Insights from the Delegated Authority Strategy Day pre-event survey results
The case for investment to achieve profitable growth
Ahead of this year’s Delegated Authority Strategy Day, the pre-event survey results paint a clear, and in many ways familiar, picture of a market still grappling with foundational challenges while simultaneously trying to move forward with more advanced capabilities.
What is different this year is the intent.
There is less interest in theory and more focus on execution. Less tolerance for fragmented processes and more urgency around scale. Less curiosity about AI and more demand for where it actually works.
Across the responses, five themes consistently emerge, each pointing to a market that understands the direction of travel but is still working through how to get there.
1. Data still dictates what is possible
If there is one constant running through every response, it is this: data quality remains the single biggest constraint on progress.
“Data quality and consistency are the biggest barriers to transforming DA Operations.”
“Each Coverholder structures their bordereaux differently… this inconsistency makes automation difficult and increases operational effort.”
The issue is not new, but its impact is becoming more acute as firms attempt to automate, scale and improve oversight. Poor data is no longer just an operational nuisance. It is actively limiting underwriting insight, slowing decision-making and increasing cost.
Several respondents pointed to the same root causes:
- Inconsistent bordereaux formats and standards
- Lack of validation before submission
- Heavy reliance on spreadsheets and email chains
- Fragmented data flows across brokers, MGAs and carriers
The result is a continuous cycle of rework.
“We need shared technology to minimise that wasted effort… chasing too much query resolution up and down the distribution chain.”
What needs to change
The direction is clear. The market needs to move upstream.
- Pre-submission validation to stop bad data entering the process
- Standardised templates and mapping rules to reduce interpretation
- Shared data frameworks or APIs to remove duplication
- Clear ownership of data quality across the chain, not just at ingestion
- Until that happens, every downstream initiative, from AI to analytics, will struggle to deliver consistent value.
2. AI has moved from curiosity to accountability
AI is now embedded in almost every conversation, but the tone has shifted.
This is no longer about what AI could do. It is about what it should be doing today.
“AI capability - practical case studies of where it can be applied today.”
“I’d like to identify real operational challenges that agentic AI can solve.”
The areas of interest are highly practical:
- Bordereaux mapping and validation
- Data enrichment and standardisation
- Due diligence and onboarding checks
- Fraud detection and routine processing
There is also a clear recognition that AI must fit into existing workflows, not sit alongside them.
“How do we achieve everyday use of AI and how do we train our people to integrate AI into their workflows?”
The emerging tension
Despite the interest, there is still hesitation.
“How do insurers get comfortable with delegating a concept that they are barely comfortable utilising themselves?”
This speaks to a deeper issue. Delegated Authority is built on trust and control. Introducing AI into that model raises questions about governance, accountability and risk.
What will good look like?
The next phase of adoption is likely to be:
- Targeted and use-case driven, not enterprise-wide transformation
- Human-in-the-loop, particularly for oversight and decisioning
- Focused on augmentation, not replacement
The winners will be those who can demonstrate measurable improvements in efficiency, accuracy or control, not just technical capability.
3. Operational transformation must link to underwriting outcomes
A notable shift in this year’s survey is the emphasis on outcomes rather than activity.
Respondents are not asking how to make processes faster. They are asking how to make them more effective.
“Linking operational transformation to underwriting profits.”
“Focus on effectiveness not efficiency.”
This is a critical distinction.
Too many transformation programmes have historically focused on cost reduction or process optimisation without clearly improving underwriting performance.
The survey suggests the market is now looking for:
- Better risk selection through improved data
- Earlier identification of performance issues
- Clearer alignment between operations and underwriting intent
The scale challenge
At the same time, firms are under pressure to grow delegated portfolios without proportionally increasing headcount.
“Management of higher volumes of DUA business with existing levels of resource.”
“Without automation this will cripple this growing marketplace.”
What needs to change
True transformation requires:
- end-to-end process redesign, not isolated fixes
- integration across systems and stakeholders, not point solutions
- clear feedback loops into underwriting decisions, not just reporting
In short, operations need to move from being a support function to an active contributor to portfolio performance.
4. Governance is under pressure to become both stronger and lighter
Governance and oversight remain central to Delegated Authority, but the current model is increasingly seen as inefficient.
“Structuring governance and signoff without creating bottlenecks.”
“42 schemes… 42 annual due diligence returns… there’s got to be a better way.”
There is a clear frustration with:
- duplication of effort across insurers
- inconsistent requirements and interpretations
- delays in onboarding and audit processes
- excessive manual intervention
At the same time, no one is suggesting reducing control.
The challenge is balance.
“Maintaining strong oversight and data transparency while still keeping the speed and flexibility that delegated authority is supposed to deliver.”
The shift towards proportionate oversight
A recurring theme is the idea of “Minimum Effective Oversight”.
This points to a more risk-based approach where:
- oversight is tailored to product, geography and partner risk
- standardised data supports continuous monitoring
- pre-bind discipline reduces the need for post-bind correction
The role of data and AI
There is growing interest in using technology to:
- automate parts of due diligence
- provide real-time performance monitoring
- reduce reliance on periodic audits
If implemented correctly, this could move governance from retrospective to proactive.
5. Transformation is still a people problem
Perhaps the most consistent and underestimated theme is the human one.
“Stakeholder buy in at a senior level.”
“Recruitment and resourcing within DA are still being seen as a cost not a benefit.”
“Achieving consistent adoption of new processes and behaviours across all DA stakeholders.”
The challenges are well understood:
- competing priorities and limited resource
- resistance to change across the value chain
- varying levels of capability across brokers, MGAs and TPAs
- difficulty embedding new processes into BAU
The reality of change
Even the best-designed solutions will fail without:
- clear executive sponsorship
- aligned incentives across stakeholders
- ongoing training and capability building
- visible benefits for each part of the value chain
One respondent captured this succinctly:
“Getting the team to see the end vision of what it could look like compared to current processes.”
Looking ahead to a market moving from intent to execution
Taken together, the survey results highlight a market at an inflection point.
The priorities are not new, but the urgency is.
- Data must improve because everything depends on it
- AI must prove value in specific, measurable ways
- Operations must directly support underwriting performance
- Governance must evolve to enable, not constrain
- People and culture must keep pace with change
What is encouraging is the level of alignment.
Across insurers, brokers and MGAs, there is a shared understanding of the challenges and, increasingly, a shared view of what good looks like.
The next phase is execution.
And that is where the real differentiation will emerge.
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