Executives from S&P Global Market Intelligence and Accenture outlined how enterprises are recalibrating artificial intelligence strategies heading into 2026, with a growing focus on measurable returns, industry-specific use cases, and governance challenges as adoption accelerates.
The discussion, hosted by Justine Iverson of S&P Global Market Intelligence, was positioned as an interactive, slide-free webinar intended to reflect what speakers said they are hearing in “hundreds of client and partnership engagements” across banks, asset managers, and corporates. Iverson was joined by Francis Hintermann, Global Lead of Research at Accenture (NYSE:ACN); Jesse Kramer, who leads M&A and investments for S&P Global; and Elena Tesoni, who leads strategy and business transformation for S&P Global Market Intelligence.
ROI pressure rises as AI moves from pilots to impact
To illustrate that shift, Iverson referenced analysis using S&P Global’s earnings call transcript dataset. She said AI mentions in earnings calls rose about 4.5% from Q3–Q4 2023 to the end of last year, while mentions of AI cost savings and positive sentiment around those savings increased 57% over the same period.
Hintermann reinforced the ROI theme but argued the narrative is expanding beyond “horizontal” productivity use cases such as customer service, knowledge management, and IT. He said Accenture is seeing “verticalization,” where AI is increasingly applied to core industry value chains, creating revenue growth opportunities in addition to cost reductions. He cited an Accenture survey of CXOs released around Davos two months earlier, stating that 78% emphasized revenue growth as the priority in coming years. As an example of industry-specific impact, he pointed to life sciences, saying AI can accelerate and reshape drug discovery and R&D processes.
Funding remains strong and partnerships keep evolving
Iverson argued that despite speculation about where the market is headed, capital continues to flow into AI. Citing data from CapIQ, she said $95 billion was raised across 143 funding rounds in 2025 for AI-specific firms, excluding chip manufacturers and data center providers, and described it as nearly tripling from 2024.
She also said partnership ecosystems are evolving quickly, noting that AI firms are partnering with each other and that S&P Global has been active in partnerships. Tesoni later echoed that theme, emphasizing continued collaboration between data providers, vertical solutions, and model developers to unlock specific customer workflows.
Enterprise adoption: a mix of tools, plus governance and “shadow AI”
Audience polling during the webinar suggested many organizations are testing multiple tools. In one poll, Iverson said most respondents indicated they had trialed or adopted between one and four AI tools in the last 12–18 months, with nearly 19% indicating five or more.
In a second poll on what firms have adopted internally, responses pointed to a mixed tool environment, with ChatGPT highlighted as a common choice and Claude described by Iverson as showing adoption momentum in investment banking and financial services workflows. Participants also pointed to workflow-specific tools and home-built internal solutions.
Hintermann said one of the biggest developments over the past year has been the way AI is changing work itself. He warned about “shadow AI,” describing how employees sometimes use personal accounts when enterprise versions are unavailable—creating risks related to intellectual property, responsibility, and ethics. He framed this as an executive imperative: provide sanctioned tools and pair them with upskilling and reskilling to close what he described as a growing skills mismatch.
He also referenced an index Accenture built with Wharton intended to help users compare skills to market trends and quantify mismatch, arguing that “talent reinvention” will be a key factor influencing whether AI adoption at scale delivers the expected ROI.
From efficiency to transformation, with trust as a core requirement
Tesoni said the pace of development has been “incredible” and “frenetic,” and argued that adoption has moved beyond efficiency to broader business transformation—how companies remain relevant and pursue growth in a new operating paradigm.
She outlined two main lenses S&P Global Market Intelligence uses internally:
- How customers are changing work with AI, such as buy-side firms using AI to ingest and synthesize large datasets—including proprietary data—to support alpha generation.
- How S&P Global Market Intelligence is changing internal workflows, including customer support and data operations, to automate repetitive tasks and refocus teams on higher-value work.
Tesoni emphasized “trust” and governance, saying quality and brand reputation require controls as adoption expands. Iverson later addressed hallucinations and guardrails, describing an approach of tightening model boundaries and grounding outputs in trusted data—preferring to decline answers rather than provide inaccurate responses—and encouraging validation by users.
Hintermann added a phrase he attributed to Accenture CEO Julie Sweet: “human in the lead,” emphasizing that responsibility, direction-setting, and boundaries remain human-led even as AI is embedded more deeply into workflows.
IPO outlook and the question of AI market froth
Kramer discussed the IPO market heading into 2026, arguing that valuations have become more uncertain as investors reassess what makes companies valuable amid rapid AI innovation. He said firms unable to show “real AI traction” may face weakening valuations, while companies benefiting from AI—such as frontier labs and firms that control key data infrastructure—could form a stronger IPO candidate set.
In audience polling on which company might IPO first, respondents were split among Anthropic, OpenAI, and “neither,” reflecting what Iverson characterized as a mixed outlook.
On a question about OpenAI’s leverage and how debt could affect IPO timing, Kramer said private and debt markets may continue funding innovation and growth, but public readiness will require more rigorous financial disclosure. He cited questions including the degree to which infrastructure-related debt sits on company obligations and the role of related-party revenue. He also suggested that as model providers seek profitability, compute economics could shift, potentially raising prices for enterprise users and increasing the need for efficiency-focused AI deployment.
Looking ahead, Hintermann said he sees “sovereign AI” becoming a growing focus, driven by geopolitics and questions about localizing parts of the AI stack across regions. Tesoni predicted continued scaling of high-impact enterprise use cases, while also noting consumer AI tools are advancing quickly and increasing bottom-up pressure on enterprises to keep pace in controlled environments.
Speakers closed with consistent advice: experiment. “Try it,” Hintermann said, describing a “co-intelligence” era where users learn from agents and agents learn from users. Kramer and Tesoni similarly urged organizations to take advantage of the current period of rapid innovation and experimentation—while putting appropriate governance in place.
About Accenture (NYSE:ACN)
Accenture is a global professional services company that provides a broad range of services and solutions in strategy, consulting, digital, technology and operations. The firm works with organizations across industries to design and implement business transformation programs, deploy and manage enterprise technology, optimize operations, and develop customer and digital experiences. Its offerings encompass management and technology consulting, systems integration, application and infrastructure services, cloud migration and managed services, as well as security and analytics capabilities.
The company delivers industry- and function-specific solutions, combining consulting expertise with proprietary tools, platforms and partnerships with major technology vendors.
