Industry
Retail & Consumer Goods
Capabilities
Data & AI · Cloud-Native Engineering · Technology Advisory
At a Glance
A national retailer wanted to accelerate the adoption of generative AI but lacked the governance, platform foundations and operating model required to move beyond isolated proofs of concept. ENEXT partnered with the organisation to establish an enterprise AI platform, implement governance controls and deliver production-ready use cases that demonstrated measurable business value.
Outcomes
- First production AI use case delivered within 12 weeks
- Reduced customer-service handling times by 35%
- Established enterprise AI governance and risk controls
- Created a reusable AI platform supporting multiple business units
- Reduced time-to-market for new AI use cases by more than 50%
- Executive-approved roadmap for enterprise AI adoption
Moving Beyond AI Experimentation
Like many organisations, the retailer had invested heavily in exploring generative AI. Multiple business units had trialled chatbots, document assistants and content-generation tools, but most initiatives remained disconnected experiments.
While enthusiasm for AI was high, concerns around data security, model governance and operational ownership made it difficult to move promising concepts into production. Different teams were evaluating different technologies, creating duplication of effort and uncertainty around future direction.
The executive team recognised that the challenge was no longer identifying AI opportunities—it was establishing the foundations required to scale them responsibly.
Defining an Enterprise AI Strategy
ENEXT worked with business, technology, security and risk stakeholders to define a practical AI adoption strategy focused on measurable business outcomes rather than technology experimentation.
Through a series of workshops and assessments, we identified priority use cases, governance requirements and platform capabilities needed to support long-term adoption.
The resulting strategy established:
- A common enterprise AI operating model
- Governance controls for model risk, privacy and security
- Standard patterns for integrating AI into existing systems
- A prioritised roadmap of business use cases
- Clear ownership across technology and business teams
This provided leadership with a shared view of how AI could be adopted safely while maintaining alignment with business objectives.
Building the Platform for Scale
With the strategy defined, ENEXT designed and implemented a cloud-native AI platform capable of supporting multiple use cases without requiring each team to build its own solution.
The platform incorporated:
- Secure access to approved foundation models
- Centralised prompt and model management
- Usage monitoring and cost controls
- Data-access governance and auditability
- CI/CD pipelines for AI-enabled applications
By establishing reusable platform capabilities, new use cases could be developed and deployed more quickly while remaining compliant with organisational standards.
Delivering Early Business Value
To demonstrate value early, the retailer selected a customer-service knowledge assistant as its first production use case.
The solution enabled service representatives to query policies, procedures and product information using natural language, significantly reducing the time spent searching across multiple knowledge repositories.
The use case was intentionally chosen because it addressed a measurable business problem while providing a controlled environment for validating governance, operational processes and platform capabilities.
Its success helped build organisational confidence and secure support for further AI investment.
The Result
The first production AI solution was delivered within 12 weeks and reduced average customer-service handling times by approximately 35%.
More importantly, the retailer established the foundations required to scale AI adoption across the enterprise. Governance controls, security requirements and operational processes were embedded into the platform from the outset, reducing the risk associated with future deployments.
Subsequent AI initiatives were delivered more than 50% faster than earlier proof-of-concept efforts, with teams able to leverage common platform services rather than starting from scratch.
The retailer now has a repeatable approach to AI delivery—combining governance, engineering and business value in a way that supports sustainable adoption rather than isolated experimentation.