Spoiler: the answer is “Yes!”

In today's fast-evolving AI landscape, the Proof of Concept (POC) is critical for validating innovative AI applications in business settings. 

The artificial intelligence field has become saturated with bold claims about what current technology can achieve. As a result, investors and business leaders have grown sceptical and wary of exaggerated promises. To combat this distrust, an increasing number of businesses can benefit from a well-made proof of concept.

Here's a streamlined guide to how to approach building a POC for your AI project.

Understanding AI POCs

POCs serve as practical demonstrations to verify whether an AI solution can meet the intended goals. They ought to cover both the technical AI side and, where necessary, the UX and integration side. They often comprise multiple ML models and an infrastructure to drive them. 

These proofs of concept must accomplish three key objectives:

• Demonstrate that existing technology can perform the expected tasks from the project brief.

• Show that the team has the technical capabilities to build the proposed AI solution.

• Provide evidence that the user experience for a non-deterministic system is sufficiently intuitive, reliable, secure and seamless.

The value of swift development

Time is of the essence in the competitive business environment. AI POCs should be developed promptly—ideally within 4 to 6 weeks maximum. This timeline allows for a quick assessment of the AI's viability and potential impact, enabling a faster decision-making process within a reasonable budget and scope of effort. It also helps the team take into account the rapidly changing AI technology landscape.

Prioritising security and functionality

AI POCs must be developed quickly, but they should also meet security standards - the most likely approach here is to develop them in a sandbox environment. However, the application of a process like Continuous Alignment Testing will ensure the security and reliability of the project.  

Usability is another critical factor - POCs should be straightforward, allowing for easy evaluation of their effectiveness. Do not treat UX and design as afterthoughts—they're at the core of every solution. Whether AI fades into the background or takes centre stage, deeply consider the new paradigms of interaction.

Iteration of the design during the POC process is driven by discovering new abilities and functionality through development and thorough user testing. Create a more intuitive and useful interface that embraces the idea of augmented intelligence - where the AI enhances the human, and vice versa, seamlessly.

Demonstrating feasibility and garnering support

An effective POC proves your AI project's feasibility and helps gain internal stakeholders' buy-in. The user-friendly interface means stakeholders can see directly what is possible without having to imagine the potential. It also showcases the AI's capabilities, making communicating its value and potential easier to investors, partners, or executives who may be sceptical.

Methodology, collaboration and rapid experimentation

At Artium, we employ a methodology that we suggest leaders use to ensure the speed, quality, and security of AI POCs.  We integrate our Continuous Alignment Testing framework to guarantee that each POC meets the highest safety and reliability standards. We also advocate adopting Extreme Programming (XP) practices to further enhance development speed and quality.

In addition our rapid iteration through experiment cycles leads to better choices of technology and user experience.

Our highly collaborative approach involves frequent communication with clients all through the POC process to co-create the POC. We recommend that any POC process includes this high-touch point communication with stakeholders. We conduct a series of rapid experiments early in the development process, enabling us to identify and implement the most effective AI solution quickly and then iterate on it for the duration of the engagement.

What happens after a POC

The journey to building a successful POC consists of the following non-sequential steps: 

• Design experiments

• Track the experiments

• Build for speed

• Get user feedback 

Artium’s AI Lift-Off package covers these steps to completion, where the POC is ready for (internal) primetime. Different groups will have different needs; however, we have found that often, the next steps include:

• Build & share the story with leadership/ investors/ board members

• Get greenlit for production

• Build for scale

Taking an AI POC to production requires your team to do something like our Production AI Applications offering. Because AI POCs are much more than a prototype, one may think they are ready to go out into the world; however, they are less than MVPs. There are a few reasons that the next steps are crucial to success:

• Implementing scalable infrastructure

• Automating data pipelines

• Integrating with existing systems

• Hardening the code

• Enhancing reliability

• Managing edge cases

• Updating the AI technology

• Optimising the user experience

Integrated AI development 

In the realm of AI, the journey from concept to production is often fraught with complexity and uncertainty. While your internal teams possess the drive and innovation to embark on this journey alone, the unique challenges of AI demand specialised expertise to navigate successfully. At Artium, we offer more than just an external hand; we provide a partnership that enhances your capabilities, ensuring your AI POCs are ready to become secure, reliable, and scalable solutions ready for the real world.

Recognise the importance of crafting and evolving a POC into a solution that garners trust  Then be ready to rapidly take your POC to production. This commitment gives your leaders and investors the confidence that you can build what is necessary, ensuring that your AI solution is not just a concept but a competitive advantage.