AI Proof of Concept: Starting AI Projects and Testing Solutions

According to Deloitte[1], 83% of AI adopters have already achieved substantial (30%) or even moderate (53%) economic benefits from 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 start with AI Proof of Concept (PoC). PoC stands for proof of concept, and it is a prototype created to check the feasibility of the AI project and the requirements to reach success. It can be very helpful, so keep reading to discover how to develop AI Proof of Concept and understand if your AI solution has practical potential or not.

What is AI PoC?

An AI PoC is a tangible demonstration of the practical application of AI technology, showcasing its ability to fulfill its intended purpose and contribute value to a business. It’s essentially a trial run that encapsulates several crucial aspects:

An AI PoC is characterized by its modest scale and minimal risk. It is designed to gauge the feasibility and potential advantages of an AI solution without committing significant resources upfront. This cautious approach allows businesses to test the waters before making substantial investments.

You may find it interesting: AI Consulting Agency – How To Pick The Perfect One?

The primary objective of an AI PoC is to validate various elements, such as the technology itself, the chosen approach, and the data requirements. Doing so offers insights into whether AI can effectively address the targeted business problem.

Moreover, an AI PoC is especially pertinent in scenarios where projects involve innovation, high levels of risk, complexity, or uncertain data quality. It is a strategic tool to mitigate potential risks associated with venturing into uncharted territory.

Conducting an AI PoC typically entails defining clear success criteria, gathering and analyzing sample datasets, training and testing the AI model, and subsequently evaluating the outcomes. Throughout this process, there’s a concerted effort to strike a balance between technical feasibility and potential business value, ensuring that the AI solution holds the promise of delivering tangible benefits.

Ultimately, a successful AI PoC is characterized by its ability to yield reliable results that align with the overarching business objectives. It should also provide a roadmap for scalability, demonstrating how the solution can be expanded and integrated into broader business operations. Conversely, an unsuccessful AI PoC isn’t viewed as a failure but rather as a valuable learning experience. It offers insights into areas that require refinement and improvement, guiding future endeavors toward greater success.

AI PoC is a crucial step in validating the viability of an AI solution and building confidence before full-scale implementation. It helps organizations de-risk their AI endeavors.

The role of PoC in 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 AI 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 AI PoC.

Identify opportunities, define problem, and check the available data for AI projects

Opportunity identification

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?

Problem definition

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 AI Proof of Concept. 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.

The most intensive part of this stage is training and tuning. If everything is done right, the data scientists will understand which parameters the constructed models converge on most effectively.

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 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 consulting company.

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.

What are the benefits of PoC in AI projects?

• Risk reduction

An AI Proof of Concept (PoC) serves as an invaluable tool for risk mitigation. By conducting a PoC, potential issues and challenges can be identified at an early stage, long before full-scale implementation commences. This proactive approach enables teams to address technical and functional risks promptly, significantly reducing the likelihood of encountering major setbacks or failures down the line.

Essentially, a AI Proof of Concept acts as a safety net, allowing organizations to navigate the complexities of AI implementation with greater confidence and certainty.

• Resource optimization

PoC facilitates a thorough assessment of the resources required for the AI project. This assessment helps prevent resource over- and underallocation, ensuring that the project operates efficiently and effectively within budgetary constraints.

Ultimately, this leads to greater efficiency and cost-effectiveness throughout the AI implementation process.

• Confidence boost

A successful AI Proof of Concept not only validates the technical feasibility of an AI solution but also instills confidence among stakeholders. By demonstrating that the proposed AI initiative can deliver tangible results and address the identified business challenges, a PoC reassures stakeholders about the viability and potential of the project. This newfound confidence can galvanize support from key decision-makers, investors, and other stakeholders, fostering a more conducive environment for the successful implementation and adoption of AI technology within the organization.

• Market validation

In addition to assessing technical feasibility, a PoC also provides an opportunity to gauge customer response and validate market demand for the AI solution. By testing the solution in a real-world context, organizations can gather valuable feedback from customers and stakeholders, gaining insights into their preferences, needs, and pain points.

This market validation process helps organizations make informed decisions about the viability and potential scalability of the AI solution, ensuring that it aligns with market demands and customer expectations.

• Compliance assurance

PoC plays a crucial role in ensuring that the AI solution meets legal standards and regulations. By conducting a PoC, organizations can identify any potential compliance issues or gaps early on and take corrective measures to address them. This proactive approach helps mitigate the risk of non-compliance and ensures that the AI solution adheres to the necessary legal and regulatory frameworks, thereby avoiding potential legal liabilities and reputational damage.

• Cost reduction

One of the significant benefits of conducting a PoC is its potential to reduce costs associated with AI implementation. By identifying and addressing issues early in the development process, organizations can avoid costly fixes or modifications later on. This early intervention not only minimizes the risk of budget overruns but also enhances cost-effectiveness by streamlining the implementation process.

• Roadmap provision

The outcomes of a PoC serve as valuable inputs for the development of a comprehensive project plan or roadmap for the AI initiative. By analyzing the results and insights generated during the PoC, organizations can refine their project objectives, milestones, and timelines, ensuring that they align with business goals and priorities. This roadmap provides a clear and structured path forward for the AI implementation, guiding teams through each stage of the project lifecycle and facilitating effective project management and coordination.

• Informed decision-making

A PoC provides decision-makers with concrete evidence and insights to make well-informed choices about the AI initiative. By evaluating the outcomes of the PoC, decision-makers can assess the feasibility, viability, and potential impact of the AI solution on the organization. This evidence-based approach enables decision-makers to weigh the risks and benefits associated with the AI initiative accurately, making informed decisions about whether to proceed with, refine, or pivot the project.

Ultimately, this fosters a culture of data-driven decision-making within the organization, enhancing the likelihood of success for the AI initiative.

• Groundwork for the full project

If the PoC is successful, it lays the groundwork for the full-scale implementation of the AI project. By validating the AI model and demonstrating its effectiveness in addressing the identified business challenges, the PoC provides a solid foundation upon which to build the full project. This includes refining the initial AI model, scaling up the solution, and transitioning from the development phase to the deployment phase.

By leveraging the insights and learnings gained from the PoC, organizations can streamline the implementation process, saving time and resources while maximizing the chances of success for the full project.

• Data optimization

A PoC offers organizations an opportunity to strategically assess their existing data infrastructure and make informed decisions about data optimization. By analyzing sample datasets during the PoC, organizations can identify any data quality issues, gaps, or redundancies that may impact the effectiveness of the AI solution. This data optimization process may involve data cleansing, enrichment, or integration efforts to ensure that the AI model has access to high-quality, relevant data for training and testing purposes.

By optimizing the data infrastructure during the PoC, organizations can enhance the performance and accuracy of the AI solution, laying the groundwork for its successful implementation and adoption.

• Technology Integration and Interoperability Testing

A PoC offers an excellent opportunity to test how well the new AI solution integrates with existing systems and technologies. This is crucial for ensuring smooth operations and avoiding disruptions once the solution is fully implemented. Interoperability testing during the PoC phase can identify potential issues early, allowing for adjustments before scaling.

• Customization and Personalization

Through a PoC, businesses can tailor AI solutions to their specific needs and challenges. This customization ensures that the final product is highly relevant and capable of delivering maximum impact. Additionally, it allows for personalization, enhancing user experience and satisfaction.

• Accelerating AI Adoption

By demonstrating the practical value and feasibility of AI solutions in a tangible way, PoCs can play a pivotal role in accelerating the adoption of AI technologies within organizations. Seeing real-world applications and benefits helps in gaining buy-in from key stakeholders, including executive leadership and potential users, thereby smoothing the path for broader implementation.

AI Proof of Concept: Starting AI Projects and Testing Solutions FAQ

What is a Proof of Concept (PoC) in the context of AI projects?

A Proof of Concept (PoC) in AI projects is a prototype or trial run that demonstrates the practical application of AI technology. It helps assess the feasibility and potential benefits of an AI solution before full-scale implementation.

How does a PoC help in AI projects?

A PoC helps in several ways:

• It mitigates risks by identifying potential issues early on.
• It optimizes resource allocation by assessing the required resources.
• It boosts confidence among stakeholders by validating the feasibility and potential of the AI solution.
• It validates market demand and compliance with industry standards.
• It reduces costs by identifying and addressing issues early in the development process.

Why is conducting a PoC important before investing in AI projects?

Conducting a PoC allows organizations to validate the viability of their AI solution and reduce the risk of failure. It provides concrete evidence and insights to make informed decisions about proceeding with, refining, or pivoting the AI initiative, ultimately increasing the chances of success.

What steps are involved in conducting a PoC for an AI project?

The steps involved in conducting a PoC typically include defining success criteria, collecting and analyzing sample data, training and testing the AI model, and evaluating the results. It’s crucial to strike a balance between technical feasibility and potential business value throughout the process.

How does a successful PoC contribute to the overall success of an AI project?

A successful PoC yields reliable results that align with business objectives and provide a roadmap for scalability. It lays the groundwork for the full-scale implementation of the AI project, saving time and resources while maximizing the chances of success.

What are some common challenges organizations may face during a PoC for AI projects?

Some common challenges include data quality issues, technical complexities, resource constraints, and ensuring alignment with business goals. However, these challenges can be mitigated through careful planning, collaboration, and leveraging the expertise of AI consulting partners.

How can organizations ensure a successful PoC for their AI projects?

To ensure a successful PoC, organizations should:

• Clearly define success criteria and objectives.
• Invest in high-quality data and robust infrastructure.
• Collaborate with experienced AI consultants or partners.
• Continuously evaluate and iterate on the PoC process based on feedback and insights.
• Align the PoC outcomes with long-term business goals and strategies.

How can organizations leverage the outcomes of a PoC to drive future AI initiatives?

The outcomes of a PoC can serve as valuable learnings and insights to inform future AI initiatives. Organizations can use the insights gained from the PoC to refine their AI strategies, optimize resource allocation, and identify new opportunities for innovation and growth. Additionally, the success of a PoC can help build momentum and support for broader AI adoption within the organization.

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.

This article is an updated version of the publication from Jul 1, 2019.

References

[1] 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.