What the TINtech Data Jam 2026 survey reveals about the next phase of digital transformation in insurance
Every year ahead of TINtech Data Jam, we survey senior insurance leaders, technology specialists, data professionals and operational decision-makers to understand the issues they believe will have the greatest impact on their organisations over the next 12-24 months.
This year's survey generated hundreds of comments covering AI, data, underwriting, operations, digital trading and technology strategy.
The findings are fascinating.
Not because the industry is talking about AI. That is hardly surprising.
What stands out is how the conversation is changing.
Compared to previous years, there is noticeably less interest in the theoretical potential of AI and significantly more focus on implementation, governance, operating model redesign and measurable outcomes.
The market appears to be moving beyond experimentation and into execution.
That shift creates enormous opportunities for insurers and brokers that get it right.
It also exposes a new set of challenges that many organisations are only beginning to confront.
AI is becoming an organisational challenge, not a technology challenge
For much of the past three years, AI discussions have focused on models, tools and use cases.
This year's survey suggests the market is beginning to realise that the harder challenge sits elsewhere.
Many of the questions weren't about technology at all.
Respondents wanted to understand:
How operating models need to evolve
How underwriting roles may change
How organisations should be structured
How to manage change and adoption
How to develop the right skills and leadership capabilities
One respondent asked:
"How to manage the organisational change due to AI initiatives?"
Another wanted to explore:
"Restructuring the organisation to make maximal use of Agentic AI."
This is a significant shift.
Technology is advancing rapidly. The bigger challenge now is redesigning organisations that were built around human workflows, manual processes and functional silos.
Many insurers have spent the last decade digitising existing processes.
The next phase may require rethinking those processes altogether.
Data quality remains important.
Data accessibility may be the bigger challenge. For years, data quality has dominated industry discussions.
It remains a major issue.
But the survey suggests a more nuanced challenge is emerging.
Respondents repeatedly referenced the need to make trusted data available across systems, teams and increasingly AI-driven workflows.
Questions focused on data orchestration, master data management, standards, governance and making data accessible for AI agents.
One respondent asked:
"What are the key considerations from data readiness perspective to be able to scale AI initiatives?"
Another highlighted:
"How to make data available for Agents to work effectively."
This matters because AI changes the economics of data. Historically, poor data quality created inefficiency. In an AI-enabled organisation, inaccessible data creates limits on automation, decision-making and scale.
The next competitive advantage may not come from having more data. It may come from making trusted data available wherever and whenever decisions need to be made.
The industry is entering the era of agentic operations
No topic generated more interest than agentic AI.
But what is particularly interesting is the nature of the questions being asked.
The industry is no longer asking whether agentic AI is real, it’s asking how to deploy it safely.
Respondents wanted practical guidance on governance, trust, oversight, interoperability and value realisation.
Questions included:
"How do we make this real with the technology estate we have today?"
"I’d like to see examples of successful application."
"What is the value realised to date."
Perhaps most tellingly, one respondent asked:
"How will we ever trust the outputs if it only takes one mistake to ruin trust?"
This highlights the balancing act facing insurance leaders.
The opportunity is significant as AI agents could transform operational workflows, underwriting support, claims processing, reporting and customer service.
Yet insurance remains a business built on trust, accountability and governance.
Success will depend on organisations finding ways to increase autonomy without losing control.
The winners are unlikely to be those who automate the most.
They are likely to be those who create the highest levels of confidence in automated decision-making.
Legacy transformation is becoming a business model issue
Legacy technology continues to feature prominently in the survey which is hardly surprising.
What is changing is the context.
Historically, legacy modernisation was viewed primarily as a technology challenge. Today it is increasingly becoming a strategic business challenge.
Many respondents recognise that future operating models, digital trading ambitions and AI capabilities are constrained by technology decisions made years ago.
Questions included:
"How we digitise fully in such a legacy world."
And:
"How we are going to move from legacy to continuous transformation."
This is where many organisations face difficult decisions.
Do they continue enhancing existing platforms or do they replace them?
Or do they create new capabilities around them?
There is no universal answer.
But there is a growing recognition that the cost of maintaining complexity is rising.
Not simply in terms of IT spend, but in slower decision-making, duplicated effort and reduced ability to exploit emerging technologies.
Governance is becoming a competitive differentiator
One of the strongest themes running throughout the survey was governance.
Not governance as a compliance exercise. Governance as an enabler.
Respondents repeatedly highlighted concerns around AI oversight, explainability, bias, accountability, regulation and risk management.
This reflects a growing maturity in the sector.
The conversation is moving beyond "How quickly can we deploy AI?" towards "How do we deploy AI responsibly and at scale?"
The organisations that answer that question successfully may gain an important advantage.
Strong governance creates trust.
Trust accelerates adoption.
And adoption ultimately determines whether investments in data and AI translate into improved underwriting performance, faster operations, better customer outcomes and sustainable growth.
The opportunity ahead
The most encouraging aspect of the survey is the overall tone.
There is little evidence of organisations standing still.
There is curiosity.
There is ambition.
There is a willingness to learn from peers and challenge existing assumptions.
Most importantly, there is a growing recognition that the next phase of digital transformation will not be defined by technology alone.
It will be shaped by how effectively organisations combine data, technology, governance, operating model design and people.
The industry has spent the last few years proving what AI can do.
The next few years will determine which organisations can successfully turn that potential into measurable improvements in underwriting performance, operational efficiency, customer experience and growth.
That is where the real transformation begins.
Sign Up to TINsights
Where we share our latest blogs, industry reports and insights
