Why a Ground-Up Approach is Necessary

When talking to customers, we often hear that they are nervous to adopt AI because it is perceived to be a huge upfront cost with unclear outcomes and can cause significant disruption.

We don’t think that you should move forward with anything until you are fully comfortable that it is going to give you the results you want. That is why we believe in the adopt and expand approach to AI implementation.

The adopt and expand model is a valuable approach for AI development for several reasons.

Reduced Risk and Investment

Start Small: Our model encourages starting with a well-defined, achievable task for the AI. This allows for a smaller initial investment and faster deployment, minimizing risks associated with developing complex AI solutions from scratch.

Validate the Concept: A successful initial application demonstrates the AI’s capabilities and provides valuable insights into its strengths and weaknesses. This reduces the risk of investing heavily in a solution that may not meet expectations.

Iterative Learning and Improvement

Continuous Feedback: By deploying the AI in a specific task, you gain real-world user feedback and performance data. This feedback loop allows for ongoing refinement and improvement of the AI’s capabilities.

Gradual Expansion: As the AI proves successful in the initial task, you can gradually expand its reach to tackle more complex or related tasks. This iterative learning process allows the AI to build its knowledge and skillset organically.

Focus and Efficiency

Clear Goals: The adopt and expand model keeps the development process focused on achieving specific, measurable goals. This avoids the potential for feature creep and scope bloat that can often plague large-scale AI projects.

Resource Optimization: By starting small and expanding gradually, you can optimize resource allocation. Development efforts are initially focused on a well-defined task, maximizing your return on investment before expanding further.

Increased Adoption and User Buy-in

Early Wins: A successful initial application demonstrates the value proposition of AI to stakeholders. This can generate excitement and support for further adoption of AI across the organization.

User-Centric Development: The iterative learning process allows for incorporating user feedback throughout the development cycle. This ensures the AI is addressing real user needs and is more likely to be adopted and used effectively.

Minimal Disruption

Keep your existing tech stack: You can integrate our product as much or as little as you want. Use it alongside your existing tools to optimize their value.

See What We Can Do for Your Business