By Khadim Batti, Co-founder & CEO, Whatfix
The surge in AI investments across Indian enterprises is truly remarkable, with 93% signalling plans to boost their AI budgets in 2025 and generative AI use having multiplied five times since 2023. However, beyond these impressive numbers lies a profound challenge: driving meaningful, large-scale adoption. Across the spectrum of companies, from nimble startups to established conglomerates, generative AI tools are becoming commonplace. Yet, their real success will be gauged not by mere usage stats but by their tangible impact on automating and improving core operational workflows. Achieving this requires shifting from one-off enablement campaigns to sustained, user-focused engagement models that skilfully address both technical and human factors.
The challenge that employees face towards AI adoption in Indian enterprises is not rooted in capability gaps or lack of enthusiasm, but stems from insufficient contextual understanding. Organisational experiences reveal that mandating users to move between disparate systems enables them to craft their own prompts or proactively seek AI assistance without much experience, which often results in digital friction, underutilisation or complete abandonment. These challenges intensify across diverse workforces spanning multiple languages and regions. The strategic imperative demands Agentic AI integration within existing workflows, allowing users to access capabilities intuitively without disruption. Drawing on insights from the IndiaAI Mission and enterprise trends projecting a 5.44% productivity boost by 2030, here are a few critical strategies that can help move AI usage from hype to habit in your organisation.
Context is king: Cutting through the prompt puzzle
Prompt engineering is quickly becoming a centrepiece in realising enterprise AI’s full potential. Expecting every user to create the “perfect” prompt from scratch or unearth the best commands unaided adds friction, particularly in India’s fast-growing sectors, where 85% of organisations prefer building AI solutions internally but only 23% have full confidence in their teams to lead innovation, according to the latest State of Enterprise Technology Survey 2025. The most effective approach proactively identifies a user’s goals, role, and immediate context within applications to surface relevant prompts instantly, without forcing users to have deep technical know-how or disrupting their flow. By analysing user behaviour such as frequently repeated actions or hesitation points where tasks stall, companies can deliver prompt suggestions that automate impactful steps and lighten cognitive load, ensuring AI tools become natural extensions of the workflow.
Analysing behavioural patterns, such as frequently performed tasks, repetitive queries or areas of hesitation that result in workflows with high dropout rates, enables organisations to deliver intent-aware intelligent prompt recommendations that automate high-impact actions while reducing cognitive load. This shift from reactive input to intelligent, behaviour-aware guidance proves essential for sustaining usage and accelerating AI value realisation across the enterprise, particularly in rapidly expanding technology and services sectors where agentic AI adoption is accelerating toward over 90% penetration by this time next year.
Unlocking the mystery of adoption metrics
Traditional measures like active user counts offer little more than a surface-level snapshot of AI adoption. For India’s enterprises, which are investing heavily yet struggle with trust in their internal innovation capabilities, it’s crucial to delve deeper into behavioural data. Understanding how users enter AI workflows, which prompts engage them most effectively, conversion rates from interaction to completed tasks, and differences in adoption across departments provides richer insight. Capturing engagement metrics at this granular level enables leaders to diagnose adoption barriers, refine prompt strategies dynamically, and continuously improve AI’s business impact, turning AI adoption into a transparent, controllable, and scalable driver of enterprise value.
For Indian businesses, capturing and analysing engagement at the user level enables leaders to move beyond surface metrics, uncover adoption bottlenecks, optimise prompt strategies, and calibrate AI experiences in real time. This analytical depth turns adoption from opaque into a measurable, controllable, and continuously improvable business value driver, helping Indian firms not just embrace AI, but compete effectively on the global stage.
AI can build confidence the right way
Building workforce confidence around AI remains a key hurdle given the uneven distribution of AI fluency across teams—even within digitally advanced Indian IT ecosystems. Overcoming this requires embedding just-in-time learning resources tailored to user roles and scenarios directly inside the applications employees use daily. Offering interactive onboarding, scenario-based microlearning, and guidance in multiple languages not only meets users where they are but respects the linguistic and cultural diversity that characterises India’s workplaces. This approach helps alleviate hesitation, foster trust, and accelerate AI fluency across complex organisations.
Interactive onboarding, role-specific guidance and scenario-driven micro-learning should be integrated directly within enterprise applications, meeting users at their operational points while continuously reinforcing knowledge. This approach must accommodate the multilingual and multicultural realities of the Indian workplace.
AI adoption for the long haul
AI adoption represents a dynamic, evolving journey, rather than a singular milestone. In-application feedback loops, combined with behavioural analytics, provide leaders with the intelligence necessary for fine-tuning adoption strategies. Understanding prompts, resonance patterns, persistent friction and optimisation opportunities for enhanced usage and outcome becomes critical.
Treating adoption as a continuous process that evolves alongside workflows, user requirements, and business priorities ensures AI continues to deliver value beyond launch phases, achieving sustainable scale. In the years ahead, distinctions between AI tools and work tools will blur as AI agents become embedded in operations like email or chat, surfacing insights, anticipating needs and streamlining tasks across every organisational function.
Materialising this future requires adoption strategies that go beyond awareness or training initiatives. It must be habitual, contextual and measurable. Successful organisations will not just deploy AI; they will design systems, structures and cultures where AI becomes as intuitive as basic communication actions such as clicking “send.”
As Indian enterprises continue to ramp up AI investments and innovation, the true differentiator will be those organisations that embed AI seamlessly into daily workflows, transforming excitement into consistent, measurable value and building a culture where AI is not just a tool but a vital partner in everyday work.