According to Deloitte, 83% of AI adopters have already achieved substantial (30%) or even moderate (53%) economic benefits from the AI projects. Besides, the more AI projects these organizations deploy, the higher their chances of realizing the benefits are. We know you want your AI project to succeed and improve your company’s position on the market. But how to join that 83% of happy organizations? Obviously, the number of deployed AI projects is not the only factor responsible for good results. You may complete ten or twenty AI projects, and still fail in terms of reaching economic benefits.
To avoid such an unpleasant problem, we recommend you to start with PoC. PoC stands for proof of concept, and it is a prototype created to check the feasibility of the AI project and requirements to reaching success. It can be very helpful, so keep reading to discover how to develop PoC and understand if your AI solution has a practical potential or not.
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The Role of PoC on AI projects
You already know that the main mission of PoC is understanding if the AI projects is worth working on. However, such a definition is very broad, while PoC plays a very important role in machine learning and AI projects. So, here are a couple of things PoC enables you and your team to do:
- Testing software, hardware and the methodologies which are going to be used during the AI project
- Delivering more concrete and immediate value
- Gaining essential experience, skills and confidence in working with artificial intelligence and machine learning
- Identifying potential problems and finding efficient solutions
- Comparing different approaches without spending too much time
If you skip the proof of concept stage and do all the above-mentioned things when already working on the AI project, you may fail or simply exceed your budget. Missing the deadlines is probably the least serious problem you may have in case you neglect developing PoC.
Identify Opportunity, Define Problem and Check the Available Data for AI Projects
The first step to make when working on the proof of concept is identifying the opportunity. If you already have an AI solution in mind, start with answering these questions:
- Which results are you planning to achieve with your AI solution?
- Why the solution matters to your company?
- How can you be sure that the solution is going to be effective?
If you don’t have an idea yet, but still want to start an AI project, check what other companies in the industry do with artificial intelligence. Are there any problems that haven’t been solved with AI yet? If yes, consider them as your potential opportunities.
In case you have several opportunities, don’t try to cover all of them at the same time. Choose one and focus only on it — this little trick will help you to reach success. If it is hard to make a choice, here are some questions to help you. Answer them and make a decision.
- Have you ever worked on similar AI projects? Do you have enough experience?
- Is the opportunity’s scope well-bounded? Or will it require a very large and complicated solution?
- Do you have all the essential technologies?
- Will the expenses fit your budget?
- Is the business impact worth your effort?
When you are done with choosing an opportunity, you can start defining the problem and profiling the data. This should be done in details, so make sure that you have enough experts on your team. If no, discuss everything with your AI development partner. Remember, that you can also outsource AI proof of concept — it is not crucial to hire an in-house team. In any case, here is a new set of questions to answer in order to shape the problem:
- Do you have all the essential data? If no, is it easy to get it? How much time will it take?
- Is there any hardware and software you prefer to use when working on the AI project? If yes, why? Who is the supplier? What is the total cost of ownership?
- Will your AI solution be self-serviced or provisioned in the data center? Why?
- How will you keep your data and results secure?
- Will your solution be cloud-based or on-premise?
Create AI Projects and AI Solutions
The problem is defined in detail, so now it is time to start working on the PoC of AI solution. You will have to build the solution itself, enable the AI frameworks to support it, and, finally, visualize the product. Remember, that artificial intelligence and machine learning require high-quality data, so you will definitely need data scientists. Ideally, they should cooperate with IT systems architects — that’s the most efficient way to design the architecture. And, again, the main rule to follow in case your existing team is missing the required professionals — implement with an experienced data science consulting company. This strategy will help your solution to see the world faster, while you will save your time and budget.
When the solution is completed, move to the model construction. During this stage, the data scientists use training data to enable iterative test runs. As a result, they check the accuracy of the constructed models and understand how to train and tune them.
And here comes the most intensive part of this stage — training, and tuning. If everything is done right, the data scientists understand what parameters the constructed models converge in the most effective way.
Test the Results of AI solution from your initial projects
As always, the results you got must be tested — otherwise, you may face pretty serious issues during the PoC deployment stage. Here are some evaluation criteria you may want to use:
- Does the solution deliver the results correctly? Are the results repeatable?
- Are the results delivered in time?
- Does the solution use all the data sources correctly?
- Can the solution be integrated with third-party services and data sources on the basis of standard protocols?
- Will the solution survive in case the volume of data will significantly grow?
- Will the solution adapt in case some other circumstances will change at some point?
- Are the decisions made by the artificial intelligence system fair? How is it possible to check this?
- Is the system able to complement its decisions with explanations? Are these explanations clear for users? And why a decision can actually be questioned?
Plan Production Deployment
Finally, all the tests are over, so you can deploy your AI PoC (proof of concept). We recommend deploying with AI partner and internal team (if you had to cooperate with an AI partner, of course). Why? Well, that’s simple. In this case, you were a client and your AI partner — a contractor. They worked on your PoC solution, and they know how to plan its deployment in the right way. In turn, your internal team will use this product, so they need to know as much about it as possible. It doesn’t really matter if you are going to sell the solution or use it only in your own company — you still have to understand how it works. And you will get this understanding if you deploy the product together with your AI partner.
After the deployment, you can optimize your solution in order to enhance it, or scale it up to make it suitable for other parts of your business. And if the latter scenario is not possible, you can start working on new AI solutions to optimize business processes.
Now you know what proof of concept is and how it works. However, if you still want to know more about how to use AI solutions to improve operations or solve business problems, we are here to help. Just get in touch with us, and we will quickly get back to you.
 Tom Davenport, David Schatsky. Dispelling five myths about cognitive technology. Dec 18, 2017. URL: https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/dispelling-five-myths-about-cognitive-technology.html. Accessed Jul 1,2019.