AI Proof of Concept – Steps and Benefits

The widespread interest in AI technologies has ushered in a new age of technological innovation, with companies, start-ups, and AI researchers coming up with their own AI solutions to accomplish different objectives.

While these innovative AI solutions hold immense benefits, they’re quite costly to implement. Even with an in-house IT team, companies spend about $6,000 to $300,000 on AI projects [1]. The price disparity depends on the complexity of the AI models, the quality of data fed into the models, and integration with existing systems.

Still, it’s worth noting that not all AI projects are successful, and some organizations lose a lot of money on unsuccessful custom AI solutions. Fortunately, with AI proof of concept (POC), they can sidestep hefty losses and invest in effective AI solutions that help them achieve their bottom lines. This essentially involves creating a scaled-down prototype of an AI solution before investing time and resources into the actual product.

This article highlights the fine details of AI proof of concept as an application testing method and its many benefits in business.

AI proof of concept – what is it?

AI proof of concept, or POC, is a method for testing AI software to check its functionality and feasibility before expending time and resources on a real-world project. It usually forms part of the software development cycle and helps the company or organization figure out whether an ML initiative is a sound investment.

The motive of an AI POC is to test and validate the underlying concept behind the ML project. This typically involves looking at what the initiative is meant to accomplish and charting the way toward these objectives. The process also looks into the potential benefits of the project and what hiccups it may encounter during its development and implementation.

Usually, AI engineers will create a miniaturized version of the proposed model. Next, they’ll test the AI under controlled conditions and check the output. The results will inform the next development phase of the POC, where they will help decide whether to proceed with or abandon the initiative altogether.

However, in most cases, the involved parties usually tweak the small-scale AI system, addressing underlying issues before advancing to full implementation.

An AI proof of concept demonstrates the practical application of the ML system and checks its overall feasibility. Unlike standard software POCs, AI POCs are usually complex since they involve gauging more than just information input and output.

Rather, it involves checking the model’s ability to learn and adapt to dynamic data patterns and changing external conditions. Some AI systems also have ethical implications, meaning technicians must assess potential biases in the information fed into the system.

Benefits of conducting an AI proof of concept

While conducting a POC might seem unnecessary in the AI development cycle, it has far-reaching benefits. Here’s a brief look at the notable benefits of conducting this exercise and why it’s crucial for artificial ML projects.

Reduces business risk

As mentioned, developing an artificial intelligence product is expensive. However, by running a POC, businesses can ensure they get value for funds allocated to their AI initiatives. The process examines the core elements that dictate the success of these projects without using too much time and resources.

Remember, AI undertakings in business are a delicate balancing act between expenditure and returns. A POC helps ensure that the returns exceed the investment.

Furthermore, a proof-of-concept exercise allows companies to use their own data on the AI systems and check their compatibility. It also checks whether the information the business provides is sufficient or whether it needs to refine its collection and preprocessing techniques.

This fine-tuning of data processes gives businesses better insights into their figures and can help identify bottlenecks and leaks that waste money.

Identifying potential challenges

AI model development comes with issues that might be costly to address post-development. POC helps identify these potential issues without spending much money. The development team will test a small-scale version of the product and uncover the underlying issues. Should the issue prove too complex or expensive to solve, they can inform the stakeholders to decide whether to discard the initiative and transition to a new one.

Helps convince stakeholders and investors

Despite its widespread benefits, three in five people are still wary of AI systems and technologies [2]. A POC is one way to convince skeptical investors and stakeholders of the many advantages of this technology.

In doing so, your business can receive appropriate funding for the full-scale implementation of these systems. Demonstrating the effectiveness of this technology can also lead to the adoption of artificial intelligence technologies in your organization to boost efficiency and productivity.

Helps build expertise

Running POC exercises is an excellent way to acquaint in-house IT staff members with ML technologies. These staff members can gain first-hand experience with ML systems and grow their technical skills. This will improve your organization’s talent pool and reduce or eliminate the need for outsourcing when working on future initiatives.

Gives you a competitive advantage

Today’s business landscape is incredibly competitive, and wasting resources on endeavors that don’t translate could have you playing second fiddle to your competitors. A POC helps you invest in feasible initiatives with significant returns so your company can maintain a competitive edge.

You can also use the results from your proof-of-concept tests to woo investors by demonstrating the viability of your technological initiative. With enough funding, you can implement the said technologies and boost productivity across the board. This gives you a clear competitive advantage as you stay ahead of your business peers.

Provides a better understanding of system limitations

An ML POC will give you better insight into your ML system’s limitations so you don’t bite off more than you can chew. We’re talking about limitations with performance, scalability, and integration with existing systems. That way, you can set realistic expectations and make well-informed decisions about what endeavors to undertake and improvements to consider.

Read more: Proof of Concept (PoC) in Data Science Projects

Stages of an AI proof of concept

An artificial intelligence POC is an intricate and systematic process consisting of several steps. It usually involves the following stages:

Stage 1: Identify the problem the artificial intelligence system will solve

Technicians create ML systems with the primary objective of solving specific problems. As such, the first step in the proof-of-concept process is determining what issues the ML system is meant to solve. This involves identifying the exact use case for the system in your business or organization.

This use case must be of immense value for your business to validate the endeavor. If you’re unsure where to implement AI in your company, you can borrow a leaf from your competitors, get ideas, or consult IT experts. Of course, you can always have multiple use cases, provided they add value and bring you closer to your goal.

The following questions should help you pinpoint possible use cases.

1. Do you have any problem you wish the product to solve?
2. How effective are other technologies in solving the problem?
3. Does your organization have the capacity to take on this project?
4. What are the likely risks of implementing ML technologies to solve the problem?

Stage 2: Compile a list of business objectives

Once you have identified the problem the ML systems need to solve, the next step is to compile a list of company objectives. Here, you’ll need to consider how the AI system can integrate with your current technology to achieve organizational objectives.

To do so, you’ll need an in-depth understanding of your current systems and processes. You can assess part of the system or general workflow that may need improvement or adjustment. Next, you can compile a comprehensive list of business objectives before moving to the subsequent POC stage.

Stage 3: Creating hypotheses

With the objectives in place, the technical team will think of machine learning and technological solutions to achieve them. This may involve using a single ML system or combining a couple to hit the target. Sometimes, the team may suggest integrating AI systems with other technologies to do the same.

This stage usually involves brainstorming multiple solutions and picking the most feasible one. Don’t be alarmed by the number of suggested hypotheses flouted by the technical team. You only need to conduct a POC for promising hypotheses and can discard the rest.

Stage 4: Experiment design

The fourth stage lays the groundwork for the POC process. Experiment design means setting up the experimental environment and ensuring you can achieve the necessary testing conditions. It also involves specifying the data required for the process.

Organizations have a handful of options for gathering the necessary data for the operation. These options include:

• Checking the availability of the required data in storage hardware
• Hiring data scrapers
• Using pre-built applications to generate synthetic/semi-synthetic data
• Buying datasets from reputable providers
• Obtaining data from open sources

However, gathering the data is just part of the experiment design process. Technicians must polish it to sufficient standards. This usually involves:

• Organizing and structuring the data
• Cleaning the data to eliminate errors and inaccuracies
• Filling any gaps in the data
• Combining related and filtering out unnecessary data (if applicable)
• Conducting feature engineering and modifying existing data fields

The clean, structured data is then split into three categories, namely:

• A training set, which the system uses to learn
• A validation set for use in the feedback loop to validate the system
• Testing set to gauge the system’s performance

With everything in place, the development team sets up the testing environment. The testing environment consists of three main components:

• Tools and platforms: These are software necessary for testing the POC model’s performance. The most popular platforms for testing ML systems include Tensorflow and Deep Checks, among others.
• Infrastructure: This describes the physical and computational resources required for the procedure. The technical team will utilize on-site servers, storage devices, and other technologies. Organizations without these technologies can utilize cloud services or outsource to managed service providers.
• Simulated/real-world settings: This is the environment in which the POC is conducted. The development team will decide whether to conduct the operation in a simulated environment with controlled conditions or a real-world setting.

The choice depends on the team’s objective, the accuracy requirements, and the risk tolerances.

Stage 5: Creating the small-scale model

The development team will now build a miniaturized version of the ML model. They can either build the system from scratch or buy an existing solution. We recommend the former since it allows greater flexibility and allows the technical team to build the system to your organization’s exact specifications. However, you’re better off buying an existing solution if building a custom one will cost much more than a pre-built AI system.

After building the small-scale system, the technical team must train and implement the algorithm. These processes require considerably expensive infrastructure. However, organizations can choose to outsource to MSPs or use cloud resources. The better option depends on factors like:

• Security: If the machine learning system requires sensitive information, you’re better off conducting the POC exercise on-premises.
• Costs: Compare the cost implications of purchasing the required infrastructure versus using cloud resources or outsourcing the processes.
• Performance: When internal systems can’t handle the processing load, you’ll have no option but to use cloud or managed service providers.
• Accessibility: Businesses with multiple branches in different locations will benefit more from cloud services, which will allow easier accessibility. Businesses with a single location will do just fine with on-premises operations.

Stage 6: Conducting the proof of concept

The sixth stage forms the crux of the entire POC exercise. Here, the process transitions from planning to action, and the technical team will conduct a series of tests to check whether the ML system works as expected. The team will perform these tests according to the hypotheses they created in the third stage.

Testing involves loading the required data into the small-scale systems and providing real-time information when necessary. As mentioned, the technical team can adopt one of two testing techniques: controlled testing with tailored conditions and environments and real-world testing using actual organization data or getting it from the external environment.

Stage 7: Assessing the model’s value potential

Once testing concludes, the team must decide whether the model meets or falls below expectations. To draw valid conclusions, the technical team will:

• Check whether the ML system has met the objectives highlighted in Stage 2
• Get feedback from users through surveys, reviewing forums, and workshops
• Gather information on how the organization or system performed before and after the system implementation
• Partner with other departments to conduct a cost-benefit analysis and determine future return projections

The technical team will then document all the findings after completing the assessment. The final document should be devoid of technical jargon and should instead be simple and straight to the point. This document will be circulated to all interested parties, including stakeholders, board members, and investors.

Stage 8: Deciding the next moves

In light of the new information, management and interested parties must make a decision on what steps to take next, which boils down to three main options:

• Creating new hypotheses and adjusting the ML system to achieve failed objectives
• Scaling the POC to a full-scale model for real-world implementation
• Abandoning the initiative altogether

How long does it take to implement a POC?

The time it takes to implement an AI POC usually depends on the project’s complexity. Full implementation generally takes three to six months, but it could be a lot longer for complex, large-scale systems. Other factors affecting the duration of POC implementation include:

• Problem complexity: As we discussed earlier, the more complex the problem, the longer the POC implementation will take. Complex data structures are also a lot more difficult to implement because they require complicated preprocessing methods, which organizations might have to outsource to third-party providers.
• Availability of high-quality data: Most development teams spend plenty of time finding quality data from reliable sources. The collection step can drag on for months, not to mention the technical team has to clean and refine the data, which further adds to the duration. However, the implementation of small-scale projects with minimal quality requirements takes a shorter time.
• Technical resources: Implementation of these technological initiatives may require extensive hardware resources. Companies with limited access to such resources will often grapple with longer loading times or waste time looking for MSPs with the kind of computational hardware they need.

Read more: AI Proof of Concept: Starting AI Projects and Testing Solutions

Measuring ROI and mitigating risks before implementation

Implementing a machine learning POC has lots of risks but holds immense potential for significant returns. The responsibility of eliminating risks before full-scale implementation rests on the technical team, who may do the following to mitigate risk and increase the likelihood of significant returns:

Create a definite AI governance framework

This framework will clarify the responsibilities, processes, and policies for the initiative’s implementation. It should also clearly outline risk mitigation protocols, ethical guidelines, and lastly, compliance protocols, specifically NIS2 compliance [3] checks and similar measures.

Promote AI literacy in your business

As mentioned, one of the key advantages of an AI POC is the refinement of in-house skills. By adopting AI training programs and workshops, your staff will be well-acquainted with the various AI tools and can easily identify security threats and other risk factors.

Ensure data security and integrity

Data is a critical part of any AI initiative. It’s important to have strong safety rails to keep your data safe. This is especially true for companies and organizations handling sensitive information and must comply with applicable privacy regulations. To secure your information, consider implementing the latest security measures like SSL encryption, multi-factor authentication, and biometrics.

Ensure your AI system is transparent and understandable

The more complex your AI system is, the harder it is to identify security threats and bugs. As such, be sure to implement an AI that’s easy-to-understand and intuitive. Doing so also makes it easier for investors and stakeholders to opt in. This transparency also promotes accountability and confidence among involved parties.

With risk mitigation on lock, you need to check whether the initiative is worth the investment. Measuring ROI involves the following:

• Reviewing your objectives: The development teams should go back to the pain points that the AI model is meant to address. Failure to address these specific issues is a negative ROI
• Creating measurable goals: The team should have a clear picture of what successful AI implementation is for the organization. This entails having measurable goals and definite targets. For instance, the goals could be increasing engagement by 20% or expanding conversion rate by 30%, etc.
• Keep track of key performance indicators: Keep a keen eye on all the crucial metrics that the AI will affect. This may be anything from error reduction percentage to customer satisfaction, or even profit margins. Check these metrics before and after the implementation to gauge whether the AI delivers as expected.
• Prioritize your goals: While you may be tempted to tackle everything at once, a better approach is to prioritize. Prioritize objectives that have the most profound impact on your bottom line or that bring in the most returns.

AI Proof of Concept: Final thoughts

An AI/machine learning proof of concept is absolutely vital to developing a machine learning system that not only works but achieves or exceeds your organizational goals. However, it’s worth noting that most in-house IT teams may be incapable of putting this entire exercise to fruition. The good news is that you can always outsource to an AI service provider or hire technicians on contract briefly to conduct the POC.

That said, organizations and companies should have realistic expectations about their AI systems. While machine learning systems are incredibly beneficial, they won’t instantly solve all organizational problems. However, investing in a thorough POC and an experienced AI technical team is a step in the right direction, and companies get to learn the true potential and limitations of this revolutionary technology.

References

[1] webfx.com. AI Pricing: How Much Does Artificial Intelligence Cost? URL: https://www.webfx.com/martech/pricing/ai. Accessed on September 12, 2024
[2]kpmg.com. Trust in Artificial Intelligence. URL: https://tiny.pl/s-6ns4g8. Accessed on September 13, 2024
[3]nis2directive.eu. NIS2 Requirements. URL: https://tiny.pl/6y-ndf8z. Accessed on September 12, 2024