Organizations citing the benefits they’ve achieved from AI initiatives often point to productivity gains and cost savings. When it comes to the ROI of AI, these are familiar, easily quantifiable metrics, and they tend to reinforce what companies are already doing by showing how much time and expense are being saved. This mindset, however, can trap organizations in a narrow view of performance. AI is transforming how we work and compete, which demands an evolution in how we measure success.

The more traditional ROI measurements are not suited to capturing the broader impact of AI, because AI’s outcomes are often distributed across time, teams, and capabilities. If your organization is still using legacy metrics and scorecards, you could already be falling behind—even if those metrics look good. What’s needed is a way to assess whether your organization is becoming more adaptive, more innovative, and more resilient—whether you’re prepared not only to operate, but to thrive, in a continuously changing environment.

Just as AI is enabling a fundamentally new way of working, measuring success in this rapidly changing environment will require a whole new approach.

AI ROI is a moving target. Lagging indicators like hours saved, budget trimmed, or headcount reduced can show short-term efficiency gains. These metrics, however, often fail to signal whether an organization is getting better at anticipating changing markets, launching new revenue lines, or developing the future-ready workforce. Many of the benefits of AI—such as improved customer satisfaction or an increased ability to deliver meaningful innovations—are intangible and measured over longer time horizons, which makes achieving the quick wins many organizations prize somewhat difficult.

This misalignment between what’s easy to measure and what actually drives sustainable growth can be detrimental to an organization. When leaders are rewarded more for incremental gains in speed or cost reduction, their teams will likely continue to concentrate their efforts in these areas, thereby reinforcing legacy structures rather than systemic reinvention. As a result, organizations may be well optimized for yesterday’s priorities but unprepared for tomorrow’s disruption.

We recommend a more robust set of metrics to answer questions like: 

• Are we building the ability to shift strategic direction in response to external signals?
• Are we accelerating the pace at which good ideas become revenue-generating products and services?
• Are we investing in human adaptability alongside technical performance?

Because traditional scorecards can’t address such questions, they serve more as rearview mirrors—offering clarity only in hindsight. What’s needed is a lens that looks forward.

Slalom has developed the PIVOT framework to help organizations evaluate their AI transformation from a more forward-looking perspective. This framework defines five critical dimensions:

People: Are we building adaptable, AI-literate talent?

Innovation: Are we converting ideas into new revenue streams?

Vision: Are we planning for multiple scenarios—and not just in the next quarter, but well beyond?

Operations: Are we agile enough to realign, and do so quickly?

Technology: Do we have the infrastructure to scale AI responsibly?

Each domain represents a set of capabilities that traditional metrics often ignore. The point isn’t just to check progress but rather to align measurement with strategic intent.

To monitor success in each of these PIVOT dimensions, organizations must track new metrics that reflect whether AI is helping in areas such as improving learning, accelerating innovation, strengthening decision-making, and enabling the business to adapt to shifting market conditions.

The following are some of the ways forward-thinking companies are reframing the way they measure success:

By not focusing purely on the typical, monetary-based measurements of speed/productivity/cost reduction, these metrics encourage leaders to ask better questions and create better behaviors that could result in a more agile, adaptable organization.

If you’re part of your organization’s innovation team, some of the metrics listed above may be familiar to you. But these and other metrics we’ve compiled in our PIVOT framework and scorecard are meant to measure AI success beyond a single department to transform the entire organization. They promote cross-functional collaboration, which is essential for a genuinely adaptive enterprise.

This emphasis on AI-driven agility and adaptability will be an enabler for new and exciting business models. Lean, AI-enabled organizations aren’t just efficient—they’re highly inventive, and deeply attuned to how fast they can build, test, and adapt. That’s the competitive terrain every enterprise must prepare for.

But achieving this level of disruption requires more than just tech—you need to fundamentally shift what you measure.

Learning velocity is as important as operational efficiency. Experimentation and strategic agility are essential to long-term planning. To lead in an AI-enabled economy, organizations must build the capacity to reconfigure themselves—continuously. That means tracking:

• How quickly teams acquire critical future skills
• How frequently strategy adapts to changing conditions
• How many ideas translate into new revenue or IP
• How well decisions flow through the organization
• How fast initiatives move from planning to execution

Each of these signals tells a story about readiness. By measuring these capabilities consistently, leaders can identify where transformation is taking hold—and where it’s getting stuck.

AI is already reshaping your industry. If you want your organization to do more than keep up, now is the time for a new definition of AI success and a better way to measure how you get there.