Industry
Energy, Resources & Utilities
Rebalance the energy equation
Highlights
- Water utilities worldwide are operating in an era of unprecedented complexity. Climate volatility, increasing water scarcity, tightening regulatory scrutiny, rising customer expectations, and escalating cyber risks are converging to challenge the resilience of traditional operating models.
- Against this backdrop, artificial intelligence for water articulates a clear and actionable vision for transformation, showing how data, artificial intelligence, and human-centric design can be orchestrated to drive safer operations, smarter decision-making, and more sustainable outcomes across the end-to-end water value chain.
- How can utilities move from isolated pilots to scalable, enterprise-wide impact and accelerate their journey towards a resilient, intelligent, and future-ready water business?
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Why resilience, real-time data, and AI are becoming essential for the future of water utilities
Across regions, water utilities are facing a converging set of structural and systemic pressures. Increasing water scarcity and prolonged drought conditions are projected to place severe stress on the United Kingdom, Europe, the Middle East and North Africa, India, and parts of the Americas over the coming decades. These challenges are compounded by climate change, driving more frequent heatwaves, floods, and runoff-related pollution. Rising levels of environmental contamination, including persistent and difficult-to-remediate chemicals, add to the challenge.
At the same time, water utilities, as operators of critical yet ageing infrastructure, are becoming increasingly exposed to cybersecurity threats. On the other hand, regulatory expectations continue to intensify around environmental, social, and governance performance, water reuse, and transparent reporting.
The clear implication is that resilience, transparency, real-time data, and artificial intelligence are no longer optional capabilities. They are becoming foundational requirements for a sustainable, secure, and future-ready water sector.
How AI and real-time data are enabling zero outages, zero emissions, and smarter water ecosystems
The digital and AI transformation imperative is to enable a smarter, safer and sustainable future across the water value chain.
| Zero boundaries | Zero outages, leakages and spills | Zero complaints | Zero emissions |
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Why AI is becoming critical for managing water scarcity and building resilient, sustainable utilities
Freshwater accounts for only around 2.5 per cent of global water resources, while groundwater, frozen reserves, and surface water are coming under increasing pressure. In this context, the adoption of AI in the water sector is no longer discretionary. It is a strategic necessity to strengthen resilience and deliver sustainable outcomes:
- AI enables water utilities to build resilience to droughts, floods, and extreme weather events,
- Optimise the management of scarce water resources across surface, ground, frozen, and recycled supplies
- Enhance regulatory compliance while strengthening trust with customers and regulators.
- Help reduce operational risk and cost while accelerating the realisation of business value
Driving the “Four Zero” outcomes through AI-powered decision intelligence and automation
How AI improves decision-making: faster, safer, and more efficient outcomes
| Themes | Zero complaints | Zero emissions | Zero outage | Zero leakage |
| Usecase | Flow to full treatment (FFT) regulatory reporting | Wastewater treatment Digital twin |
Sensor fingerprinting Anomaly detection |
Smart metering Leakage detection |
| Objectives | Automate compliance reports for thresholds and alerts in near real-time to act quickly on water quality | Optimise wastewater treatment by simulating changes before implementing them for the waste catchment | Quick detection of sensor drift/malfunction for predictive maintenance | Early detection of water loss or abnormal usage patterns |
| Expected outcome | Increase water quality | Reduce carbon emissions | Reduce downtime | Reduce water leaks |
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Tracking these metrics month over month allows you to build a business case: fewer people doing more value-added work, cost savings, risk reduction, and better uptime.
Procurement role 2.0
For a procurement specialist, the current role means handling RFx and tenders, supplier negotiation, PO issuance, invoice matching, supplier performance monitoring, and risk flagging.
With the Agentic AI workflow in place, the role will change.
Tomorrow’s role with agentic AI
The procurement specialist will now focus on strategic supplier partnerships, innovation in raw material sourcing (e.g., scrap circular supply, green inputs).
The new role will involve overseeing agentic systems, including monitoring exceptions and reviewing dashboards of events flagged by AI.
The person will now drive category strategy, risk-scenario planning, ESG, and sustainability sourcing, and ultimately be the process architect, defining rules, workflows, training data for AI, managing change, and ensuring data integrity.
Role eliminated (or sharply reduced): Repetitive tasks — PO issuance, manual invoice matching, routine sourcing, commodity price benchmarking, risk-alerts triage.
Implications for staffing: Procurement departments will shift from large, transactional teams to smaller, strategic teams. Many specialists will either be reskilled or replaced by AI.
AI procurement plan
To capture value and enable this shift, a steel company should follow these phases:
1. Foundation – Data and process
Clean up the supplier master, spend-cube, contract database, PO, and invoice history. Note: Poor data or broken processes can reduce the benefits of AI.
Standardise the PO process, inventory triggers, supplier qualification, and performance monitoring.
2. Pilot targeted use-case
Pick a high-volume, low-complexity category (e.g., consumables, refractory, scrap sourcing).
Implement agentic AI to automate sourcing and PO for that category, measuring: cost per PO, cycle time, cost savings, and FTE hours saved.
3. Scale broadly
Extend agentic AI to strategic categories (such as raw materials and fuel) with more complex rules and risk analytics.
4. Build dashboards for key metrics (see above).
5. Redeploy procurement specialists to strategic roles.
6. Recognise risks: AI only works well if data, processes, and governance are solid. Without that, you may amplify errors.
AI procurement limits
- Data and process readiness: Many organisations overestimate AI’s immediate impact. Legacy companies remain wary about the effects of such an autonomous approach on relationship management. The procurement functions are fragmented to such an extent that multiple agencies are engaged in the process if outsourced, making it a mammoth task to integrate such operations at scale.
- Human supplier relationships: In strategic sourcing, human judgment, trust, and relationships still matter. Complete replacement may be unrealistic for high-complexity categories.
- Change management and adoption: Automation often hits resistance. Success requires change management, governance, and a culture shift.
- Transparency and compliance: Agentic AI must operate within existing regulatory, ESG, and audit frameworks — governance overhead may reduce some gains.
- Domain specificity: Steel procurement has unique features (volume, geography, scrap and ores, regulation), so generic procurement-AI frameworks must be tailored.
- Human in the loop: It will be learned during implementation as to how much human intervention is needed to apply these workflows.
- Nevertheless, the economics and technology make it clear: the procurement specialist role will change—very substantially—and many of today’s procurement tasks are mature enough for replacement by Agentic AI.