Enterprises today face a stark reality: the breakneck pace of AI-driven disruption means that traditional operating models are no longer sufficient.
As Seema Noronha, Global Practice Director, Operating Model Transformation, Digital Business Services, HCLTech, explained on the HCLTech Trends and Insights podcast, “the way AI is disrupting and the pace of change in the industry is really very rapid.” Organizations must ask themselves: are they truly agile, scalable and customer-centric enough to thrive in an AI-powered world?
Spotting the warning signs
Noronha identifies three critical indicators that an operating model is past its sell-by date: “whether we are able to respond with agility, whether our operating model is easily scalable and whether we are able to innovate continuously and be highly customer-centric.”
When AI pilots fail to scale, cross-functional collaboration stalls or rollouts drag on, leaders should conduct a rapid diagnostic across six pillars of strategy, structure, people, process, tools and governance.
Key questions include: “Is AI integrated into the strategy, or is it just another initiative?”, “Does our organization structure enable cross-functional collaboration?” and “Do we have platforms and tools to support the rapid development and scaling of AI?” If the answer to any of these is no, it’s time for an overhaul.
Embedding customer feedback
HCLTech’s The Blueprint to AI-Led Operating Model report found that only 17% of organizations believe they’re “using customer feedback to its full potential,” while 70% say they incorporate feedback at some stage but struggle to leverage it effectively. A product-aligned operating model solves this by embedding feedback “at every stage of the lifecycle,” said Noronha, because teams are “structured around customer-facing products, around customer journeys,” making them accountable for outcomes. They work in “continuous discovery and delivery cycles in agile feedback loops,” integrating insights from usage analytics, support-ticket trends, product adoption directly into sprint planning and quarterly roadmaps.
Yet 94% of respondents report “challenges in collecting and analyzing customer data,” hindering those feedback loops. Noronha points to AI analytics and observability tools as key enablers to process real-time sentiment, usage and friction metrics, feeding these insights back into product decisions without delay.
Rethinking ROI for AI investments
Traditional ROI calculations focus narrowly on “cost savings and efficiency gains,” but AI’s value compounds as models mature. Noronha argues for “a multi-pronged approach” to measurement: combining quantitative metrics, including revenue impact, productivity uplift and time-to-market improvement, with qualitative gains such as customer satisfaction (CSAT), hyper-personalized experiences and loyalty. Speed to insight and decision-making, employee engagement and strategic innovation potential all belong in the value loop. “It’s a continuous value loop rather than a direct measure like ROI,” she said.
Navigating regional nuances
The report highlights distinct regional challenges. In APAC, rapid digital growth coexists with “talent fragmentation,” so enterprises should prioritize “scalable platforms” and “investing in talent uplift.” Europe’s mature markets demand compliance with stringent data-privacy laws, green operations and Responsible AI frameworks; areas where “customer trust and transparency” must take center stage. The US, by contrast, thrives on “innovation-driven” and “autonomous teams,” requiring an operating model that “optimizes for speed and scale.” Noronha advocates a federated approach that tailor’s structures and priorities to each market’s culture, regulations and customer needs.
Cultivating culture and mindset
A striking 95% of organizations acknowledge that shifting to a product-aligned model requires a cultural transformation as much as structural change. HCLTech supports clients through focused change management, combining top-down sponsorship with “cultural ambassadors” at every level. Successful programs anchor people in the “why,” breaking down “limiting beliefs and behaviors,” such as task-orientation, into “enabling” shifts like curiosity and outcome-focus. Storytelling workshops, training and continuous reinforcement, through joint planning, co-creation sessions and leadership role-modeling, embed new behaviors in everyday work. Crucially, Noronha emphasized providing “psychological safety” so teams can experiment, learn, and adapt.
Building trust and shared accountability
Transitioning from project-centric to product-centric teams hinges on trust, transparency and shared accountability. Noronha recommended co-creating a team charter: “a contract among teams” outlining purpose, goals, values and working agreements, which is reinforced in OKR sessions and planning ceremonies.
Transparent data dashboards address the 40% of teams seeking better collaboration platforms, while joint retrospectives and celebrations foster empathy and collective success. Leadership coaching shifts managers from command-and-control to a coaching style, empowering teams to deliver against shared outcomes.
Where to begin? Top three priorities
For CEOs ready to embark on this transformation, Noronha offered three starting points:
- Align leadership on a compelling vision
“Get your leadership together, aligned on a compelling vision and purpose as to why we are doing this.” A shared north star secures executive commitment and galvanizes teams. - Educate decision-makers on the product model
From value measurement to operating-model implications, leaders must “understand what we are getting ourselves into.” Peer learning, case studies and targeted training help decision makers grasp the scale and duration of the journey. - Map your journey and leverage partners
Only then should organizations draft a roadmap: assessing existing capabilities, defining skill gaps and identifying partners who can “accelerate this journey” with proven tools and expertise.
By systematically addressing strategy, structure, culture and metrics, and by embedding customer feedback and continuous value loops, enterprises can evolve into truly AI-led organizations, ready to surf the next wave of innovation.
