What happens when your pricing data and workflows can’t keep up with the pace of change?
That’s the challenge driving transformation in commercial insurance today. And it’s what brought together leaders from Coherent, EY, and Indico Data for a candid conversation about how AI and automation are reshaping underwriting—not in theory, but in practice.
Together, they discussed from actual field experience what smart carriers are doing right now to tackle old problems - fragmented logic, integration headaches, regulatory uncertainty, and talent constraints - with new methods.
Below are six grounded takeaways from that conversation with one clear throughline:
Transformation is possible, if you start in the right place.
The urgency around pricing modernization isn’t new. But this moment feels different.
Alice Borman, Actuarial Transformation Partner at EY, laid it out plainly:
[05:27] “Everyone has agreed for a while for the need for change and more robustness around pricing and technology. Ultimately, insurance is a data business, but we have managed with paper and giant spreadsheets for a long time.”
What’s changed? The group discussed multiple forces converging at once: regulatory pressure, market softening, and greater availability of practical tooling.
Tom Wilde, CEO at Indico Data, called it a turning point:
[08:55] “We describe this as the dawn of the decision era where all of this investment in cloud data, and now AI, creates the answer to the sort of so-what question: Why did we spend all this time and money aggregating all of this data?”
And Bryan O’Neal, Head of Sales Engineering at Coherent, noted that expectations are shifting across the actuarial stack:
[06:55] “I talk to actuaries all week long and they always want more and more data out of their business processes. They expect all of the systems upstream of them to be able to provide it.”
In short: the pressure (and desire) to act is familiar. But the ability to act is what's new.
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Now, before you plug in automation or AI, your logic needs to be clean, centralized, and actually usable.
If your pricing models are fragmented, buried in shared drives, or tied to manual workarounds, AI won’t solve the problem — it’ll just accelerate the mess.
Alice called out the persistent friction amongst carriers:
[20:50] “If you've already put information about a risk into the system you shouldn't then have to be putting it into your pricing model and then again into your exposure management modeling system and then into your PAS.”
And when you solve that integration problem, the payoff isn’t just speed. It’s smarter, more strategic underwriting:
[21:22] “That means you can use more and more different models… and the underwriters are really doing the thinking and the stuff they're really good at rather than administration.”
But even when systems are connected, a deeper issue often remains: the pricing logic itself is still locked inside spreadsheets. Bryan pointed to the risks that come with that setup:
[23:45] “You're seeing data loss at the rekeying step. You're seeing governance questions around ‘how do you even know that the underwriter is using the right version of Excel?’.”
Coherent Spark offers a new approach:
[24:34] “With Coherent, you can take that spreadsheet, upload it to [our platform] and immediately have a working API that you can plug into a workbench or an admin system or some other process that needs to consume that logic.”
Start with clean logic. Then automate.
We are seeing plenty of excitement around full-scale automation. But the real gains come from targeting the right tasks — the ones that slow everything down but don’t require judgment.
Tom offered a helpful distinction:
[19:24] “Think of it as lowercase A ‘automation’ versus capital A ‘Automation’… If you can do a good job laying out the steps in the process that lead to success, then isolate those steps and make decisions around where is really ripe for automation. Where are we going to add value by automating it and driving speed, or data fidelity, or data completeness.”
It's a reminder that you don’t need boil the ocean. Focus first on removing the repetitive, administrative work that drains time and adds no strategic value.
Carriers, think about the data that fuels your models — and how that data moves through your systems.
Tom reframed the challenge using a supply chain metaphor:
[15:54] “You’ve got to think about the raw materials - that is first, second, and third-party data – and drive the schema that is the data you will use.”
So what is it you need to decide? Do you have the data to make that decision? Is that data accessible when and where it’s needed?
For most carriers, not reliably.
Bryan pointed to the root cause:
[13:45] “The hardest part of doing any [transformation] project is the integrations. You end up with lots of jumps between applications – and data is lost at every jump. It’s like lossy compression, if you will.”
Even when the data exists—in a broker email, in a PDF, in a CRM—it often can’t move without manual effort. That creates latency and limits how pricing teams can respond.
Automation and AI will only deliver results if the data supply chain is already working.
Innovation doesn’t mean skipping compliance. It means designing systems that are fast, but still traceable.
Bryan put it clearly:
[37:10] “Regulators are going to want to see the same kind of checks and processes at every step of the game that you see now. If you want an AI to make a decision, you're going to have to show human governance.”
Tom added that explainability has to be built-in, not retrofitted:
[39:37] “The regulators are going to show up and [ask]... What data was used, what prompts were used, what model was used? You have to design with regulatory as one of the critical stakeholders.”
In other words: structure your data and automation decisions today so you don’t have to rebuild under pressure tomorrow.
Tools matter. Models matter. But people are what make it all work.
Alice described how the shape of pricing teams is changing:
[42:56] “As technology becomes a more important way of deploying our models, what kind of technology skill set do we need to have embedded in the team? We increasingly see developers embedded within actuarial teams.”
Tom also suggested looking at the employee experience as a differentiator:
[45:28] “Will there be a carrier who decides to really focus on making that employee experience — the underwriter’s experience or the claims analyst’s experience — just first class, as a way to attract and retain talent?”
And Bryan championed individual experimentation at the edges:
[46:27] “This is such an exciting time to be a high-agency person who's willing to go out to the frontiers of these new tools. All of a sudden you might find ways to have 10x the throughput that you were before in your job.”
Empowering your people to explore what’s possible might be the most strategic move you make.
Underwriting transformation isn’t about chasing the next shiny tool.
It’s about fixing what’s already slowing you down.
Fragmented logic. Manual workflows. Hard-to-trace decisions.
Luckily, the path forward doesn’t require a massive overhaul. Just three moves:
Do that, and the bigger opportunities—AI-driven triage, smarter model reuse, faster quote response—stop being futuristic. They start being feasible.
And if Tom Wilde is right, this work sets the foundation for something even bigger:
[32:36] “We’re witnessing a steady transition from underwriting being done mostly inside the four walls… to a future where we may not ask the insured anything.”
That kind of future demands flexible systems, transparent models, and teams empowered to adapt as fast as risk does.