- Insights title Insights to Lead With Digital | Cognizant
- Insights Blog title Insights Blog | Cognizant
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The first phase of AI maturity is establishing a shared understanding of how AI will be used in the organization and how it will affect operations and workers. Alignment on business goals, which guide strategy and performance, is critical to successful AI adoption.
Banking scores 99 in this maturity dimension, just one point below the cross-industry average. In large part, this is due to a critical disconnect between financial services leaders’ perception of the effectiveness of their AI communications versus employees’.
For example, 93% of executives believe they have clearly communicated their AI strategy, compared with just 75% of employees. Similarly, far more leaders (91%) believe employees know how AI should be used in their specific roles, compared with employees (68%).
Even understanding which tools are available to use is a source of confusion. Fully 89% of executives say their organization has a clear policy on enabling access to AI tools, compared with just 72% of workers.
This ambiguity presents significant challenges for a tightly regulated sector. In practice, employees who face uncertainty as to which tools they can use internally are quick to seek options elsewhere. Sixty-one percent of employees admit they resort to non-sanctioned AI tools when their organization's in-house tools fall short.
Skilling matters because AI can create value only when employees know how to apply it safely and effectively. Banking ranks highly, with a score that’s 31 points above the cross-industry average, placing it second among 10 industries in this maturity dimension.
A total of 70% of banking employees have completed AI-related training provided by their organization in the past 12 months, well above the cross-industry rate of 54%. By comparison, 46% of employees in life sciences, another highly regulated sector, have completed training.
Training budgets also demonstrate this strong commitment, with 80% of banking organizations allocating more than $10 million to AI training initiatives.
Yet, there is also a critical disparity among those receiving training, with access meaningfully tilted toward more senior roles. Executive leadership leads with an 80% training completion rate, while junior employees, including analysts, operational staff and process specialists, trail at 59% (see Figure 2).
Closing this gap matters. Front-line workers often have a clearer view of where AI can add significant value in their team or organization. Among banking employees, 93% say AI has the potential to significantly impact their work, whether that means working faster or performing tasks at a higher level. Training offers a vital pathway to workers delivering these gains.
Gap between executive and junior employee training rates
The high skilling rates in the sector seem to be paying off. When compared with the cross-industry average, workers in the banking and financial services sector have higher proficiency across the full range of AI tools available (see Figure 3).
The industry’s sixth-place ranking reflects the progress all industries have made in this area, resulting in a tight clustering of scores. Where banking and financial services stands out is in workers’ proficiency in some of the newest, most advanced AI technologies, such as agentic AI, where more than half of banking workers say they have clear or expert levels of proficiency, fully eight points above the cross-industry average.
High proficiency across all AI tools
Despite this strong foundation, the sector is struggling to turn AI initiatives into tangible returns. Workers themselves say they’ve seen AI-driven productivity gains, with over three-quarters (76%) reporting productivity gains of up to 20%. Of those, nearly half (48%) reported more than a 10% productivity gain. When asked what productivity gains they’d expect to see if given the right resources, 25% of workers said they’d be able to realize productivity gains of more than 20%.
Yet, industry executives paint a different picture. Only 36% of senior leaders say they have already seen measurable worker productivity gains from AI deployments, which is 6 percentage points below the cross-industry rate of 42%, and many do not expect to see gains for several years.
Despite pressure from consumers and competitors alike, the sector has many sources of friction slowing it down. Unsurprisingly, a tight regulatory environment is a key inhibitor for many, with 57% of executives saying data privacy concerns inhibit adoption, and 41% citing the effect of regulations.
The sector is also hindered by complex, aging infrastructure. Eighteen percent say their existing technology estate is preventing them from realizing their AI ambitions, while 40% worry about the availability of compute power, the highest share among all industries analyzed. More concerns center on aging infrastructure. Only one-quarter of executives say their data quality or data lineage are excellent.
When translating worker experiences of productivity into the real-world, this makes a significant difference. For example, a relationship manager might use AI to prep for a client meeting in minutes instead of an hour—a real win on paper. But if the data lives across legacy systems that don't communicate with each other, the tool can only achieve a partial view, so the bank never sees the process itself get faster.
Banking and financial services leaders are investing heavily in AI. The sector ranks among the top three AI spenders, allocating 8.7% of its annual technology budget to AI tools. On average, leaders expect this to increase by 11.4% over the next two years. Yet, how this investment is paying off is still uncertain, leaving the industry ranked fifth in this maturity dimension.
A closer look at the data reveals an investment profile distinctly shaped by the industry's regulatory and risk management imperatives. Financial services organizations are 8 percentage points more likely than the cross-industry average to say their largest AI investment area is risk, compliance or governance frameworks (see Figure 4).
While critical, this investment focus demonstrates the delicate balancing act the sector must perform as it allocates funding. Despite scoring comparatively low among its peers for areas like data readiness and access to computing resources, the sector is also less likely to allocate funding to technology infrastructure (29% versus the 32% industry average).
Spending focus reflects compliance concerns
Introduction #spy-1
A look at banking and financial services on the AI maturity path #spy-2
subnav- Awareness: Communication disconnects present risks#spy-21
subnav- Skilling: Widespread training but tilted toward the top#spy-22
subnav- Adoption: High proficiency is combined with a clear understanding of AI’s value#spy-23
subnav- Productivity: Compliance and aging infrastructure#spy-24
subnav- ROI: High spending levels with a focus on compliance#spy-25
Moving up the AI maturity path #spy-3
