Beyond the hype: A strategic AI framework for MGA success

Successfully navigating the modern risk landscape requires MGA leaders to move with discipline, not just speed. The evidence clearly points to a strategic imperative: embrace AI where it provides augmentation and efficiency but exercise extreme caution where it demands full autonomy and carries high governance risk.

Gartner’s forecast that at least 40% of agentic AI projects will fail by 2027 due to unclear value and poor risk controls is not a warning against AI but against unstructured adoption. For MGAs, who rely on trust and specialized capacity, a failed AI project can be devastating to both capital relationships and reputation.

In this final part of the series, we outline a strategic framework with four actionable recommendations for MGA leaders to ensure their AI adoption is not part of that projected 40% failure rate but a source of sustained competitive advantage.

Four actionable recommendations for MGA leaders

Embed governance before you invest

The most successful AI projects begin with the audit trail, not the algorithm. Given the industry’s highly regulated nature, governance must be the first layer of your strategy, not an afterthought.

• Establish a human-in-the-loop mandate: For all high-stakes decisions (e.g. final pricing, claim denial, significant risk selection), mandate that the AI acts as a copilot to the underwriter or claims professional, never the sole pilot. Autonomy does not warrant opacity. 
• Define explainability standards: You must be able to explain an AI-driven decision to a broker, a regulator, and an insured. Implement systems that track data lineage — knowing the exact source, transformations, and time stamp of every data point used to train and run the model — and model influence to quantify precisely which features (e.g. loss history, geography, policy limits) contributed to the final score or decision. This complete traceability ensures all outputs are transparent, traceable, and auditable.
• Prioritize “invisible” AI for maximum ROI: The highest-impact AI projects are often the least visible to the end customer. Avoid projects driven by market hype and instead focus on automating the mundane, complex, and repetitive tasks that bog down your underwriters.
• Target document intelligence: Use AI to automate the ingestion and triage of unstructured data (e.g. ACORD forms, emails, loss runs). This immediately cuts cycle time, ensures data accuracy, and frees underwriters to focus on risk analysis instead of data entry. 
• Focus on generative convenience: Deploy GenAI for internal efficiency, such as summarizing long claims files, drafting first-pass responses to common broker queries, or generating regulatory report sections.

Fortify your data foundation

AI models are only as good as the data they consume. Many MGAs struggle to scale AI because their data is siloed, messy, or inconsistent. Investing in your data architecture is the non-negotiable step to unlocking AI value.

• Centralize and standardize: Break down data silos and consolidate core underwriting, claims, and policy data into a single, clean data source. Use a modern platform to ensure data is standardized, current, and accessible via APIs. 
• Validate for bias: Before using an AI model for risk selection or pricing, run bias audits against the training and production data to ensure the system is not unfairly or illegally discriminating.

Shift underwriters from experts to super-users

The goal of AI is not workforce replacement but workforce reinvention. Successful adoption requires MGAs to redefine the underwriter’s role and shift their focus from data entry and calculation to complex judgement and relationship management.

• Reskill for judgement and prompt engineering: Train underwriters not just on using the new tools but on interpreting model outputs and effectively leveraging GenAI through advanced prompt engineering. 
• Measure strategic impact: Change performance metrics to reward underwriters for strategic outcomes, such as portfolio quality, loss ratio improvement, and speed-to-market on new risks rather than just volume.

Choose a trusted, insurance-native partner

Partnering with vendors who understand the unique compliance and risk-aversion of the insurance industry is essential. A general technology provider cannot replace the insurance-native platform that has trust built into its DNA.

• Demand security and compliance: Select a partner that explicitly prioritizes data privacy, security, and regulatory adherence over simply offering the latest AI trick. Their solutions must be built to handle sensitive policyholders and financial data. 
• Prioritize seamless integration: The AI must integrate with your existing core systems. If the solution requires a massive, disruptive overhaul, it raises the complexity, cost, and, ultimately, the risk of failure.

Your AI future at Vertafore

With Vertafore’s AI-powered platforms, you can scale your success with confidence. AI is transforming how insurance professionals work and provides tremendous potential for you and your business. Tangible AI value comes from three main areas: generative convenience, document intelligence, and data enhancement.

These foundations have been in use for years in our industry and will remain a significant percentage of AI use. AI is powering the future of the entire insurance distribution channel. With Vertafore, you can work smarter and faster with AI solutions built at the intersection of innovation and trust.

Learn more about Vertafore's MGA solutions.