process is a mess, AI gives you an automated mess, faster. Operations is the reality check, the quality control, the team that makes sure AI stays grounded in how work actually gets done.

And then there’s the orchestration layer – IT enabling the infrastructure and integration, and executive leadership making sure the whole effort is coordinated and resourced. Not owning AI, but making sure every area’s contributions connect.

Coordination, Not Control

The instinct, once you see all this decentralized AI activity, is to centralize it. Pull it all under one roof. Create governance. Establish standards. Control the spend. And some of that instinct is right – you do need coordination, shared learning, and a way to measure what’s working. But there’s a critical difference between coordination and control, and getting it wrong will kill the very energy you’re trying to harness.

When shadow IT first emerged, the organizations that tried to stamp it out missed the point entirely. Those departments weren’t going rogue – they were solving problems. The smart CIOs recognized shadow IT for what it was: proof that information and technology had become essential to every part of the business. The right response wasn’t to shut it down. It was to ask better questions. How do we share what’s working? How do we connect these islands so the data flows? How do we invest in a way that maximizes the return across the whole organization instead of optimizing each department’s corner?

The same questions apply to AI, and the answers follow the same pattern. You don’t need an AI czar who approves every tool and every experiment. You need a framework that helps each functional area understand what it contributes, how its work connects to what other areas are doing, and where the organization needs to build shared capabilities – data infrastructure, governance, skills development – that no single department can build alone.

This is where a framework like the Five Building Blocks comes in. Operational Excellence, Customer Connection, Product Intelligence, Data Mastery, Team Dynamics – each one maps to the contributions we’ve been talking about. Operations brings discipline. Sales and Marketing bring the customer voice. Product Development brings innovation. Finance and Operations bring data rigor. And the human dimension – how your teams communicate, learn, and adapt – runs through everything. AI readiness isn’t a score on a maturity model. It’s the organizational muscle that comes from every area doing its part and someone making sure the pieces fit together.

The question most organizations ask is “are we ready for AI?” And the honest answer, for most of them, is that they’re more ready than they think – because the skills, the data, and the institutional knowledge already exist in every functional area. What’s missing isn’t capability. It’s coordination.

In the articles that follow, we’re going to look at each of these functional areas in detail – what Finance brings, what Sales and Marketing bring, what Product Development and Operations each contribute, and what IT and executive leadership provide as the connective tissue that holds it all together. Then we’ll tackle the hard question of how you actually measure AI readiness across the organization. Not with a vendor checklist or a maturity model that tells you to buy more software, but with a practical framework built around what your people already know and what your business actually needs.

Because the answer to “whose job is AI readiness?” isn’t the CIO’s. It isn’t the AI team’s. It’s the same answer we should have given about digital transformation when we had the chance.

It’s everyone’s job. And most of your organization is already doing it.

If you’re thinking about how to coordinate AI readiness across your own organization – or just want to follow this series as it develops – join our mailing list.

Related Articles

• Organizational Readiness for AI Adoption and Scale – Google Cloud’s practical guide on scaling AI across departments, with emphasis on cross-functional champions and change management
• AI Readiness: The Four Steps CEOs Need to Take – Harvard Law Forum piece on why AI transformation is a leadership challenge, not a technology one
• Understanding AI Readiness: How to Prepare Your Digital Workplace – Comprehensive view on aligning AI with business goals through culture, governance, and data foundations