Indian enterprises hit 47 % mark with enterprise AI live use-cases

Indian enterprises are embracing enterprise AI at a new scale, with 47 % of companies now running multiple AI use-cases live in production. The trend signals a shift from pilots to performance, but investment levels and governance remain critical bottlenecks.

AI adoption crosses tipping point
The enterprise AI market in India has reached a critical inflection point. The main keyword enterprise AI India comes into play as 47 % of Indian firms now report having more than one AI use-case live in production (up from a smaller number still in pilot). This shift indicates that AI is no longer confined to experimentation, but is embedding into core workflows.

From pilot to production: what’s changing
Until recently many organisations treated AI as a pilot project or proof-of-concept. Now, firms are scaling up across functions such as operations, customer service and marketing with real ROI expectations. The report shows that 91 % of technology decision-makers cite “speed of deployment” as the key factor when deciding whether to build in-house or buy AI solutions. At the same time, 76 % believe generative AI (GenAI) will have significant business impact, and 63 % feel ready to leverage it effectively.

Where AI use-cases are being deployed
Key business functions receiving attention include: operations (63 % of enterprises), customer service (54 %) and marketing (33 %). These areas are driving enterprise performance rather than just cost-cutting. For example, firms are automating document processing, using chatbots for support, and applying GenAI models for content or marketing optimisation. The keyword GenAI India enterprises is relevant here given the growing role of generative models.

Investment and governance: the weak links
Despite the enthusiasm, investment levels tell a more cautious story. More than 95 % of organisations still allocate less than 20 % of their IT budgets to AI, and only around 4 % allocate more than 20 %. That means while adoption breadth is increasing, depth of investment remains modest. Governance, data readiness and ROI measurement are emerging as key blockers. Without these, scaling AI may yield sub-optimal results.

Talent and operating-model shifts
The shift to enterprise AI is also driving changes in workforce and operating models. About 64 % of companies report workforce transformation focused on standardised tasks, yet 59 % say there remains a shortage of skilled AI talent. Hybrid human-AI workflows are becoming the norm, with new roles combining domain expertise, data-science and AI-agent interaction. The keyword AI talent India highlights the emerging demand.

Why this matters for India’s growth story
Embedding AI into business processes provides Indian enterprises with an opportunity to leapfrog inefficiencies. As India positions itself as a technology-enabled economy, enterprise AI adoption becomes a competitive differentiator. Firms that move from experimentation to mature deployment will gain first-mover advantage. However, if the investment and governance gap persists, India risks witnessing a broad base of pilots with few scale-ups.

Takeaways

• 47 % of Indian enterprises now have multiple AI use-cases live, signalling shift from experimentation to production.
• Investment levels remain low: most firms allocate under 20 % of IT budget to AI despite high expectations.
• Deployment is concentrated in operations, customer service and marketing functions, driven by speed of impact.
• Talent shortages and weak governance/data-readiness remain key barriers for scaling enterprise AI.

FAQs
Q1: What does “live use-cases” mean in this context?
It means AI solutions are not just in pilot or testing phases but are running in production environments as part of business operations, affecting outcomes on an ongoing basis.

Q2: Which types of companies are adopting enterprise AI in India?
The survey covers a broad set of 200 organisations across 20+ industries including government bodies, start-ups, Indian firms and multinationals’ India arms. The trend spans large and mid-sized enterprises.

Q3: Why is investment still low despite strong adoption numbers?
Because firms are cautious about scaling AI across the enterprise. Many are spending on targeted use-cases rather than broad transformations. Issues like data infrastructure, model governance and measurable ROI are slowing larger allocations.

Q4: What should enterprises focus on next to make enterprise AI work?
They should prioritise data readiness, build robust model assurance and governance frameworks, design human-AI collaboration workflows and track business outcomes beyond cost reduction to strategic differentiation.