AI in Housing: What Decision Makers are asking right now

Plentific recently hosted a webinar on “AI in Housing: A Leader’s Guide to People, Performance and Trust”, bringing together over 120 housing professionals to explore how AI is moving from concept to real-world application.

The session was chaired by Harry Palmer, Editor at Housing Executive, and featured insights from:

• Nick Atkin, CEO, Yorkshire Housing

• Emily Shaw, Head of Product, Plentific

• Guy Marshall, Independent AI and Digital Advisor for Housing

Together, the panel explored how AI is already being applied across housing operations, and what this means for decision makers navigating change in a regulated, service-driven environment. As discussed, AI is not simply a technology shift, but a broader evolution in how organisations think about performance, service delivery, and decision-making.

Given the level of engagement during the session, it was not possible to address every question live. To continue the conversation, we’ve answered some of the most relevant questions below, sharing additional insights on how housing providers can move from AI curiosity to real-world impact.

Q&A: perspectives from the panel

Some housing providers report achieving call deflection rates of up to 40% using GenAI. Is this a realistic target for others?”

There was a general sense that while this level of deflection is achievable, it tends to reflect more mature implementations. In many cases, organisations are more likely to see incremental gains initially, with higher levels dependent on integration across channels, data quality, and service design.

Where should organisations start with AI?

The discussion pointed towards starting with clearly defined, high-impact use cases rather than broad transformation programmes. Areas such as repairs, customer contact, or compliance were highlighted as natural entry points where value can be demonstrated relatively quickly.

Who needs to be involved?

AI adoption was consistently framed as a cross-functional effort. Bringing together operational, data, and frontline perspectives early on appears to be critical in ensuring that solutions are both practical and adopted effectively.

What are the most practical frontline use cases?

Repairs diagnostics and triage emerged as a strong example. The ability to analyse incoming requests and support better allocation and prioritisation has clear operational benefits, particularly in reducing repeat visits and improving resolution times.

Are regulators slowing progress?

The panel view suggested that regulators are less a barrier and more a shaping force. The emphasis on governance, auditability, and accountability is influencing how AI is implemented, often leading to more robust and sustainable approaches.

How should organisations think about investment in such a fast-moving space?

There was a shared perspective that focusing on foundational capabilities such as data, governance, and internal skills may be more effective than committing too heavily to specific tools. Building adaptability was seen as more valuable than trying to anticipate the “right” solution.