Implementing AI into your enterprise workflows is an imperative in 2026, but getting locked in with one vendor limits your adaptability as your strategy evolves. And for organizations in highly regulated industries, security concerns are just as important as AI efficiency. The answer for many enterprises is an on-premises approach that lets them keep data (and costs) under control.
The way we work is shifting
As technology capabilities grow, people will move from doing almost half of work tasks (47%) now to performing about a third on their own, working with technology on another third, and letting technology tackle the rest by 2030, according to World Economic Forum 2025 data.
At complex enterprises, it will be tough to ensure this transition is smooth and efficient without a new approach. Siloed, disparate technologies across the organization and compliance challenges are hampering your ability to move forward. To do that, your organization needs a flexible approach that keeps you in control of your data — and your spend.
The reality of enterprise automation
As AI enthusiasm has grown, the cracks in poorly planned projects have started to show. In 2026, Gartner estimated that 50% of generative AI projects had failed by the end of 2025, citing issues like a lack of data readiness, weak change management, and underestimated costs as organizations tried to scale.
The inability to scale can grind projects to a halt when AI initiatives aren’t built on solid, proven automation first. In our study of more than 700 senior IT and finance executives, we found that:
- 84% said automation was a necessary first step for successful AI implementation.
- 81% said adding AI to their automated processes created better outcomes, compared to just 18% achieving transformation from AI alone.
- 58% of leaders with mature automation practices reported significant value from AI projects, versus 42% of leaders whose automation strategies were just getting started.
The leaders reported that starting with strong automation practices creates the technical
infrastructure needed for scalable AI (48%), provides the clean data foundation that AI needs to operate successfully (47%) and delivers the standardized, repeatable processes AI needs to be effective (47%).
Leaders also shared that the value of AI was easier to measure when it was based on the standardized baselines automation creates. When so much is riding on your AI initiatives and investment, you have to be able to demonstrate the value they add to the enterprise.
The hidden risks enterprises are facing
AI moves fast, and enterprise leaders know they need to keep up. In a 2025 EY study, executives surveyed said they needed to rethink their approach to managing the speed and scale of tech disruption and adoption (69%). Yet 63% of those executives also said their organization needed to increase resilience through more iterative and adaptable transformation. In short, to keep up with evolving technologies, enterprises need to remain agile and take strategic steps when adopting AI solutions.
Risk 1: Getting stuck with one AI vendor
AI solutions can become outdated or compromised suddenly, leaving you with all your eggs in the wrong AI basket if you don’t have the ability to pivot quickly. That’s why avoiding vendor lock-in is so important.
Your automation platform’s AI choices shouldn’t dictate your entire AI strategy. As better models emerge, you want an automation platform that lets you change course when needed to avoid setbacks. You also need the flexibility to adapt how and where you use AI as your business needs change.
Risk 2: Losing control of your data
A variety of factors are driving enterprises to move data from public cloud storage to on-premises solutions, including cost and performance issues, but data sovereignty is also a growing concern.
Deloitte’s 2026 State of AI in the Enterprise report shared that 83% of business leaders think AI sovereignty is at least moderately important. Moving data from the cloud to on-premises storage helps you stay compliant with regulations that require you to store data within the regulating country’s borders.
For instance, while the United States requires U.S.-based cloud providers to share data with agencies on demand, the European Union’s General Data Protection Regulation (GDPR) bars transfer outside EU borders. On-premises data gives you greater control over regulatory compliance.
Controlling costs by preventing data breaches with greater data oversight and automation is another crucial factor. IBM’s Cost of a Data Breach study for 2025 showed that when a data breach occurred, organizations with extensive security automation faced average breach costs of $3.62 million, compared to $5.52 million for organizations without it, reducing the impact by $1.9 million. By keeping your data closer, you can soften the financial blow of a data breach if one occurs.
Risk 3: Wasting AI spend
There’s a lot of AI noise out there, but we’re starting to see some patterns in how to gain value from AI investments. A prominent, yet troubling, 2025 MIT study showed that 95% of organizations aren’t getting ROI from their generative AI investments, primarily because these tools aren’t flexible and adaptable to enterprise processes.
Starting with consistent, automated processes brings more value to AI. MIT’s study shares that the most successful AI implementations are those that are embedded within workflows and adapt easily. Eighty-four percent (84%) of the IT and finance leaders in our survey said they considered automation a necessary first step in adopting AI. Automation gives AI the structure it needs to be most effective, leading to stronger outcomes from your spend.