2026 Outlook: AI Adoption – Rolling It Out Is Just the First Step

The challenge now in 2026 is so-called operationalization, aka putting AI into play. Firms must embed AI into core processes and drive usage to deliver efficiency, cost savings, and innovation at scale.

Although AI technology continues to advance, real-world adoption by businesses remains limited. According to reporting by The Economist ─ based on data from the U.S. Census Bureau and the Massachusetts Institute of Technology ─ just one in ten firms with more than 250 employees has embedded AI into production processes, and most AI pilots have delivered no return on investment. Similarly, according to Cutter’s 2025 Data Management Benchmarking survey, while 58% of investment management firms use more than one Generative AI (GenAI) engine, only 4% have well-established AI capabilities within the business with consistent tools and appropriate support. For firms to unleash tangible ROI from their AI initiatives, they must push beyond their pilot programs and establish governance, benchmarks, and cultural engagement strategies that ensure consistent, safe, and value-driven AI use across the organization.

AI Is Here ─ But Deploying Tools Isn’t Enough

For investment management firms, as well as firms across other industries, AI creates valuable opportunities to grow more efficiently and scale. Enterprise licenses for GenAI engines, chatbots, and agentic assistants are now common, and many firms are endeavoring to make AI a key part of their processes, ranging from AI-enabled workflows for reporting, reconciliation, and compliance to data analysis, summarizing research, drafting emails, and rote tasks. Yet simply deploying AI tools alone does not deliver measurable value, or it only delivers value in small pockets, as hurdles to true workflow integration demonstrate.

What are those hurdles? First, simply deploying GenAI tools like Microsoft Copilot and ChatGPT does not guarantee usage. Investment firms should not expect employees to go from not using AI at all to knowing how to use it to drive innovation and efficiency. Yet many firms have this expectation. Furthermore, a gap exists between what AI often promises to deliver and what it can reliably manage at scale ─ both in terms of executives’ expectations for ROI and users’ ability to apply it effectively without sufficient training.

Limitations in technology, data quality, potential for AI “hallucinations” (false information presented as factual), and regulatory constraints mean firms cannot always use AI for every task, even when technically possible. Differences in AI comfort levels across generations, organizational roles, seniority, and business areas present additional challenges. And within investment management itself, the complexity and sensitivity of business operations create an added layer of concern.

The broader reality is that, even for younger employees or innovative firms, AI also represents a fundamental change in how work gets done. Simply making GenAI tools or AI agents available does not ensure that shift. The real challenge for firms is in moving AI from simple deployment for occasional use to embedding it as a crucial component of a firm’s core workflows and processes.

Scaling AI Throughout the Organization

Without defined goals, proper governance, and cultural engagement, firms often stall in their early AI efforts and true ROI eludes them. Depending on their size, maturity, and use cases, firms can take a variety of approaches to support effective AI enablement. These include setting benchmarks and prioritizing high‑impact use cases, as well as starting with small, achievable steps, strengthening data and technology foundations, and establishing clear KPIs. Together, these approaches offer a flexible toolkit that firms can adapt to their needs and stage of AI maturity. In the year ahead, we expect that more firms will formalize these goals and apply greater scrutiny to AI performance, usage, and ROI.

But we also expect to see firms defining their AI objectives in tandem with a stronger governance approach so that strategy and controls reinforce each other. This includes establishing pragmatic structures and policies by using cross‑functional committees, updating acceptable use and access policies, and maintaining inventories that track use cases, datasets, users, and vendor AI features. To support this, firms this coming year will likely deepen their cultural and training efforts through phased education and “trust but verify” practices that align with their risk appetite while positioning governance as an enabler.

Scaling AI is not a one-time feat, but an ongoing effort, which blends clear objectives, robust governance, and cultural commitment to turn early experimentation into sustained, measurable value. Even once AI is successfully integrated into core workflows, firms will continue to face challenges around cost management, regulatory processes, and ongoing change management as the technology evolves. Ultimately, success depends on investment firms treating AI as a living capability or an unconventional new hire ─ and not a static solution.

Cutter Research in 2024 explored investment management AI use cases, and in 2025, we revealed that member firms had advanced beyond AI exploration and were readying themselves for more meaningful AI usage. In 2026, our research will further examine these trends and explore the ways that firms are strategically advancing their AI programs and driving AI usage and adoption. If you are interested or want to share insights on the topic, please reach out at [email protected].