Dec 24, 2025
Outlook for 2026: What tech leaders in insurance brokerages need to get right
A forward looking perspective on how AI will reshape commercial insurance brokerages by 2026, and what technology leaders must prioritize now to turn experimentation into lasting competitive advantage.
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Commercial insurance brokerages are approaching a decision point. AI is no longer theoretical, and it is no longer optional. By 2026, the difference between market leaders and laggards will not be who experimented with AI, but who translated it into measurable operational advantage. Conversations with more than 50 CIOs, CTOs, and Heads of Innovation across Europe and the US point to a clear theme: the opportunity is massive, but execution is where most firms stumble.
From experimentation to expectation
Commercial insurance brokerages are moving into a phase where AI is no longer optional experimentation but an expected part of the operating model. Conversations with more than 50 CIOs, CTOs, and Heads of Innovation across Europe and the US show the same tension. The potential upside is widely understood, with estimates pointing to 40 to 60 percent reductions in administrative overhead. Yet most implementations fail to translate into day to day impact. Copilot licenses are rolled out firm wide, but usage remains low. In parallel, unmanaged AI usage is growing quietly across broker teams, creating new exposure around data privacy, regulatory compliance, and professional liability. By 2026, this gap between intent and execution becomes impossible to ignore.
Commercial insurance brokerages are moving into a phase where AI is no longer optional experimentation but an expected part of the operating model. Conversations with more than 50 CIOs, CTOs, and Heads of Innovation across Europe and the US show the same tension. The potential upside is widely understood, with estimates pointing to 40 to 60 percent reductions in administrative overhead. Yet most implementations fail to translate into day to day impact. Copilot licenses are rolled out firm wide, but usage remains low. In parallel, unmanaged AI usage is growing quietly across broker teams, creating new exposure around data privacy, regulatory compliance, and professional liability. By 2026, this gap between intent and execution becomes impossible to ignore.
Commercial insurance brokerages are moving into a phase where AI is no longer optional experimentation but an expected part of the operating model. Conversations with more than 50 CIOs, CTOs, and Heads of Innovation across Europe and the US show the same tension. The potential upside is widely understood, with estimates pointing to 40 to 60 percent reductions in administrative overhead. Yet most implementations fail to translate into day to day impact. Copilot licenses are rolled out firm wide, but usage remains low. In parallel, unmanaged AI usage is growing quietly across broker teams, creating new exposure around data privacy, regulatory compliance, and professional liability. By 2026, this gap between intent and execution becomes impossible to ignore.
The 80 20 imbalance becomes a growth ceiling
The core operational problem has not changed. Brokers still spend more than 30 hours per week on non revenue activities such as data entry, coverage review, submission handling, and proposal formatting. That leaves a narrow window for advisory work, relationship building, and prospecting. In a more competitive market, this imbalance directly caps growth. Firms that cannot reclaim broker time will struggle to keep up with peers that can respond same day, quote faster, and provide more proactive guidance without increasing headcount. By 2026, time allocation is no longer an efficiency issue. It becomes a strategic constraint.
The core operational problem has not changed. Brokers still spend more than 30 hours per week on non revenue activities such as data entry, coverage review, submission handling, and proposal formatting. That leaves a narrow window for advisory work, relationship building, and prospecting. In a more competitive market, this imbalance directly caps growth. Firms that cannot reclaim broker time will struggle to keep up with peers that can respond same day, quote faster, and provide more proactive guidance without increasing headcount. By 2026, time allocation is no longer an efficiency issue. It becomes a strategic constraint.
The core operational problem has not changed. Brokers still spend more than 30 hours per week on non revenue activities such as data entry, coverage review, submission handling, and proposal formatting. That leaves a narrow window for advisory work, relationship building, and prospecting. In a more competitive market, this imbalance directly caps growth. Firms that cannot reclaim broker time will struggle to keep up with peers that can respond same day, quote faster, and provide more proactive guidance without increasing headcount. By 2026, time allocation is no longer an efficiency issue. It becomes a strategic constraint.
Why most AI programs stall before impact
Most stalled AI initiatives fail for predictable reasons. Leadership teams attempt to evaluate too many use cases at once, creating analysis paralysis. Committees meet regularly, vendor demos pile up, and months pass without measurable results. Meanwhile, the underlying work remains unchanged. The brokerages that break this cycle do something counterintuitive. They narrow aggressively. They commit to one high volume, high friction workflow with clear metrics and a short decision horizon. Renewal quote comparison is a common starting point because it is bounded, measurable, and close to revenue. By 2026, the ability to prioritize execution over exploration will define successful technology leadership.
Most stalled AI initiatives fail for predictable reasons. Leadership teams attempt to evaluate too many use cases at once, creating analysis paralysis. Committees meet regularly, vendor demos pile up, and months pass without measurable results. Meanwhile, the underlying work remains unchanged. The brokerages that break this cycle do something counterintuitive. They narrow aggressively. They commit to one high volume, high friction workflow with clear metrics and a short decision horizon. Renewal quote comparison is a common starting point because it is bounded, measurable, and close to revenue. By 2026, the ability to prioritize execution over exploration will define successful technology leadership.
Most stalled AI initiatives fail for predictable reasons. Leadership teams attempt to evaluate too many use cases at once, creating analysis paralysis. Committees meet regularly, vendor demos pile up, and months pass without measurable results. Meanwhile, the underlying work remains unchanged. The brokerages that break this cycle do something counterintuitive. They narrow aggressively. They commit to one high volume, high friction workflow with clear metrics and a short decision horizon. Renewal quote comparison is a common starting point because it is bounded, measurable, and close to revenue. By 2026, the ability to prioritize execution over exploration will define successful technology leadership.
Standardization before automation
AI struggles in environments shaped by years of organic growth and acquisition. Most brokerages operate with significant process variation across teams, carriers, and offices. Senior brokers rely on personal spreadsheets built over decades. Documentation is sparse or nonexistent. In this context, automation breaks easily and adoption collapses. Leaders who succeed accept an uncomfortable truth. Some level of mandated standardization is required to unlock value. The firms that move forward are willing to define one standard workflow, even if it disrupts long standing habits. By 2026, leadership willingness to enforce change in a narrow but critical area becomes a key differentiator.
AI struggles in environments shaped by years of organic growth and acquisition. Most brokerages operate with significant process variation across teams, carriers, and offices. Senior brokers rely on personal spreadsheets built over decades. Documentation is sparse or nonexistent. In this context, automation breaks easily and adoption collapses. Leaders who succeed accept an uncomfortable truth. Some level of mandated standardization is required to unlock value. The firms that move forward are willing to define one standard workflow, even if it disrupts long standing habits. By 2026, leadership willingness to enforce change in a narrow but critical area becomes a key differentiator.
AI struggles in environments shaped by years of organic growth and acquisition. Most brokerages operate with significant process variation across teams, carriers, and offices. Senior brokers rely on personal spreadsheets built over decades. Documentation is sparse or nonexistent. In this context, automation breaks easily and adoption collapses. Leaders who succeed accept an uncomfortable truth. Some level of mandated standardization is required to unlock value. The firms that move forward are willing to define one standard workflow, even if it disrupts long standing habits. By 2026, leadership willingness to enforce change in a narrow but critical area becomes a key differentiator.
Closing the gap between technology and commercial reality
Another recurring failure mode is the disconnect between IT success metrics and business outcomes. Technology teams optimize for processing speed, uptime, and accuracy. The business cares about placement ratios, retention, and revenue concentration. This misalignment produces systems that perform well in testing but fail in production on complex, high value accounts. Leading brokerages address this by forcing genuine cross functional collaboration. Senior brokers are embedded into technology decisions, while technologists spend time shadowing frontline workflows. By 2026, organizations that still run parallel tech and business initiatives will find themselves outpaced by teams that align around shared outcomes.
Another recurring failure mode is the disconnect between IT success metrics and business outcomes. Technology teams optimize for processing speed, uptime, and accuracy. The business cares about placement ratios, retention, and revenue concentration. This misalignment produces systems that perform well in testing but fail in production on complex, high value accounts. Leading brokerages address this by forcing genuine cross functional collaboration. Senior brokers are embedded into technology decisions, while technologists spend time shadowing frontline workflows. By 2026, organizations that still run parallel tech and business initiatives will find themselves outpaced by teams that align around shared outcomes.
Another recurring failure mode is the disconnect between IT success metrics and business outcomes. Technology teams optimize for processing speed, uptime, and accuracy. The business cares about placement ratios, retention, and revenue concentration. This misalignment produces systems that perform well in testing but fail in production on complex, high value accounts. Leading brokerages address this by forcing genuine cross functional collaboration. Senior brokers are embedded into technology decisions, while technologists spend time shadowing frontline workflows. By 2026, organizations that still run parallel tech and business initiatives will find themselves outpaced by teams that align around shared outcomes.
Working around legacy systems instead of replacing them
Most enterprise brokerages still rely on policy management systems deployed more than a decade ago. These systems lack modern APIs, trap data in proprietary formats, and consume a large share of IT budgets. Full replacement remains expensive, risky, and slow. The firms making progress accept these constraints and work around them. They target specific data bottlenecks, unlock value incrementally, and prove ROI before expanding scope. This pragmatic approach allows progress without betting the organization on multi year transformation programs. By 2026, incremental gains compound faster than ambitious but fragile overhauls.
Most enterprise brokerages still rely on policy management systems deployed more than a decade ago. These systems lack modern APIs, trap data in proprietary formats, and consume a large share of IT budgets. Full replacement remains expensive, risky, and slow. The firms making progress accept these constraints and work around them. They target specific data bottlenecks, unlock value incrementally, and prove ROI before expanding scope. This pragmatic approach allows progress without betting the organization on multi year transformation programs. By 2026, incremental gains compound faster than ambitious but fragile overhauls.
Most enterprise brokerages still rely on policy management systems deployed more than a decade ago. These systems lack modern APIs, trap data in proprietary formats, and consume a large share of IT budgets. Full replacement remains expensive, risky, and slow. The firms making progress accept these constraints and work around them. They target specific data bottlenecks, unlock value incrementally, and prove ROI before expanding scope. This pragmatic approach allows progress without betting the organization on multi year transformation programs. By 2026, incremental gains compound faster than ambitious but fragile overhauls.
Low adoption and shadow AI usage are symptoms of the same issue. When official tools do not deliver immediate value, brokers turn to consumer AI tools using personal accounts and unsecured workflows. This creates significant exposure as regulatory scrutiny increases and carriers introduce AI related exclusions. Blocking AI drives usage underground. Allowing unrestricted access increases risk. Leaders who navigate this tension successfully provide secure, compliant tools and accept long adoption curves. They start with willing teams, low risk workflows, and let results drive demand. By 2026, managing adoption and risk together becomes table stakes.
Low adoption and shadow AI usage are symptoms of the same issue. When official tools do not deliver immediate value, brokers turn to consumer AI tools using personal accounts and unsecured workflows. This creates significant exposure as regulatory scrutiny increases and carriers introduce AI related exclusions. Blocking AI drives usage underground. Allowing unrestricted access increases risk. Leaders who navigate this tension successfully provide secure, compliant tools and accept long adoption curves. They start with willing teams, low risk workflows, and let results drive demand. By 2026, managing adoption and risk together becomes table stakes.
Low adoption and shadow AI usage are symptoms of the same issue. When official tools do not deliver immediate value, brokers turn to consumer AI tools using personal accounts and unsecured workflows. This creates significant exposure as regulatory scrutiny increases and carriers introduce AI related exclusions. Blocking AI drives usage underground. Allowing unrestricted access increases risk. Leaders who navigate this tension successfully provide secure, compliant tools and accept long adoption curves. They start with willing teams, low risk workflows, and let results drive demand. By 2026, managing adoption and risk together becomes table stakes.
The most forward looking brokerages are already looking past automation toward augmentation. Same day renewals instead of multi day cycles. Queryable portfolio intelligence instead of static PDFs. Continuous risk monitoring instead of annual reviews. These capabilities depend on one foundation: transforming decades of unstructured data into structured, usable intelligence. The firms that invest in this foundation now will not just operate more efficiently. They will redefine what clients expect from a modern insurance brokerage by the end of the decade.
The most forward looking brokerages are already looking past automation toward augmentation. Same day renewals instead of multi day cycles. Queryable portfolio intelligence instead of static PDFs. Continuous risk monitoring instead of annual reviews. These capabilities depend on one foundation: transforming decades of unstructured data into structured, usable intelligence. The firms that invest in this foundation now will not just operate more efficiently. They will redefine what clients expect from a modern insurance brokerage by the end of the decade.
The most forward looking brokerages are already looking past automation toward augmentation. Same day renewals instead of multi day cycles. Queryable portfolio intelligence instead of static PDFs. Continuous risk monitoring instead of annual reviews. These capabilities depend on one foundation: transforming decades of unstructured data into structured, usable intelligence. The firms that invest in this foundation now will not just operate more efficiently. They will redefine what clients expect from a modern insurance brokerage by the end of the decade.
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Protect your clients with more insight.
Grow your brokerage with more confidence.
See how Vantel can fit your workflows, security requirements, and book of business in a live session with our team.
Protect your clients with more insight.
Grow your brokerage with more confidence.
See how Vantel can fit your workflows, security requirements, and book of business in a live session with our team.
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© 2026 Vantel Inc. All rights reserved.
Know the difference.
Choose the right cover.
Vantel helps brokers compare coverage faster and make clearer, more confident decisions.
© 2026 Vantel Inc. All rights reserved.
Know the difference.
Choose the right cover.
Vantel helps brokers compare coverage faster and make clearer, more confident decisions.
© 2026 Vantel Inc. All rights reserved.
