A third of professional services leaders say poor data is blocking effective AI adoption
New UK research from Dayshape suggests professional services firms may have an AI readiness problem. While two-thirds of senior leaders say investing in new technology is now a top business priority, a third identify poor data quality as the biggest barrier to using AI effectively.
Professional services organisations are rapidly increasing their focus on artificial intelligence as a driver of growth. But as greater scrutiny falls on returns on investment in the technology, many are being held back by issues with data and integration.
New research from Dayshape has surveyed 200 UK senior leaders across professional services, to find the top barriers facing firms, as they bid to get the most from AI. At the top of the agenda, concerns around data quality now outweigh cost, integration challenges and skills shortages when it comes to successful AI adoption.
Blind spots
Investing in new technology is the top priority for 61% of organisations, with 50% of leaders also ranking it as their main personal focus for the year ahead. Pointing as to why that is, Dayshape noted that its poll shows leaders want real-time access to exactly the kind of insights AI could unlock.
With 86% of leaders claiming their firm’s talent is “fully utilised”, 38% said they were hoping for real-time insights on capacity and availability – to improve the allocation of talent and resources to key projects. AI boosts here could also be crucial in helping the 42% of firms address the fact burnout is their top challenge to retention.
Meanwhile, 37% of leaders identified profitability insights – categorised by team, project, or service line – could be a top benefit. And 36% also said live forecasting and scenario modelling would be similarly important.
Clearing barriers
Unfortunately, barriers to using AI effectively persist. However, Dayshape’s study points to these being less about problems with the technology, or access to it, and more about the conditions needed to make it work. A 34% portion of respondents noted that poor data quality was the single biggest barrier to effective AI adoption. This was followed by integration with existing systems cited by 32%, while 28% said cost and investment requirements had been prohibitive. And finally, 22% bemoaned a lack of internal capability – related to skills and recruitment in human teams – when it comes to using AI.
To address this, looking ahead, firms plan to expand AI further into business-critical areas linked to forecasting, staffing and client delivery. Around a third say they plan to increase use of AI in client delivery tools, capacity modelling, project and resource planning, and workforce optimisation. Work will still be needed to overcome the growing tension between AI ambition and the operational foundations needed to support it, though.
Andrew Bone, vice president of product at Dayshape, commented, “Many people in professional services firms already struggle with poor data quality, disjointed systems and internal silos, placing a huge drag on operational effectiveness. Similarly, AI initiatives will not meet expectations if they are layered on top of those foundations. The organisations seeing the most value are the ones focusing not just on adopting AI, but on strengthening their data and building the internal capability to use these tools effectively.”
