By Jake Sloan, VP of Global Insurance at Appian
This is the year insurers move decisively beyond isolated experiments to operationalise AI across the entire business. Success is now defined by a powerful, simple principle: AI must drive the sheer volume, while human experts are freed to conquer the complexity.
Moving on from manual
Manual document handling, whether in physical mailrooms or outsourced document factories, is rapidly becoming a competitive liability. It’s slower, riskier from a compliance perspective, and introduces errors and delays into the earliest stages of the underwriting and claims cycle.
The solution to this volume-based problem is intelligent document processing (IDP), which is now table stakes for any forward-looking insurer. IDP can ingest submissions, claims documents and supporting evidence in real time, extracting relevant data while preserving full audit trails. It’s also now moving beyond front-end intake into underwriting, claims adjudication and compliance.
With information flowing seamlessly into underwriting and claims workflows, experts can spend less time searching for data and more time applying decisioning.
Why automation is risk-dependent
Insurance has always operated on a spectrum of risk, and automation must follow the same logic. Not all risks are created equal, and neither should they be processed in the same way.
Routine, low-complexity products like bicycle or basic travel insurance lend themselves to high levels of automation. In these cases, AI can assess eligibility and price risk, and process straightforward claims with minimal human intervention.
However, that approach does not translate to complex risks such as large industrial sites or bespoke commercial programmes. These risks demand expert decisioning, contextual understanding and experience. Here, AI’s role is not to replace decision-makers, but to accelerate analysis by surfacing relevant insights, flagging anomalies and coordinating workflows across teams.
We are already seeing this balance achieved through connected underwriting and connected claims models, where AI supports human decision-making without obscuring accountability. The result is faster, more consistent decisions, without sacrificing control.
Governance will be the difference-maker
With intelligent document processing and connected workflows becoming ubiquitous in the industry, it’s governance that will ultimately separate the leaders from the laggards in the months and years ahead.
Insurance profitability has always been tightly linked to governance, given the industry’s regulatory intensity. At the same time, insurers are under pressure to deliver hyper-digital, highly personalised experiences at scale. Those pressures only intensify as AI becomes embedded in core workflows.
Regulatory scrutiny of AI is increasing globally, with regulators raising expectations around transparency and control. Notably in August of this year the EU AI Act takes effect in Europe. Insurers need auditable AI decisions, full decision lineage and clear ownership models. Recent headlines involving opaque AI systems denying claims have reinforced a simple truth: governance is a trust issue for the whole industry.
In this environment, strong governance becomes a competitive differentiator, enabling insurers to adopt AI sustainably and at scale, rather than being slowed by risk or regulatory uncertainty.
Breaking through the cultural barrier
The biggest barrier to enterprise AI adoption is rarely the technology itself. More often, it’s organisational culture. Nowhere is that truer than in insurance, where historically profitable firms have often underinvested in change.
As AI investment accelerates, cultural change must keep pace. While many leaders talk about agile and iterative development, a surprising number of insurers still operate in a waterfall mindset. Lengthy requirements documents are handed over to IT, only for solutions to emerge months later that technically meet specifications but fail to address real business needs or human problems.
For AI to deliver value at scale, that pattern must change. Business teams need to trust IT to deliver iteratively, and IT needs clear priorities rather than exhaustive upfront specifications. Insurers should embed AI into day-to-day workflows through continuous improvement rather than relying on one-off transformation programmes.
AI as the accelerator, not the decision-maker
The future of AI in insurance is about accelerating decision making and ensuring experts spend their time where it matters most.
In 2026, insurers that strike that balance – of automation for volume, expertise for complexity, with governance always a central principle – will make the shift from isolated AI tools to enterprise-wide capability. That is how we as an industry make good on AI’s promise.
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