AI adoption has reached a critical turning point. Enterprises have moved beyond asking whether AI has value; now, the question is how to extract it. Yet despite massive investment, meaningful transformation remains elusive. Companies keep falling into the same trap: asking, “What can this tool do?” instead of, “What business problem are we solving?”
The result is a flood of disconnected pilots, rising costs, and outcomes that never materialize.
Enterprises Are in the Age of ‘What Works’
This isn’t a failure of ambition, it’s a failure of focus. Generative models are being rolled out across front to back offices. In marketing, content teams are experimenting with automated agents for research, and finance departments are testing predictive analytics. But without a clearly defined outcome and a proper data foundation, these efforts rarely connect back to measurable business value.
In today’s ‘what works’ era, enterprises often jump into fragmented deployments or pilots that ultimately prove unsustainable or fail to deliver lasting impact. That’s why more enterprises are embracing outcome-driven AI, a service-oriented model in which success isn’t measured by deployment but by results. The idea is simple: start with the business goal, define the KPIs, and build backwards from there.
Holding AI Accountable
One company leading the shift is Gruve, which helps enterprises operationalize AI through a service-first approach. Rather than offering a traditional software platform, Gruve designs and implements custom AI systems with one key difference: clients only pay when results are delivered.
This model forces alignment from day one. Gruve works with stakeholders to define success, whether that’s reducing onboarding time, cutting resolution rates in support, or eliminating manual steps in finance ops. Only after agreeing on those metrics does the build begin.
“AI isn’t valuable just because it’s powerful,” said Tarun Raisoni, CEO and co-founder of Gruve. “It’s valuable when it changes business outcomes, when it reduces churn, closes revenue gaps, or eliminates inefficiencies. That’s the bar we hold ourselves to.”
A Look at Real-World Impact
Take onboarding, for example. Companies like Hitachi and Texas Credit Union are applying AI to eliminate friction in new-hire processes. At Hitachi, AI shaved four days off the onboarding process, while Texas Credit Union used automation to streamline system access and reduce HR workload, cutting the time required per hire by nearly 40%. In both cases, success began with identifying pain points, aligning on metrics, and building custom solutions around them.
The same outcome-first approach has proven critical in security. A global financial services leader had stalled for more than two years trying to modernize its firewall infrastructure across 40 countries and 80,000 employees. Compliance gaps and complex third-party dependencies made progress nearly impossible. By partnering with Gruve and Cisco, the company restarted the project, aligned stakeholders across geographies, and established a compliant, scalable migration framework that enabled more than 100 firewalls to be modernized. The outcome wasn’t just new systems in place; it was a stalled project restarted, completed, and delivering measurable security and compliance improvements.
Gruve applies the same principle across industries. In logistics and operations, outcome-driven orchestration has helped clients move from pilot testing to full-scale impact, accelerating workflows and unlocking measurable cost savings across core functions.
The Outlook: From Experimentation to Execution
The AI landscape is overflowing with dashboards, copilots, and agents. But the enterprises seeing real ROI aren’t the ones stuck in pilot purgatory; they’re the ones tying AI directly to business performance.
“We’re moving into a post-pilot world,” Raisoni said. “The companies that win with AI won’t be the ones who adopted the flashiest tools; they’ll be the ones who made AI operational. Execution is the differentiator now.”
As enterprises grapple with tool sprawl and internal fatigue, outcome-based services may be the discipline AI needs: less about what’s possible, more about what actually gets done.
