From recording to institutional practice.
AI Governance for Public Infrastructure: An Executive Session at BBMB
After nearly eight years with the Bhakra Beas Management Board (BBMB), I had the privilege of closing that chapter in a way that brought together two areas that have shaped my professional journey: public-sector technology and responsible AI.
As my final professional engagement at BBMB, I delivered an intensive session on Artificial Intelligence and its Governance Frameworks exclusively for the Top Management of Bhakra Beas Management Board.
The session went beyond the question of what AI can automate.
The focus was on a much more important question for public institutions:
How can critical public infrastructure adopt AI responsibly, securely and in the public interest?
The discussion covered several dimensions that leadership needs to consider before introducing AI into organisational processes, including:
- Responsible AI adoption in critical public infrastructure
- AI governance frameworks and institutional accountability
- Emerging government guidelines and regulatory considerations
- Data governance and protection requirements
- Risks associated with AI deployment
- Procurement considerations for AI-based systems
- Accountability and oversight mechanisms
- Leadership guardrails for AI implementation
- Balancing innovation with security, transparency and public interest
One of the central messages of the session was that AI governance cannot be treated as an IT-only responsibility.
When an organisation deploys an AI system, the implications can extend far beyond the technology itself—to procurement, data management, cybersecurity, legal compliance, operational processes, employee responsibilities and ultimately public accountability.
That makes AI governance fundamentally a leadership issue.
Having spent almost eight years working within a public-sector institution, the discussion was also informed by the practical realities of how government organisations operate—the importance of established processes, accountability structures, regulatory requirements, procurement mechanisms and the challenges involved in introducing new technologies into existing systems.
This intersection between technology and institutional reality is what makes AI adoption particularly interesting in the public sector.
The response from the leadership was deeply encouraging. Beyond the appreciation received after the session, the Top Management proposed that I continue conducting specialised sessions over weekends to regularly educate and upskill BBMB personnel on responsible AI frameworks and adoption.
For me, that proposal was particularly meaningful.
It demonstrated that the conversation had moved beyond awareness of AI towards a genuine interest in building organisational capability for responsible AI adoption.
After eight years at BBMB, I leave with immense gratitude for the opportunities, experiences and people who shaped my understanding of public-sector systems.
Those eight years taught me that technology does not operate in isolation.
It operates within institutions—with rules, people, responsibilities, constraints and, most importantly, consequences.
That perspective is something I carry forward as I continue to work at the intersection of AI governance, ethical technology, public-sector transformation and responsible AI adoption.
The question that increasingly drives my work is:
How do we govern AI in the public interest?
Because the future of AI in government should not simply be about deploying more technology.
It should be about deploying technology thoughtfully, securely, transparently and accountably.
And perhaps the simplest way to express the principle is:
AI suggests. You decide.