Deep expertise across the leading AI platforms
Each engagement starts where the client is. Pick a stage to find the work closest to your situation.
You’re spending on multiple tools, but you do not know what the business is getting for it.
Employees were paying for several overlapping AI tools. Jetson matched each platform to the work and cost profile of each role.
Platforms survive in the P&L long after anyone questions them. Jetson traces every contract to the business value it delivers.
Teams are saving time in pockets, but the gains haven’t scaled across the business.
Chat tools speed up the old workflow, then gains stall. Jetson’s enablement work takes teams from about 5% to about 25%.
Executives, budget owners, IT and knowledge workers each start somewhere different. Jetson brings them onto one plan.
Faculty needed to reflect how AI is used in the industries their students will enter, without weakening core skills or independent thinking.
Your team has taken AI as far as it can, and the next step needs focused ownership and a clear plan.
Early investments across several AI platforms came together under one firm-wide strategy tied to revenue, margin and risk.
Rollouts led by a single point of contact miss most of the value. Jetson gives every department a voice in defining its own AI future.
You’re executing to plan and seeing the impact you were told AI would have.
A tightly coupled legacy platform paid for itself whether or not anyone used it. The rebuild makes cost scale with work done.
A legacy platform carrying heavy technical debt was rebuilt from the first commit with Claude Code, by a much smaller team.
Every stage, from customer call to validated production code, now runs through one consistent agent-based process.
The latest cases we track at ReturnOnAI.ai, filtered by industry.
Image: ReturnOnAI.aiR1 achieved a 70% reduction in preventable denials by deploying AI agents that analyze historical claims data, real-time payer signals, and operational context to catch denial risk before billing. The gains came from synthesizing payer-provider interaction data at scale and embedding domain expertise from R1's AI innovation lab, R37, directly into workflow automation tools. Health system finance leaders should treat denial pattern data as a strategic input to leadership decisions, managed at the front end of the revenue cycle where prevention is still possible.
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Image: ReturnOnAI.aiAccenture set a $29 per-user funding limit on Claude usage to manage AI tool spending across its workforce. The cap creates a deliberate constraint that forces employees to make conscious choices about which LLM to use for which task rather than defaulting to the most powerful or expensive option. Senior leaders building AI budgets should treat per-user spending caps as a governance mechanism, because they generate real data on which tools employees reach for and why.
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Image: ReturnOnAI.aiShift Technology is targeting a tenfold increase in output per employee, aiming to produce the equivalent of 7,000 full-time workers from a staff of 700, by routing AI tasks to models matched to their complexity and cost. The company benchmarks models weekly, assigning simple tasks to cheaper models and the most demanding work to premium ones, treating inference tiering as an ongoing operational discipline. Senior leaders building AI into claims or fraud workflows should treat model cost benchmarking as a standing function, because token consumption is rising faster than prices are falling and unmanaged usage has already cost some enterprise teams tens of thousands of dollars in a single month.
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Image: ReturnOnAI.aiITMF and ITA research found that causal AI cut scrap by 10 percent on a nonwovens production line, one of the few concrete results the sector can point to so far. The broader finding is that fragmented data systems and skills shortages are stalling adoption far more than any limitation in the AI itself. Senior leaders should audit their data infrastructure before scaling AI investment, because poor data does not get corrected by better models; it gets amplified by them.
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Image: ReturnOnAI.aiExpedia directs at least 80% of its technical workforce onto a defined golden path of approved AI tools, reserving open experimentation for a smaller group of designated AI champions who vet new models before broader rollout. The company treats AI models as interchangeable commodities and anchors its competitive advantage in its proprietary travel data, deliberately avoiding lock-in to any single vendor or model. Senior leaders should note that Expedia's approach treats governance structure and data infrastructure as the durable assets, while the AI models sitting on top of them are expected to be swapped out as better options emerge.
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Image: ReturnOnAI.aiWharton Human-AI Research's fourth annual survey of 1,416 US enterprise leaders and employees finds that only 28% of enterprise leaders report strongly positive AI returns, even though 80% report some positive return. Departments that measure AI against growth outcomes such as net-new revenue or sales conversion are nearly three times as likely to report strong ROI as those measuring efficiency alone, 35% versus 13%. Senior leaders watching their own AI budgets should treat measurement design as a strategic choice: what you decide to measure determines whether AI spending surfaces real business value or disappears into hours-saved reports.
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