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Before choosing an AI/ML solution you should do a Proof-of-Concept (POC) project. This is a quick and cost-effective way with lots of benefits to ensure your AI/ML choice is the right one for your needs. A POC will not only result in valuable learning, but as importantly give you the chance to see the AI/ML you are considering in action using your data to address an important business need. Further the resulting solution becomes one you can use as a template or solution accelerator to perform updates or similar analysis.
Experts agree that conducting a rapid and well-defined test to prove the value of machine learning to an organization is an essential first step on the journey to ML adoption. With a disciplined approach, a Proof of Concept (POC) can provide a measurable demonstration of the capability of AI/ ML technology to address an important business need.
We have collaborated with our clients on numerous successful POCs, and we know what it takes to make a POC successful. In this series we share the best practices we’ve discovered as we will explore the Key Questions for a Successful POC, Checklist for a Successful POC and Post POC Best Practices.
So, let’s begin with the Key Questions for a Successful POC.
The leadership team at Lityx has compiled a list of questions, some obvious and others a layer or two deeper, that should be answered in advance of any POC engagement
Why should you conduct a Proof-of-Concept (POC) trial?
We are addressing two primary uses of a POC in this article, the ability to leverage ML to solve a particular need and the evaluation of a solution provider.
- Solving a need with ML: The POC process, should yield enough information and results to build an effective business case for the purchase of the technology.
- Evaluating a solution provider: Determining whether the machine learning platform and the support team deliver the capabilities, and customer support expertise necessary for successful deployment.
What should you try to learn from the POC and why?
Learning is the primary reason for a POC. This includes education around the process of applying the new technology, which problem types make the most sense, and a measure of the amount time and resources involved. In addition, a POC trial should yield insights into what is needed internally to support machine learning including staffing needs and stakeholder commitment. Ultimately, the best POC outcome is a clear monetary impact on a business need that is also an organizational priority.
In addition, you are evaluating the software platform and the team that supports it. This means that efforts should be made at the outset to actively observe and record the pros and cons of the software including the team that is provided to support your POC trial. Keep a close eye on the process, the level of collaboration and the experience and expertise levels of the vendor team as this should be a key part of evaluating the success of the engagement.
Who should be involved? Who should the POC team include?
A POC is a collaborative effort, so it is critical to have your key stakeholders involved from the outset. This may include participants from multiple internal teams and lines of business that will be impacted by the POC and the eventual deployment of machine learning technology. Once your in-house team is assembled, determine the amount of vendor input you require to select the right POC and executing the trial to get maximum value. Many vendors don’t provide POC consulting services so be sure to clear this up in advance.
Pro Tip: Determine what your acceptable timeframe is and what you expect to achieve. Often a shorter, more focused POC is the most effective approach. We recommend conducting a rapid POC trial as part of evaluating a machine learning platform, including the ease of use of the platform, the feature set, and the vendor experience.
Be sure to visit our POC special offer page as an initial next step.
Read Part 2: Checklist for a Successful Proof of Concept (POC)
Read Part 3: Post POC Best Practices