Case Study: How Baker Hill Used AI to Boost Employee Efficiency by 30%
Written by Tyler Claypool, Baker Hill; Vance Monical, Baker Hill; and Mary Dougherty, TechPoint
The CEO of Baker Hill was scrolling through his news feed, seeing article after article about how AI is transforming businesses. One headline caught his attention, but before he could click, he received an email from a board member asking about the company’s plan to improve employee efficiency with AI.
At the next Executive Leadership Meeting, the CEO posed a clear challenge to the leadership team:
Use AI to increase employee efficiency by at least 30%.
Industry Context
Founded in 1984, Baker Hill provides innovative lending, risk management, and analytics solutions to financial institutions across the U.S. With around 200 employees, it operates in a competitive fintech landscape where speed, accuracy, and compliance are critical to success.
The financial technology industry is evolving rapidly, shaped by digital transformation, increasingly complex regulatory requirements, and rising customer expectations for real-time, secure services. To remain competitive, fintech firms must innovate while ensuring compliance and security.
AI offers a path forward by automating repetitive tasks, enhancing decision-making, and enabling employees to spend less time on low-value work and more time on strategic, high-impact activities.
AI Implementation Strategy
Baker Hill’s AI strategy focused on three pillars:
- AI Adoption & Enablement: Building AI literacy, piloting tools, and scaling adoption.
- Governance & Risk Management: Ensuring responsible, compliant, and secure AI use.
- Data & Model Management: Controlling access, monitoring vendors, and preparing for regulation.
Solution and Implementation
AI Journey
Baker Hill’s adoption journey began with forming an AI committee to guide strategy and governance, along with a pilot test with M265 Copilot. Once the team was ready to roll out Copilot company-wide, they ensured the full team received AI 101 training and prompt guidance resources. The AI champions identified support teams and solicited feedback on use cases so they could pilot additional tools for coding and automation.
Observations
The Baker Hill team purchased M365 Copilot for the team despite early limitations and reservations about the application. Early meeting summaries showed it was less effective than ChatGPT. However, it was decided this was still the best tool for Baker Hill. In retrospect, the Baker Hill team shared that there should have been more gate keeping with the AI platforms prior to implementation company wide. This would have provided the team with stronger baseline information. Shadow AI revealed gaps in blocking the unapproved tools sooner. The business leaders prioritized efficiency gains, while Compliance and Risk teams emphasized responsible, compliant use of AI.
Outcomes
Through their AI adoption process, Baker Hill increased AI adoption across business units, with efficiency gains in routine tasks. They also established a formal AI governance structure to ensure security and compliance. Baker Hill created a data protection framework that reduced risks of sensitive information exposure. This all laid the groundwork for continuous innovation, with AI now embedded into workflows. The team also learned valuable lessons from pilot projects which will help inform and structure future scaling.
Conclusion
Through a structured AI strategy balancing adoption, governance, and compliance, Baker Hill positioned itself to achieve its goal of improving employee efficiency by 30%. While early challenges included managing shadow AI and addressing Copilot’s performance gaps, the organization’s commitment to responsible adoption created a foundation for long-term success.
AI is now enabling employees to focus on higher-value work, strengthening both competitiveness and compliance in an increasingly complex fintech landscape.
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