Laura Jesson
Director, Research
Laura Jesson has more than 25 years of experience in the financial services industry as a project manager, business analyst, and consultant. She joined Cutter Associates in 2013. Prior to Cutter, Laura spent eight years at Wells Capital Management and Evergreen Investments, where she managed investment operations projects, including those focused on outsourcing, client reporting, and trade management. She previously worked at Omgeo as a member of the Central Trade Manager (CTM) product design team. Laura started her career at Andersen Consulting (now Accenture) as a process consultant supporting a variety of projects for clients in the financial services industry. Laura earned her bachelor of arts in economics and psychology from Amherst College. She holds the Project Manager Professional (PMP) certification.
Recent research assignments and publications include the following:
- AI Use Cases in Investment Management: Infinite Possibilities
- Business Intelligence Tools
- Cutter Benchmarking: Client Reporting
- Cutter Benchmarking: Firmwide ESG Capabilities
- Cutter Benchmarking: Managing Vendors and Service Providers
- DataOps: In Theory and Practice
- Enabling Data Analytics
- ESG Data Management
- ESG: It’s a Jungle Out There ─ A Look at the Data Provider Landscape
- ESG Investing: One Vision, Many Lenses
- Execution Management Systems
- Hybrid Work Arrangements and Their Impact on Firm Culture
- Using RPA to Automate Business Processes
- The Role of the CRM for Asset Managers
Alijah Poindexter
Research Analyst
Alijah Poindexter is an experienced professional in financial services research and consulting, with a background spanning banking, healthcare, asset management, and fintech. He joined Cutter Associates in 2025, where he supports the firm’s research initiatives with a focus on research production and design, analysis, and content development. Prior to Cutter, Alijah was a senior research associate at Datos Insights, producing market research on commercial banking, digital payments, and healthcare payments. He also led multiple client consulting engagements, delivering strategic, data-centric advisory work. Earlier in his career, he served as associate editor at Bank Automation News, where he focused on banking coverage and industry events, and held program and research analyst roles. Alijah earned his BBA from Austin Peay State University in Clarksville, Tennessee.
AI continues to dominate conversations at investment management firms. Firms that find themselves behind in developing the necessary governance for AI programs are looking for practical ways to get focused (sooner rather than later). That governance gap creates a barrier between experimentation and live AI implementations that deliver measurable business outcomes. A vital first step is to develop a comprehensive, well-organized AI inventory.
Firms have used traditional AI for a long time, yet there is no question that asset managers and asset owners are eager to put GenAI and the power of LLMs to use. As firms adopt more types of AI across increasing numbers of divisions and workflows, they’re shifting their focus from simply trialing and deploying AI capabilities to measuring the success and ROI of AI initiatives, increasing knowledge, and supporting long-term adoption and usage. Despite exploration and ongoing AI initiatives, some firms lack confidence in how to accomplish it while balancing the right level of AI governance. Cutter’s 2025 AI Governance research found that 47% of firms have established formalized AI governance practices, with another 27% implementing them. While this is good news, more work remains.
Without consistent AI governance, firms may struggle to move beyond experimentation. With AI governance, firms can operationalize AI and realize its business value. From the same AI Governance study, Cutter found that a significant percentage of firms (at least 45%, as noted below) lack a defined/complete inventory of AI initiatives. Firms should make it a priority to develop and maintain a complete AI inventory.
AI inventories are centralized records of a firm’s AI tools, models, data, and users, enabling firms to establish proper oversight, risk management, and awareness. For firms, AI inventories serve as critical communication tools that provide the details needed to assess the overall AI program and are often among the first items requested during client due diligence and audits.
Asset managers and asset owners are responsible for overseeing their AI assets, whether built in-house, used by in-house resources with vendor-provided technology, or by an outsourced provider on the firm’s behalf. Having complete knowledge of AI utilities is a baseline expectation, and the AI inventory serves as a place to store those details.
Aspects of AI Governance Program in Place (for Firms With an AI Governance Program Underway)
A complete AI inventory should include a description that characterizes the purpose and expected outcome/benefit of each AI utility and note the extent of use, such as the business domain/user group for which it is (or is to be) used. The inventory should also note the approved datasets for use in the solution. Firms should establish a process for granting permission to intended business users/user groups, and have the ability to report which users have access to AI utilities.
A complete AI governance inventory will include many data points, including a listed name for each AI utility, plus the following:
• A description/purpose
• Expected utility/benefit and business domain/user group
• Status: Some suggested statuses are requested, approved, in development, in pilot, in production, terminated
• Type of model and provider of the AI solution: In-house development, hybrid (built upon a large vendor framework), vended (vendor name if appropriate)
• Data in/out: Training data used, key date flow, notes of relevant data-related risk areas: Public information only, PII data, intellectual property
• Relevant contacts: owner
• Relevant dates: dates or releases, updates, changes in status, etc.
Without a complete AI inventory, firms lack transparency. An AI inventory is a straightforward tool that can help answer fundamental questions, especially as firms seek to expand AI use and adoption. Firms without the requisite inventories risk exposure to unclear AI accountability, unmanaged flows of data and information, shadow AI use, and substandard governance and risk management overall. It is standard practice to know whom to contact if something goes wrong in the front or back office, or which initiatives are underway across the firm’s business operations. Whether in initial deployment or over the long term, AI requires the same approach.
Cutter recommends that firms track all statuses from inception (when they are requested) through trial/test, production, and termination (however your firm identifies the lifecycle stages of each AI utility), with notes and key dates captured. Terminated AI solutions/processes or initiatives that failed to progress to production are important data that serve multiple purposes. Capturing reasoning and justifications for halting, stalling, or terminating initiatives can help improve future evaluations and decision-making.
For more information, see Cutter’s 2025 research, AI Governance.
For obvious reasons, AI inventory maintenance is an ongoing task for firms, but one that will pay off. With strong inventory maintenance practices, firms have a better chance of keeping their related risk registers and other artifacts up to date.
Across our research interviews, only a small number of firms disclosed that they maintained their AI inventory information in one centralized location. Most described a fragmented, disparate situation, with information kept in multiple places such as spreadsheets, project request forms, user permission request/approval systems, etc. Many firms report they can pull this information together when needed, but few have followed through.
As a 2026 call to action, look to pull your AI inventory together. It will serve as an interesting exercise to see what it takes to find the data, but more importantly to determine what you can learn from it and how it can help you advance your firm’s broader AI program. Taking this step will enable you to move your AI programs forward with safety, transparency, and effectiveness.
Cutter Research has issued an AI Usage survey. If you would like to see how your firm compares with peers, please reach out to us at [email protected] to participate in the survey.