Most Companies Are Wasting AI on Marginal Gains, Missing Real Competitive Advantage
Organizations are deploying AI to save employee hours and cut operational costs-but this incremental thinking is leaving real money on the table, according to research firm Forrester.
The numbers tell the story. Among AI decision-makers surveyed, 43% measure productivity improvements and 41% measure efficiency gains. Only 32% tie AI outcomes to profits or revenue.
This disconnect matters. A company that saves 10,000 employee hours looks productive on paper. It won't cover infrastructure costs or drive business growth.
The Strategy Problem
Between 5% and 15% of organizations have an effective AI strategy, with the percentage likely closer to 5%, according to Brian Hopkins, vice president for emerging technology at Forrester.
"Efficiency is not strategy; it's project management," Hopkins said. "You're trying to make your current processes incrementally better."
The approach creates a structural problem. To realize productivity gains, companies must prove the solution works-then eliminate jobs. The people affected have little incentive to cooperate.
"Do you think those people who will be let go are going to help you do that? They're not. It's messy, ugly work," Hopkins said.
Workflow Redesign, Not Tool Deployment
The solution requires rethinking how work flows through an organization, not bolting AI onto existing processes.
Mike Flynn, technology sector consulting leader at EY, calls the current approach "trapped work." Companies add AI tools and computing costs without removing significant work from the system.
"If you think about bolting AI onto your business problem, as you continue to add AI, the amount of effort that's going on continues to increase," Flynn said. "Versus redesigning your processes such that AI is built into your process."
Christine Park, chief AI transformation officer at Branch, frames it plainly: "AI is being treated like a feature when it should be treated like a transformation."
True transformation requires changes across three dimensions-how work is organized, how people are trained, and how success is measured. It's not a technology problem alone.
Governance Keeps Companies Stuck
One reason organizations default to productivity improvements: they're the safest option.
Material use cases-like repricing decisions or supply chain optimization-require model risk management, audit trails, and governance structures most enterprises lack, according to Thomas Prommer, former president at design, IT, and AI company Huge.
"Internal productivity is the only use case the organization can actually safely test with current governance," Prommer said. "They're doing copilots because copilots don't need a model risk committee."
Companies that moved beyond incremental gains did so because a single P&L owner staked their number on it. That executive pressure forced the organization to build necessary governance and take on material risk.
What Actually Works
Organizations that want competitive advantage should focus on four areas:
- Define business outcomes and success metrics before deploying AI
- Identify specific use cases aligned with those outcomes
- Build a structure to plan, test, and deploy applications
- Scale using cloud infrastructure, frontier models, and embedded agents
The difference between winning and losing with AI comes down to strategy, Hopkins said. "Strategy is where you apply massive force, based on an insight you have, that gives you strength and weakens your competition."
That means finding an insight competitors haven't seen and building a capability they can't replicate. It's harder than saving hours. It's also the only path to real competitive advantage.
For executives developing AI strategy, understanding the difference between efficiency and transformation is foundational. AI for Executives & Strategy resources can help clarify how to structure AI initiatives for business impact rather than incremental gains.
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