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Enterprise AI Shifts From Pilots to Operational Scale

Market Movers: The Opening Bell

January 27, 2026

Video Transcript

AI is emerging as the next stage of AI, moving between automation to systems that can act, adapt and support complex decision making for enterprises.

The promises greater efficiency as well as new revenue opportunities and a fundamental shift in how work gets done.

But that autonomy also brings new complexity.

In 2025.

AI became a core enterprise priority.

So as we head into 2026, where do we go?

From here while joining me live this morning is probably Savik, senior director in AI solutions and strategy at Moody's.

Welcome back, Pa.

Great to have you here.

Great to be here.

Well, it's hard to believe, but 2026 is well underway, and as we head into the year, markets are expected to remain bullish on artificial intelligence.

But what is the market getting bright as we kick off the year and what are they overlooking.

So I think the markets are definitely understanding that infrastructure is a big play and there is scale there for certain, so they've been pricing things like cloud, chip, you know, compute that's been very popular, but what they're not looking at right now is how does agentic AI and AI tools start to incorporate into business decisions and business outcomes.

And so that's something that 2026 is going to show.

Are the AI different organizations that we have there, are they going to be able to provide solutions for workflows, for business operations?

And if so, what's the impact going to be on AI stocks in general?

I think 2026 is going to be very telling.

Yes, and especially as we head into earnings season for sure.

So when it comes to enterprise adoption, you mentioned agentic AI, so that is expected, but also Gen AI, where do you both of them stand when it comes to enterprise?

Yes, so I think What we've seen in terms of enterprises is they're past the kind of innovation phase, but they're not fully operational yet, and you're seeing them pass that innovation phase in terms of single tooling of AI where it becomes very specific tasks.

But the next stage is agentic solutions.

How is that multitask workflow is going to be applied to business operations, so it's things like risk management, reporting, operational supply chain type of risk assessments, and that's something that we'll see if AI is going to be durable through those business decisions in 2026.

Yes, and we're here at the New York Stock Exchange and we just heard a first trade bell and as we head into 2026, there's a lot of anticipation when it comes to IPOs.

So what a potential wave of AI-related IPOs signal about AI.

What does it look like and what are your expectations?

Yes.

I think that when you're looking at the IPOs, there's a lot of rumors, right?

So we've got data bricks, maybe even Anthropic Coreweave, a bunch of others, and it's going to be very telling for the market.

The market is going to be able to get a lot of information from that right now those IPOs, you're going to be able to see public investing is not going to be about what's the vision.

They're going to be looking at more of the tangibles, right?

They're going to be looking at.

The revenue, the customer adoption, they're going to be looking at margins and so if that happens with AI, you're going to see that there is clear sort of renewed expectations of AI becoming integral to business outcomes again the operational, the risk management, the workflows, all of those kind of efficiencies and productivity gains that we're expected to see.

If that doesn't happen, well then I think the market is going to decipher between.

Is it purely infrastructure spend, or are these enterprises going to be able to get value from these AI tools?

But either way, the IPOs are going to be able to really sort of renew the expectations of is there sustained or sustainable enterprise value within AI for 2026.

Yes, and as AI becomes embedded in decision making, I think it's very important to understand security regulation amidst all this innovation that is taking place, especially with certain industries such as healthcare, financial services.

So what do you think is important for us to keep our eyes on?

Yes, I think it's one of those.

Those boring terms compliance, governance, well, at least not the fun ones, right, but unfortunately it may be painful, but it needs to be applied at the beginning of setting those expectations and the beginning of the integration of AI, especially Agenech AI.

So it really it has to tie into those business decisions and business outcomes.

How do you make sure you have interoperability between the data, but how do you make sure that that is auditable so it's consistent.

How do you make sure that you can actually have compliance around all of these different processes with so many different data sets?

I mean, like Moody's for example, we provide so many different data sets to clients sometimes in a workflow that will be over 30 different data sets and different tools in there.

How can you audit that?

How can you make sure they're consistent?

So I think that's going to be incredibly important, as you said.

Yes, and as we head into 2026, what do you think will separate the AI winners from the laggards and Also, can you break this down on a company level and also across markets?

Yes, so I think winners and la guards, let's take that first.

So I think the winners are the ones that are going to be able to apply integration of data, so data that is AI compatible, a huge thing.

The ability to tie in from the start the work flows that tie into business outcomes, so whether that is supply chain risk management, whether that is KYC onboarding of clients or the asset management of the banking world, whether that is a credit memo production and reporting for banks and even any type of underwriters in the corporate sphere.

And then finally it's going to be really change management.

That's the big thing.

How is the talent within the organization going to be able to skill up, to utilize these, these tools, and make sure that it's spread across the organization?

And I think you're going to see.

A type of adoption gap between organizations that let's say are well capitalized and they're sort of more cost conscious and cost struggling peers.

And with the prices of cloud compute going up because there's quite consolidation there, that's going to be a challenge for these enterprises.

Yes, and finally, Pablo, before I let you go, you mentioned a keyword there, and that is talent.

When the conversation comes up with artificial intelligence and the labor force, what do you think will actually happen here?

What are the implications for the workforce?

Yes.

It's really about, you know, there's a lot of discussions around workforce reductions and there's a lot of, I think arguably some type of challenges there or worry around what's going to happen with the workforce, but I think what will happen is it's going to be a difference between the people that are skilled with AI, the companies that are AI first or AI forward if you like, and the companies that think about it as a secondary thing.

And I think that the workforce will change a little bit.

The structure will change in terms of a lot of the remedial tasks, repetitive tasks, will probably be shifted around, and then you're going to have a lot more decision makers and strategic thinkers that can apply these efficiencies with AI.

To get better outcomes.

So I think there's going to be more of a shift as opposed to let's say a reduction of employees.

Well, Pablo, great having you here as we kick off the new year.

Always appreciate your time and your insights, and I'm sure as the year gets underway this conversation will continue to involve.

So I look forward to having you back.

Look forward to.

Thank you.

As 2026 unfolds, artificial intelligence is entering a decisive phase inside enterprise environments. The conversation is no longer about experimentation or proof-of-concept tools, but about operational scale, governance, and measurable value. In a recent discussion with Pavlé Sabic, senior director of Generative AI Solutions and Strategy at Moody’s, the focus turned to how AI is reshaping enterprise decision-making, efficiency, and competitive positioning.

A central theme of the discussion was the rise of Agentic AI. Unlike earlier generations of automation, Agentic AI systems are designed to operate autonomously within defined guardrails, supporting complex workflows rather than executing isolated tasks. Sabic explained that many enterprises have moved beyond the innovation phase, but full operational adoption remains uneven. The next stage of AI maturity will emphasize multitasking capabilities, allowing AI systems to simultaneously support risk assessment, operational analytics, and supply chain optimization within core business processes.

Market expectations are also evolving. With a wave of anticipated AI-focused IPOs expected in 2026, including firms such as Databricks, Anthropic, and CoreWeave, investor scrutiny is sharpening. Sabic noted that markets are shifting away from aspirational narratives toward tangible performance metrics. Revenue growth, customer adoption, margin expansion, and demonstrable operational integration will matter more than vision alone. Companies that cannot clearly articulate how AI drives sustainable value may find public markets less forgiving.

As AI becomes more deeply embedded in regulated sectors like financial services and healthcare, governance and compliance are emerging as non-negotiable priorities. Sabic stressed that successful deployment of Agentic AI begins with clear standards around data interoperability, auditability, and regulatory alignment. Enterprises that build governance into their AI foundations from the outset are more likely to scale responsibly and avoid costly setbacks as regulatory scrutiny increases.

The widening gap between AI leaders and laggards is becoming increasingly visible. According to Sabic, organizations that excel are those that integrate AI directly into business workflows rather than treating it as a standalone tool. These firms use AI to enhance critical functions such as risk management, client onboarding, and decision support. By contrast, companies that delay integration or struggle with fragmented data systems risk falling behind. Talent development also plays a decisive role, as enterprises must ensure their workforce is trained to collaborate effectively with AI-driven systems.

Concerns about AI-driven job displacement remain part of the broader conversation, but Sabic offered a more nuanced view. Rather than eliminating workforces, AI is reshaping them. Routine and repetitive tasks are increasingly automated, while demand grows for roles centered on judgment, oversight, and strategic decision-making. Enterprises that adopt an AI-first mindset and invest in reskilling their employees are better positioned to unlock productivity gains without eroding institutional knowledge.

As enterprises move deeper into 2026, AI integration represents both a major opportunity and a structural challenge. Agentic AI, when deployed with strong governance and workforce alignment, has the potential to redefine how organizations operate at scale. The coming year is likely to set lasting standards for enterprise AI adoption, shaping not only technology strategy but also the future intersection of finance, data, and sustainable entrepreneurship.

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