This is article one in a six-part series exploring new research findings from HCLTech and Raconteur. You can read the rest of the article series in ‘The Blueprint for AI Leadership’ here or download the full report here.
Enterprise AI has moved beyond isolated experimentation within individual functions and is increasingly being embedded across core business processes, enterprise applications and day-to-day workflows. As adoption scales, organisations are beginning to realise tangible gains in productivity, decision-making and customer experience.
It’s clear that the benefits of AI are no longer in question. New research from HCLTech and Raconteur finds that 90% of organisations believe GenAI and agentic AI are already having either a major or at least some impact on operational workflows and process efficiency, as well as on data accessibility (91%) and knowledge access and productivity enablement (90%). A majority of respondents (59%) also believe that investing in AI is essential to remaining competitive.
However, even as these investments accelerate, the research points to a clear divergence in how organisations are converting those investments into business value. AI Leaders, who represent 18% of the sample, are maximising ROI not simply by using AI to reduce costs, but by applying it to drive growth, innovation and improved customer experience.
AI Followers, by contrast, represent 60% of the sample. While they are realising returns from AI investments across business functions, they significantly trail Leaders in the higher-value use cases that are reshaping operating models and competitive advantage.
Who are AI Leaders & Followers?
AI Leaders, who represent 18% of the sample, are maximising ROI not simply by using AI to reduce costs, but by applying it to drive growth, innovation and improved customer experience.
AI Followers, by contrast, represent 60% of the sample. While they are realising returns from AI investments across business functions, they significantly trail Leaders in the higher-value use cases that are reshaping operating models and competitive advantage.
This gap is especially visible in the adoption of agentic and autonomous systems. AI Leaders are four times more likely than Followers to scale these capabilities, reinforcing how unevenly organisations are moving from functional AI deployment to enterprise-wide transformation.
This reveals a broader structural and operational challenge. While many organisations generate value from AI, a persistent gap remains between what they expect AI to deliver and what they can realistically achieve. This is particularly visible in commercial outcomes, where only 18% of organisations report AI significantly impacting revenue generation. This gap is not only technical, spanning data, architecture and enterprise applications, but also personal, reflecting shortcomings in leadership alignment, ownership and the ability to drive change.
The winners are not simply those deploying more models or launching more pilots, but those using AI to redesign the enterprise itself. In that sense, AI advantage is becoming less about feature-level augmentation and more about structural reinvention: how humans, agents, workflows, systems and data operate together as a single, harmonised model.
Efficiency and transformation
Many organisations currently measure the ROI of AI investment primarily through process speed and efficiency improvements (28%) or cost savings and productivity gains (23%). These indicators suggest that much of AI’s value is still assessed through a transactional lens, providing only a partial view of its broader impact, particularly in areas such as revenue growth, product innovation and long-term competitiveness.
Such a narrow lens can limit the ability to unlock AI’s full value. “You can focus on efficiency-driven AI, or you can focus on transformation,” says Sadagopan Singam, executive vice president and global head, enterprise platforms and edge services, digital business services at HCLTech. “Efficiency-driven AI is about process automation, whereas transformative AI is about reimagining and redesigning the processes themselves.”
The goal, he argues, is to reach a state of orchestrated intelligence by aligning business goals with AI, embedding continuous workforce learning and balancing AI capabilities with governance and accountability. “These are the key factors separating Leaders and Followers. If all of them are in place, then you’re actually creating a self-reinforcing loop that feeds more success,” says Singam.
You can focus on efficiency-driven AI, or you can focus on transformation
This is the crucial distinction between productivity and advantage. Productivity is local: a task is completed faster, a report is generated more quickly and a support response is drafted with less effort. Advantage is systemic: decisions improve, workflows compress, exceptions are handled better and the enterprise begins operating with a different rhythm, quality and resilience. One is an efficiency gain; the other is a moat.
Larger organisations appear better positioned to make progress towards this state. While they may face legacy challenges, they are also more likely to have the resources to address foundational requirements – such as security and data discipline – while simultaneously advancing multiple AI initiatives. In fact, firms with revenues exceeding $10bn are almost seven times more likely to report that AI ROI significantly exceeds expectations and are more likely to be driven by clearly defined use cases.
AI initiatives, however, are typically distributed across multiple functions and enterprise systems rather than owned centrally, including technology and infrastructure (76%), data and analytics (74%) and operations (47%). While this reflects AI’s cross-functional relevance, it also creates risks. Without clear leadership and alignment to business goals, accountability can become fragmented, slowing decision-making and diluting impact.
Running the right race
Smaller organisations are not excluded from success. Those that address structural challenges and focus on enterprise-wide transformation rather than isolated use cases can still generate significant value. “It’s not only about the size of the organisations,” says Singam. “It’s also about where and how they want to apply AI.”
In practice, many firms operate with different definitions of success. “Organisation A may call something a success while organisation B may view the same outcome as a failure,” he notes. “So organisations shouldn’t get too obsessed with how to win, because winning is completely subjective.”
Despite these differences, however, the barriers to scaling AI are widely shared.
At a structural level, legacy systems continue to limit organisations’ ability to scale AI effectively, while integration complexity, technical debt and vendor lock-in further compound these challenges, particularly for those operating on fragmented technology foundations.
Organisations shouldn’t get too obsessed with how to win, because winning is completely subjective
organisational readiness adds a second layer of constraint. Leaders are significantly more likely than Followers to believe they are investing sufficiently in organisational and talent readiness (67% vs. 37%), highlighting a clear divide in how prepared organisations feel to support AI-driven change.
Workforce-related challenges are frequently cited, but the findings suggest they are less decisive. Instead, progress appears to be shaped more by structural limitations and uneven organisational readiness than by employee resistance alone. Where organisations begin to diverge is in how they respond to these constraints.
This is where the idea of a full-stack reboot becomes essential. AI Leaders are not just modernising one layer of the stack. They are progressively rewiring the full enterprise suite: data and semantics, process definitions, application estates, interoperability, governance models, talent design and workflow orchestration. In other words, they are asking not how AI can sit on top of the business, but what parts of the business must be rebooted so AI can become operationally intrinsic.
AI Leaders also tend to adopt more distributed ownership models, moving beyond ‘HiPPO’ (Highest-Paid Person’s Opinion) decision-making. Their ability to orchestrate AI across complex technical and organisational environments is a key differentiator. As Singam notes, while a single enterprise AI platform may be ideal, “organisations very rarely get there today, so the ability to orchestrate multiple platforms is important.”
The Agentic AI question
The shift towards Agentic AI will further test organisational readiness. According to Singam, this transition will intensify competitive divergence. “It will be winner takes all,” he says. “What is the ideal human-to-agent ratio? What are the best metrics for measuring this?”
Overall, confidence in AI’s transformative potential is high. Yet in many organisations, AI remains concentrated in areas where technical and workforce foundations are already strong, delivering incremental gains rather than systemic change. These gains are often measured using metrics that fail to capture AI’s full impact on growth and competitiveness.
As Agentic AI scales, this measurement challenge will become more acute. Counting usage, tokens and isolated productivity gains will not be enough. The more meaningful questions will be sharper: are workflows now more reusable, has decision-making improved, are cycle times compressing without control breakdowns, has institutional memory accumulated, do acquisitions integrate more quickly and has the enterprise become easier to change rather than harder? Positive answers to these questions signal a structural edge.
The organisations most likely to pull ahead share clear characteristics: sustained investments in architectural modernisation, robust data foundations, strong leadership alignment and cultures that naturally empower employees to use, challenge and extend AI capabilities. These attributes enable organisations to move beyond efficiency gains and use-case expansion to a third stage: AI-driven relevance, where intelligence is embedded directly into enterprise applications, workflows and operating models.
Importantly, Followers are not locked into their current position. “The winner of tomorrow is going to be a Follower who’s agitated about the gap,” says Singam. “With sufficient self-awareness and decisive action, organisations can still close the gap and potentially leapfrog today’s Leaders.”
Key research findings
Discover the essential takeaways from HCLTech’s new research with Raconteur.
1. Enterprise AI is delivering real results, but mostly incremental gains. True transformation requires reimagining workflows, not just automating them.
2. A growing divide separates AI Leaders, who tie investment to clearly defined use cases, from Followers still stuck in reactive implementation mode.
3. Measuring ROI through cost savings and efficiency alone misses the bigger picture. AI-driven relevance means inventing entirely new business models.
4. Technical barriers like integration complexity and legacy debt remain persistently under-addressed, while employee resistance is less of an obstacle than many organisations assume.
5. Successful adoption depends as much on culture, leadership and workforce upskilling as it does on data and underlying architecture.
6. The gap between Leaders and Followers will only widen as Agentic AI matures, but self-aware and decisive Followers can still leapfrog today’s Leaders if they act now.
This is article one in a six-part series exploring new research findings from HCLTech and Raconteur. You can read the rest of the article series in ‘The Blueprint for AI Leadership’ here or download the full report here.
This is article one in a six-part series exploring new research findings from HCLTech and Raconteur. You can read the rest of the article series in 'The Blueprint for AI Leadership' here or download the full report here.
Enterprise AI has moved beyond isolated experimentation within individual functions and is increasingly being embedded across core business processes, enterprise applications and day-to-day workflows. As adoption scales, organisations are beginning to realise tangible gains in productivity, decision-making and customer experience.
It’s clear that the benefits of AI are no longer in question. New research from HCLTech and Raconteur finds that 90% of organisations believe GenAI and agentic AI are already having either a major or at least some impact on operational workflows and process efficiency, as well as on data accessibility (91%) and knowledge access and productivity enablement (90%). A majority of respondents (59%) also believe that investing in AI is essential to remaining competitive.
