Staggering numbers reveal a significant gap in the world of Artificial Intelligence (AI): While a whopping 97% of large enterprises have allocated budgets for agentic AI, only a mere 18% have actually deployed it. This stark contrast, highlighted in Qlik's latest AI study, begs the question: What's holding these businesses back? Let's dive in and find out.
The study, conducted with Enterprise Technology Research (ETR), surveyed over 200 enterprise technology decision-makers. The results paint a picture of ambitious intentions clashing with real-world hurdles. James Fisher, Qlik's Chief Strategy Officer, sums it up: "I'm still surprised by the gap that we see between expectations and commitments to AI and particularly agentic AI, but still a lack of adoption across the enterprise."
This represents a substantial leap from Qlik's 2024 research, where only 37% of organizations had formal AI strategies. Now, that number has surged to 69%. However, a significant 46% of respondents anticipate it will take three to five years to fully operationalize AI at scale.
Budgetary Pressures and Fragmented Funding: The Reality Check
Fisher points out that overall IT budgets are often under pressure, making it challenging to invest heavily in AI, especially agentic solutions. This leads to tough decisions and trade-offs. While 56% have a dedicated AI innovation budget, a majority (60%) still draw from IT/Technology budgets, and 42% rely on line-of-business funds. A striking 79% agree that agentic AI is crucial for their organization's strategic plan within the next three to five years.
Data Foundations and Skills Gaps: The Core Challenges
Data quality, availability, and accessibility emerge as the primary roadblocks to AI adoption, cited by 56% of respondents. But here's where it gets controversial: while 77% express confidence in differentiating agentic AI from other tools, only 42% believe their organization possesses the internal expertise to design and deploy it without external help. Fisher notes that these data-related issues have been persistent barriers for good use of analytics, traditional AI, and generative AI.
Integration with existing systems ranks second at 49%, followed by a lack of internal expertise at 48%. Interestingly, only 13% mentioned multi-agent systems when defining agentic AI, with most focusing on autonomous decision-making (61%) and task automation (49%).
Fisher offers a measured perspective: "This is still relatively nascent in terms of the grand scheme of things, and that transference of skills out into the wider ecosystem takes time."
Security and Governance: Addressing the Risks
Cybersecurity vulnerabilities top the list of deployment concerns at 61%. IT Operations is the primary target area for implementation (72%). Legal and compliance exposure (51%) and a lack of explainability and auditability (47%) also raise concerns. Fisher emphasizes the evolving role of stakeholders, highlighting the increased involvement of legal teams alongside cybersecurity, data security, and governance teams.
On governance, Fisher observes: "The starting point for all of this comes down to policy. That sets the foundation for the governance of how all of our people work with AI in doing their job..."
Where Deployment Works: Success Stories in Action
Fisher provided customer examples showcasing successful AI deployment. The common thread? Building AI on existing data foundations rather than attempting complete transformation.
- A North American specialty chemicals distributor launched a generative AI assistant for sales and customer service within two months, connecting it to existing document repositories. Roughly 40 people now use it daily. The company can now hire commercial talent from outside the chemicals industry.
- A global industrial manufacturer with 3,500 employees took a similar approach with unstructured technical content. Setting up each new knowledge base took approximately 15 minutes plus indexing time. The speed convinced leadership it could scale AI use without a large data-engineering project upfront.
- An Asia Pacific entertainment group improved attendance forecast accuracy from roughly 70% to over 90% using predictive capabilities layered onto their existing Qlik Cloud Analytics deployment. These forecasts now drive labor scheduling and operational planning in near real-time.
- A European food producer built AI-powered demand forecasts for premium organic meat products, reducing forecast deviations to around 1%. This cut over-production, reduced costly downgrades of organic meat to conventional, and lowered storage costs while supporting sustainability goals.
Fisher summarizes, "Right now today, to prove value from AI and agentic, I believe we have everything we need. The question is, how distributed is that across the Enterprise Architecture? How distributed is that across the vendor ecosystem, and what does it cost for an organization to pull that together?" He adds, "When you're holding a hammer, everything kind of looks like a nail. I think we're slowly beginning to figure out which pieces of technology to use in which particular use cases."
My Take: What Separates the Successes?
Looking back at the headline numbers - 97% budget commitment, 18% deployment - what did the successful 18% do differently? The customer examples offer a clear answer: They focused on connecting AI to existing data and demonstrating value quickly.
The definitional confusion may explain the wider gap. Only 13% mentioned multi-agent systems when defining agentic AI – arguably its distinguishing feature. Most conflate it with autonomous decision-making or task automation. If organizations don't understand what they're buying, it's harder to scope deployments that deliver.
Fisher emphasizes policy and governance as starting points. The customer examples add another dimension – bounded scope. The chemicals distributor didn't attempt enterprise-wide transformation – it connected an AI assistant to SharePoint and existing Qlik data in weeks.
After three years of research highlighting persistent implementation challenges, these examples offer a ray of hope for organizations willing to start small.
What are your thoughts? Do you agree with the findings, or do you see other factors at play? Share your perspective in the comments below!
