Most enterprises have yet to tap into the transformative power of AI, focusing instead on incremental productivity and efficiency gains that don\u2019t lead to competitive advantages, according to a report from research firm Forrester. The report, titled Accelerate Your AI Voyage<\/em>, was covered by CIO in an article published on 29 April 2026. That article is the sole source for this piece.<\/p>\n For business leaders deciding what to do about AI at work, the findings are a wake-up call: if your organization is like most, you may be measuring the wrong things and missing the bigger opportunity.<\/p>\n Forrester surveyed AI decision-makers and found that 43% measure productivity improvements gained through AI, and 41% measure efficiency gains. But only 32% tie AI outcomes to profits or revenue. That gap reveals a fundamental disconnect: companies are optimizing for internal metrics that don\u2019t show up on the bottom line.<\/p>\n \u201cSaving 10,000 employee hours might look good on paper, but it won\u2019t cover the GPU bill, let alone drive reinvention,\u201d Forrester analysts write in the report. \u201cThis incremental thinking forms the basis of a fundamental disconnect in the promise of AI\u2019s transformative potential.\u201d<\/p>\n Brian Hopkins, vice president for emerging technology at Forrester, estimates that only between 5% and 15% of organizations currently have an effective AI strategy \u2014 with the percentage likely closer to the low end. By focusing on productivity or efficiency gains, most organizations miss out on the true power of AI, he says.<\/p>\n \u201cEfficiency is not strategy; it\u2019s project management,\u201d Hopkins says. \u201cYou\u2019re trying to make your current processes incrementally better.\u201d<\/p>\n Giving employees a copilot to see what they\u2019ll do with it is not a winning approach, Hopkins adds. \u201cThis whole idea that we\u2019re going to incrementally invest in productivity and somehow that\u2019s going to capture the potential that AI offers, it\u2019s a fool\u2019s errand,\u201d he says.<\/p>\n Meanwhile, AI productivity improvements often depend on job cuts on the other side of deployment, he adds.<\/p>\n \u201cThe problem with incremental productivity improvements is that in order to realize the benefits that your CFO demands, you have to implement a solution, prove that it works, and then you have to fire people,\u201d Hopkins says. \u201cDo you think those people who will be let go are going to help you do that? They\u2019re not. It\u2019s messy, ugly work.\u201d<\/p>\n The Forrester data echoes another recent survey from AI agent platform vendor Decidr, which found that 40% of US businesses are getting the bulk of their AI value from ChatGPT-style tools, instead of agents or custom AI models.<\/p>\n Other IT leaders also see the problems highlighted in the Forrester research. Many companies are focused on horse-and-carriage-level AI strategies in a world that\u2019s moving to self-driving cars, says Christine Park, chief AI transformation officer at mobile link tracking platform provider Branch.<\/p>\n \u201cThis is exactly what happens when the market moves faster than the operating model,\u201d she says. \u201cLeaders are optimizing for narrow efficiency inside functions instead of rethinking how the work itself should fundamentally change.\u201d<\/p>\n Productivity and efficiency gains won\u2019t significantly move the needle for most organizations, she adds. True AI transformation won\u2019t enable individual functions, but it requires coordination across all workflows.<\/p>\n \u201cAI for cost efficiency raises the floor, so what?\u201d Park says. \u201cIf it\u2019s just an efficiency play, you\u2019re not going to win beyond short-term gains. If you consider cost avoidance versus efficiency, we can grow without proportional headcount growth, but you need true transformation to raise the ceiling.\u201d<\/p>\n Instead, smart organizations will focus on AI as an amplification of both revenue and the experience of people, she adds. The nature of work is changing, with it now happening in multidimensional workflows instead of step-by-step tasks, she says.<\/p>\n \u201cAI is being treated like a feature when it should be treated like a transformation,\u201d she says. \u201cThat means taking a human-centered lens and leaders changing how we train people, define roles, and measure success. AI is a human shift, not just a new tool.\u201d<\/p>\n Organizations should look for business-wide workflow transformation, adds Mike Flynn, technology sector consulting leader at professional services provider EY. Many organizations are focused on task-level automation instead of redesigning workflows from end to end, he says.<\/p>\n By focusing on task-level gains, companies add AI tool and computing costs without removing significant work from the system, leading to what Flynn calls \u201ctrapped work.\u201d<\/p>\n Organizations need to take an AI-centric approach to all their workflows and try to redesign processes to remove repetitive human work as much as possible, Flynn recommends. Organizations should then add in human intervention when it\u2019s needed, he says.<\/p>\n \u201cIf you think about bolting AI onto your business problem, as you continue to add AI, the amount of effort that\u2019s going on continues to increase, versus redesigning your processes such that AI is built into your process,\u201d he adds.<\/p>\n Creating an enduring AI strategy goes beyond rolling out a few AI tools for employees, Flynn says, adding that EY runs clients through an AI value blueprint that takes them through potential outcomes of various AI strategies.<\/p>\n \u201cFirms are realizing that this is not as easy as just enabling and giving people tools that they can do something with and kind of bolt onto their existing jobs,\u201d he adds. \u201cTo me, the big thing is thinking about re-engineering your operational processes. It\u2019s a process transformation and a people transformation as much as it is around specific AI.\u201d<\/p>\n Most organizations aren\u2019t yet ready to take the next step, however, suggests Thomas Prommer, former president at design, IT, and AI company Huge. Material use cases such as repricing and supply chain decision-making require model risk management practices and audit trails that most enterprises don\u2019t have yet, he says.<\/p>\n \u201cInternal productivity is the only use case the organization can actually safely test with current governance,\u201d Prommer adds. \u201cThey\u2019re doing copilots because copilots don\u2019t need a model risk committee.\u201d<\/p>\n In addition, the transition from incremental to material gains driven by AI requires someone or something to force a change, such as a CEO, activist investor, or competitive shock, he adds. CIOs rarely can drive the change alone, he says.<\/p>\n However, some organizations have moved past productivity savings because they don\u2019t show up on profit-and-loss (P&L) statements, Prommer says.<\/p>\n \u201cIf you save an engineer 90 minutes a day, that doesn\u2019t show up on the P&L \u2014 it shows up as, \u2018We shipped 15% more features,\u2019\u201d he says. \u201cBoards want a line item. Companies that moved to material use cases did so because they had a single P&L owner willing to stake their number on it.\u201d<\/p>\n Forrester\u2019s Hopkins urges organizations to rethink AI strategies and focus on material changes, despite the difficulties. If organizations aim high enough, they can use AI to enable entire business transformation and find AI uses that drive competitive advantages, he says.<\/p>\n Forrester advises IT and business leaders to focus on four key areas. (The source article does not detail these areas, but the recommendation is clear: move beyond incremental thinking.)<\/p>\n If organizations take the right approach, they can deploy AI in ways that create real competitive advantages, Hopkins says.<\/p>\n \u201cStrategy is where you apply massive force, based on an insight you have, that gives you strength and weakens your competition,\u201d he adds. \u201cYou have an insight that your competitors don\u2019t see and establish a capability that your competitors can\u2019t replicate.\u201d<\/p>\n If your AI efforts are focused on productivity or efficiency, you\u2019re not alone \u2014 but you\u2019re also not likely to gain a competitive edge. The data shows that most companies are stuck in incremental mode, measuring hours saved rather than revenue gained. To break out, consider these takeaways from the source:<\/p>\n At Faida Workforce, we help organizations navigate these challenges. Our services include AI strategy consulting, workflow redesign, and change management. We disclose this relationship plainly: we offer these services, but our goal here is to inform, not to sell. The source article provides the evidence; you decide what to do with it.<\/p>\n The article was produced with the assistance of AI tools and reviewed by a human editor for accuracy and adherence to editorial policy.<\/p>\nThe Evidence: Productivity and Efficiency Dominate, Profit Lags<\/h3>\n
Why Incremental Gains Don\u2019t Move the Needle<\/h3>\n
Governance and Leadership Gaps Hold Companies Back<\/h3>\n
What This Means for Your Organization<\/h3>\n
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Sources<\/h3>\n