Central to the conversation was a broader interpretation of ROI. Although return on investment is still significant, businesses are increasingly pursuing a more expansive definition. This involves a quest for return on intelligence, which translates into improved decision-making, enhanced resilience, innovative business models, better customer engagement, and work redesigned for a future where human and AI systems collaborate.
Setting the Stage for Return on IntelligenceRomal Shetty, CEO, Deloitte South Asia, inaugurated the Forum by presenting AI ROI as a matter of leadership and enterprise value. The opening discussion moved beyond the limited perspective of AI as just another technological investment or a cost-cutting effort.
The essential question was whether AI is merely a tool for organizations to reduce costs or whether it amplifies intelligence across the entire enterprise. This distinction set the tone for the Forum. The value of AI cannot solely be assessed on efficiency; it must also consider the quality of decisions it facilitates, the resilience it fosters, and the new avenues it creates for growth.
This viewpoint holds particular relevance for India. While scale provides a significant advantage, it does not inherently generate enterprise value. The discussion emphasized trust and governance, human-led AI transformation, and return on intelligence as central to India’s opportunity. The takeaway was clear: AI can transform corporate operations and enhance national capabilities, but only when ambition aligns with responsible implementation.
The Global Value Reservoirs of AI
The next phase of the Forum shifted from ambition to execution. Jim Rowan, Head of AI, Deloitte US, led discussions on what organizations need to achieve scalable AI adoption and align investments with tangible business value.
The session outlined AI value in three primary areas. Efficiency remains a key focus, as organizations aim to automate tasks, enhance productivity, and minimize friction in operations. Experience is redefining customer and employee engagement through innovations like chatbots, avatars, video rendering, and novel AI-enabled interactions.
The third area, Growth AI, promises transformation. It highlights how organizations can leverage AI to rethink business models, penetrate new markets, and discover new pathways for growth. While efficiency is the most pressing investment area, the Forum presented growth as the next frontier for businesses seeking strategic advantages via AI rather than mere incremental enhancements.
This section of the Forum also illustrated the pace of innovation. With frontier model companies, hyperscalers, and tech providers launching new capabilities rapidly, enterprise leaders face the challenge of distinguishing between hype and reality. The challenge lies not just in investing in AI but identifying which investments truly matter, how they relate to business value, and how this value can be articulated to boards and executive leaders.
Trust, Governance, and Boardroom Dynamics
As AI scales, critical decision-making extends beyond technology teams. Beena Ammanath, Global AI Institute Leader, Deloitte US, guided a discussion that analyzed AI investments, value creation, trust, and governance in the context of the boardroom.
The session examined how enterprises and investors assess AI through an economic value lens. While cost efficiency remains a significant driver for AI adoption—evident in contact centers, software development, and enterprise productivity—the more compelling inquiry is whether AI can generate new economically valuable outputs.
This broader perspective transforms how organizations evaluate AI use cases. In sectors like legal services, for example, the value may extend beyond simply speeding up contract reviews. It could also arise from the downstream effects of quicker decisions, expedited intellectual property development, and enhanced business velocity.
The governance aspect is equally crucial. As enterprises experiment with AI, sandbox environments are emerging as vital spaces for testing, validating, and refining use cases. The conversation also underscored the importance of involving compliance, risk, and legal teams early in the process, right from the pilot stage. This proactive approach enables organizations to conduct necessary due diligence before moving AI into full-scale production, ensuring trust serves as a foundation for expansion rather than an obstacle.
Assessing AI Value Across the Enterprise
The Forum then addressed the practical challenges of scaling value creation. Prashanth Kaddi, Partner, Deloitte India, led a session exploring how enterprises implement AI across industries and how they can better measure returns.
The discussion revealed that AI now penetrates nearly every aspect of the enterprise, influencing operational processes, the creation of products and services, customer interactions, and how dealer and partner networks derive value. AI agents are being utilized in both software development and business workflows, with conversational assistants facilitating faster customer responses while organizations seek ways to convert these interactions into sales, orders, and improved customer outcomes.
A more sophisticated ROI framework emerged through four perspectives. Direct value pertains to revenue growth and cost savings, while indirect value considers benefits that show up upstream or downstream in the value chain. Efficiency value encapsulates productivity enhancements and the ability to redirect personnel to more impactful work. Opportunity value reflects the potential work made possible as AI reshapes the economics of time, effort, and resources.
This approach is vital, as AI value is rarely confined to isolated instances. An enhancement in one business area often creates ripple effects elsewhere. As AI measurement matures, organizations will need to progress beyond simple metrics like hours saved, focusing instead on linking AI to revenue, resilience, customer conversion, operational efficiency, and the creation of new opportunities.
Strategically Scaling Agentic AI
The transition from pilot projects to large-scale implementation marked the next key theme. Ashvin Vellody, Partner, Deloitte India, led a dialogue on the strategic decisions enterprises must navigate as they scale AI and agentic AI.
Scaling AI represents the intersection of ambition and operational practicality. Organizations must ascertain which internal skills to cultivate, which hyperscalers and technology partners to collaborate with, the types of agents to develop, and the pace at which they should adapt to change.
The conversation framed platform decision-making around four priorities. Control is essential as agents distribute their functions and require governance layers that can monitor, orchestrate, and activate them systematically. Context is crucial; agents need clean, connected, and meaningful enterprise data. Connectivity is imperative as large organizations operate across numerous internal and external systems. Compliance remains central, with security, accountability, privacy, and explainability becoming non-negotiable elements in the agentic landscape.
This session also reflected evolving views on the build versus buy debate, as democratized AI stacks empower internal capability development while specialized providers continue to advance rapidly. As organizations scale, roles and metrics may also evolve, with increased emphasis on outcomes, customer satisfaction, and delivered value, alongside new roles such as agent supervisors and AI product managers.
Industry Impact and Engineering AI Value
The Forum then expanded its focus to industry realities. Vinay Prabhakar, Partner and Leader, Sales, Alliances, and Pursuit Excellence, Deloitte South Asia, led a session on how organizations are creating significant impacts using AI.
A core theme was the necessity to concentrate not only on the science of AI but also on the engineering of AI value. Models are vital, whether large, small, or specialized. However, the true challenge lies in constructing effective applications that employ those models to address business challenges.
The discussion contextualized AI historically. If the steam engine augmented physical labor, AI has the capacity to enhance human intellect. However, as seen in past transformational changes, its complete effects will require time to be fully realized.
The session also highlighted the organizational tensions that many companies face. AI can inspire enthusiasm, induce pressure from business teams, and trigger a surge of pilot projects across various markets and functions. Without discipline, this may result in redundant use cases, heightened costs, privacy issues, and ambiguous ROI.
The pathway to value necessitates structure. Organizations need a clear vision for AI integration, reinforced by specific missions, use cases, and forums for continuous evaluation of opportunities. The objective is to channel curiosity into initiatives that have merit, relevance, and measurable value.
The Next Wave of Agentic and Physical AI
The next phase of ROI focused on AI that not only generates insights but also acts on them. Sanghamitra Pati, Applied AI Strategic Growth Market Leader, Deloitte USI, led a session on the ascent of agentic AI, physical AI, and the infrastructure necessary for enterprise transformation.
The conversation highlighted a distinct shift. It is no longer a question of whether AI will reshape enterprises, but rather when and how it will happen. Three forces are coming to the forefront: agentic and generative AI are transforming industries, physical AI is paving the way for autonomy in mobility, and infrastructure is evolving to support the scalable integration of these technologies.
Agentic AI was positioned as a defining component of future enterprises, where specialized agents may interact similarly to how departments and functions do today. These agents will require human collaboration in unprecedented ways.
Physical AI introduces another dimension as autonomous systems extend beyond vehicles and robotics, necessitating substantial computational power for model training, real-world behavior simulation, and reliable deployment of intelligent systems. Without robust, controlled, and disciplined infrastructures, the ambitions for enterprise AI cannot yield scalable transformations.
Redesigning Work for AI Value
The final segment redirected attention to people, work, and operational models. Dheeraj Sharma, HR Transformation Leader, Deloitte USI, led the closing conversation on why realizing AI value necessitates a redesign of work and the integration of intelligence into workflows.
The session addressed critical questions organizations are grappling with. Where is the return on technology investments? How can AI be expanded without disrupting existing efficiencies? How can enterprises operationalize AI and evolve into AI-first entities while prioritizing human roles?
Three common pitfalls shaped the dialogue. The first is technology-only thinking, where organizations acquire tools without reimagining the workflows those tools are intended to enhance. The second is the process trap, where AI is introduced into existing workflows without questioning whether those workflows should be restructured. The third is a limited focus on training that lacks support from a cultural transformation.
A more transformative path encourages organizations to scrutinize how work is actually accomplished, including workflows, touchpoints, roles, decision-making processes, and collaboration patterns. Given AI’s probabilistic and non-deterministic nature, human judgment, work design, and cultural elements become pivotal to realizing value. Models alone will not suffice for ROI; enterprises require operating models that facilitate effective collaboration between human workers and AI systems.
From Ambition to Measurable Impact
Deloitte’s AI Forum 2026 clarified that enterprise AI has embarked on a new chapter. The initial phase was characterized by experimentation and adoption, while the next will focus on outcomes, governance, scalability, and measurable value.
The discussions traversed from leadership vision to execution, from trust to measurement, from industry-specific use cases to agentic and physical AI, and finally to the reconfiguration of work. Collectively, these themes emphasized that AI ROI transcends a singular metric; it is a holistic enterprise discipline.
The transition from return on investment to return on intelligence encapsulates the upcoming challenges. Intelligence gains significance only when it yields impact, and impact matters solely when it generates value.
