By Luke Smith, Principal Consultant
Deloitte’s Tech Trends 2026 report opens with a blunt assessment. The foundations built for cloud-first strategies are not designed for the demands of AI. Infrastructure struggles to support the economics of AI workloads, processes designed for human work do not translate easily to agents, and security and operating models built for a different era cannot keep pace with systems operating at machine speed.
For CMOs and CXOs in financial services, this is not just a technical observation. It is a commercial warning. Research from Boston Consulting Group shows that despite years of investment, 74 percent of companies still struggle to achieve and scale value from AI. The gap between ambition and measurable business impact remains significant.
Financial services organisations are under increasing pressure to scale AI across customer engagement, decisioning and personalisation. The expectation is clear. Faster responses, more relevant interactions and improved lifetime value. Yet as initiatives scale, many organisations find that customer experience does not improve in line with investment. In some cases, it deteriorates.
The issue is not the ambition to scale. It is the foundations on which that scale is being attempted.
What we see across financial services helps explain why. AI is being layered onto operating models that were never designed to support real-time, customer-centric decisioning. Data models remain fragmented across products and channels. Content supply chains are too slow to support dynamic engagement. Processes are still optimised for manual handoffs rather than automated decision flows. Teams are structured around delivery rather than outcomes.
As AI scales, these constraints become more visible. Decisions happen faster, but they are not necessarily better. Journeys feel inconsistent rather than joined up. From the customer’s perspective, the experience does not feel more intelligent. From a leadership perspective, growth stalls.
This is where Deloitte’s message lands hardest for marketing and customer leaders. Scaling AI on foundations built for a different era does not unlock growth. It exposes the limits of existing operating models.
In financial services, customer growth depends on the ability to respond to behaviour, context and need in the moment. That requires more than models and automation. It requires foundations that allow data, decisioning and content to move at the same speed as the customer. When those foundations are missing, AI amplifies complexity rather than reducing it.
From a marketing transformation perspective, the risk is clear. Investment continues, but confidence erodes. Boards begin to question why customer metrics are not shifting. Teams struggle to keep up with increased decision volume, and experience quality becomes harder to control. AI starts to feel expensive rather than enabling.
The organisations that avoid this trap approach scaling differently. They treat AI as a catalyst for rebuilding how customer value is created, not just how decisions are executed. They simplify and unify data models around customer and business value rather than systems. They redesign content operations so engagement can adapt in real time. They align teams and governance around journeys and outcomes.
Most importantly, they are clear on what success looks like. Rebuilding foundations is justified through its impact on acquisition, retention, engagement and lifetime value. Customer experience becomes the measure of whether transformation is working.
This is the commercial lens missing from many programmes. Foundations only matter if customers feel the difference.
For marketing transformation leaders, the challenge is not choosing whether to scale AI. That decision has already been made by the market. The real challenge is ensuring that scale translates into better customer journeys, stronger customer relationships and sustained customer growth.
Key takeaways for financial services leaders:
- Deloitte’s warning is commercial as much as technical, because scaling AI on outdated foundations directly impacts customer experience and growth
- Customer growth stalls when fragmented data models, slow content operations and human-centric processes prevent real-time, relevant engagement
- Scaling AI exposes operating model weaknesses rather than fixing them, increasing cost and complexity without improving outcomes
- Rebuilding foundations only matters if it enables better customer journeys, higher adoption and measurable impact across acquisition, retention and lifetime value
*Source: Deloitte, Tech Trends 2026. Opening perspective on enterprise AI foundations and operating models.
* Source: BCG, AI at Scale research series.
