This is the 9th edition of our industry newsletter with musings, observations and ideas regarding the challenges and opportunities facing market data management leaders.
Insights from our global AI leaders
AI has been circling the edges of market data for years. Then almost overnight, it became central to every discussion; and the pace is unlike any technology shift this industry has lived through.
We’ve been deep in this work ourselves, but for this piece we wanted to zoom out. We brought together five of our domain experts – Amjad Zoghbi, Christophe Plouvier, Steve Richardson, Laura Wynthein, FIA and Deepak Rajagopal – to discuss where AI is genuinely moving the needle in market data management.
What follows is a distilled view of the five shifts already reshaping the space, and why this moment will look very different six months from now.
For years, market data teams have had to chase information across systems; contracts in one place, usage in another, compliance information hidden somewhere else. Everyone knows the pain of stitching that picture together manually.
AI is cutting straight through that.
The first meaningful shift has been the rise of AI assistants that can pull in, interpret and reason across huge volumes of siloed detail at once. Add a chat interface on top and suddenly people can ask a straight question and get a clear, multi-domain answer in seconds.
Christophe captured the shift: “You can ask one question, and the AI draws on compliance, usage, contracts – insights that used to sit in different worlds now surface in seconds.”
This isn’t a small efficiency gain. It’s a structural step-change.
Teams are finally working from the full picture, not piecing things together after the fact. This means higher quality responses, faster decisions, tighter controls, and materially better commercial outcomes.
We’ve already seen the impact of this in our Xmon usage management platform. It already included powerful base technology, automated reporting, and tailored expert recommendations. But when we added the AI Assist natural language interface to it just one month ago, usage jumped. Why? Before AI, users needed to stitch reports together or create custom dashboards if they wanted to see the data a certain way. With AI, that’s no longer necessary. Additionally, it can bubble up insights, not just data ─ making users more powerful in their decision-making.
And this shift is happening now, at pace. As Laura put it: “If you’re not using AI today, you’re already behind.”
Chatbots are the warm-up act.
The real shift now is AI that doesn’t just explain things… it makes decisions and initiates actions, bringing the human in the loop at the right time in the process.
We’re moving into a phase where AI is at the core of the workflow. Amjad called this “intelligent automation” – systems that take meaningful action.
Think of a workflow that decides whether a license request should be approved or escalated. AI agents that communicate with stakeholders and propose alternate licensing options based on contractual and usage realities. AI that streamlines manual and time-consuming tasks and helping teams focus on high-impact work.
This is a very different class of capability, the moment where interpretation, reasoning, and action snap together.
Christophe summed it up: “We’ve unlocked chat and interpretation. The next phase is agentic workflows, where the AI connects the steps and enables faster, clearer decisions consolidating multi-source inputs.”
For the first time, technology is taking on core workflow decisions, not just sitting beside them. And when the system begins to act – not just answer – the operating model starts to change with it.
Once AI is making decisions internally, the next step seems obvious: systems start talking directly to each other.
Amjad summed it up with this example: “We’re heading toward agents in TRG Screen’s platforms talking to other agents within our client’s ecosystem. We expect agent to agent communication to be a big part of system integrations. That’s coming in the next couple of years.”
When that becomes reality, market data workflows will shift from rigid, API-driven exchanges to adaptive, autonomous coordination, where agents proactively reconcile entitlements, usage, and requests across our clients’ systems. This will enable faster decisions, fewer manual interventions, and far more resilient integrations throughout their market data ecosystem.
The technology blocks already exist, but the governance for this world largely doesn’t.
And once governance catches up, this will redraw how the ecosystem works – and it will happen faster than most anticipate.
One of the strongest shared views, and a direct counter to the current headlines, is that AI doesn’t replace human expertise. It amplifies and makes it more essential.
As AI accelerates the mechanics (reading, comparing, validating, routing) the real judgment work becomes more critical. Subtle licensing nuance, commercial ripple effects, borderline usage cases, interpretations that shape audit exposure… none of this disappears. AI just uncovers it faster.
Deepak captured it perfectly: “AI is your GPS, it gets you there faster, but you still need to watch the road and steer.”
And the warning from Google’s CEO last week landed squarely in this context: you can’t trust these tools blindly. Even the most advanced models produce fluent, confident answers that could be directionally wrong without the wider context.
This leads to the other trap: treating generic chat models as if they understand this domain. They don’t. As Steve cautioned, “A general model will always give you an answer, and it can be convincing. It won’t tell you it doesn’t understand the rules, and that’s exactly where firms get exposed.”
Yes, AI absolutely expands capacity. But human judgment is still the thing that keeps the entire system honest.
That becomes more, not less, important as automation grows.
Turning on a chatbot isn’t transformation.
Testing a summarizer isn’t transformation.
Pilots scattered around the organization aren’t transformation.
Right now, most firms are collecting features, not holistically considering how AI can really move the needle in how they operate.
AI’s transformational impact will only become real when it starts reshaping the core machinery: how risk is managed, how governance works, how approvals flow, how entitlement integrity is maintained, how audit exposure is controlled, how teams interact, how vendors are handled, and how the operating rhythm actually runs.
Anything short of that is surface level.
Amjad cut through the noise: “This isn’t a technology play. Everyone has access to the same models. The difference is the expertise behind them, and how you embed that into real workflows.”
And Laura reinforced the same point: “Adoption only moves the needle when it reduces friction, improves clarity and plugs into how people actually work.”
That’s the dividing line emerging right now: some firms are gathering AI features, others are redesigning their operating model around them.
The gap between those two groups is widening, and it will widen fast.
Where this leaves the industry
Across all five shifts, one truth is clear: AI is no longer an experiment. We are already building features using AI that were not possible a few years ago.
It is restructuring the machinery beneath the surface of market data. Decisioning, interpretation, workflows, cross-system connection, governance, and the role of expertise itself.
The pace is fast – far faster than cloud, and far faster than most structures can absorb. Six months from now, parts of this conversation will already feel like the early phase of something bigger.
That’s why this moment matters. The firms moving now are building the muscle the rest of the industry will need later. Those waiting for clarity will be reacting to a landscape that has already changed.
Why the real work starts now!
Anyone in market data knows the rhythm of this time of year.
You’ve just staggered out of the budgeting cycle – the spreadsheets are closed, Finance has finally stopped asking questions, and the business is (mostly) aligned on what it thinks it’ll need next year.
You exhale, mutter a quiet “thank ______ that’s done,” and try to reclaim your sanity.
Except… it isn’t done.
Yes, you have a budget (even if it’s not formally approved yet), but what you really have are the guardrails you now need to operate within. Come January 1, you’ll spend the year defending it, explaining it, adjusting it and keeping it honest.
And that’s when the real work begins.
The teams that cope best aren’t the ones who ‘budget well.’ They’re the ones who understand that budgeting never really stops. The only true event is the sign-off. Everything after that is about communication, visibility and control.
The dynamics you wrestled with during budgeting don’t disappear in January.
In most firms, the people who set the budget, the people who manage it and the people who spend it sit in completely different pockets of the organization.
That triangle has to stay alive all year. Without that cross-functional communication, you lose sight of the details and leave space for the cracks to appear.
Staying aligned is how you stay in control. Especially when it comes to the market data expense category and the tools you need to manage it effectively.
The first responsibility is understanding your run rate and what’s already locked in.
Some costs will rise whether you like it or not and ignoring that only sets you up for trouble. Having a clear view of your market data vendor book, where you have flexibility and where you don’t, helps keep expectations grounded on all sides.
And then there’s the operational diligence.
If contract data and renewal dates aren’t maintained properly, the consequences are painful – hundreds of thousands in some cases. This is the price of poor hygiene, not bad luck.
Beyond the day-to-day mechanics, there’s the strategic layer.
Treating each contract as a standalone transaction hides duplication and leaves value on the table.
Taking time – we recommend twice a year – to look at major vendor relationships holistically changes the conversation. You see where products overlap, where enterprise terms might make sense and where spend has crept without anyone noticing.
It’s the difference between reacting to renewals and actively shaping them.
No matter how tight your controls, unexpected costs will appear.
Misuse happens. License caps get breached. Exchanges audit at the worst possible moment. And mid-year contract resizing is almost always negotiated on the vendor’s terms, not yours.
If you’ve managed to secure a contingency, the real win is not burning through it. Use it to get proactive. Initiatives like market data education programs – which we’re now seeing across several major banks – can cut down the surprise costs that usually blow holes in budgets.
And if you didn’t secure a contingency this time, start gathering the evidence now. Finance may not love the idea of budgeting for ‘problems,’ but your own history may be the strongest case for securing one next time.
Keeping pace with the business: the moving target factorThen there’s the business itself, which never stays still.
Headcount changes, desks expand or shrink, priorities evolve, and users occasionally click through license terms they absolutely should not have accepted. Every one of those movements lands somewhere in your budget.
Keeping a regular dialogue with the business keeps your numbers tethered to reality. They also give you the ‘why’ behind changes, which is exactly what Finance will ask for when spending moves.
It’s never enough to say something changed; you need to explain the story behind it.
This is the weakness vendors seek out.
When procurement, market data and the end users aren’t aligned, the vendor will go straight to the business – it is the path of least resistance.
The pitch will be compelling, the users will agree and suddenly you’re unpicking commitments made without any understanding of cost or licensing implications.
A united front is your best defensive position.
If there’s one takeaway in all of this, it’s that budgeting is not the finish line. It’s the starter pistol.
The discipline you apply now – complete data, strong oversight, regular reviews, honest communication, proper alignment – is what makes next year’s budgeting cycle bearable.
Manage the year well and the next round feels like a continuation of a process you already own. Manage it poorly and you’ll be right back in the September scramble.
One way or another, the real work starts now.
We're having revelations about AI every day as we adopt it across our business and add it to select solutions. Is your team having a similar experience? And as far as budgeting, do these observations hit home? We hope both stories provide food for thought and some actionable insights.