Corporate CEOs reframe artificial intelligence as a core driver of growth rather than an experiment

CEOs of major public companies are increasingly positioning artificial intelligence not as an experiment but as the engine of their next phase of growth, laying out multiyear spending plans and the specific outcomes they expect from that investment. According to a Forrester analysis of Q3 2025 earnings transcripts from 20 firms across retail, financial services and manufacturing, chief executives described AI initiatives aimed at raising operational efficiency, deepening customer engagement, empowering employees and creating new revenue streams through on‑demand expertise.

Executives stressed that these are not trivial line‑item costs but material programmes measured in billions of dollars and stretching across years. Industry commentary and earnings transcripts underscore four foundational capabilities that companies say must be in place for AI to deliver: trustworthy data, a modern computing stack, software engineering capacity, and workforce upskilling.

CEOs repeatedly returned to the challenge of data. Bank of America’s Brian Moynihan told investors that substantial pre‑AI investments were necessary to underpin reliable outcomes: “The $3 billion we spent on data from 2014 to 2019 to get the data perfect in this company or [as perfectly] as we could; perfect is beyond reach. It takes that much work.” That emphasis on data quality mirrors other corporate accounts showing firms investing in both quantitative datasets and proprietary knowledge assets to prevent AI from producing plausible but incorrect outputs.

Leaders also highlighted the centrality of technology architecture. Capital One’s founder and CEO Richard Fairbank framed AI opportunities as resting “on the shoulders of our modern technology stack,” saying the bank’s cloud‑native transformation positions it to bring AI “into the heart of the business.” Executives from technology and services companies have described a five‑layer stack, spanning infrastructure, intelligence, data, orchestration and experiences, required to operationalise AI at scale.

Companies are already seeing AI change how software is produced and how operations run. Former Walmart chief executive Doug McMillon reported that when AI assists development, “more than 40% of the new code is either AI-generated or AI-assisted.” Analyses of Q3 2025 earnings across broader sectors find similar patterns: firms such as Intuit, Microsoft, Nvidia, Salesforce and SAP pointed to AI‑driven product development and efficiency gains, while logistics companies described automation and robotics improving throughput and costs.

Manufacturers and life‑sciences firms provided converging evidence. Thermo Fisher Scientific’s Q3 2025 discussions referenced partnerships with AI providers to enhance productivity and customer service, signalling that industrial and scientific businesses are applying generative models to operational and customer‑facing processes as well as to R&D workflows.

Boards and management teams are also tackling the people dimension. CEOs say they are investing in training and reskilling so employees can use AI tools safely and effectively; Walmart, for example, has described programmes to help staff adapt to an “AI‑powered workplace.” Forrester’s jobs forecast projects that automation and AI will displace a modest share of roles by 2030 while affecting many more jobs’ content, a dynamic that companies are attempting to manage through workforce development.

Market‑level data points to a rapid normalisation of AI in corporate narratives. Reporting shows a record number of S&P 500 earnings calls in 2025 referenced AI, a trend analysts interpret as firms pivoting from talking about macroeconomic “uncertainty” toward investments in technology as a growth lever. That rhetorical shift accompanies concrete measurement efforts: Goldman Sachs’ David Solomon described his firm’s multiyear AI effort and said progress would be tracked across goals that include client experience, profitability, productivity, resilience, employee experience and risk management.

Not all accounts are uniform. Some companies emphasise internal data and bespoke models; others lean on partnerships with external AI providers. The precise mix of cloud services, proprietary tooling and third‑party models varies by industry and by how far along a company’s prior digital transformation has been. Firms that modernised their data and applications earlier report a competitive edge, while others are investing heavily now to catch up.

Financial officers and investors are watching costs and returns closely. Earnings calls and analyst write‑ups show CFOs tracking near‑term implementation costs alongside projected medium‑term benefits in efficiency and new products. Capital One and several large banks pointed to both substantial prior tech spending and ongoing investment in talent and compute as essential to unlocking AI value.

Taken together, the accounts gathered from Q3 2025 suggest a consensus among corporate leaders: AI requires sustained capital, technical platforms, disciplined data practices and active talent programmes, but when those elements are combined, executives expect measurable gains across operations, customer relationships and new business models. The narrative from the C‑suite is clear, AI is now central to corporate growth agendas rather than an optional experiment.