Goodyear presentation slide reading “Transforming a 128-year-old company with a startup mindset,” with a speaker at a podium and audience.

Goodyear CIO Raman Mehta Makes Speed A Technology Strategy

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Mehta’s “speed” agenda is only credible if it is governed as a portfolio, not celebrated as a series of quick wins. The article’s real management signal is that Goodyear is pairing experimentation with explicit sponsorship, kill switches and outcome measures. For CIOs, that is the difference between novelty theatre and scalable value. The practical question is not whether AI can be deployed faster, but which processes deserve accelerated decision rights because the cost of delay is higher than the risk of controlled failure.

The strongest implication is organizational, not technical. Moving from project IT to product IT changes accountability: business leaders must co-own metrics, funding and adoption, while technology teams become responsible for end-to-end performance rather than delivery milestones alone. That requires a cleaner operating model for prioritisation, since “lighthouse” initiatives can quietly compete for the same scarce data, plant, and domain experts. Leaders should ask whether there is a single portfolio forum that can arbitrate trade-offs across manufacturing, supply chain, finance and customer service.

His data emphasis also exposes a governance requirement that many AI programs underplay. If master data, ontology and knowledge graphs are the foundation of “enterprise grade intelligence,” then data ownership, stewardship and exception handling become executive responsibilities, not back-office tasks. A useful next question is which data products have formal service levels, who is accountable for corrections, and how quickly a bad model input is quarantined before it affects credit, inventory or production decisions.

Finally, the talent model matters as much as the technology. “Learning by evidence” is sensible, but only if it is tied to workforce planning, role redesign and manager incentives. Otherwise, AI-native specialists and domain experts will remain adjacent rather than integrated.

When Raman Mehta became chief information officer of The Goodyear Tire and Rubber Company in December 2025, he joined an iconic manufacturer undergoing consequential change. The 128-year-old company generated $18.3 billion in 2025 sales and employs approximately 63,000 people across 48 manufacturing facilities in 19 countries. Its products serve consumer, commercial, aviation, farm, motorsports and recreational markets.
Goodyear had also sharpened its portfolio through the Goodyear Forward transformation plan, which produced $1.25 billion in cumulative segment operating income benefits by the end of 2025. Mehta’s mandate spans IT, digital strategy, cybersecurity, infrastructure, customer experience, dealer operations and portions of supply chain and logistics.

From Pilots to Lighthouse Projects

Mehta introduced that mindset through a simple message: “Momentum builds clarity.” Rather than wait for perfect data or a comprehensive multiyear design, he wants teams to deliver something consequential and learn from the result. His definition of speed is tied to measurable outcomes, not the number of experiments underway.
Operational discipline comes first. Infrastructure must be resilient and cybersecurity must be sound. On that foundation, Mehta wants Goodyear to move beyond personal productivity tools and apply AI to large, cross-functional problems. During his first six months, he worked with supply chain, manufacturing and finance leaders to identify lighthouse projects with executive sponsorship and a clear connection to financial performance or customer experience. “You need to go beyond the pilots where you are actually shipping something meaningful that adds to the bottom line, that adds to better customer service,” he explained. Early wins can also counter the inertia surrounding legacy processes. Once teams see a difficult problem solved in weeks rather than years, the discussion shifts from whether the organization is ready to what it should tackle next.Sign Up

Enterprise Grade Intelligence Starts With Clean Data

The largest constraint is the quality and structure of the enterprise data beneath the models. Many corporate systems were designed for an era in which rules produced a dashboard, a person interpreted the result and a workflow continued. AI can increasingly reason and act, but unreliable source data makes those capabilities dangerous in processes demanding precision.
“Generative AI is mostly probabilistic, but in an enterprise, you need very deterministic outcomes,” Mehta noted. “You just cannot put a customer on a credit hold based on some probability. It has to be very accurate.” Goodyear is concentrating on core master data entities such as customers, suppliers and product bills of material. The company is also using agents to identify quality problems, recommend corrections and uncover relationships across its data lakes. Mehta calls the intended result enterprise grade intelligence, or EGI: intelligence grounded in governed company data, business context and dependable controls. That includes teaching models Goodyear’s ontology, building knowledge graphs and recovering the tribal knowledge that often sits in spreadsheets between systems of record.
Shared data products carry explicit commitments for quality, timeliness, governance and security. Subject matter experts can then build analytics and agentic workflows without reconciling competing numbers from different geographies. “Everybody is operating on the same set of information,” Mehta said.

Product IT Changes the Role of Technologists

Mehta is moving the organization from project-based IT toward product IT. Technology leaders are expected to understand the business domain they serve, work with a business counterpart and identify the performance measures their product will influence. The approach draws on IT’s end-to-end understanding of processes such as order to cash and record to report. The shift requires a different talent model. As AI enables specification-based development and new forms of software engineering, measures such as lines of code are losing meaning. Instead of assigning generic courses, Goodyear emphasizes what Mehta calls learning by evidence. Employees develop skills on live projects, with AI-native practitioners working alongside colleagues who hold deep domain knowledge.
“We are creating mentorship where people who are native AI are teaching people who are domain experts how to mesh the two worlds together,” he highlighted. The objective is not to turn every domain expert into a machine learning engineer. It is to combine business context with technical capabilities so teams can redesign a process instead of merely automating it.

Building the Connected Factory

Manufacturing is where Mehta’s data, product and AI agendas converge. Goodyear’s plants vary considerably in age, equipment and automation. The first requirement for a connected factory is therefore a governed backbone capable of collecting time-series data from machines, programmable logic controllers and production steps. On top of that layer, Mehta envisions manufacturing execution systems that understand the characteristics of a good tire, recognize deviations and trace problems through raw materials, machine tolerances and specifications. The longer-term goal is a closed loop in which the system recommends or takes corrective action within defined boundaries.
Mehta is also watching physical AI. Vision-language models could allow robots to learn by observing a person rather than requiring every action to be programmed. He sees potential applications in plant safety, inspection and monitoring. Experimentation will remain necessary, but every initiative needs a business sponsor, clear guardrails and a kill switch. Projects that fail should be stopped without stigma, with the knowledge carried into the next effort. Mehta calls that return on learning. Goodyear has branded its broader platform “Good AI,” reinforcing that it is a joint undertaking between technology and the business. “AI is compounding the knowledge every day,” Mehta underscored. “If you’re not starting today, you’re already a year behind your competition. We are committed to get on the racetrack, literally, and keep moving.”
Original Posteterhigh/2026/09/29/goodyear-cio-raman-mehta-makes-speed-a-technology-strategy/

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