Strategy and Data as the Foundation for AI Success in Wealth and Asset Management

Firms are discovering that AI amplifies what they already have. Strong foundations enable competitive advantages, whereas weak foundations accelerate existing problems.

AI adoption in wealth and asset management is now firmly underway, yet outcomes remain strikingly varied. Firms demonstrate strong intent, launch pilots and proof-of-concepts, and build growing expectations, though some have trouble scaling and achieve little to no measurable value. The pattern is so prevalent it demands examination: why do some organisations generate genuine returns while others see pilots stall and promising initiatives quietly abandoned?

At Solve, we’ve identified six essential pillars that separate success from wasted investment: Strategy, Data, Client Experience, Operations, Talent, and Controls. This three-part series explores each pillar – its role, its importance, and how successful firms approach it.

We begin with the two foundational pillars that determine everything else: Strategy and Data.

Strategy: Intent and Ownership

Strategy establishes direction, boundaries and priorities. With it, AI initiatives connect to commercial outcomes. Research shows that organisations with defined AI strategies are 3.5× more likely to achieve critical benefits and almost twice as likely to report revenue growth. Yet only 22% of organisations say they have a visible, documented AI strategy. (Thomson Reuters, 2025)

Many firms mistake activity for strategy. They gather use-cases, run pilots, bring in vendors and call this an “AI programme.” But without clear prioritisation and ownership, AI becomes an operational distraction: months spent on proof-of-concepts that never scale beyond test environments. Others anchor strategy around technology capabilities rather than business needs, creating solutions that perform well in isolation but can’t integrate with existing workflows.

A practical AI strategy starts at the top. The CEO and board must take ownership to ensure buy-in at the enterprise level and alignment with overall business objectives. Without senior mandate, the organisational changes AI requires – from data governance to workflow redesign to performance metrics – lack the authority and urgency to overcome institutional resistance.

With ownership in place, the next step is clarity of purpose. AI efforts must be anchored to defined business outcomes: for example, capacity release, productivity gains, improved client experience, risk reduction, or cost-to-serve efficiency. Each AI initiative should directly support these goals with clear metrics, accountable owners, prioritisation, and governance boundaries.

Data: Integrity

If strategy provides the blueprint, data supplies the raw material. AI systems are fundamentally dependent on the information they learn from, reason over, and retrieve from. Without trustworthy, well-structured, well-governed data, AI performance becomes inconsistent at best, unreliable at worst.

The scale of the challenge is clear. Financial services firms report that inaccurate or incomplete data affects around 6% of annual revenue (Fivetran & Vanson Bourne, 2024), and more than half of organisations acknowledge their data is not yet “AI-ready” (Gartner research, 2025). The performance gap between firms with mature data foundations and those without is also becoming harder to ignore: organisations with stronger data and infrastructure capabilities achieve around 50% greater revenue growth and over 55% more cost savings from their AI initiatives compared with less mature peers (IDC Enterprise AI Maturity Study, 2025).

Many firms assume their existing data is “good enough” for AI. They point AI tools and large-language-models at decades of information fragmented across CRMs, portfolio management systems, back-office platforms, custodians and document archives, to discover that outputs require constant manual verification. In practice, their data often contains duplicates, conflicting fields, missing identifiers or legacy taxonomies. These inconsistencies, manageable for human interpretation, get amplified exponentially through AI systems, making outputs unreliable and hurting adoption.

Data readiness requires deliberate construction across five dimensions: quality (accuracy and completeness), structure (organisation and discoverability), interoperability (ability to move across systems), governance (ownership and controls), and use-case alignment (relevance to intended outcomes). Firms don’t need perfect data – an impossible standard – but they need data that is accurate enough, current enough, and consistent enough for intended applications.

The practical starting point involves identifying datasets that power the highest-value workflows or use cases and systematically addressing issues there first. This essential work of cleaning duplicates, fixing missing fields, standardising formats, and establishing ongoing monitoring is the prerequisite for successful AI implementation and adoption.

Before Anything Else

Strategy determines intention; data determines integrity. Together, they create the foundation upon which all other capabilities rest. In Part 2 of this series, we will explore the next two pillars Client Experience, and Operations.