Why The Proof Of Concept Matters In AI

Test, Test, and Test Some More


If you want to apply AI in your business, most likely a provider will create a POC first. Proof of concepts can help identify where, how, and when to adopt AI; learn more about it here.

This article features direct insights from Craig Ganssle, CEO and Founder of Cadre AI and Farmwave.

Even though artificial intelligence, or AI, is more accessible than ever, that doesn’t make it completely risk-free for any business owner — or scam-free, at that. Introducing AI into your business may sound like a gamble, but, as time’s gone on and the technology’s developed, proof of concepts (POCs) have emerged in all sectors and industries worldwide.

Most AI projects fail or never fully get started because:

  1. The innovative minds leading the project have a difficult time convincing senior leadership that AI is the next strategic step to take, leaving most AI initiatives without support.

  2. Companies are looking to add AI as a quick solution or silver bullet to their complex problems.

  3. Leadership will blindly pay for full implementation — diving straight in — rather than seeing a test first.

  4. Whomever is leading the charge is thinking short term without considering how AI will affect other business functions after implementation.

If your company’s current business plan is stable and your future strategies don’t incorporate AI, now’s the time to incorporate AI. It may feel unwise to try something new when things are going well, but at its core, AI is created to improve human lives, which means your lives, your employees’ lives, and your customers’ lives, too.

Obviously, rushing into something you don’t fully understand could risk failure. This is where proof of concepts comes into play.

Craig Ganssle: Proper planning is often overlooked at this stage. This doesn't mean you need to spend weeks overthinking this. It's as simple as finding value in solving the problem. If there's value it's worth it.

What A Proof of Concept Is

A proof of concept, or a POC, is evidence (data) obtained from a pilot project designed to demonstrate a specific AI’s capability to address a business problem. POCs have been in the tech industry far longer than AI’s been around, but they’re specifically important to emerging technologies, like AI or machine learning (ML).

Why Does POC Matter in AI?

No technology should be deployed without proof it’ll work, and AI is no exception to this. AI’s applications reach far and wide for enterprises of all sizes, so testing the software is imperative to ensure it’ll reap benefits down the line.

Unfortunately, many companies skip POC when they include AI and it just doesn’t work.


How Proof of Concepts Work


Any emerging technology needs to be applied conservatively through testing and slow implementation. Newer developments in AI, like Deep Learning or Natural Language Processing, are usually implemented over the course of 6 months.


Craig Ganssle: “Be sure to outline the POC process carefully. Contractor and customer should be clear on the process, deliverables, and investment.”


A POC plan should address how AI will support organizational goals, objectives, and/or other business requirements. These factors aren’t the primary focus of a POC test, but they do demonstrate to anyone questioning whether AI is the best next step for the company or not how the technology works.


The POC process should also include:

  • Clearly defined criteria for success

  • Documentation for how the POC will be carried out

  • Some evaluation components; and

  • A proposal for how to move forward, should the POC prove to be successful

Careful plan development is the key to showcasing whether the AI will ultimately deliver the results you’re seeking or not. Expect flaws to occur no matter what — just plan for them.


De-risking AI


No one wants to waste money on something they don’t understand. Or, when they do, hoping it fixes the problem, nothing comes of the spending. Some companies will see POC — and AI as a whole — as a waste of money or a risky cost, but there’s no need to be scared of the price. You can’t put a price on peace of mind.


Craig Ganssle: “If you're doing A&B, you will automatically reduce risk for C. Throwing money at a problem is not a solution - it's wasteful. Through proper planning, and a solid POC strategy, finding value and developing a working prototype to validate is all that's needed.”


How To Use POC To De-risk AI


1. Perfect for the crawl, walk, and run method


Don’t just jump into something headfirst; you’ll shock your leadership right into doubt. Instead, start slow and emphasize your interest in long-term thinking with AI adoption. Giving your leadership time to process and get on board also gives your POC time to develop.


2. Reinvest your savings into your AI journey


POCs are a fraction of the cost of full implementation and allow twice the early insight necessarily to course-correct before onboarding more AI. For example, if your vision didn’t meet the actual outcome of the POC, you’ll spend less money by cutting off the project versus spending more on a prototype, full-scale project, etc.


A vs B — An Example of POC


When a company chooses the POC route rather than full AI implementation, the results are drastically different. In AI and Squirrel Food: Running A Proper POC, we’re introduced to two companies — one that implements a strategic POC plan, and the other rushes to find a result.


Note: a result. Not the best result.


In this example, Company A is looking to build a visual AI to “monitor and watch for ground activity for dams in Brazil to prevent natural disasters and save lives from dam breaks and massive flooding.”


Company B is wanting to “test an existing piece of hardware to monitor harvest loss on corn or soybean crops…”


The end result: Company A built an accurate and successful AI model to achieve its objective; Company B’s hardware didn’t work at all.


The reason why is clear once the differences are outlined:

  • Company A dedicated time and resources to a POC

  • Thus, Company A carefully planned and budgeted their project to best fit their test

  • Company B didn’t have dedicated time or resources, meaning their tests revolved around simulations of scenarios rather than real life

  • This led Company B to have inaccurate and inadvisable data, ultimately realizing the test as pointless.

  • Company A understood that automation, machine learning, and data science are not “one-size fits all” — conservative, strategic planning is required.


Proof Enough?


Adopting AI is the inevitable trend we will see in the oncoming years whether we adapt to it or not. Many business leaders are spearheading efforts to adopt safe and ethical AI, but many will drag behind, too.

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