Off-the-Shelf vs. Proprietary AI: What’s Right for Your Digital Strategy?

Insight

Technology

Written by Scott Cook | 04 Jul 2025

A man wearing a beanie and glasses sits at a desk in a dimly lit workspace, focused on his computer. He is using AI image generation software displayed on a large monitor and a laptop. The interface shows input fields for describing images and generation controls. A warm desk lamp and mug add to the cosy, creative atmosphere.

Industry Context
AI adoption is accelerating rapidly across sectors, presenting companies with a crucial decision: should they build custom AI models tailored to their unique needs or leverage powerful off-the-shelf platforms like GPT-4, Google Vertex AI, or Azure OpenAI? Each path offers distinct advantages and challenges that must be carefully weighed.

Past Challenges
Many firms have struggled with unclear AI strategies, leading to over-investment in costly bespoke models or becoming overly reliant on inflexible APIs with high usage costs and limited ability to customise. This often results in inefficient spending and reduced agility.

Real-World Examples

Airbnb developed a proprietary machine learning platform that improved dynamic pricing accuracy by 20% and reduced inference latency, showcasing the value of a tailored approach.
Startups like Cohere and Anthropic have raised significant funding ($500M+) to build specialised large language models (LLMs) with a focus on enterprise privacy and explainability.
Slack integrated GPT-4 through Salesforce’s AI layer to boost productivity, though this exposed some prompt leakage risks, highlighting governance challenges in off-the-shelf solutions.

Opportunity
Off-the-shelf AI platforms enable rapid innovation and immediate impact, especially for companies looking for quick wins. On the other hand, proprietary AI models offer greater control, enhanced security, and competitive differentiation, particularly when trained on sensitive or industry-specific data, such as in life sciences.

Risks
Deploying AI solutions comes with potential pitfalls, including soaring cloud compute costs, AI hallucinations, governance challenges, and the risk of exposing personally identifiable information (PII) through unstructured data. These issues can threaten return on investment and regulatory compliance if not properly managed.

Positive Future, XPS Role
At XPS, we partner with clients to carefully map out their AI strategy, balancing the benefits of quick deployment with the need for long-term control. We help build secure, compliant, and high-performing AI foundations tailored to your business goals, whether leveraging off-the-shelf solutions or creating proprietary models.