As commerce enterprises race to adopt generative AI and keep pace with rapid innovation, many are building strategies around a single large language model (LLM) or vendor ecosystem. While this approach can initially accelerate deployment, it also introduces a new form of lock-in, where models, workflows and, crucially, business data become tightly coupled to a single provider.

With AI evolving at an unprecedented pace, organisations risk becoming tied to technology decisions that may no longer serve them long term, in terms of performance, cost or indeed capability. As concerns regarding the risks associated with vendor lock-in grow, there is a growing recognition of the value of enterprise AI strategies that are open and modular by design.

Lawrence Roycroft explains why Bring Your Own Model (BYOM) is key to achieving the freedom of choice that will define the next phase of AI commerce enterprises…

Freedom of Choice

AI investment continues for brands, retailers and online commerce businesses, with a maturing market and growing technology understanding increasing confidence in AI’s power to materially improve business performance. However, the value of the investment to date remains mixed, with just one-in-eight (12%) CEOs confirming AI has delivered both cost and revenue benefits according to the latest PWC CEO survey.

Notably, these successful organisations are two to three times more likely to have embedded AI extensively across products and services, demand generation and strategic decision making. Furthermore, organisations that have established strong AI foundations, including technology environments that enable enterprise-wide integration, are three times more likely to report meaningful financial returns.

This research highlights the importance of the growing debate about AI methodology and best practice. Organisations have already made an investment and have likely standardised on a model – whether it is OpenAI, Anthropic, Gemini, or a private deployment. As AI adoption expands across the business, strategic decisions about open versus proprietary technologies will have long-term ramifications for cost, innovation and, ultimately, return on AI investment.

Proprietary LLM Risk

While there are without any doubt a number of powerful proprietary AI platforms offering strong business potential, the pace of innovation is changing the landscape rapidly. Being locked into one provider represents significant operational risk for any brand, retailer or online commerce business . Innovation is, by default, constrained to one vendor’s development roadmap and release cadence. Investment is driven not by business goals but by the pricing strategy adopted by that vendor.

Further, reliance on a single LLM, however powerful, constrains an organisation to a single AI product ecosystem – which might be fine until that technology is, somewhat inevitably, superseded in this fast-moving market. As it is not easy to integrate other algorithms to proprietary LLMs, organisations have limited opportunities to embrace new and innovative technologies alongside this single vendor ecosystem.

As and when a commerce organisation decides to migrate to a new commerce platform or embrace another AI model within its current solution, switching between proprietary LLMs or vendor ecosystems is not straightforward. Ownership and portability of data is becoming a vital consideration. Questions around data switching rights, interoperability and vendor dependency are already gaining regulatory attention in the EU. Organisations are becoming more aware of the risks associated with locking critical business data into proprietary platforms.  There is also growing awareness of the risks to Intellectual Property (IP) associated with the learning models that have been created. These models are enormously valuable to any organisation, and the thought of leaving behind potentially years of development and innovation due to the constraints of single vendor proprietary models should give any brand, retailer and online commerce business  pause for thought.

BYOM Empowers Innovation

With AI increasingly being used across multiple applications, from email to CRM, ERP to warehouse management, interoperability is a vital consideration. While there are a few very large companies reliant upon a single vendor ecosystem for the entire tech stack, the vast majority of organisations have a complex mix of vendors. Cross business AI adoption will by default demand integration with diverse systems – and that means organisations need the freedom to choose between AI models that best meet operational needs today and in the future. A Bring Your Own AI Model (BYOM) not only allows organisations to choose whichever LLM is best suited to a specific requirement today but it also provides the opportunity to embrace new technologies both as they emerge and as they support business opportunities.

This open, modular approach aligns to the growing awareness of the value of best of breed, iterative implementations. It recognises the power of composable technology that allows components to be assembled and customised as required in line with changing business needs without rebuilding the entire platform or tech stack. Critically, it ensures learning IP is retained by the organisation, not lost to proprietary vendor lock in.

As organisations increasingly look to deploy both AI agents across these diverse technology stacks, any platform that fails to offer a true open API or locks data in, represents an immediate constraint to realising business goals. And while promises of composability abound throughout the proprietary AI vendor ecosystem, according to the MACH Alliance’s 2026 global study on AI and composable architecture, 89% of respondents reported that standards and certifications are missing for AI in composable environments. Commitment to the Model Context Protocol (MCP) which allows AI systems to seamlessly connect with external data sources, tools, and workflows, is key to empowering the BYOM approach.

Conclusion

The pace of AI innovation continues, and brands, retailers and online commerce businesses that embrace an open BYOM approach will be ready to explore and embrace whatever comes next. This is especially relevant as open-weight models – AI models whose underlying parameters are published openly for anyone to run and adapt, rather than locked behind a single vendor’s proprietary system – continue to mature. Over the next 18 to 24 months, these models are expected to close most of the gap with proprietary systems, giving organisations the freedom to run powerful AI on their own infrastructure and terms, rather than a vendor’s. A BYOM approach ensures organisations are ready to adopt these models the moment they meet business needs, rather than being tied to whichever proprietary system they committed to early on.

Rather than committing to a single AI ecosystem, by adopting open, agentic AI frameworks that support multiple LLMs, organisations can avoid an expensive lock in mistake. Side-stepping dependency on a single LLM or vendor ecosystem will allow brands and retailers the freedom to select the right model for the right task based on factors such as performance, cost and capability.

A BYOM AI strategy provides organisations with the ability to integrate diverse data sources together. It delivers ownership not only of the data but, critically, the learning.  And it is modular by design, adding modules, changing models and swapping technologies at any time to deliver end-to-end business agility. Essentially, BYOM puts organisations in control of the AI driving commerce.