TLDR
A HCLTech and Raconteur survey of 500 enterprise decision-makers found 90% report AI is transforming workflows, yet only 18% say it is delivering significant revenue impact. The firms clearing that bar are four times more likely to scale agentic AI, define measurable use cases at 73% versus 22% for laggards, and secure senior leadership sponsorship at 63% versus 36%. The gap is not about access to technology; it is about execution discipline. Clear metrics, C-suite buy-in, and structured workforce upskilling separate the minority from the rest. For Australian business leaders, the findings amount to a pointed directive: stop running pilots and start targeting revenue.
KEY TAKEAWAYS
Near-universal adoption, minimal revenue conversion
Only 18% of enterprises say AI is delivering significant revenue impactverifiedVerified Source: hcltech.com, according to HCLTech's global research report released on 21 July 2026.[1] That figure lands against a backdrop of near-total adoption: 90% of the 500 enterprise decision-makers surveyed said generative AI and agentic AI are already transforming workflows.[1]
The report, produced in partnership with Raconteur and titled 'The Blueprint for AI Leadership', draws a sharp line between activity and outcome. Ninety-one per cent of respondents cited improved data access from AI initiatives, and 90% reported productivity gains, yet top-line growth remained elusive for the vast majority.[1]
What AI leaders do differently
HCLTech segments respondents into AI Leaders and AI Followers based on their ability to convert adoption into measurable business results. AI Leaders define measurable use cases at a rate of 73%, compared with just 22% for AI FollowersverifiedVerified Source: hcltech.com.[1] Senior leadership sponsorship follows a similar pattern: 63% of AI Leaders secure it, against 36% of AI Followers.[1]
Pawan Vadapalli, Corporate Vice President and Global Head of Digital Business Services at HCLTech, said the divergence comes down to how deeply AI is embedded in the business. Vadapalli said the organisations pulling ahead are not just running more pilots; they are rethinking how the business works, embedding AI into everyday decisions and workflows, and that coordinated shift across leadership, culture and foundations turns AI from a tool into real, long-term advantage.[1]
The agentic AI advantage
One of the starkest separators between the two cohorts is the willingness to scale autonomous systems. AI Leaders are four times more likely to scale agentic and autonomous AI than AI FollowersverifiedVerified Source: hcltech.com.[1] Agentic AI refers to systems that pursue multi-step goals with minimal human intervention, moving well beyond single-task automation.
Vadapalli said AI has entered a decisive phase, and success will come down to how well organisations bring people, data and technology together.[1] AI Followers concentrating on isolated tools and one-off pilots are, by contrast, accumulating a structural disadvantage that compounds with each quarter they fail to scale.
Workforce and data readiness as the hidden foundation
Workforce transformation separates the two groups as sharply as any technology choice. Ninety-three per cent of AI Leaders have structured workforce upskilling programs in place, compared with 20% of AI Followers, a 73-percentage-point gap the report identifies as a core enabler of scaling.[1] Embedding continuous learning into the organisation allows AI Leaders to deploy new capabilities faster without repeated onboarding cycles.
Data readiness sits alongside upskilling as a foundational condition. The 91% of respondents who said AI improved data access masks a quality issue: accessing data and having that data structured for autonomous AI decision-making are distinct problems.[1] AI Leaders address both simultaneously, treating data infrastructure and talent development as prerequisites rather than afterthoughts.
What Australian business leaders should act on now
The findings carry direct implications for Australian business leaders operating in an environment where AI investment is accelerating but board-level scrutiny of returns is tightening. The HCLTech and Raconteur data make the case that execution discipline, not technology spend, is the variable that determines whether AI investments reach the revenue line.
Three actions emerge from the report's findings. First, attach a revenue or margin metric to every AI use case before deployment, mirroring the 73% of AI Leaders who define measurable outcomes upfront.[1] Second, move C-suite and board sponsorship from passive awareness to active accountability, closing the gap between the 63% of AI Leaders and 36% of AI Followers who secure that backing.[1] Third, build a structured upskilling program now; waiting until agentic systems are already deployed leaves organisations scrambling with a workforce not prepared to operate them.
HCLTech published 'The Blueprint for AI Leadership' on 21 July 2026, with Raconteur conducting the fieldwork across 500 enterprise decision-makers globally.[1]
SOURCES & CITATIONS
FREQUENTLY ASKED QUESTIONS
What did the HCLTech and Raconteur AI report find?
What separates AI Leaders from AI Followers in the report?
What is agentic AI and why does it matter for revenue outcomes?
Jonas Valenti writes about search and how businesses get discovered. He has spent years watching what makes a company visible online, and is unsentimental about tactics that no longer work.
