AI Proof of Concept: The Benefits of Kickstarting an AI Software Development Project With AI PoC

AI Proof of Concept: The Benefits of Kickstarting an AI Software Development Project With AI PoC

Mateusz Łach - June 21, 2022

AI is on the rise. AI is revolutionizing every industry, from pharma to logistics and telecommunications. AI has been making its way into our lives, but the processes behind successful AI software development strategies are still rather not well known. Research by Capgemini says that only 27% of data-related projects can be considered successful, and up to 85% of AI projects fall without bringing the expected value. AI investments may be significant, and testing and validating AI-based solutions at the early stages of project development is essential. Getting a real return on investment in AI projects is not always easy.

AI Proof of Concept (PoC) has become one of the most popular AI application testing methods. AI PoC is very useful for evaluating certain AI features and functions. It also allows companies to understand how AI can solve their business problems before investing resources into building full-fledged AI products. This article aims to provide tips on starting your next machine learning project with AI Proof of Concept.

Now, let’s get to the meat of it and get you up to speed on the benefits of PoC.

What Is an AI PoC?

In software development, Proof of Concept (PoC) is a project generally used to verify if specific theories or concepts can be applied in real-life business processes and if they are worthwhile enough to justify the cost of the entire project. An AI PoC is a working artificial intelligence solution to help an organization understand whether an AI-based project can be successful or not.

To reap the benefits of AI, running a PoC should be the first step in building your ML  models or AI solutions. This approach allows you to measure the business benefits of implementing artificial intelligence without investing in a complete implementation.

Why Is Deploying a PoC Critical in AI Development?

A PoC helps you determine if what you’re trying to achieve is feasible and if doing it with AI is worth it in the first place.

A Proof of Concept is a critical stage in implementing an AI-based solution. It serves as a showcase of the potential of AI and helps to determine whether or not the AI solution will be successfuWhat’st’s important, PoC always uses the real output to solve a real problem.

A PoC is like trying on clothes before buying. The first-hand experience of touching the fabric and looking in the mirror can answer many questions that a product description can’t. This is important, especially if there is a degree of uncertainty about whether a solution will work. An AI proof-of-concept will tell you whether a solution can provide the value you expect and, if so, how much benefit it would bring. This way, you can determine whether the benefits are worth the investment.

In general, AI PoCs are helpful in the decision-making process because they allow organizations to:

  • Test out AI and different methodologies
  • Test different AI development partners. This is especially important in AI development as such projects are typically time and resource-consuming, but may not offer the results expected. AI technology partners which offer a PoC approach will accelerate your benchmark for AI-based solutions, and save you from losing money on a non-viable project.
  • Analyze and compare multiple solutions quickly
  • Develop AI competence, knowledge, and confidence
  • Secure buy-in for future artificial intelligence projects. An AI PoC can be instrumental in translating how AI fits into the business process flow and getting more people on board.

How Long Does It Take To Implement a PoC?

A Proof of Concept is significantly shorter than an entire project as it compresses the project into a pilot mini-project that only includes testing specific assumptions to find out if the initial idea is feasible and applicable in practice.

Usually, the process takes from a few days to a couple of weeks.

What Are the Benefits of PoC in Machine Learning Projects?

Whether your PoC enters production or is abandoned, there are several benefits resulting from pursuing this approach.

Minimizing Business Risk

By implementing a PoC, a company can verify a few core elements and ensure they are headed in the right direction without committing too much time and resources. More specifically, with a PoC, the company can test an initial AI model on its data and know whether it already has all the data it needs, or whether it should enrich its database with further internal or external data.

Launching an AI-driven app without a solid amount of training data is a common mistake. There is little benefit to implementing ML preemptively and expecting it to benefit the business before gathering data. It’s almost always better to focus on gathering and organizing the data and gaining insights manually before considering machine learning.

Use cases for ML can be found in almost every business, but this is always a balancing act between investment and return. In a small business with little data, AI processes won’t make much difference, and analyzing the data manually might still be a better option.

Improving Your Data-Collection Practices

The additional long-term value is the opportunity to learn how to better structure or collect data that may be used in the future to generate new AI solutions or services. In a world increasingly driven by AI, this is an essential factor to consider.

Getting People on Board

If successful, an AI Proof-of-Concept can be the first step of a change management process toward implementing new technology in a company. It helps to generate buy-in. It can convince your stakeholders and explain why they should trust AI.

Laying the Foundations for Your First AI Project

Building an AI Proof-of-Concept will involve developing a machine learning model. In some cases, if the PoC model is satisfactory, it can be further tuned and set in the deployment stage.

The Artificial Intelligence PoC Process

Conducting a PoC is the most cost-effective approach for emerging technologies such as AI to demonstrate quick wins and build confidence.

PoCs allow you to compare different solutions quickly and test vendors’ capabilities. Proper analysis and effective implementation are important factors in a successful AI-based project: a proof of concept can help a business see if a particular process needs to be replicated in the same way when using AI or if it’s better to combine AI with other processes and techniques.

In summary, PoCs benefit the decision-making process by allowing the business to:

  1. Test the technologies and methodologies
  2. Deliver more immediate and concrete value
  3. Analyze and compare multiple solutions quickly
  4. Develop your AI skills, knowledge, and confidence

AI Proof of Concept - Development Stages

AI Proof of Concept - Development Stages

Stage 1: Identifying the Objective and Selecting an AI Solution

Identifying why you want AI in the first place should be the first step in planning a PoC. Be clear about what you want to accomplish with AI, why it matters for your business, and how confident you are that it will serve the purpose.

In order to determine where and if AI has the potential to bring significant benefits we recommend starting small, with low investment workshops - AI Design Sprint – a set of collaborative sessions structured with the Design Thinking approach. To make the most out of the AI’s potential, it’s worth organizing a Design Sprint workshop that is focused on machine learning solutions. Since the customer needs are always its core focus, the solutions prototyped in the DT mindset have much more chance to fulfill the customer-oriented business goals.

AI Design Sprint materials

On-site AI Design Sprint workshops

Machine learning problems often fail because of an insufficient definition of the problem that has to be solved, and the deliverables that the AI Proof of Concept should bring. With this in mind, start with a clear definition of the objective. For example, let’s say the PoC model must recognize hand gestures with 80% accuracy. This objective determines a very specific threshold for the feasibility of the model. Anything below 80% means abandoning the model or iterating.

Apart from defining the problem/objective, the main focus in the PoC preparation stage is data selection and data preparation.

Stage 2: Initial Data Science - Data Selection and Preparation

At this stage, you need to employ data scientists who will run initial data screening, selection, and preprocessing (or preparation). The data science step will give you advice on additional data collection strategies (when needed), narrow the PoC scope, and give confidence in further model development.

You have to decide how much data to use to train a model, which features to feed into the training, and which algorithms to use. There will be unexpected challenges, and sometimes more than one solution can be employed to solve them.

What’s important, however, is always to test your model against your project objective. This way, the decisions are anchored on business outcomes and not purely on technology.

It is essential to have good data when you are training machine learning models. It’s best to use historical data that has been verified and classified by a subject matter expert (e.g., an economist who understands the domain). Using insufficient data, such as misclassified records, will result in incorrect assumptions and an underperforming model, which won’t help you as much as it can.

Data Selection

Once the problem is defined, the next step in the preparation stage is data selection. In this step, we help companies explore their own data sets and determine which data is valuable information and if there are any missing data points in the data sets. Also, in this step, it is determined which internal and external data is needed and what are the data sources that can be considered. Once the data set has been selected, the selected subset needs to contain the most significant number of characteristics possible, preferably those with the best representation.

Data Preparation

After selecting the data, the next step is to prepare the data. Data preparation can include sorting, structuring, preprocessing, and filling the missing data points. Once the data is analyzed and prepared, the next stage of the proof of concept can begin – the development of the model (machine learning algorithm).

For the AI Proof of Concept to be carried out, the original data must be transformed and prepared. It is essential for different AI algorithms to be tested to select the most appropriate one regarding the results attained. A data cleaning process must be applied to eliminate noise, detect, correct, or disregard possible errors or faults. The result achieved can make it necessary to reconsider the dataset selected for the Proof of Concept. The chosen data set could be manipulated by dataset transformation techniques, including the possibility of combining, transposing, eliminating, or dividing records to adapt them to the desired processing mode.

What to Do When Your Datasets Are Limited?

If you keep receiving an incorrect or inaccurate output in a testing phase, you may have fed your model with too little data. That’s not the best news since gathering data for training and testing purposes is often the most costly and time-consuming part of the artificial intelligence implementation process. What to do in such a situation to avoid additional costs? There are a few options you may consider. Once you ensure that the issue is not the low quality (duplicated data, missing records, etc.) but the quantity, you can reach out for one of the following methods.

Stage 3: Model Development and Testing

The development stage is where the initial model is developed, tested, and tuned. It is the core stage in proof of concept. To avoid interfering with “live” business operations, the PoC should be developed separately from actual live production systems.

Development and Modeling Phase

In the modeling stage, a Machine Learning technique will be chosen. You can add custom or pre-defined machine learning algorithms. You can try different machine learning experiments and create an ML model. Train your model over a set of data with an algorithm. Various algorithms have different trade-offs. The process of modeling and developing begins by selecting the most appropriate artificial intelligence technique and algorithms to solve the problem and defining the test dataset used for validation. The model is then developed and establishes the appropriate parameterization according to the nature of the data and objectives by documenting the results with test data collection. If different parameterizations correspond, repeat the previous step by selecting the model showing the most optimal results.


Once the model is built, the next step is testing. Testing the model involves checking how well the model works on data it has never seen. This testing procedure allows data scientists to see how well a model generalizes and how well it will perform in a production setting. Furthermore, this testing can also involve checking the logical steps that an algorithm has learned and checking whether this matches with the knowledge and intuition of business teams.

Stage 4: Validation

This is the final stage in the proof of concept, where the initial insights are gathered, results evaluated, and recommendations issued. This is a crucial stage that all company stakeholders attend, so the outputs and recommendations are discussed and validated. They are usually used by the client for their deployment roadmap, data collection, or data governance roadmap.

The next step is to compare the results to the project objective. It should provide answers to the two questions you set out to answer. In addition to knowing the project’s feasibility, you’ll find out how long (and how much) it took to achieve the optimal results.

What Happens After the PoC Project Ends?

After the PoC is built and validated, you can choose to:

Successful PoC, additional data science step for model retraining and fine tuning model’s performance or abandoning machine learning  implementation

What to do after the AI Proof of Concept?

A: Bring the AI Proof of Concept Into the Production Environment

Operationalizing the PoC entails connecting the PoC with other systems. This may require writing APIs for the systems, embedding the PoC into a more extensive pipeline, or another approach to bringing the PoC online. These efforts will have an associated cost, and the costs should be able to be quantified based on the metrics gathered during the implementation of the PoC.

A detailed approach and an implementation plan should be developed, including cost and time estimates, timelines, and a high-level estimate of what will be required to move forward with the PoC and start building the actual project.

B: Iterate and Optimize the Results Further

The process is iterated until a new set of results is ready for review if further optimization is desired. It is possible to follow more than one approach, depending on the type of the project. For example, if your objective is to categorize Tweets, you may want to compare the results of a modified and an unmodified natural language processing algorithm. You may want to test different approaches to see which one gets you closer to the goal.

As you evaluate several implementations, you may have to adjust one, some, or all of them. Consideration of this kind can help you balance the benefits vs. investments equation. Additional engineering resources and hardware may be necessary when trying multiple approaches, but the potential benefits may or may not justify the extra effort.

C: Abandon the PoC

AI is not a silver bullet for all problems and also has its challenges. AI can increase productivity by taking over tedious tasks from humans, resulting in performance and efficiency gains, but it can’t do everything. There are a couple of considerations that stakeholders should bear in mind:

  • AI cannot replace every task
  • To reimagine processes and upskill talent, investments are needed
  • It is essential to plan for software malfunctions
  • Have realistic expectations of AI’s capabilities

How to Tell a PoC Was Successful?

The business’s success depends on implementing projects that can rapidly deliver business value and demonstrate relevant business impacts. For emerging technologies such as AI, deploying a PoC is considered the most cost-effective way of showing quick wins and building confidence.

To be successful with an AI-based project, proper analysis and practical implementation are essential: a proof of concept can help a business determine whether a particular process should be in the same way as AI or if it should be combined with other strategies.

Do You Really Need to Implement an AI PoC Project?

Yes. PoC is the best way to determine if a specific AI model can accomplish positive benefits for a particular use case. In other words, a proof of concept aims to explore and validate how well a particular business problem can be solved by AI.

The conclusion from a PoC project may be that you don’t need AI, or the benefit doesn’t justify the cost. And that’s ok. Some issues are just not suited for artificial intelligence. Apart from looking at the problems you wish to solve, you also need to consider the internal skills your teams may lack. As part of the evaluation of your AI project, your partner vendor should be able to provide you with the guidance you need.

Your AI project needs a PoC if:

  • You want to identify the issues you are trying to solve and make sure you have realistic expectations about AI.
  • You want to know if the end value offered by the solution justifies the effort and investment in the project,
  • You want to check if the chosen plan of workflow is correct,
  • You want to determine the limitations for the solution of the specific problem,
  • You want to make sure the solution is technically feasible and
  • You want to check whether the proposed solution matches your expectations. For many companies, it also helps avoid possible future technical problems.


Leveraging technology has always been vital for organizations looking to improve their bottom line and gain a competitive advantage. Similarly, AI plays a crucial role in our future as we enter the next industrial revolution. AI is gaining popularity, and its benefits spread across solutions and industries. As long as an organization is looking to gain competitive advantage and productivity from AI, the benefits continue to be substantial.

Ready to start your AI journey? Find out how our AI works and request a proof of concept to see how it can be applied to your application. Even if investing in AI is not on your agenda today, it’s definitely something worth considering. If you’re looking to develop your own AI product or trying to find a provider of AI software development services, our team of experts will discuss your project.

Our AI Design Sprint is a low-investment workshop that aims to kickstart any AI implementation project and give strategic benefits. We apply a set of tools for each step of the design-thinking process to help our clients leverage the power of AI and machine learning algorithms and turn these technologies into a tangible competitive advantage. Get in touch with us to start your AI adoption and digital transformation journey.

Oh, and two more things. If you’re looking for places to start - read some of my recent articles within this series - on Tips for Smooth AI Adoption and a Quick Guide - Artificial Intelligence for Business Leaders.

About the author

Mateusz Łach

Mateusz Łach

AI & Digital Business Consultant

Linkedin profile

Mateusz is a digital strategist and innovation enthusiast. He enjoys building new products and concepts, often with the help of AI. Mateusz joined Nexocode with the mission to consult startups, mid-size companies, and enterprises on their digital transformation journey and help them benefit from custom artificial intelligence solutions.
Responsible for overall business development and sales activities. A geek of new technologies.

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