From hype to high impact: When and why purpose-built AI beats general-purpose AI

For forward-thinking firms, the hype about AI is almost as great as the deployment challenges. Despite enormous spending on AI strategies and mandates, MIT found that 95% of AI pilots failed. 

From our conversations with CIOs, it’s clear that problems arise whenever firms deviate from proven procurement strategies. Instead of racing to buy the most popular chatbot and praying for results, firms must return to first principles: begin by clearly identifying business problems and only then piloting purpose-built solutions. The objective must be to make the critical problem disappear, not to achieve some nebulous claims about increased productivity.

For general tasks, ChatGPT and Claude are truly impressive. But for critical business workflows, purpose-built AI like Ren offers five significant benefits:

#1: Relevant context improves the user experience

By definition, general-purpose chatbots must be able to handle a wide range of prompts, which means they simultaneously have both relevant and irrelevant context for any single prompt. Even when IT teams connect chatbots to internal tooling and datasets, chatbot answers generally create a lot of noise for users.

Purpose-built AI eliminates the burden of providing context or sifting through noise for each prompt. Technical onboarding ensures IT teams exclusively integrate the right datasets and tools, and users have a focused experience.

#2: Built-in prompts increase adoption

General-purpose chatbots are blank canvases where users have to write their own prompts. That is not only tedious but takes skill. Ask two users to do research on the same topic and they will have wildly inconsistent results.

With purpose-built AI, prompts are included if not entirely obfuscated behind buttons. R&D teams are dedicated to refining prompts per use case to ensure optimal results, so that your users can simply click a button and get consistent and proven results.

#3: Proprietary data drives differentiated results

Every consumer in the world has access to ChatGPT, which in turn has access to public data. When firms buy the same general-purpose chatbot as other firms, they should expect more or less the same capabilities and results.

Purpose-built AI requires resources and expertise to evaluate, procure, integrate, and deploy. Along with the aforementioned context and prompts, purpose-built AI often leverages proprietary and licensed data in a cost-effective way to deliver differentiated results.

#4: Ensemble of models reduces your technology spend

Each general-purpose chatbot has various modes and models which users either have to manually select or hope are properly defaulted for each prompt. Across chatbots, benchmarks per skill vary dramatically and change frequently, meaning that firms may have to purchase multiple models to achieve wide-ranging peak performance.

Purpose-built AI deploys an ensemble of models, selecting both the best and most cost-effective model for the task under the hood. Some technology vendors, like Databricks, develop proprietary AI which outperforms frontier models at a fraction of the time and cost.

#5: Enterprise partnerships give you strategic influence

Unless your firm is one of the largest organizations in the world, you will be dwarfed in size by any company with a frontier general-purpose chatbot. Your firm will have absolutely no influence over their roadmap or strategic direction. If users have any questions, they can submit a ticket into the void.

Software-as-a-service developers of purpose-built AI won’t just be vendors – they’ll be responsive partners to solve your evolving business problems. They offer strategic expertise from pilot to deployment, customer references, and tailored contracts and commercials.