Adoption outruns integration
Projections from the sources below show 1.1 billion people will use artificial intelligence (AI) by 2031, and most Fortune 500 companies already rely on AI to varying degrees, but the gap between industries is large. For instance, among financial services companies that have begun testing Generative AI, only 8% had successfully scaled it across the enterprise. The other companies are reluctant to fully integrate the technology. Some executives describe AI as a "moving train" that is tricky to board and even harder to steer. Some settle for the role of fast followers and others just find a place in the middle of the pack. The technology itself has become a commodity and it is how you execute that will separate the firms that scale from the firms that stall.
Shadow AI fills the governance gap
Leaders are unable to set standards to govern AI usage if they are not able to identify it. Studies show that more than half of workers hide their AI use on key tasks. As a result, governance becomes a vacuum. Employees are aware of this phenomenon because about 70% of Americans also believe that companies cannot be trusted to use AI responsibly. It is not surprising that most companies are reluctant to leave low-stakes AI pilot programs.
Legacy systems tax every gain
According to the sources, 92% of businesses consider their production systems to be at least partly legacy systems. Maintaining these systems costs UK banks about £3.3 billion a year and it consumes 24% of their IT budgets. Without access to clean and adequate data, the best AIs will produce essentially no output. So building any advanced AI stack on top of old and obsolete data pipelines is like an engine starved of fuel.
So-so automation consumes the time it saves
Automation has the potential to revolutionize businesses, and when successfully implemented, can provide substantial gains in productivity. "So-so automation" describes AI that handles a task but returns unreliable output. Employees must in these cases evaluate the results, which often takes longer than just performing the task manually. Hallucinations have been reported to affect 56% of businesses, but only 32% of companies have taken action to mitigate the issue. Hallucinations occur when AI makes incorrect assumptions, and if managed correctly, can have a positive impact on productivity. A Harvard study found that workers using AI completed tasks 25% faster, produced 12% more output, and achieved 40% higher quality results. But, realizing these gains requires more than just installing software. Firms must maintain a "human in the loop" approach, not merely as a productivity choice, but also to comply with consumer expectations and compliance requirements.
The skills gap lands before the technology gap
In the next 10 years, the Financial Services Skills Commission expects more than 40% of the sector's skilled workforce to retire or leave the sector. Automation will put more than 119,000 clerical jobs at risk in the same time frame. Over half of the organizations in the sector are not equipped for data science and are therefore unable to fully utilize AI. The kind of skills in highest demand by the sector is trying to balance technical literacy with core human capabilities. This includes analytical thinking (risk planning, data interpretation, market analysis, and software quality assurance) combined with interpersonal skills vital for stakeholder influence, relationship management, and complex business communication.
What the 8% do differently
Successful firms believe that integrating AI will fundamentally alter their business. AI projects that view business transformation through a narrow lens will be uncompetitive and will fail. The industry’s focus is shifting from Generative AI to Agentic AI. Generative AI can assist and enhance human work. In contrast, Agentic AI allows for the automation of more complex and continuous work. This shift will require businesses to confront “the untestable.” A case study involving PwC and a leading UK bank demonstrated that by using AI to test AI, checking for bias, hallucinations, and toxicity, firms can build the necessary trust to move from pilot to scale. Both firms developed a system that filtered out these things and promoted trust in AI. Subsequently, a strict governance framework was created to define the boundaries of the business, and shift the firm’s workforce to perform more judgmental and complex work, and away from routine work.
Beyond the Hype Cycle
About 3 out of 4 business leaders expect to boost their AI budgets in the years ahead. Rising spending doesn't tell the whole story though. A company needs to implement AI solutions the right way to see the largest impact. The leadership team needs to ensure they've collected the right data to implement AI. Patience is essential for businesses to modernize their underlying systems. One question that is worth asking of any roadmap:
Is your organization redesigning and streamlining work around what AI does well, or just automating what it already did?