Don’t be fooled by the dire headlines about companies struggling to demonstrate value from AI initiatives. A small group of organizations is proving otherwise.
The ServiceNow Enterprise AI Maturity Index 2026 identifies a group of Pacesetters whose average maturity score is nearly 30 points higher than the others in the study. That represents just 21% of those surveyed. Pacesetters are achieving 160% return on investment (ROI) today, and that figure is projected to reach 194% within two years.
Pacesetters aren’t smarter, better funded, or better equipped. They’re just building their organizations around it AI while most organizations deploy the technology to improve existing processes.
The distinction comes down to strategy. Most organizations start by asking where they can use AI. Pacesetters start by determining the business outcome they want. The former approach leads to isolated use cases and no plan for how to use the efficiency gains. The latter leads to transformation and long-term results.
A bimodal AI strategy—running efficiency initiatives (mode 1) in parallel with innovation initiatives (mode 2)—is the difference between deploying AI and being transformed by it.
In conversations with executives, the 20-minutes-saved problem comes up again and again. Implementing AI to save everyone's time sounds impressive until you ask people what they plan to do with those time savings. The reality is that time saved creates no actual value unless it’s consolidated into meaningful change.
The math is straightforward. Scale AI, automating 20% of work across 10,000 employees, and you've theoretically unlocked the equivalent of 2,000 full-time employees. But what would they do?
Without answering that question, organizations face three possible dead ends:
- Employees do less work.
- Marginal time savings evaporate into the organizational structure.
- Headcount gets reduced without any reinvestment plan.
The first two produce no ROI. The third captures short-term savings while surrendering long-term competitive positioning.
The 20-minutes-saved example illustrates an uncomfortable truth: Most decision-makers don’t know what to do with AI. Efficiency without a destination isn't a strategy.
Bimodal AI strategy is straightforward in concept. Mode 1 deploys AI to automate, optimize, and free resources—whether cash, time, or people. Mode 2 takes those freed resources and channels them into new business models, new service lines, and new markets. Mode 2 must be defined before mode 1 begins, and both modes run in parallel, not in sequence.
This is an organizational AI strategy. A chief information officer can deploy AI but lacks the authority to consolidate freed capacity across the organization and redeploy it toward something new. That decision cascades through HR, organizational design, finance, and other affected business units.
Redeploying IT resources to fund product development initiatives, for instance, creates organizational implications that extend well beyond any single executive's domain.
This is why bimodal AI is fundamentally about leadership. The destination—what mode 2 will build—is an executive commitment that must precede mode 1’s implementation. Without that commitment from the top, efficiency gains may be impressive on paper, but they’re immaterial to the business.
Most companies approach AI as a cost play: automate, optimize, reduce. CEOs declare that the organization must adopt AI, teams deploy solutions, and then leadership discovers the investment is generating costs without commensurate returns.
Deploying AI for its own sake means using new technology to do old things marginally better. That's not transformation. That's just an expensive upgrade, while others are investing in future competitive advantages.
Pacesetters—the 21% of organizations that have committed to using AI to develop entirely new capabilities rather than maximizing existing ones—are at least six times more likely to use AI to create new products, services, and revenue channels, according to the Enterprise AI Maturity Index. They're optimizing faster than others, and they’re building new capabilities that their competitors can't easily replicate.
AI-fueled optimization has a ceiling. Eventually, an organization will have automated everything it can and cut every cost. Meanwhile, competitors aren’t just operating more efficiently; they’re building future businesses that render their current models obsolete.
The strategic question shifts from “where can AI save us money?” to “what will we build with what AI saves us?”
A European home furnishing giant shows how this works in practice. The company deployed an AI customer service solution that automated a significant share of customer inquiries.
Some companies would have conducted layoffs as a result. Instead, the company retrained 8,500 call center workers to become remote interior design consultants. Customers share photos of their rooms and receive personalized furniture recommendations. That service has since become a meaningful revenue stream.
The difference wasn't superior technology. The company understood before deploying mode 1 what mode 2 would deliver. Cost savings became reinvestment capital for a new business model.
That's bimodal AI, enabling organizations to use technology to augment human creativity rather than simply eliminate human workers.
Organizations abandoning AI projects are failing at strategy. Before the next AI investment reaches the approval stage, leadership must answer one question: What will we build with what this saves us?
If the answer is, “We'll determine that later,” the organization isn't ready for AI. The investments will be made, but there will be no returns on them.
Bimodal strategy requires leaders to commit to a destination before deployment, to decide what they want to become, not just what they want to automate.
Pacesetters have chosen to build for what they are today and for what they want to become. What will you choose to do?
Find out how you can maximize the return on your AI investments.
