Enterprise AI in 2026: Execution, resilience, and value
FintechTV recently interviewed Pavlé Sabic, Moody’s Senior Director of Generative AI Solutions and Strategy, to discuss how AI is transforming enterprise decision-making, driving efficiency, and reshaping competitive positioning. Read our takeaways below.
After several years of experimentation, enterprise AI is no longer defined by pilot projects or isolated automation tools. The central question is now whether AI can operate reliably at scale and meaningfully influence business decisions, workflows, and outcomes.
That shift was the focus of a recent FINTECH.TV interview with Pavlé Sabic, Senior Director of AI Solutions and Strategy at Moody’s, who described 2026 as a decisive year for enterprise adoption.
“What we’ve seen in terms of enterprises is they’re past the innovation phase, but they’re not fully operational yet,” Sabic said. “They’ve moved beyond single tooling of AI for very specific tasks. The next stage is agentic solutions.”
From automation to decision-making systems
Sabic noted that markets have largely focused on the infrastructure layer of AI adoption. Cloud services, chips, and compute capacity have been widely recognised and priced in. What remains less well understood is the challenge of embedding AI directly into business decision-making and operational workflows.
“What the markets are not looking at right now is how agentic AI and AI tools start to incorporate into business decisions and business outcomes,” Sabic said. “That’s something 2026 is going to show.”
Task-level automation can improve efficiency, but operational AI reshapes how organisations manage risk, allocate resources, and execute strategy. Sabic pointed to areas such as risk management, reporting, supply-chain assessments, and credit workflows as critical tests of whether AI systems can prove durable in real business environments.
IPO markets as a reality check
The anticipated wave of AI-related IPOs in 2026 may further clarify this transition. According to Sabic, public markets are likely to place greater emphasis on execution than on narrative.
“Public investing is not going to be about what’s the vision,” he said. “They’re going to be looking at the revenue, the customer adoption, the margins.”
Those indicators, he suggested, will help determine whether AI companies are delivering sustained enterprise value or remain primarily tied to infrastructure spending.
“The IPOs are going to renew expectations around whether there is sustained or sustainable enterprise value within AI for 2026,” Sabic said.
Governance moves to the foreground
As AI becomes embedded in operational decision-making, governance and compliance take on greater importance. Sabic acknowledged that these considerations are often viewed as unglamorous, but argued they are essential to scaling AI responsibly, particularly in regulated sectors such as financial services and healthcare.
“Those boring terms, compliance and governance, unfortunately, they need to be applied at the beginning,” he said. “Especially with agentic AI, it has to tie into business decisions and business outcomes.”
He highlighted the complexity of auditing AI-driven workflows that rely on large numbers of datasets and tools, underscoring the need for interoperability, auditability, and consistency across systems.
What separates AI leaders from laggards
Looking ahead, Sabic outlined several factors likely to differentiate AI leaders from laggards at both the company and market level. These include the ability to integrate AI-ready data, design workflows that link directly to business outcomes, and manage organisational change effectively.
“Change management is the big thing,” he said. “How is the talent within the organisation going to skill up to utilise these tools and make sure it’s spread across the organisation?”
He also noted that rising cloud and compute costs could widen the gap between well-capitalised organisations and more cost-constrained peers, reinforcing uneven adoption across markets.
Implications for the workforce
On labour impacts, Sabic cautioned against simplistic narratives focused solely on workforce reduction. Instead, he described a structural shift in how work is organised.
“I think what will happen is a shift,” he said. “A lot of remedial and repetitive tasks will be shifted around, and you’re going to have more decision-makers and strategic thinkers that can apply these efficiencies with AI to get better outcomes.”
A defining year for enterprise AI
Taken together, Sabic’s perspective suggests that 2026 will be less about experimentation and more about proof. Enterprise AI’s long-term value will depend on whether organisations can integrate these systems into core operations in a way that is scalable, governed, and economically meaningful.
For investors and credit analysts, this transition offers a clearer lens through which to assess execution capability, operational resilience, and the sustainability of AI-driven value creation.