As featured in Retail Destination magazine, Gareth Jordan, director for Retail Advantage Insights from ART Software group, outlines the strategic AI roadmap for retail destinations to unlock value.
Few industries are as well positioned for AI as retail. High volumes of daily decisions, tight margins, and operational complexity create significant opportunities to improve efficiency and performance. While AI capabilities are rapidly improving and LLMs (Large Language Models) in general have become much more widely available, the gap between organisations that embed this ubiquitous technology into everyday decision-making and those that treat it as a standalone initiative is widening. The difference is increasingly defined not by tools, but by data foundations, operating models, and contextual understanding. We are supporting Retail Destinations with digital environments where operational, commercial, and customer data can be understood together, enabling organisations to move from fragmented reporting to integrated intelligence.
The opportunity lies in integrating AI into the systems, data, and workflows that underpin retail destinations and property portfolios. As organisations move from experimentation to enterprise-wide adoption, success will depend on the strength of these foundations.
Common barriers to scaling
Despite rising investment, many organisations struggle to move beyond pilots. The most common barriers are fragmented data, inconsistent data quality, governance requirements, and misalignment between technology initiatives and business priorities.
In most cases, the constraint is not access to AI, but the ability to build the foundations required to deploy it consistently across assets, teams, and workflows.
Research consistently shows that organisations generating the greatest value from AI focus on a small number of high-impact use cases and scale them across the enterprise, rather than pursuing large numbers of disconnected pilots.
Here are some key points to remember:
- Data integration creates a unified view of performance, enabling faster, more confident decision-making.
- As AI becomes more accessible, competitive advantage increasingly comes from context rather than technology alone.
- Common challenges include inconsistent source data quality, growing security and governance requirements and the rapid expansion of data volumes. However, a well-designed platform can help mitigate these and bring control, clarity, and scalability.
- Cloud-first solutions bring strong advantages to data and AI integration, particularly through scalability and real-time access to insights across multiple sources.
Lay the groundwork
AI programmes often fail when organisations start with tools rather than integration.
In retail environments, data is typically distributed across leasing systems, tenant reporting platforms, footfall analytics, marketing tools and financial systems. Without a connected data environment, reporting remains fragmented, and decision-making is based on inconsistent and isolated views of performance.
Effective integration begins by connecting these sources into a unified data layer. Cloud-based architectures support scalability and enable faster access across systems. However, value is driven less by connectivity alone and more by standardisation. This ensures that data is structured, aligned, and usable across business roles such as leasing, operations, marketing, and asset management.
This establishes a single foundation for performance management across an entire portfolio.
Move from visibility to insight
Once data is integrated it can be transformed into something consumable. Visualisation and analytics tools translate complex datasets into structured, decision-ready insights. Each retail destination has distinct operational and reporting needs, requiring flexible analytics environments rather than fixed reporting structures.
Flexibility through custom fields, reports, and interactive charts allows people to move beyond isolated metrics to understand how key performance drivers interact. Footfall, tenant mix, occupancy, and trading performance cannot be viewed independently; they must be understood as part of a connected system.
This shift enables faster and more aligned decision-making across functions such as leasing, marketing, finance, and operations. Instead of reconciling multiple reports, stakeholders can work from a shared view of performance drivers across assets and portfolios.
Where differentiation actually comes from
As AI capabilities become more widely available, the technology itself becomes less distinctive, but the context surrounding each retail destination remains unique.
Customer behaviour, asset mix, trading patterns, local demographics, marketing activity, leasing structures, historical performance, and operational realities all inform future performance. AI systems that lack this context can only provide generic outputs.
AI models can be purchased, licensed, or accessed, but the organisational knowledge built from years of customer interactions, trading performance, leasing decisions, and operational experience cannot be replicated. Our experience shows that organisations create the greatest value from AI by combining this proprietary context with advanced analytical capabilities that are designed to empower and enhance expertise.
Address security and governance early
As data environments expand, governance becomes increasingly important. Data often spans multiple stakeholders from landlords to asset managers, leasing teams, centre management, marketing functions, tenants, service providers, and external partners. Each group may need different levels of visibility, depending on their role, responsibilities, and commercial relationships.
Ensuring the right people have access to the right information is essential. Governance provides the rules, structures, and accountability needed to manage this complexity. This includes defining ownership of data, setting clear access permissions. Establishing approval processes and ensuring that information is secure, accurate, consistent and up to date.
Strong governance not only supports compliance but also builds trust in the insights generated. Cloud-based platforms support this by providing scalable security controls with centralised permissions and clear oversight of how data is accessed and used.
Build enrichment, not just connectivity
A key step in increasing AI value is moving from data access to data enrichment. While integration connects systems, enrichment adds meaning.
In retail environments, enrichment is what transforms raw data into usable intelligence. For example:
- Movement data gains value when combined with sales performance, tenant locations, weather, events, and marketing activity.
- Sales performance becomes more actionable when viewed alongside staffing levels, promotions, customer flow and demographics.
- Asset performance becomes more interpretable when internal and external drivers are analysed together.
Over time, this enriched data environment becomes the foundation for more advanced AI use cases that explain performance and suggest future patterns, not just report it. It also becomes a long-term source of competitive advantage.
Build adoption, not just capability
Even advanced analytics environments deliver limited value if they are not widely adopted. Successful organisations focus on embedding insights into existing workflows, tools that help improve data literacy, and ensuring decisions are consistently grounded in evidence. The challenge is less technical and more behavioural, ensuring insights are used consistently across teams and decision cycles.
From Adoption to Advantage
The next phase of AI adoption in retail will not be defined by who experiments with the most tools, but by who builds the strongest intelligence foundations. For retail destinations and property portfolios, the priority is clear: connect data, enrich it with context, govern it effectively, and embed intelligence into everyday decision-making. Those that do this will be better equipped to anticipate change, improve performance, and build more resilience in data-driven retail destinations that will define future placemaking.
Looking to explore your AI roadmap? Let’s discuss.
