Strategy before technology: Establishing the foundation for measurable AI value

Artificial intelligence (AI) has become a significant strategic investment across wealth management. As its deployment evolves from experimentation into day-to-day operations, firms are increasingly expected to demonstrate the business value of those investments.

Many firms, however, are still laying the groundwork to measure AI’s return on investment (ROI). According to recent research from F2 Strategy, 67% of wealth management firms now have a dedicated AI budget, yet 65% still lack a formal methodology for measuring AI success.

Measuring AI’s outcomes starts with decisions made before deployment. Firms need to define the business outcome an investment is expected to improve, determine how success will be measured and identify the information and capabilities needed to demonstrate that impact. Establishing those parameters upfront gives firms a stronger foundation for evaluating whether an initiative delivers business value.

Strategy before technology

The starting point for an AI investment should be to define the business outcome it is expected to improve. That might mean increasing advisor capacity, deepening client engagement, reducing operational friction, improving service quality, managing risk or creating capacity for growth. Those priorities should determine where AI is applied, how success is defined and how impact is measured.

Every firm's AI strategy should align with a broader business strategy. Treating AI as a business transformation initiative, rather than a technology budget line item, shifts the focus from deploying tools to improving revenue, managing risk, controlling costs and improving advisor and client experiences.

Build the infrastructure for AI scale

Meaningful ROI measurement begins with leadership alignment. Executive teams should agree on the business goals for AI and corresponding key performance indicators (KPIs). Establishing those expectations upfront creates accountability and a baseline for evaluating results.

AI affects nearly every part of the business, from advisor workflows and operations to compliance, service and technology. Firms should engage key stakeholders early, prepare the organization for change and develop a strong business case before enterprise-wide deployment. Without that alignment, even promising initiatives might remain isolated pilot projects rather than scale into enterprise capabilities.

Reliable information is equally important. Policies, procedures, marketing materials, meeting recordings, research and other institutional knowledge all influence the quality of AI-generated responses. Without trusted information, answers might be faster without being better.

Before expanding AI across workflows, firms need to reduce data fragmentation, establish common business definitions and assign clear ownership for critical information. For some firms, this may include creating a centralized data lake that makes trusted enterprise data more accessible across the organization. Strong data governance supports responsible AI use and creates greater consistency in how outcomes are evaluated.

Firms also benefit from scalable capabilities that can support multiple use cases rather than treating each AI initiative as a separate project. Shared infrastructure creates consistency in how AI value is measured, making it easier to evaluate new initiatives against common business objectives and success criteria while reducing the need to rebuild foundational capabilities for each use case.

Establishing the right measurement framework

With those foundations in place, firms can establish a measurement framework that connects AI investments to the business outcomes they are designed to improve. The calculation should include the full cost of implementation, including technology, integration, data preparation, governance and ongoing oversight. It should also establish a baseline for the business outcome before implementation, giving firms a meaningful point of comparison as results emerge.

Some outcomes are easier to quantify. Hard metrics such as reduced manual work, shorter cycle times, lower service costs and improved operational capacity provide visibility into efficiency gains. These benchmarks can help firms determine whether AI is improving processes and creating business value. For example, reducing routine service requests through self-service capabilities or accelerating processes such as account opening can improve both operational efficiency and client experience.

Other outcomes are less tangible but equally important. For instance, advisor satisfaction, employee engagement and Net Promoter Score (NPS) can help firms evaluate whether AI is improving client and advisor experiences. These metrics can provide early indicators of stronger retention, higher service quality and greater organizational capacity.

Firms should also plan for “harvesting” AI value. Time saved through AI does not automatically become ROI. An hour returned to an advisor has greater value when it is redirected toward deepening client relationships, prospecting, financial planning or similar higher-value work. Firms should determine upfront how AI-generated capacity will be redeployed, whether toward growth, better service, expanded AI capabilities or operational savings.

Ultimately, every metric, whether hard or soft, should connect back to the business objective that inspired the AI initiative.

Seven steps to implement before establishing KPIs

These steps can help firms align AI initiatives with clearly defined business objectives:

1. Secure executive sponsorship and communicate a shared vision.
2. Prepare the organization through stakeholder engagement and change management.
3. Build data governance frameworks, create common business definitions and establish defined ownership models.
4. Prioritize AI initiatives based on business value, feasibility and risk.
5. Identify scalable AI capabilities that support multiple use cases.
6. Define how AI outcomes will be governed, evaluated and tied to business value.
7. Establish an ongoing process to measure outcomes and revisit AI initiatives as needed.

Firms that invest in these foundational capabilities are better positioned to generate measurable AI ROI by connecting AI to clear business outcomes, trusted data, organizational alignment and disciplined value capture.

The strongest ROI measurement programs begin before the first KPI is established, with the strategy, governance and operating discipline needed to make the results meaningful and the mechanisms needed to turn AI-enabled improvements into measurable business value.

Anthony Lancaster is the chief data officer at Osaic. With over 25 years of experience across the wealth management, insurance and annuities sectors, he directs the company's enterprise data infrastructure, artificial intelligence (AI) strategies and digital advisor platforms.