Demystifying AI: The Journey from Prototype to Production

As the Lead AI Solution Consultant at Deeper Insights, I frequently get questions from clients who find AI intimidating or are often concerned about getting the buy-in from key stakeholders. Today, we're breaking down the process of AI solution development. It's not just about building the models, but also about creating cost-effective and efficient strategies.

Accelerate Innovation

Typically, before any prototyping and MVP development takes place, we follow a method known as the AAII (Accelerated AI Innovation) program. This program serves to understand which key features are most meaningful to the customers and advise on different approaches to AI adoption that are aligned with their specific needs. This strategy encompasses several key considerations, such as:

• What value does AI bring to the customer?
• Is the desired AI application feasible given the available data?
• What infrastructure is required to support the AI model?

In short, the AAII program aims to provide our customers with a roadmap for successful AI adoption, covering everything from current capabilities to future project preparation.

Mitigating Risk with the AAII Program

Our AAII program is designed to mitigate the risks associated with AI adoption. A study from Gartner suggests that 85% of AI projects fail. We aim to reduce this figure significantly by providing the best tools, advice, and industry best practices, thus helping our customers make better-informed decisions about their AI journey.

The AI Roadmap: A Key to Success

An AI roadmap can offer clear directions for successful projects with AI adoption. It may involve focusing on specific data collection efforts, identifying which models can address current business problems, which type of software architecture will enable the scaling of existing AI solutions or assist in preparing for upcoming projects.

The Power of Experimenting with PoCs

A lot of our work involves supporting our clients to better understand the AI adoption process. One method we often employ is creating a Proof of Concept (PoC) using customer-specific data or when some more experimental work is needed to de-risk the next steps. The PoC allows us to trial unique models tailored to the customer's needs without the cost or complexity of building a full infrastructure. It's a low-risk environment to explore the possibilities of what AI can do for their business.

From Prototype to MVP

Deeper Insights often develop prototypes, which typically include a simple model but with an interface that clients can interact with. As with the PoCs, the prototypes won't scale up as a full solution, but it helps visualise what can be achieved.

If successful, the next step is to identify the minimum features for creating a Minimum Viable Product (MVP). Here, our team of data scientists and engineers design a scalable infrastructure with one or more AI models that aligns with the customer's needs. It's only at this stage that we delve into building a solution that's ready for the rigours of everyday business use.

Key Takeaways

1. Demystifying AI: AI needn't be daunting. Through prototyping and PoC, we can illustrate the value of AI in a cost-effective and tangible way.
2. The AAII Program: This four-week course is designed to guide customers through AI adoption, giving them the insights they need to make informed decisions.
3. Reducing Risk: We aim to reduce the risk associated with AI projects, offering our customers tools and advice to successfully navigate their AI journey.

Thanks for reading and remember, AI doesn't have to be big and scary. In fact, it can be an accessible, practical tool that provides significant benefits to your business. See what the Accelerated AI Innovation Program (AAII) can offer your enterprise.