FutureIsNow Intelligence | Signals over noise
- Signal 1: Enterprise adoption moved forward, but unevenly
- Signal 2: India’s GCCs are moving beyond cost arbitrage
- Signal 3: Agentic AI raised the stakes on control
- Signal 4: The production gap became a management problem
- Signal 5: Model choice became an architecture decision
- Signal 6: Evaluation and observability became core infrastructure
- Signal 7: Data quality and knowledge access remained decisive
- Signal 8: Voice and local-language interfaces offer a distinct India opportunity
- Signal 9: Compute, energy and cost are strategic constraints
- Signal 10: Workforce change is selective, not uniform
- What enterprise leaders should do in 2026
- The FutureIsNow view
2025 was a year of acceleration in enterprise AI—but not a clean transition from pilots to autonomous operations. Models became more capable, open-weight options broadened the field, and organisations increasingly tested AI in real workflows. At the same time, data readiness, evaluation, governance and the difficulty of scaling across the business remained stubborn constraints.
This retrospective separates the durable signals from claims that should not be repeated without evidence. It is not a leaderboard of models or a list of dramatic company anecdotes. It is a guide to the shifts that matter for Indian enterprise leaders heading into 2026.
Signal 1: Enterprise adoption moved forward, but unevenly
EY’s 2025 survey of more than 200 Indian enterprise leaders found that 47% had multiple generative AI use cases live in production, while 10% were scaling use cases across the business. The same research highlights the gap between running several deployments and transforming an enterprise operating model. These are survey findings, not a claim that AI is already embedded everywhere.
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Why it matters: the next competitive advantage comes from repeatable implementation—integrating AI with data, processes and controls—not from announcing the largest number of pilots.
Source: EY, The AIdea of India: Outlook 2026.
Signal 2: India’s GCCs are moving beyond cost arbitrage
EY’s GCC Pulse Survey 2025 reported that 58% of India-based GCCs were investing in agentic AI, with another 29% planning to invest within a year. It also found that 67% were creating dedicated innovation teams or incubation programmes, while 52% shared accountability for global decisions.
Why it matters: GCCs can become places where AI products, data capabilities and global workflows are designed—not just where centrally defined processes are delivered. Whether that ambition becomes reality depends on decision rights, talent, governance and measurable ownership.
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Source: EY GCC Pulse Survey 2025.
Signal 3: Agentic AI raised the stakes on control
AI systems are increasingly being designed to plan tasks, call tools and carry out bounded actions, rather than only generate text. But the word “agent” covers a wide range of capabilities, from a workflow that uses a few APIs to systems with more complex planning. Adoption and autonomy should not be conflated.
Why it matters: organisations need permission boundaries, evaluation, audit logs, escalation paths and human approval for high-impact actions. The more an AI system can do, the more important it becomes to govern what it may do.
Signal 4: The production gap became a management problem
Enterprise AI performance depends on data quality, integration, reliability, evaluation, security and process redesign—not just model capability. A demo can succeed on curated inputs and fail when users introduce incomplete records, unusual cases or conflicting instructions.
Why it matters: AI leaders should measure successful task completion and business outcomes, maintain representative evaluation sets, track failures in production and calculate total operating cost. A pilot should have explicit scale, revise or stop criteria before it begins.
Related reading: The AI Pilot Trap: Why Enterprise AI Struggles to Scale.
Signal 5: Model choice became an architecture decision
Closed models, open-weight models and smaller specialised models serve different needs. The strongest choice depends on task quality, latency, throughput, privacy, deployment constraints, language requirements and total cost—not a universal claim that one model is best or that smaller models always win.
Why it matters: enterprises should benchmark models on their own representative workloads, use the least expensive option that meets quality and risk thresholds, and retain the ability to change providers where practical. Model routing can help, but only when it is tested end to end.
Signal 6: Evaluation and observability became core infrastructure
Traditional application monitoring remains necessary, but AI systems also require checks for factual grounding, relevance, task success, unsafe outputs and regressions after model or prompt changes. Automated evaluation can increase coverage, while human review remains important for high-impact or ambiguous cases.
Why it matters: evaluation is not a final QA gate. It should be part of development, release management and ongoing production monitoring. Without it, teams cannot reliably tell whether a change improved the system or simply made outputs sound more convincing.
Signal 7: Data quality and knowledge access remained decisive
Retrieval-augmented generation and enterprise search can make internal knowledge more useful, but they cannot fix missing, stale or unauthorised source data by themselves. The underlying knowledge layer must preserve permissions, document freshness and source traceability.
Why it matters: data engineering, content ownership, identity controls and retrieval evaluation are strategic AI capabilities. In many enterprise use cases, these foundations are more consequential than another model upgrade.
Signal 8: Voice and local-language interfaces offer a distinct India opportunity
Voice and regional-language systems can reduce friction in customer service, employee support and access to digital services. Their performance depends on accents, code-switching, background noise, latency, consent, privacy and reliable escalation—not only speech synthesis quality.
Why it matters: Indian deployments need testing across the actual languages and settings in which people use them. A successful English-language demo is not evidence of broad multilingual performance.
Signal 9: Compute, energy and cost are strategic constraints
AI infrastructure decisions increasingly involve more than accelerator availability. Data-centre capacity, power, cooling, network access, inference efficiency and workload placement all influence cost and reliability. Claims about specific projects or savings should be assessed individually rather than treated as proof of a nationwide compute shortage or a guaranteed economic outcome.
Why it matters: enterprise leaders should track cost per successful task, capacity utilisation and energy efficiency alongside latency and quality. Efficient architecture can matter as much as access to a larger model.
Signal 10: Workforce change is selective, not uniform
AI is changing task composition in areas such as software development, customer operations, research and back-office processing. The effects differ by role, sector and the maturity of deployment. Broad claims that AI has already eliminated whole job categories or that one developer now replaces a fixed number of employees are not useful without context.
Why it matters: organisations need to redesign jobs, retrain teams and establish accountability for AI-assisted work. GCCs have an opportunity to build skills in AI engineering, data, evaluation, security and domain-specific product ownership—not just increase tool usage.
What enterprise leaders should do in 2026
- Pick outcomes, not demos: identify a business problem with a measurable baseline and an accountable owner.
- Build a production path early: include data, integration, security, evaluation and support in the pilot design.
- Match autonomy to risk: define which actions AI may recommend, execute or escalate for approval.
- Benchmark models on real work: test quality, latency, cost and risk with representative tasks before committing to an architecture.
- Measure net value: include model usage, infrastructure, human oversight, implementation and ongoing maintenance.
- Build organisational capability: invest in process ownership, workforce development and governance alongside technology.
The FutureIsNow view
The defining enterprise AI story is not that every organisation moved from pilots to production in 2025. It is that the distance between model capability and business value became more visible. Adoption is advancing, but the winners will be those that turn AI into dependable workflows with measurable outcomes, trusted data, sound economics and clear accountability.
For India, the opportunity is larger than using global models more cheaply. Enterprises and GCCs can build the engineering, evaluation, governance and product capabilities required to operate AI at scale for global businesses. That is a more durable advantage than chasing each new model release.
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