Almost every executive I speak with carries the same private worry: that they will be asked to show real ROI on AI investment soon and they are not sure they can deliver it in time. Driving ROI on AI is the skill that will define the next decade or two of your executive career – whether you spend it struggling to stay relevant or being treated as indispensable.
The good news is that if you feel behind, you are not alone – almost everybody is.
AI adoption is high. AI confidence is not. Most organizations have bought licenses, launched pilots, stitched together tools and encouraged experimentation. But the conversation about value is still stuck at the workflow level – Copilot saving hours, support tickets closing faster. Directionally promising, but disconnected from the metrics that matter at C-suite level, like margin expansion. The executive who closes that gap becomes one of the most valuable people on the leadership team.
Let’s look at three lanes. They’re not exclusive – each attacks the same challenge of building AI capability through a different lens. Pick the one that fits your skill set, your temperament and the career you want five years from now.
1. The human value multiplier
AI excels at compressing the production layer of white-collar work. It can draft, summarize, research, code, generate options and analyze large bodies of information. Work that used to take hours or days now takes minutes.
But the more important question is: what do your people do with the capacity AI releases? If the answer is simply “produce more of the same,” the business ends up with more activity, not more value. More slides. More emails. More reports. More internal noise.
The real advantage comes when leaders redeploy human capacity upstream into the work AI cannot do well. Deciding what’s worth doing. Persuading others to support it. Reading the room. Building trust.
Take enterprise sales. Pre-meeting work – buyer research, stakeholder mapping, deck preparation – once consumed large parts of a salesperson’s week. AI compresses most of it. The salesperson who wins puts that time into two places.
The real advantage comes when leaders redeploy human capacity upstream into the work AI cannot do well.
First, they spend more of it building real relationships with buyers. Second, they prepare better – using AI to understand the buyer’s commercial pressures, sector headwinds, likely objections and decision styles. Done well, the buyer feels understood in the first five minutes.
I’ve watched this play out in our own business. A huge slice of our internal resource used to go into campaign reporting and creative production. AI absorbed a meaningful portion of that, which freed our team to go deeper on client psychographics. What we found surprised us.
Most of our clients fall into the D and I quadrants of the Dominance, Influence, Steadiness and Conscientiousness (DISC) framework, while our communication – in our enthusiasm to convey the magic of what we do – was speaking to S and C personalities.
We are rebuilding our pipelines so the messaging matches the right personality at each stage. The result is a warmer, more tailored experience for every client. We could not have done this 18 months ago – we simply didn’t have the time.
2. The agentic value multiplier
Your company buys 100 Copilot licenses. Excitement peaks in week two. Then comes a crater of disappointment and most quietly stop using it. That curve looks identical in nearly every enterprise rollout. Around 20 percent become active users. The other 80 percent drift back to old habits.
The 20 percent who stuck with it figured out something the other 80 percent didn’t – the destination of AI is agents, not chat. Your team doesn’t need better chatbots. It needs an agent layer that can get things done.
There is a huge gap between “I bought 100 Copilot licenses” and “I can reliably run 50 agents across the team with role assignment, supervision hierarchies, failure recovery, cost controls, audit logging and human escalation paths.”
The work isn’t writing better prompts, it’s treating the model like a capable but inexperienced collaborator.
The work isn’t writing better prompts, it’s treating the model like a capable but inexperienced collaborator – breaking down the task, providing context, reviewing the output, iterating. The same skill set that makes someone a good manager.
Most corporate AI training misses this. The basic layer (tool tours, prompting basics) and the expert layer (APIs, fine-tuning) are both fine. What almost nobody is training for is the layer in between: task decomposition, context assembly, quality judgement, knowing when output can be trusted.
That’s where the productivity gains actually live and where your people are stuck. Shift your organization’s AI mindset from “we bought everyone a license” to “we trained people to manage AI the way they would manage a sharp but inexperienced new hire,” and AI adoption becomes a capability-building exercise, not a software rollout.
3. The decision infrastructure builder
The third lane is for the big-picture technologists, operators and transformation leaders. Almost every company using AI has the same shape of stack: a warehouse like Snowflake, a workflow tool, a model layer plugged into OpenAI or Anthropic, a CRM, integrations holding it together.
From a distance it looks like a system. Up close, it’s connective tissue – pipes designed to move information between tools, not an architecture designed to make decisions. That distinction explains why so many enterprise AI investments fail to move board-level metrics.
The current stack answers two questions well: what information do we have and what process should run? It does not answer the question that actually drives outcomes – what should we do? That third layer is missing in most enterprises. Workflow automation is not the same as decision-making.
The market won’t ask whether you experimented with AI – it will ask whether you turned AI into measurable value.
A workflow moves information between systems – triggering an email, updating a record, generating a report. Decision-making requires more structure. What is being decided? What are the options? What context matters? What trade-offs are acceptable? When does a human need to intervene?
Without that structure, AI pilots impress in demonstrations but disappoint in production. They can generate, summarize and recommend. They cannot operate inside a consistent decision environment. The shorthand for the shift is schema versus stack.
Stack-based approaches assemble powerful tools and connect them loosely. Schema-based approaches start with how decisions actually get made in your business and let the AI operate inside that structure. One moves information around. The other compounds into the kind of consistent, auditable outcomes the board can see in the numbers.
This is the lane for the executive who thinks in architecture – not writing code, but recognizing when a vendor is selling connectivity instead of decision-making and refusing to sign for the wrong one. The companies that pick this lane early stop adding tools and start fixing structure. The ones that don’t will spend the next two years explaining to the board why AI investment never moved the margin line.
Which lane will you choose?
Three lanes. None is really about using AI. All three are about positioning yourself as the executive who translates AI investment into board-level results – then building the track record to prove it. You don’t need all three. But you do need credibility in one.
The next few years will reprice executive talent. The market won’t ask whether you experimented with AI – it will ask whether you turned AI into measurable value. The executive who can answer that clearly will be harder to replace, easier to hire and more likely to shape the future direction of the enterprise.
