The Three Things Every Insurer Should Ask Their DA Technology Partner in 2026

A problem-led guide to data + process automation readiness for insurers

In delegated authority, technology isn’t just “software.” It’s the system your underwriting operations rely on to ingest bordereaux, standardise data, route exceptions, and produce the evidence that your stakeholders (and regulators) expect.

And in 2026, the bar has moved again.

The study, The Value of AI in the UK: Growth, people & data, produced with SAP and Oxford Economics, validates that the average UK business is already realising significant returns of 17% (£2.7m), from AI investments, with ROI forecast to almost double to 32% (£7.5m) by 2027. Market expectations are shifting toward AI-enabled automation as a baseline, not a bonus. The British Chambers of Commerce indicate that 46% of B2B service firms, are now using AI. At the same time, many teams are re-checking whether their current partner’s roadmap, continuity, and incentives still align with what the business needs next.

This article is a simple checklist: three questions to ask any DA technology partner, designed to cut through demos, decks, and “AI-washing.”

1) “What gives us confidence you’ll still be investing in this platform two years from now?”

Why this matters in 2026

DA platforms are long-lived. Switching is possible, but no one wants to do it twice. The real risk isn’t the workflow you automate this quarter, it’s whether your partner’s incentives support long-term R&D, service continuity, and roadmap stability, or whether your platform is ultimately shaped by forces unrelated to client outcomes.

What to ask for (evidence, not promises)

Use this mini-checklist:

• Roadmap governance: Who decides what gets built? Clients or investors?

• Product investment signals: What shipped in the last 6 months that materially improved DA operations (not just UI)?

• Retention plan: What’s the retention plan for the team that actually runs delivery and support through change?

• Commercial predictability: What typically changes in pricing and packaging over time and why?

  
  

Red flags

• “We can’t share roadmap detail.”

• “AI is coming soon” (without showing production outputs).

• Vague answers about support continuity and product investment cadence.

2) “Show us your AI in production: what does it do on real bordereaux this week?”

Why this matters in 2026

A widening gap has emerged between vendors who talk about AI and platforms that have AI-enabled automation running in production, processing bordereaux data at scale, improving data quality, and surfacing useful underwriting signals.

What “AI in production” should look like (in delegated authority)

Ask your partner to demo with your sample data (even a subset):

• Bordereaux mapping + standardisation: How are formats learned, mapped, and normalised?

• Data quality improvement: What validation/enrichment happens automatically vs. manually?

• Exception handling: How does the workflow route anomalies—who sees them, when, and with what context?

• Measurable outcomes: Time saved, error reduction, and evidence of improved quality (not just “accuracy claims”).

One killer follow-up

“Which parts of the AI output can we audit and explain—field-by-field—if challenged?”

Because if AI can’t be explained, it becomes an operational risk, not a capability.

3) “How will you partner with us through change beyond the initial implementation?”

Why this matters in 2026

DA operations don’t stand still. Data requirements evolve. Delegated arrangements change. Workflows get redesigned. The question is whether your partner measures success by ongoing client outcomes, or by project milestones and transaction timelines.

What to ask for (so you don’t buy a one-off implementation)

• Operating model: What does “steady-state” support look like once you’re live?

• Continuous improvement cadence: How are enhancements prioritised, tested, and deployed?

• Proof points: Average client tenure, examples of long-term platform evolution, and references who have scaled with them.

• Migration confidence: What’s the practical plan for onboarding and data migration, and what risks do they proactively manage?

Red flags

• “We’ll handle it” (without a clear migration approach)

• A partner who can’t articulate how they run governance, changes, and roadmap alignment with clients over time

A simple scoring rubric you can use internally (15 minutes)

Score each category 1–5:

1. Stability & roadmap confidence (clarity + evidence)

2. AI in production (demonstrated outputs, measurable results)

3. Workflow + exception governance (controls, auditability, routing)

4. Partnership model (continuous improvement, longevity, references)

   
   

If any category is a 1 or 2, you don’t have a platform problem, you have a partner-risk problem.

Conclusion

If you’re re-evaluating your DA technology stack in 2026, start with the three questions above, then insist on evidence. The best partners will welcome scrutiny, because they can show stability, demonstrate AI in production, and point to long-term client outcomes.

FAQs

What is delegated authority (DA) technology?

DA technology supports the operational workflows and data processing behind delegated underwriting, typically including bordereaux ingestion, validation, mapping/standardisation, exception routing, and reporting.

What’s the difference between “AI-enabled” and “AI in production”?

“AI-enabled” often means roadmap intent. “AI in production” means the platform is currently using machine learning to process live data and produce measurable operational outcomes (speed, quality, insight).

What should we ask to avoid AI-washing?

Ask to see AI outputs on real bordereaux, ask how exceptions are handled, and ask what parts of the result are auditable/explainable.

Is switching DA platforms risky?

Any platform switch has risk, but the highest-risk situation is staying with a partner whose incentives, roadmap, or capability maturity no longer align with your operating reality.