Shaping the AI Narrative: Trust, Outcomes and Responsibility

Detailed Summary

• Moderator (Mridu Bhandari) set the stage by referencing the “seven chakras of aligned global cooperation” (human capital, inclusion, trust, resilience, science, resources, social good).
• She introduced the panel from left to right: Paul Hrubad (Australia), Divyesh Vithlani (FAB), Erik Ekudden (Ericsson), and Hari Shetty (Wipro).
• She asked Paul to explain his self‑described “AI‑masked economist” title and to discuss how trust can be built without throttling innovation.

Key Insight – Paul Hrubad

• AI is not merely a technology issue; it is an public‑value problem.
• Trust is foundational – it enables innovation rather than inhibiting it.
• Governments must meet citizens where they are, tailoring AI communication and rollout to the existing level of AI familiarity.

2. Trust, Innovation & the Role of Government

• Public‑policy perspective (Paul)
  • Emphasised a democratic, participatory approach: engage citizens early, explain AI jargon, and align AI projects with clearly stated public problems.
  • Stressed that transparency and accountability are required to satisfy rising global demand for AI governance.

3. The Network as an Enabler of Trust (Ericsson)

3.1 AI‑Enabled Infrastructure

• Erik Ekudden described how modern cellular networks (5G/6G) have become the “intelligent fabric” that carries AI inference for billions of devices.
• Current AI workloads already run on billions of smartphones; upcoming use‑cases include agriculture, hospitals, smart manufacturing, and AI‑powered wearables.

3.2 AI Glasses & Edge Computing

• AI glasses (real‑time navigation, language translation, live prompts) require edge inference; the device off‑loads heavy models to the network.
• The network must evolve from a passive carrier to an active, secure, low‑latency platform for distributed AI.

3.3 Sustainability

• AI training is energy‑intensive, but future inference will move to the edge, reducing overall power draw.
• Ericsson argues that network energy consumption is ~1 % of global electricity, yet AI‑enabled networks can offset up to 15 % of emissions in other sectors (e.g., reducing travel).

4. Governance & Platform‑First AI in Banking (First Abu Dhabi Bank)

4.1 Platform‑Centric Trust

• Divyesh Vithlani highlighted the platform‑first approach: building a secure AI platform with layers for data, model, knowledge, context, and use‑case APIs.
• The platform embeds ethical AI, data governance, and compliance directly into the workflow, making AI as easy to use as opening Excel.

4.2 Operational Guardrails

• AI governance mirrors traditional banking controls (people, processes, technology) but adds agents, models, data as new risk vectors.
• “AI must oversee AI” – AI‑driven guardrails monitor for hallucinations, bias, and compliance violations.

4.3 Metrics Tracked

• Micro‑productivity (co‑pilot usage), enterprise‑level ROI (cost reduction, error mitigation), and speed of response to market changes are the three pillars of value measurement.

5. Proof‑of‑Promise Framework (Wipro)

• Hari Shetty articulated four pillars for moving from AI hype to scalable impact:

Pillar	Description
Problem‑first	Identify a concrete business problem before selecting a model.
Enterprise‑centric architecture	Recognise legacy heterogeneity, fragmented data, and diverse personas; create integrated pipelines.
Continuous reliability	Solutions must work every minute, every hour, not just in a pilot proof‑of‑concept.
Earned trust	Long‑term model stability (no hallucinations) builds user confidence.

• Wipro positions itself as “client zero” – internal deployments must succeed before any external sale.

6. Accountability & Responsible AI at National Level

• Paul returned to the question of accountability:

  • Governments need a clear, communicated AI strategy, spreading benefits across regions, sectors, and marginalized groups.
  • Accountability mechanisms include measurement frameworks, reporting outcomes, and independent oversight.

• Erik added a hierarchical view of agents: top‑level, highly capable agents and lower‑level, task‑specific agents each require tailored guardrails and clear domain ownership.

7. Dynamic Oversight & Agent Governance (Banking)

• Divyesh described a two‑plane architecture:

  • Execution plane – runs AI workloads.
  • Control plane – monitors, logs, and enforces policies.

• Governance mirrors human onboarding: agents receive progressive responsibilities and are subject to performance appraisals, “agent university” training, and token‑usage monitoring.

8. Measuring ROI & Trust Scores (Wipro)

• Hari argued that ROI should be reframed as a capability shift rather than a pure cost‑benefit calculation.
• Early productivity gains are a leading indicator; later outcomes include cost reduction, quality improvement, cycle‑time cut‑backs.
• Trust scores track failure rates, hallucinations, and compliance breaches to gauge model suitability for a given process.

9. Real‑World Use Cases

Organization	Use‑case	Value Delivered
Wipro (energy refinery)	AI‑driven flame analysis for combustion efficiency	More granular insight than traditional sensor thresholds, enabling predictive maintenance.
Ericsson	AI‑enhanced network services for mission‑critical enterprises	Tailored latency & quality‑of‑service, unlocking new revenue streams.
FAB	AI‑augmented fraud detection & capital‑efficiency workflows	Faster anomaly identification, improved risk response.

10. Future Outlook – AI‑Native Nations & Networks

• Paul foresaw AI‑native nations distinguished by capability, competence, curiosity, and an enthusiastic ecosystem rather than pure compute or data‑center capacity.
• Erik projected AI‑native networks that deliver real‑time, personalized experiences across wearables, drones, and robots, requiring an “intelligent fabric” that scales with user‑specific QoS.

11. Risk Perception & Trust Readiness

• Divyesh: risk is manageable, not overstated; appropriate tool‑sets can mitigate it.
• Erik: public sector may over‑estimate risk, potentially stalling innovation, while private sector tends to under‑estimate.
• Paul: governments are moving from cautious to more active AI adoption, balancing risk with emerging guardrails.

12. Closing Vision – 2030 & Beyond

• Panelists imagined 2030: new job titles, ubiquitous AI avatars, frictionless financial services, dramatically higher decision velocity, and AI agents acting as digital colleagues.
• Consensus: trust, transparency, and inclusive governance are the linchpins to realizing this future.

Key Takeaways

• Trust is the foundation of AI innovation – it must be built early through transparent, citizen‑centric policies.
• Secure, high‑performance networks (5G/6G, edge computing) are the intelligent fabric that enables AI at scale across devices and industries.
• Platform‑first governance (FAB) integrates ethical AI, data stewardship, and compliance directly into the AI workflow, making AI as easy to use as everyday software.
• Wipro’s “proof‑of‑promise” stresses problem‑first thinking, enterprise‑wide integration, continuous reliability, and earned trust to move beyond pilots.
• Dynamic, two‑plane oversight (execution vs. control) allows agents to be onboarded, monitored, and held accountable like human employees.
• ROI should be reframed as a capability shift; early productivity gains lead to downstream cost, quality, and speed benefits, while trust scores measure model reliability.
• AI‑native nations will be distinguished by capability, competence, curiosity, and inclusive enthusiasm, not just data‑center capacity.
• Risk is manageable when appropriate governance, monitoring, and performance mechanisms are in place; both over‑ and under‑estimation of risk can hinder progress.
• Sustainability is achievable: shifting AI workloads to edge inference reduces overall energy consumption and can offset emissions in other sectors.
• The future (2030) foresees AI‑driven seamless experiences, rapid decision‑making, and AI agents acting as trusted digital colleagues—provided trust, transparency, and inclusive governance keep pace.

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