Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I'm a technology and transformation leader who has delivered enterprise-scale AI innovation across HR, Finance, and Supply Chain. From leading 6 Generative AI transformations and 14 pilots to embedding LLMs, copilots, and agentic automation into mission-critical workflows, I specialize in taking AI from strategy to production. My career mission is clear: build human-centric AI that drives measurable business outcomes and empowers people.

Q2. How do you see enterprise AI maturity evolving, especially as organisations move from isolated pilots to embedding generative and agentic systems into core business processes?

Enterprise AI maturity is shifting from “model experimentation” to “process ownership.”
Early pilots proved technical feasibility; the next phase embeds GenAI and agentic systems directly into mission-critical workflows (NOC operations, finance close, customer support, supply chain). Success now depends less on model quality and more on governance, observability, SOP-driven agents, human-in-the-loop controls, and change management. Mature organisations treat AI as a digital workforce layer, not a tool.

Q3. How do you see the competitive landscape shifting among hyperscalers, model providers, and platform integrators as enterprises seek more specialised AI solutions?

The market is fragmenting into clear but interdependent roles:
•    Hyperscalers (Azure, GCP, AWS): dominate infra, security, data gravity, and enterprise distribution
•    Model providers (OpenAI, Anthropic, Mistral, open-source): compete on reasoning depth, controllability, cost curves, and efficiency
•    Platform integrators / SIs (Accenture, Deloitte, etc.): increasingly decisive—owning process re-engineering, agent orchestration, compliance mapping, and verticalisation
Enterprises are moving away from “best model” thinking toward “best system for my domain.” This strongly favours integrators and domain-specialised platforms over standalone model vendors.

Q4. How important is the ability to embed AI directly into existing enterprise workflows, and how does that influence selection criteria?

This is now non-negotiable.
AI that sits outside ERP, ITSM, CRM, OSS/BSS, or DevOps pipelines fails to scale. Selection criteria increasingly prioritise:
•    Native integration with existing workflow engines and tools
•    Ability to execute SOPs, not just generate text
•    Auditability, rollback, and explainability
•    Minimal disruption to operating models
In practice, enterprises choose AI that adapts to their workflows, not workflows rewritten for AI.

Q5. How do you interpret the biggest whitespace opportunities emerging as enterprises shift from GenAI experimentation to operational deployment?

The largest whitespace is “AI operations” rather than AI models:
•    Agent observability, lifecycle management, and cost governance
•    SOP-to-agent translation platforms
•    Industry-specific agent frameworks (telecom NOCs, banking ops, pharma QA, energy networks)
•    AI reliability engineering (hallucination control, fallback logic, escalation models)
Companies that turn AI from probabilistic output into deterministic business execution will define the next wave of value creation.

Q6. How do you see the environmental footprint of AI models influencing future choices around model design, deployment, and optimisation?

Expect increasing preference for:
•    Smaller, task-optimised models over monolithic LLMs
•    On-prem/edge inference for high-volume workloads
•    Token-efficient prompting and agent reuse
•    Energy-aware scheduling and model routing
Sustainability will increasingly align with cost optimisation, making efficient architectures both greener and more economical.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

“What mission-critical enterprise process runs materially better today because of your AI—and what breaks if your platform is removed?”
This question cuts through demos and benchmarks to expose:
•    Real operational dependency
•    Switching costs
•    Defensibility beyond model access
•    Long-term revenue durability
If the answer is vague, the company is likely still in the experimentation phase—not the value creation phase.
In summary:
The winners in enterprise AI will not be those with the most innovative models, but those who embed intelligence into execution, governance, and outcomes. AI is becoming less magical—and far more operational.