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Analysts have urged technology leaders to manage expectations, especially for gen AI, which often promises more than it delivers in the short run. CIOs must help CFOs see AI as a long-term strategic play, while that unmet expectations of immediate returns on AI investments will cause many enterprises to scale back efforts sooner than they should.

But not every enterprise is struggling. Conversations with four seasoned IT leaders paint a more balanced picture. From large multinationals to SME innovators, many organizations are already generating measurable value from AI and share a pragmatic framework for CIOs: focus on the right use cases, lean into cultural readiness, measure impact creatively, and design for evolution.

Prioritize practical, high-impact use cases

At global semiconductor company AMD, AI is treated like any other strategic IT investment — it’s useful only if it delivers business value in a reasonable time frame. Chris Wire, VP of business applications, explains that AI success often mirrors traditional technology efforts. “We evaluate the cost, benefits, and suitability,” he says. “When it aligns with our business goals, we proceed with the project.”

AMD

That philosophy translates into projects that pay back quickly. AMD has used gen AI to streamline complex tasks, like preparing R&D tax documentation, and what previously took weeks can now be completed in hours, thanks to AI tools that summarize and structure dense materials. This type of efficiency is especially valuable in high-stakes, compliance-heavy functions like finance.

Similarly, Lenovo’s Global 娇色导航Arthur Hu cites Studio AI, an in-house generative tool that slashes marketing content production time by 80% and reduces agency spend by up to 70%. The benefits aren’t only financial: sales and marketing teams gain newfound agility and are able to create personalized materials in near real-time. In addition to Studio AI, Lenovo uses embedded agents in customer support systems to detect issues early and improve call center efficiency. These digital assistants enhance agent performance and improve customer satisfaction by providing real-time suggestions and automating common resolutions.

Then there’s Upwave, a data-driven ad analytics firm, which found ROI from a customer-facing tool that uses gen AI to create campaign performance reports. The tool sifts through multichannel advertising data and distills it into clear, executive-ready insights. CTO George London says these reports are easier to understand and more widely shared, boosting customer satisfaction and internal efficiency. The platform has also begun integrating conversational interfaces to simplify campaign planning, turning complex dashboard interpretations into natural language explanations.

Across these companies, the common thread is practical implementation. Most AI gains came from embedding tools like Microsoft Copilot, GitHub Copilot, and OpenAI APIs into existing workflows. , VP of technology innovation at tech company Trimble, also notes that more than 90% of Trimble engineers use Github Copilot. The ROI, he says, is evident in shorter development cycles, and reduced friction in HR and customer service. Moreover, Trimble has introduced AI into their transportation management system, where AI agents optimize freight procurement by dynamically matching shippers and carriers.

These examples show that value creation from AI doesn’t require massive investment in bespoke platforms. Often, the best results come from building on proven, scalable technologies and integrating them thoughtfully into existing systems.

Build a culture that encourages AI fluency

Technology may be the essential element, but culture is the catalyst. Successful AI programs are supported by organizational habits that promote experimentation, internal visibility, and cross-functional collaboration. A culture of curiosity and iteration is just as critical as a strong technology stack.

At AMD, this includes hosting internal hackathons and promptathons, where business and IT teams collaborate on real-world use cases. The results have been dramatic: one hackathon generated 100 new AI ideas in a single day, with several making it into production. This open-ended creativity encourages business leaders to think beyond automation and envision new ways of working.

Lenovo takes a tiered approach to readiness. “Some teams need basic education,” says Hu. “Others are ready for agile sprints. We provide on-ramps for every level of maturity.” The company has cultivated friendly competition among departments to showcase their AI innovations, which has led to a sense of ownership and momentum across the business.

[See also: Lenovo’s Arthur Hu on the CIO’s customer-centric imperative ]

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Trimble emphasizes leadership support and structured onboarding. Almagor believes cultural investment is as important as technical enablement. “It’s not just about the tools,” he says. “It’s about helping people imagine what’s possible.” Their framework includes dedicated training programs, internal champions, and support for iterative experimentation.

For smaller firms like Upwave, cultural clarity translates to design discipline. London warns against superficial deployments, saying that sprinkling AI fairy dust rarely delivers value. Instead, he champions intentional design that starts with user needs and works backward. Upwave has found that close collaboration between product and data teams leads to more useful applications, such as AI-generated summaries that align with clients’ internal reporting formats.

Measure ROI creatively and contextually

While analysts often lament the difficulty of showing short-term ROI for AI projects, these four organizations disagree — at least in part. Their secret: flexible thinking and diverse metrics. They view ROI not only as dollars saved or earned, but also as time saved, satisfaction increased, and strategic flexibility gained.

London says that Upwave listens for customer signals like positive feedback, contract renewals, and increased engagement with AI-generated content. Given the low cost of implementing prebuilt AI models, even modest wins yield high returns. For example, if a customer cites an AI-generated feature as a reason to renew or expand their contract, that’s taken as a strong ROI indicator.

Trimble uses lifecycle metrics in engineering and operations. For instance, one customer used Trimble AI tools to reduce the time it took to perform a tunnel safety analysis from 30 minutes to just three. For Almagor, that kind of improvement speaks volumes. They also benchmark performance gains in software development, with AI tools showing 15% to 20% improvement.

Trimble

AMD tracks time savings across a range of processes, including meeting summaries and chatbot-based HR workflows. In finance, AI-driven automation is delivering 15% productivity gains. Most impressively, small yield improvements in semiconductor manufacturing — achieved through machine learning — translate into millions of dollars. AMD also maintains an internal resource catalog of over 100 documented AI use cases, which helps standardize success measurement and spread adoption.

Lenovo blends soft and hard indicators. Hu says a big part of their strategy is reducing friction: by standardizing tools, compliance frameworks, and onboarding processes, they lower the barrier to AI experimentation and scale adoption without runaway costs. Teams can launch projects more confidently and with lower overhead, creating a repeatable model for value capture.

Think long-term, but start with what works today

None of these organizations are naïve about AI’s limitations. But they view the current wave of adoption as a necessary foundation for bigger transformations. The short-term wins aren’t just about proving value — they’re about preparing the enterprise to think and act differently.

Trimble is investing in intelligent agents and multi-agent ecosystems, envisioning a future where software agents representing different business domains collaborate to optimize outcomes. Almagor imagines agents for procurement, modeling, logistics, and compliance interacting seamlessly. He foresees a shift from application-centric IT to agent-based interactions.

Lenovo is watching a similar trend. Departments are already requesting co-pilots for decision-making, with Hu seeing a future where augmentation, not just automation, becomes the norm. The long-term goal is to embed intelligence across business functions so decisions are supported in real time by data-driven insights.

At Upwave, experiments in conversational AI and visual insight interpretation point toward a more intuitive interface between data and action. London believes the next leap forward will come from co-pilots that turn insights into recommended next steps. Their aim is to remove cognitive overload for users by translating data into suggestions directly tied to campaign goals.

Upwave

AMD is also investing in expanding the internal AI community, providing playbooks and training resources that ensure AI capabilities are adopted consistently across teams. Moreover, they’re focused on governance, ensuring that data privacy, ethical considerations, and operational resilience are embedded into every AI deployment.

Across all four firms, the advice for CIOs is consistent:

• “Start with confidence,” says Almagor. “Go after use cases that are guaranteed wins.”
• “Co-create solutions with the business,” advises Wire. “That’s how you drive adoption.”
• “Understand your cost structure,” cautions London. “Using existing platforms lets you scale without overspending.”
• “Reduce barriers to entry,” says Hu. “The easier it is to try AI, the faster your organization will learn.”

AI doesn’t need to be a moonshot. Done well, it can deliver value now and compound that value over time. As these leaders show, the best AI strategies combine discipline with imagination, delivering near-term wins while laying the foundation for long-term reinvention. And as organizations mature, the strategic role of AI will likely shift from enhancement to reinvention not just to do things better, but do entirely new things altogether.

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