While most organizations are deploying predictive, generative, and agentic AI, few can directly connect those investments to revenue, customer outcomes, or profitability. As AI becomes cheaper and easier to build, the quality of the problem being solved becomes the primary determinant of value. Teams that measure success by releases, features, or utilization risk falling into […]

As the cost and effort required to create AI products fall, the quality of the problem to solve becomes the biggest determinant of value. Yet many organizations still begin with an AI idea, a new capability, or a mandate to “do something with AI,” then search for a problem to justify it. Digital and product teams must reverse the “build trap.” Successful AI investment reverses that logic: Start with a problem worth solving, understand the root causes, ideate solutions, define the capabilities required, and build an evidence-based case for investment. This report is the first in a three-part series that helps digital and product leaders build effective AI products.

Discover why financial institutions are using transaction foundation models and proprietary data to build a lasting AI advantage.

Global digital business leaders continue to look ahead to resourcefully find and execute on growth opportunities. Digital channels now account for a mean of 39% of their organizations’ revenue. To plan 2027 budgets, they must diligently lean into both discipline and creativity to invest (think change leadership, conversational experiences, centralized insights, AI content strategy, measuring AI value) and to cut (think strategy consulting, ineffective analytics, in-house AI explainability). They will budget to strategically experiment with AI personalities and syndicating product into answer engines.

AI is redefining what consumers expect from financial guidance, but trust remains the limiter. As AI takes on more advanced roles, financial institutions must balance personalization and efficiency with transparency, governance, and human oversight.

As AI becomes more prevalent in financial services, consumer comfort is rising but varies across demography and task complexity. Consumers, especially younger generations, are increasingly open to using AI for financial guidance and show little preference for where that help comes from. Comfort drops sharply as AI moves from explaining options to acting autonomously, and most consumers still trust human advisors more than AI alone. This report helps financial services leaders understand where customers expect AI tools to create value today and the types of AI experiences that consumers will adopt.

Lending Is Reaching An Inflection Point More than 80% of financial services (FS) AI decision‑makers plan to increase investments in both predictive AI and generative AI (genAI), with most firms expecting double‑digit growth. The immediate focus for the majority of FS leaders remains pragmatic: scaling origination, reducing friction caused by handoffs, and improving risk control. […]

AI is rapidly transforming the lending value chain, enabling lenders to better understand their customers, deliver personalized products and services, and make faster risk-informed credit decisions. Internal process improvements continue to dominate AI use cases today, but innovative lenders are experimenting with customer-facing applications to unlock new growth. This report helps digital lending executives understand the current state of AI in lending and provides critical insights into how AI in lending will evolve over the next 12 months.

Cowritten with Janis Teo, senior research associate Five months ago, I published a blog post about banking’s new power role, the chief digital, data, and AI officer, an executive mandate designed not just for a simple consolidation for efficiency but to unify digital, data, and AI around a single strategic vision for growth, decision-making, and […]

Financial services firms are increasing tech spending faster than the market average, signaling a deliberate bet that technology is a strategic lever and not a discretionary cost.