This is article two 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.
While most organisations are experimenting with AI, one of the clearest points of divergence identified in Raconteur and HCLTech’s new research lies in how organisations define and pursue value from AI. Among AI Leaders, 73% say adoption is driven by clearly defined use cases and measurable outcomes, compared with 22% of Followers.
This gap is not just a matter of clarity; it reflects a fundamental difference in how organisations approach AI. For Leaders, value is defined upfront and used to guide investment, prioritisation and execution. For Followers, value is often more loosely defined, emerging only through market pressure rather than being shaped from the outset.
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 distinction underpins the ability to scale. Where value is clearly articulated, AI initiatives are more likely to gain traction, secure sponsorship and translate into measurable business impact.
For many organisations, this is where the gap begins – and leadership behavior plays a critical role in translating strategic intent into execution. When leadership is visible, vocal and able to connect AI initiatives clearly to business outcomes, organisations move faster, experiment more effectively and scale promising initiatives. Where this is absent, initiatives that show genuine promise often struggle to gain sustained traction.
Among AI Leaders, 63% say senior leadership actively champions AI adoption, compared to just 36% of AI Followers. This visible sponsorship does more than signal intent; it helps align priorities, unlock investment and create the conditions for AI initiatives to move beyond isolated pilots. Where leadership is less engaged, AI efforts are more likely to remain fragmented, with weaker links to business outcomes and limited momentum.
Leaders are more proactive in preparing for workforce impact, with 54% planning for how Agentic AI will reshape roles, compared with just 16% of Followers. This enables them to anticipate change and embed AI more effectively, rather than reacting to disruption as it occurs.
AI Leaders are also four times more likely than Followers to operate within structures and processes that allow AI projects to move quickly across the organisation, an approach that prioritises agility over hierarchy. This means promising pilots are less likely to stall while awaiting cross-functional sign-offs or extended governance cycles.
While leadership support for AI may exist conceptually among Followers, true ownership of key initiatives is often less clearly defined and the links to business outcomes are less explicit. “AI strategy fails when you have fundamentally siloed conversations in which all the aspects of the enterprise fail to come together,” says Praveen Bhat, executive vice president and global head, SAP, digital business services at HCLTech. Leaders also need to tie AI initiatives clearly to business outcomes, not just operational efficiency. “You may be running certain things faster, but is it creating any impact on your dollar and end-customer value? Maybe not.”
Bhat also emphasises that AI adoption must be driven from the top down. Without senior leadership ownership, it is unlikely to receive the priority required to scale. In some organisations, the absence of sustained engagement is masked by a proliferation of strategy documents, steering committees and published principles. While these can create the appearance of momentum, they do not necessarily translate into meaningful progress.
“Leaders in the organisation who sell the benefits of AI must have a clear grasp of the infrastructure story, including the costs, constraints and gaps between what AI systems promise and what they can actually deliver in a specific organisational context,” notes Sebastian Reiche, professor of managing people in organisations at IESE Business School.
Bridging the functional readiness divide
Rather than a simple readiness gap, the data points to a more structural constraint: the maturity of core enterprise applications is limiting organisations’ ability to adopt and integrate AI. The most significant bottlenecks sit within systems that underpin core business operations, particularly ERP (35%), finance (32%) and supply chain (26%), where legacy architectures and tightly coupled processes make integration more difficult.
By contrast, functions such as CRM (20%) and product development and innovation tools (20%), where systems are typically more modular and already closer to customer-facing use cases, are less likely to act as constraints. This suggests that organisations are making faster progress in areas where AI can be layered onto existing workflows, but face greater friction where deeper integration into core systems is required.
Differences between AI Leaders and Followers are present but less pronounced than in other areas. Leaders report slightly fewer constraints across most systems, particularly in supply chain (17% vs. 29%) and finance (24% vs. 37%), indicating a greater ability to navigate or modernise complex environments. However, the overall pattern remains consistent: the biggest barriers to scaling AI are not at the edge, but at the core of the enterprise.
The implication is that scaling AI is less about extending use cases and more about addressing the limitations of underlying systems. Organisations may be able to generate value in isolated functions, but without modernising the applications that support core operations, embedding AI at scale remains difficult.
This is reflected in how AI is currently being applied. organisations are more comfortable with AI that assists or augments human work – such as content generation or customer experience optimisation – than with deploying more autonomous systems. While Agentic AI is attracting significant attention, fewer than half (45%) say they are using it effectively for workflow automation and orchestration, rising to 71% among Leaders but just 44% of Followers.
Many organisations are uncomfortable delegating critical decisions to AI without stronger controls, transparency and governance in place
Despite these structural constraints, confidence is expected to rise across all functions over the next three years, including those critical to deploying autonomous systems. This reflects a broader pattern seen between Leaders and Followers: while Leaders are far more confident in managing ongoing AI-driven change today, Followers believe they will be able to do so within the same timeframe.
This suggests a familiar dynamic, one in which ambition is advancing faster than capability and reinforces the risk that progress towards more autonomous, enterprise-wide AI may be constrained by underlying readiness. As organisations move beyond assisted use cases towards more autonomous systems, confidence begins to falter, particularly around governance and optimisation.
“Many organisations are uncomfortable delegating critical decisions to AI without stronger controls, transparency and governance in place,” says Daniel Meyer, chief technology officer at Camunda. “In industries like financial services, where customer experience, data privacy and regulation are paramount, firms simply do not have the confidence to extend AI beyond low-stakes tasks.”
This reinforces the role of leadership in determining how confidently AI is deployed at scale. Many organisations will need to strengthen governance frameworks, but without better visibility into how AI is operating across the business, progress is likely to be limited.
“It may sound obvious, but you can’t govern what you can’t see,” says Meyer. “Yet many organisations continue to deploy AI systems with little visibility into how they are actually used or how decisions are reached.”
Creating the right culture
To raise employee confidence in using AI, leadership will also need to enhance collaboration between technical and business teams. “Leaders in the organisation should invest in establishing liaison roles, drawing on people with the cultural and technical range to bridge the strategic and operational levels,” says Reiche.
Leadership also needs to create a culture that allows AI enthusiasm and talent to flourish. “Technology or data alone is not going to solve things,” says Bhat. “Without talent that is completely excited about this whole prospect of AI-enabled work, you are not going to run the entire mile.”
You don’t want AI to be an autopilot; it has to be a co-pilot
AI Leaders already recognise that employee trust is built through transparency, robust governance and clear commitments around how AI decisions are made, reviewed, challenged and adopted. Where these commitments are in place, organisations tend to see higher levels of employee confidence and, in turn, greater willingness to deploy AI across higher-stakes workflows.
“You don’t want AI to be an autopilot; it has to be a co-pilot,” says Bhat. “It’s an evolution of people’s roles, not a depreciation of them.”
Translating this value into consistent, enterprise-wide impact, however, requires coordination. Orchestration remains the critical layer between AI capability and measurable enterprise value, with leadership playing a central role in enabling it. “CIOs and CTOs are being asked to demonstrate ROI,” says Meyer.
Bhat adds that the CFO must also be central to AI strategy, given their ability to connect investment to business value. “The moment you position AI as a business driver, you need to have the CFO at centre stage.”
Ultimately, leadership determines whether AI initiatives translate into enterprise-wide impact or remain confined to isolated pilots. Yet even where leadership and value alignment are in place, scaling AI ultimately depends on whether organisations can build the workforce capabilities required to sustain it.
This is article two 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 two 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.
While most organisations are experimenting with AI, one of the clearest points of divergence identified in Raconteur and HCLTech's new research lies in how organisations define and pursue value from AI. Among AI Leaders, 73% say adoption is driven by clearly defined use cases and measurable outcomes, compared with 22% of Followers.
This gap is not just a matter of clarity; it reflects a fundamental difference in how organisations approach AI. For Leaders, value is defined upfront and used to guide investment, prioritisation and execution. For Followers, value is often more loosely defined, emerging only through market pressure rather than being shaped from the outset.
This distinction underpins the ability to scale. Where value is clearly articulated, AI initiatives are more likely to gain traction, secure sponsorship and translate into measurable business impact.
