New Delhi, Feb. 5 -- Enterprises are accelerating the shift of generative AI from pilots into production, but outcomes remain inconsistent. While early experiments often succeed, many initiatives falter once exposed to organisational complexity and real-world data.
In a recent conversation with TechCircle, Ankor Rai, CEO of Straive, explains where GenAI deployments tend to fail after pilots, why legacy AI operating models struggle at scale, and how enterprises are rethinking governance, cost control, and accountability as GenAI becomes embedded in core workflows.
Edited Excerpts:
Enterprises are moving GenAI into production faster than expected, yet results remain mixed. From your vantage point, where do GenAI initiatives most often start to fail after pilots?
The pilot itself is rarely the issue. The problems begin immediately after. Pilots are designed to succeed because they operate in controlled conditions, with dedicated teams, curated datasets, and close stakeholder involvement. Production environments are fundamentally different. They involve cross-functional teams with varying levels of technical literacy, data coming from multiple sources with inconsistent quality, and edge cases that were never encountered during the pilot phase.
What typically breaks is the organisational infrastructure rather than the technology. Enterprises invest significant effort in model selection and fine-tuning, but they often overlook the operational framework required to make these systems reliable at scale. Accountability structures are unclear, so when the system produces an unexpected output, it's not obvious who owns the issue or has the authority to intervene. Quality assurance processes that work for limited pilots don't scale to production volumes. Escalation mechanisms are often undefined, leaving users without a clear path for resolution. Without governance that defines ownership, establishes quality benchmarks, and sets clear escalation paths, even a successful pilot can become a liability in production.
Traditional AI operating models were built for predictable systems. What breaks first when those models are applied to GenAI at scale?
Monitoring is usually the first to fail, and when it does, operational control is lost. Traditional AI systems were built on assumptions of stability. With classical machine learning, teams could establish performance baselines, monitor statistical drift, and intervene when metrics moved outside acceptable ranges. Degradation patterns were gradual and observable.
GenAI operates under very different dynamics. Output quality is highly contextual and can vary significantly depending on prompts, inputs, and user behaviour. Legacy monitoring frameworks aren't designed to capture this variability. Static benchmarks lose relevance quickly, and periodic reviews fail to detect issues that can emerge within hours or days. Alerting mechanisms miss problems because they're designed to detect deviations from a stable baseline, not to assess quality in a continuously shifting context.
The operational consequence is a loss of confidence. Teams struggle to distinguish between acceptable variation and emerging risk. Control becomes ambiguous, and accountability weakens because there's no objective basis for deciding when to intervene. At scale, GenAI requires a move away from snapshot-based oversight toward continuous quality assessment, including human evaluation loops and a tighter linkage between system outputs and business outcomes.
Cost volatility and quality control are emerging as practical challenges once GenAI becomes part of everyday workflows. How are large enterprises responding?
Enterprises that manage this well treat it as an operational discipline issue rather than a technology problem. Once GenAI moves into regular production workflows, organisations shift from permissive experimentation to structured operations. This means making deliberate decisions about where GenAI is deployed, guided by business value and risk, not just technical feasibility.
Different workflows require different levels of model capability. A customer-facing chatbot handling complex queries demands stronger reasoning and tighter oversight. An internal document summarisation workflow can function effectively with a lighter model. When enterprises align model capability with workflow complexity and risk exposure, they avoid unnecessary costs and can measure return on investment more precisely.
Quality controls follow the same risk-based logic. Workflows that affect customers, regulatory compliance, or financial outcomes receive stricter validation, defined review checkpoints, and clear intervention thresholds. Lower-risk workflows operate with lighter oversight. The shift from experimentation to production is marked by replacing broad access with structured governance tied to clearly defined workflows and outcomes.
Straive works with enterprises to move GenAI from experimentation to production. What does that work typically involve beyond model development?
Beyond model development, the focus is on operationalising GenAI so it delivers sustained business value. This usually starts with resetting expectations around AI adoption, moving away from the idea of broad, immediate transformation and instead prioritising a small number of workflows where AI can be deployed quickly and responsibly.
In practice, this involves identifying low-risk, high-visibility use cases and moving rapidly from idea to working prototypes using real business artefacts such as internal documents, regulatory content, RFPs, and customer interactions. Rather than extended conceptual phases, the emphasis is on compressed discovery-to-deployment cycles that allow stakeholders to interact with functional systems early and learn through actual usage.
Human-in-the-loop operating models are central to this approach. Systems are deployed before perfection, exceptions are reviewed by subject-matter experts, and feedback is continuously fed back into the system to improve quality over time. This allows enterprises to scale GenAI without delaying deployment. Execution is often decentralised, with small cross-functional teams combining domain, engineering, data, and risk expertise owning workflows close to where the work happens.
How does Straive design governance and oversight into GenAI systems so they can operate reliably in production environments?
Governance begins with defining operational boundaries rather than imposing technical constraints. This means deciding where GenAI can operate autonomously, where it provides decision support, and where human judgement must remain central.
Three factors shape these boundaries. The first is the level of risk involved, particularly in customer-facing, regulatory, or financial contexts. The second is the nature of the task, as some decisions benefit from automation while others require contextual judgement. The third is organisational readiness, because even when technical capability exists, enterprises may not be prepared to delegate certain decisions to automation.
Governance is grounded in observed system behaviour rather than assumptions. Systems are instrumented to capture how they're used in production, including where outputs are overridden, when human review is triggered, and which inputs perform poorly. These usage patterns often reveal gaps that design-time analysis misses. Human oversight is then structured based on decision impact rather than technical complexity, ensuring the level of governance matches business risk.
What kind of impact are enterprises seeing when GenAI is embedded into core workflows rather than used as a standalone tool?
The most meaningful impact comes when GenAI is integrated directly into end-to-end workflows. In sectors such as publishing, research, and enterprise operations, GenAI is embedded into processes like content ingestion, enrichment, quality assurance, research synthesis, learning design, and decision support, with defined human checkpoints.
This shifts usage away from ad-hoc drafting toward repeatable, governed systems. Enterprises typically see reductions in turnaround time across content and knowledge pipelines, faster rollout of learning programs, shorter research cycles, and improved decision velocity. Human effort moves from mechanical tasks to judgment and analysis, while outputs become more consistent and auditable. Importantly, return on investment is tied to process-level KPIs and business outcomes rather than individual productivity gains.
Over the next 12 to 18 months, how do you expect Straive's role to evolve as enterprises scale GenAI more seriously?
The experimentation phase is ending. What's emerging is industrialised GenAI at enterprise scale with full operational accountability. As organisations treat GenAI as core infrastructure, expectations shift from enabling deployments to supporting disciplined, long-term operation.
Enterprises are increasingly looking for standardisation across use cases, shared governance principles, quality thresholds, and monitoring approaches, while still allowing flexibility based on workflow criticality and risk exposure. Integration depth increases as GenAI systems become embedded into enterprise platforms, data architectures, and security controls. There's also a growing emphasis on explainability and auditability to maintain confidence as GenAI supports more consequential decisions.
As a result, the role evolves toward enabling sustained operation, including defining production operating models, building human-in-the-loop governance, establishing clear ownership, and creating feedback loops that convert real-world usage into continuous improvement.
Published by HT Digital Content Services with permission from TechCircle.