The value AI delivers is closely connected to the environment in which it operates. It draws on an institution’s data, rules, processes and governance.

By Nkuli Mbundu, Managing Executive: Public Sector, BCX

This is the central risk in the public-sector race towards AI. Municipalities are being presented with compelling use cases in predictive maintenance, revenue protection, citizen engagement, public safety and automated decision support. Many of these applications create real value, but introducing AI into an operating environment where data, systems and processes are not yet fully connected limits the value it delivers.

A public-sector AI project may work technically and still fall short of its intended outcome if the underlying process, data or decision-making framework needs further strengthening.

AI must therefore be treated as the final layer of institutional maturity, not the first purchase in a modernisation programme.

Intelligence begins before the algorithm

Municipalities already generate significant volumes of operational information through billing systems, customer accounts, meter-management processes, asset registers, financial systems, service-request platforms and infrastructure environments. The challenge is rarely a lack of data but rather establishing trusted governance around that information so that insights are acted upon confidently and consistently.

AI depends on strong governance and builds on the foundations already in place.

An intelligent community requires far more than an AI platform. It requires resilient digital infrastructure, connected systems, trusted data, clear decision rights, capable officials and an operating model that translates insight into accountable action.

Consider predictive maintenance in water infrastructure. A model may be able to identify unusual pressure patterns and predict a likely failure. That prediction has no public value unless the municipality knows which asset is affected, can route the alert to the correct team, has the authority and resources to respond, updates the resident-facing service, records the work completed and learns from the outcome.

The useful question is not, “Can AI predict a leak?” It’s, “Can the institution act before the community loses water?”

The same logic extends to electricity-demand forecasting, refuse-route optimisation, fleet maintenance, traffic management and disaster response. A route model depends on reliable vehicle availability and completion data to improve waste collection. Fleet predictions only reduce downtime when maintenance is scheduled and funded, and better emergency coordination requires more than a dashboard. Departments need to share information and be clear about who takes action.

The distinction is critical. Too many technology projects optimise the moment of insight while ignoring the chain of accountability required to act on it.

Consider something as ordinary as recurring service-delivery complaints. AI could analyse thousands of service requests and identify that complaints about water pressure are increasing across a particular area before they become a major service disruption. That insight is useful only if the underlying records are accurate, locations are consistently captured, the municipality can distinguish duplicate complaints from separate incidents and an operational team has both the authority and capacity to investigate the pattern.

With the right institutional chain in place, municipalities move beyond detecting a problem towards responding earlier and more effectively. With it, intelligence shifts the institution from reacting to individual complaints towards anticipating service risk and intervening earlier.

Municipal revenue offers another example. AI can identify unusual consumption, billing anomalies, possible leakage and accounts that require attention. It cannot compensate for inaccurate property records, inconsistent meter data, duplicate customer profiles or weak access controls. The reliability of the model’s output will therefore depend heavily on the quality and integrity of the underlying information.

BCX’s SOLAR environment provides an example of how governance precedes intelligence. Municipal financial, billing, debtor-management, revenue and reporting functions generate structured operational data that supports stronger decision-making today and more advanced analytical capabilities over time. Dashboards already provide visibility into revenue performance, financial administration, banking activity, debtor management and operational trends.

The opportunity is for predictive capability to build on a well-governed operating environment, strengthening the value that municipalities can derive from their existing data and systems.

Data quality is often described as a technical cleaning exercise. In reality, it is the accumulated result of governance. It reflects who captures information, which standards apply, who may change it, whether systems reconcile and whether anyone is accountable for its integrity.

Keeping accountability at the centre of automation

Public-sector AI carries a responsibility that goes beyond model accuracy. Decisions can affect a person’s account, access to a service, eligibility, risk profile or treatment by the state. That makes explainability, human oversight and a route for review essential.

As government introduces automation into decision-making, explainability and accountability should remain central to the experience of the person affected.

This does not mean every use of AI must be avoided until every policy question is settled. It means institutions should distinguish between low-risk support and high-impact decision-making. An AI tool that helps classify incoming service requests creates a different level of public risk from a model that recommends whether a household should be disconnected or flagged for investigation.

The higher the consequence, the stronger the requirements for data quality, transparency, testing, human judgement and appeal.

Working with a technology partner does not change the importance of institutional oversight and accountability. A municipality should understand how a system is used, which data informs it, how performance is tested, where bias may enter and who is accountable when the output is wrong. No public institution should become dependent on a model it cannot interrogate or a provider relationship it cannot govern.

Recent examples of AI-generated content entering formal governance, policy and operational processes reinforce the importance of effective verification and quality assurance. As AI becomes more widely used, meaningful human oversight remains an essential part of responsible adoption.

For municipalities, the lesson is direct: AI-assisted output cannot bypass verification, accountability or human judgement. Use cases should be classified by risk; their purpose and data sources should be documented, and systems should be tested for bias and accuracy. High-impact decisions must preserve an explanation, a route for review and ongoing oversight after deployment.

Ethical AI needs to extend beyond policy into the way systems are designed, governed and used in practice.

Cybersecurity is part of AI governance

AI also expands the digital ecosystem that government must protect. Data may move between public platforms, internal systems, field devices, third-party providers and cloud services. New interfaces improve operational visibility, but they also create new points of exposure.

Identity and access management, continuous monitoring, data protection, third-party risk, incident response and recovery planning must therefore be designed into the AI operating model. A model trained on sensitive or compromised information is not simply a technology risk. It becomes a financial, service-delivery and public-trust risk.

This is why the infrastructure, connectivity and intelligence layers cannot be separated in practice. Asset sensors and field devices need dependable connections. Data platforms rely on governed cloud capacity, while operational teams require secure access to workflows and mobile tools. Across the environment, monitoring and cybersecurity teams depend on clear, consistent visibility.

These capabilities are most effective when assembled around the end-to-end municipal use case and governed through shared service outcomes.

This is where the broader municipal digital ecosystem becomes important. Asset-management systems, citizen-engagement platforms, financial environments, field-service capabilities, connectivity infrastructure and cybersecurity controls all contribute to the quality of information available for analysis.

The effectiveness of AI is ultimately determined by the quality, integrity and governance of the environment in which it operates.

Start with a service problem, not an AI ambition

For municipal leaders, a practical starting point is a clearly defined service problem with public value, a credible data foundation and an accountable operational owner.

It may be reducing the time taken to resolve water faults, improving billing accuracy, protecting revenue, anticipating infrastructure failure, optimising refuse collection or providing earlier notice of service interruptions. The use case should be narrow enough to govern, important enough to matter and connected to a team that can act.

Before approving an AI initiative, leaders should ask the following:

1. What public outcome will improve?
2. Is the underlying service process already understood and owned?
3. Is the data sufficiently reliable, lawful and representative?
4. Who acts on the output, and within what timeframe?
5. Can the decision be explained, reviewed and corrected?
6. How will security, resilience and third-party risk be managed?
7. What capability will remain inside the institution?

Answering these questions helps municipalities assess their readiness for a use case and identify the foundations that need to be strengthened before implementation.

The sequence matters because intelligence is not the starting point of transformation.

Government first needs resilient digital foundations: infrastructure that can be governed, secured and recovered. It then needs connected services: systems, information, channels and operational teams capable of working across institutional boundaries around a shared public outcome. Those conditions create the trusted data and accountability from which AI can generate meaningful insight.

The progression is therefore not a technology roadmap. It is an institutional capability journey. Government first builds resilient foundations, then enables connected services, and ultimately creates intelligent communities.

This is why responsible technology partners should not lead with algorithms. They should help institutions establish the foundations, connected-service environments, governance frameworks and operational capabilities required to realise value from intelligence over time. The objective is not technology adoption. It is stronger institutional capability and improved public outcomes.

The municipalities that lead the next decade will not necessarily be those that adopt AI first. They will be those that build the strongest relationship between resilience, connectivity, intelligence and public purpose.

Intelligence is more than something a municipality buys. It develops through strong governance, connected capability and the responsible use of information.

That is how resilient foundations become connected services, how connected services create intelligent communities and how technology ultimately becomes something far more important: public capability worthy of trust.