How to build a scalable, AI-ready midmarket brokerage

For much of the past decade, growth masked operational complexity across the brokerage industry. Strong premium rates, abundant merger and acquisition activity, and favorable market conditions allowed many firms to expand despite relying on fragmented systems and labor-intensive processes.

However, as rate-driven growth moderates, brokerage leaders are confronting new pressures that are exposing operational complexity. For example, PwC has warned that insurance faces a rapidly aging workforce while many carriers struggle to attract and retain the frontline talent needed. Policyholder expectations are also growing, with seamless digital experiences and hyper-personalization as a new defining force shaping the industry.

Artificial intelligence offers an opportunity to address many of these challenges, but only if brokerages move beyond viewing it as another technology investment and instead use it to build the data, workflows and servicing capacity needed to support growth.

Find the constraints hiding inside everyday work

Many brokerages assume growth challenges stem from market conditions or competitive pressure. In reality, the biggest constraints often sit inside day-to-day operations.

While McKinsey estimates that administrative tasks consume 30% to 40% of an underwriter's time, brokerage service teams face similar burdens through renewal preparation, certificate handling and commission reconciliation.

Every hour spent on manual processes is an hour that cannot be devoted to strengthening client relationships, identifying cross-sell opportunities or proactively managing renewals. At the same time, clients increasingly expect brokers to provide more proactive advice on risk, coverage and emerging exposures, placing even greater pressure on servicing capacity. The resulting revenue leakage rarely appears as a single line item on the profit and loss sheet. It hides in underdeveloped accounts, overloaded service teams, missed commissions and client relationships that receive only reactive attention.

As a starting point, leaders should map where employees spend time, identify the processes that generate the most rework and determine which activities delay revenue realization.

The next step is prioritization. Not every process requires transformation at once. Brokerages should focus first on the high-volume workflows that directly affect growth and profitability, such as renewal preparation, submission intake, quote comparison and policy checking. These processes typically consume significant servicing capacity and create bottlenecks that ripple throughout the organization.

How to build a strong data foundation before investing heavily in AI

The insurance industry is investing heavily in AI, yet many organizations continue to struggle with scaling initiatives beyond individual use cases. According to the IBM Institute for Business Value, legacy technology and fragmented data remain significant barriers to enterprise AI adoption. For brokerages, the lesson is straightforward: the value of AI depends on the quality of the data and workflows that support it.

Many brokerages still operate with fragmented client information spread across multiple systems and acquired businesses. The result is a familiar set of challenges: no single view of client, policy or exposure data, limited ability to generate actionable insights and inconsistent data quality across the organization.

This challenge is particularly acute for firms that have grown through acquisition. Multiple agency management systems, inconsistent workflows, fragmented permissions and different document practices create operational complexity that limits visibility and slows decision-making. Introducing AI into that environment often amplifies the mess rather than solving it.

Building an AI-ready brokerage begins with creating visibility. A modern brokerage needs clear systems of record, common data standards and governance processes that keep information accurate across the client lifecycle. Data should be captured once, structured early and reused across submissions, renewals, servicing, billing and reporting.

Better data enables brokerages to move from reactive servicing to proactive client management and more informed decision-making.

Redesign the workflows that directly affect growth

Technology investments deliver the greatest return when they are applied to workflows that shape revenue, margin and client experience.

Many firms still equate AI adoption with purchasing new tools and encouraging teams to experiment with copilots, chatbots and document summarizers. That approach can lead to shadow AI initiatives and agent sprawl without creating meaningful business value.

The highest-impact opportunities are typically found in submission intake, renewals, policy checking, certificate handling, client service requests and billing operations. These workflows require employees to move information between systems, review documents and perform repetitive quality checks.

The practical starting point is to identify workflows where manual effort directly limits growth. If a process slows down renewals, delays producer follow-up, weakens carrier submissions or prevents proactive client outreach, it should be a priority for redesign.

AI and automation should then be embedded into those workflows with clear measures for turnaround time, accuracy, producer capacity and revenue impact. The goal is not to replace brokers, but to create more capacity for the relationship-building and advisory work that clients increasingly value.

Use early productivity gains to fund long-term transformation

Many AI initiatives disappoint because they focus on deploying technology rather than changing how work gets done. A new tool may produce an impressive demonstration, but if it does not improve servicing capacity, producer productivity or renewal speed, its business impact will likely be limited.

For many midmarket brokerages, large-scale transformation is difficult to fund all at once. A more practical approach is to start with a handful of high-friction processes that directly affect growth and client service. Faster turnaround times, fewer manual touchpoints and increased account-manager capacity create immediate value while also freeing up resources that can be reinvested into integrating acquired agencies, improving data quality and expanding analytics and AI capabilities.

This is how transformation becomes sustainable. The most effective programs create a virtuous cycle in which operational improvements fund the next phase of modernization, steadily building a more scalable and AI-ready brokerage.

A structured path to brokerage transformation

Redesigning the brokerage operating model is not simply an efficiency exercise. It can accelerate revenue realization by reducing friction in renewals and servicing, improve producer effectiveness by giving teams more time to focus on clients and growth opportunities, and uncover revenue that often remains hidden in underdeveloped accounts, missed commissions and cross-sell opportunities.

Better data and faster response times can also strengthen relationships with carriers and managing general agencies through higher-quality submissions, greater responsiveness and stronger alignment with risk appetite. In a softer market, those capabilities can become an important source of organic growth. As operational complexity increases and AI capabilities become more widely available, the real differentiator will increasingly be an organization's ability to connect technology to better workflows, better decisions and stronger relationships across the brokerage ecosystem.

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Saleem Sheikh is vice president and practice lead, private equity in insurance, with EXL. Contact him at [email protected].