Findings from Digitate’s recent survey conducted with Sapio Research highlight a persistent challenge: AI is often implemented to reduce human workload and operational costs, yet these very factors continue to limit its broader adoption. In our last two posts, IT as the Proving Ground for AI: Driving Enterprise Innovation and The Next Phase of Agentic AI, we explored how IT has become the primary proving ground for enterprise AI, outpacing adoption in every other business function, and how enterprise AI is evolving from efficiency-focused automation to Agentic AI–powered collaboration between humans and intelligent systems.  

This “cost–human conundrum” now defines the next stage of AI maturity: balancing the promise of automation with the realities of economics, skills, and governance. This post provides an overview of the key insights and takeaways on the topic, highlighting essential strategies and actionable recommendations to drive success and informed decision-making.  

You can download the report: Agentic AI and Future of Enterprise IT    

You can also listen to our recent podcast with Avi Bhagtani, CMO at Digitate, as he reviews the survey responses, interprets the insights behind the data, and reflects on the shift from experimenting with AI to embedding it into everyday workflows. 

Internal vs. external risk mapping  

Organizations today face a dynamic risk landscape shaped by both external pressures and internal complexities, and these forces are increasingly influencing how they approach AI. On the outside, financial and security concerns dominate, cybersecurity stands out as the most pressing threat, followed closely by the rising cost of technology and broader macroeconomic uncertainty. Interestingly, many organizations are beginning to view AI not just as a vulnerability, but as a potential solution to these challenges. Yet there’s a notable gap: despite ongoing concerns about talent shortages, few are leveraging AI to improve employee retention or engagement. Internally, the focus shifts toward operational hurdles, with IT complexity leading the way, alongside concerns about profitability, cost efficiency, and resistance to change. Even here, AI is already being put to work, helping simplify systems and enhance financial performance. Together, these trends hint at a larger shift, one where AI is no longer just a tool, but a strategic response to the very risks reshaping modern organizations.  

Drawbacks and challenges  

While the potential of AI is widely recognized, many organizations continue to face practical challenges during implementation. When discussing the drawbacks of AI and automation, two main issues arise: the continued need for human oversight and the significant costs involved in deploying and maintaining AI systems.  

Despite AI’s goal to enhance efficiency and reduce manual effort, concerns remain about the level of human involvement required and the resources needed to support AI tools.  

Additionally, key obstacles limiting further AI adoption include a shortage of technical skills, difficulties surrounding data management, and budget constraints.  

Overall, these challenges highlight a core paradox in AI adoption: while AI aims to lower costs and reduce reliance on human labor, successfully achieving these benefits still requires skilled personnel and ongoing financial investment.  

The cost–human challenge overview  

Enterprises face multiple overlapping pressures when adopting AI technologies:  

  • Human resources: There is a high demand for skilled professionals essential to managing and overseeing AI systems.  
  • Financial considerations: As AI capabilities advance, associated costs like computing and compliance continue to increase.  
  • Strategic expectations: Leadership demands clear returns on AI investments, pushing for timely benefits.  

These factors create a cycle where AI deployment expands capabilities but also increases reliance on talent and budgets. Successfully managing AI requires a holistic approach encompassing governance, workforce planning, and financial strategy.  

The key challenge facing enterprises is not whether AI provides value, but how to maintain that value in the face of rising costs and constrained human resources. Success will belong to those who can effectively balance automation with human augmentation, maximizing efficiency without compromising the expert human input that drives AI’s effectiveness.  

Download the full report here: Agentic AI and the Future of Enterprise IT