Proof of Concept
Often it makes economic sense to validate an AI capability, before embarking on a full solution. POC's help with building trust, educate stakeholders, and mitigate the need for expensive failures by removing excessive uncertainty and project risks.
From assumption to a validated POC. Ready to scale.
Before a product is introduced in the market, the best way to test the water and evaluate the benefits and risks of a significant investment in AI is by iterating an AI solution’s Proof of Concept, or POC.
Essentially, an AI Proof of Concept is the ability to demonstrate the pragmatism behind a proposed idea for a corporate need. It’s the way to identify technical and logistical issues that pose the possibility to block a business from meeting its obligations.
Applied on a real-life case, Faktion’s POC’s are never prototypes; but working AI solutions that demonstrate the capability of AI, combined with robust software engineering that is capable of showing a clear path to both scaling investment and ROI.
A checklist before you begin your Faktion POC
Before you begin designing an AI POC, let’s make sure you are in good shape to initiate this process. Both your business, organization, data and users must be lined up and ready to successfully build, execute and evaluate a POC.
Business
Organization
Data
Business
Change is largely driven by the business of an organization. Therefore, often a business issue, desire or idea is a powerful change agent. Are any of the following items checked?
- Are there realistic expectations with regards to AI?
- Is there a well defined pilot as POC selected?
- Is there a realistic budget allocated for a POC?
- Is there a vision for future scaling?
Organization
Introducing a proof of concept requires the organization to embrace the operational innovation that might be the outcome of this development. Are any of the following items checked?
- Is there a mature change process and culture in place?
- Is the POC supported by a proper management mandate?
- Have all stakeholders clearly defined their evaluation criteria?
Data
A machine or deep learning POC requires a substantial amount of relevant data, in order to evaluate the effectiveness and efficiency gains of a POC. Are any of the following items checked?
- Has the relevant data been identified and present?
- Has the data the required level of quality for the POC?
- Has the data been labeled, or is that part of the POC?
Faktion's POC method;
a process for success
One of the biggest problems companies face when they want to get a firm understanding of ROI of an AI solution before they can allocate resources for execution is the approach to a successful POC. Faktion’s methodology aims to simplify the process towards a Proof of Concept in a timely and controlled manner.
