What It Really Takes to Scale AI in Enterprises
by Nidhi Agrawal
Jan 22, 2026 | 04 min read
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January 16, 2026. Bengaluru.
Leaders from across industries gathered to contribute to Vision 2030: Rewriting the CPG Playbook with AI.
As the discussion unfolded, the question surfaced: Why does AI promise so much, yet deliver so little and still not up to scale?
Almost everyone in the room had experimented with AI. Many had invested and several had impressive proofs of concept. Yet very few could confidently say that AI had become core to how their business actually runs.
AI adoption in India has not reached full scale yet. Indian enterprises operate across fragmented channels, diverse markets, and varied operating conditions. A single AI model rarely fits all. What works in one region or route often breaks in another. This makes implementing AI far harder than just evangelising its capabilities.
Although India is a hotbed for AI use cases, the complexity of operations and the wide variation in problem statements make large-scale adoption difficult.
Strategy should be in forefront, not algorithms
Several voices returned to the same foundational truth. Before adding AI to everything, organisations must first get their route-to-market strategy right.
A global technology leader from a multinational consumer packaged goods company suggested:
A simple and effective starting framework is Plan, Execute, Measure. AI should be applied to support the channel strategy, not define it. First, understand channel dynamics. Then, deploy AI where it can add real value.
Many were in favor of pilots before implementing a full fledged solution org-wide. However, measurable impact is often not derived from POCs alone.
Why do most POCs fail to move forward?
On one hand, everyone agreed that pilots are essential. On the other, many admitted that there’s no concrete roadmap beyond POCs. The reason is lack of clarity around what AI success truly means. It is observed that KPIs are either loosely defined or vague also without establishing baselines clearly.
AI is often viewed as a personal productivity tool, largely due to the rise of generative AI. However, its true potential extends far beyond content generation and individual use cases. AI should be applied to improve enterprise-wide productivity, driving efficiency, consistency, and scale across core workflows.
Understanding apprehensions and building with confidence
Many AI initiatives began with FOMO rather than conviction. Brands allocated budgets and deployed tools, but as an experiment, not with complete confidence. That’s mainly why AI projects/initiatives struggle to find sponsors.
While decision makers appreciate the capabilities of AI, they also acknowledge AI is not cheap. It demands careful investment. Choosing the right problem matters more than choosing the most advanced model.
Another strong undercurrent throughout the discussion was fear. Fear that AI might generalise what is deeply specific to a business. Fear that a model trained in one market will not work in another. Fear that today’s technology will be obsolete soon and better technology might come tomorrow.
We also heard that without proper modeling and training, AI systems do not always understand context. When they fail, they fail miserably. One wrong recommendation, one poor outcome, and confidence is lost.
Despite these concerns, the discussion made it clear that confidence in AI grows when adoption is deliberate and grounded in business reality. When organisations anchor AI initiatives to clearly defined problems, measurable outcomes, and strong ownership, AI moves from experimentation to enablement.
Trust is built through context-aware models, continuous learning, and human oversight, not blind automation. With the right foundations in place, AI becomes a reliable co-pilot that supports decision making, delivers value, and earns its place as a long-term capability rather than a short-term experiment.
Where can AI change the game?
Beyond the criticism, apprehensions, and hesitation around embedding AI into core workflows, leaders highlighted real examples and use cases where AI is delivering/can deliver value.
- Salesperson productivity to enable them to focus on the right outlets, products, and actions to maximise impact.
- Selling consistency to standardise selling behaviour through data-backed recommendations.
- Distributor effectiveness to improve inventory planning and fulfilment.
- Capturing demand signals to identify early demand patterns and enable faster, more accurate decisions.
- Image recognition to identify competition and pricing through shelf presence, competitor activity, and price gaps.
- Last-mile analytics access to deliver simple, actionable insights directly to sales representatives.
- Vernacular ordering to allow ordering in local languages and drive higher adoption.
- Voice interfaces to simplify interactions through hands-free, voice-led inputs and guidance.
- Remote and offline accessibility to ensure recommendations are accessible even in low-connectivity environments.
These may not sound glamorous, but they are real. And solving real problems is where AI earns its place.
Intelligence has to be REAL
There’s nothing artificial about intelligence.
When it comes to retail, every market, every region, every seller is unique. A generic algorithm or model cannot just work.
At Bizom, Real Intelligence comes from deep integration with on-ground sales, distribution, and retail workflows. By combining trusted RTM data with AI and agentic systems, Bizom enables decisions that are practical, explainable, and actionable at every level, from leadership to the last-mile sales representative.
The focus is not on showcasing AI, but on delivering outcomes that improve selling consistency, optimise distribution, and drive measurable growth.
What truly drives AI success at scale
The session closed on a thoughtful and constructive note, highlighting that AI success is less about technology and more about discipline. It has to be built on clear problem statements, strong ownership, measurable outcomes, human context, and above all trust.
We thank everyone who joined the session and shared their experiences, expertise, and unfiltered perspectives. The openness of the discussion is what made the conversation meaningful and grounded in reality.
Building on these insights, we are conducting an online masterclass to enable CPG leaders to build and configure their own AI Agents on January 29th. Click here to save your spot.
