How AI Integration Fuels the Agentic Enterprise

The rise of AI is undeniable, with its adoption outpacing even the most optimistic projections. In fact, the average number of AI models being used doubled from 2024. 

As organizations race to embrace the power of AI, a new frontier is emerging: the agentic enterprise. This next evolution promises unprecedented independence, empowering systems to respond to queries, autonomously manage complex tasks, optimize workflows, and drive innovation — all with minimal human intervention. However, realizing the full potential of the agentic enterprise hinges on a critical foundation: robust data management and AI integration. 

During a recent webinar, our team had a chance to speak with two Deloitte Digital leaders — Karim Trojette, Global MuleSoft Alliance Leader, and Kurt Anderson, Managing Director and API Transformation Leader at Deloitte Consulting, LLP. Together, they shared key insights from the 2025 Connectivity Benchmark Report. Read on for the top takeaways from our conversation.

AI has become a top priority for businesses worldwide, with 84% of enterprise CIOs viewing AI as crucial as the internet. Organizations are rapidly deploying autonomous agents to augment teams and processes. 

Currently, 40% have already implemented autonomous agents, with another 41% planning to do so within the next year. This surge in AI adoption is also reflected in IT budgets, which increased by an average of 61.5% since last year.

However, this rapid adoption is creating significant challenges. Digital transformation remains slow for many organizations, placing immense strain on IT teams. They must balance current capabilities with business aspirations, integrate AI across diverse applications, and maintain existing systems. 

Unsurprisingly, the report reveals that 95% of IT leaders cite AI integration as a major hurdle to seamless implementation — a percentage that has remained steady since last year’s report.  

The challenges cited – including concerns around cybersecurity, data privacy, and governance, the high cost of implementation, and the lack of in-house resources and the skills to support AI integration and ongoing maintenance — are all significant hurdles, all of which are not easily solved without an AI strategy that incorporates API management and key investments in reskilling and upskilling.

Given that AI integration is the primary obstacle to AI implementation, IT leaders are seeking ways to better overcome these challenges in integrating AI into existing processes. 

Disconnected data is a major contributor, hindering legacy modernization efforts for 83% of organizations. This disconnect especially creates friction for end-users, with 97% of IT leaders acknowledging struggles in integrating end-user experiences.

Key AI integration challenges include:

• Moving data from source systems into the data warehouse
• Correlating data in the warehouse to derive insights
• Reusing data sources across different user-facing applications
• Incorporating data-derived insights into user-facing applications

These challenges are exacerbated by outdated IT architectures, which restrict the use of data for AI across the business. Forty-one percent of respondents reported that their organization’s old IT architecture and infrastructure got in the way of their AI integration, preventing the use of data for AI applications. 

Modernizing infrastructure and enhancing cybersecurity are crucial steps to overcome these obstacles and fully realize AI’s potential.

Disconnected data and systems have tangible consequences, with 66% of respondents not providing an integrated user experience across all channels. This lack of AI integration undermines customer expectations for seamless, data-driven experiences. 

There’s a glimmer of hope to be had for IT teams, however. Of the organizations that do have a fully connected user experience, 49% report that it delivered an increased ROI. This solidifies what a further 83% of total respondents believe: any delays in digital transformation, particularly in legacy modernization, represent a missed opportunity to capture revenue.

APIs have become essential for integration, boosting IT infrastructure and improving user experiences. They allow for seamless data sharing and connectivity across systems, unlocking the full value of existing assets.

The report highlights that APIs and API-related implementations now account for 40% of company revenue. This has grown year-over-year, and is significant when you look at the growth from only 25% in 2018.

However, organizations are still grappling with API management. While 28% of respondents report that their leadership has a clear, upfront strategy for API management implemented across the majority of the organization, 7% admit their organization lacks a comprehensive strategy. 

As a result, 87% of respondents agree that API management within their organization could be improved. To maximize the ROI of APIs, 91% of IT leaders believe collaborating with a third-party would help — and it can’t come soon enough. 

With the rapid adoption of AI — and the increasing pressure from leaders to evolve into an agentic enterprise — IT teams will only further feel the strain as they manage day-to-day operations alongside AI implementation mandates. 

As AI adoption accelerates, the demand for automation is also increasing. Automation streamlines processes, reduces manual tasks, and frees up valuable IT resources. So it’s no surprise that 98% of IT leaders highlight their need for automation within their organization. 

However, it’s central IT teams that are leading the charge, governing 70% of automations across the enterprise. A well-rounded automation strategy is considered essential for AI integration, and that means enabling non-technical users with low-code and no-code solutions. 

This effort is a key component of a sound automation and AI strategy, and 65% of organizations have developed a complete or nearly complete strategy to empower non-technical users. However, nearly a quarter (24%) of respondents are still developing their automation strategies.

Integrating AI raises critical concerns around cybersecurity and data privacy. These issues complicate the integration process and require robust measures to ensure data integrity and compliance. 

As previously mentioned, cybersecurity was named as a primary concern when implementing AI — in fact, 41% of respondents cited it as a challenge. As a response, IT teams are increasingly leveraging proprietary AI models to enhance the security and governance of APIs — again, the number of models has doubled since last year. 

These models excel in detecting and mitigating security threats by analyzing traffic patterns and predicting potential breaches. They also automate compliance by enforcing policies and maintaining detailed audit trails, ensuring regulatory adherence.

To thrive in the age of AI, organizations must prioritize AI integration and establish a unified strategy that encompasses apps, systems, automations, and APIs. Key steps include:

• Modernizing IT infrastructure to support AI and data-driven initiatives
• Breaking down data silos to enable seamless data access and sharing
• Implementing a robust API management strategy to unlock the value of existing assets
• Leveraging automation to streamline processes and free up IT resources
• Prioritizing security and governance to ensure data integrity and compliance
• Collaborating with third-party experts to maximize the ROI of integration efforts

By embracing a proactive AI integration strategy, organizations can unlock the full potential of AI, drive revenue growth, reduce operational costs, and position themselves as leaders in the agentic enterprise. 

Hear more about these key trends from leaders at Deloitte Digital in the on-demand webinar, which explores the report highlights and the pivotal role of seamless connectivity  in maximizing AI’s impact.