AI development journey, particularly in the healthcare sector, is a complex and resource-intensive endeavor. But fear not because here’s where the game-changing AI proof of concept (AI PoC) comes into play. Picture this: from start to finish, your AI healthcare project spans 6 to 12 months, encompassing critical stages like data collection, model training, algorithm development, testing, and validation. Each phase demands specialized expertise and dedicated resources to ensure accuracy, reliability, and regulatory compliance.
Let’s talk financial side of things. It’s not just about investing time and effort; we’re talking about acquiring high-quality data, bolstering your computing infrastructure, assembling a top-notch team of data scientists and AI experts, focusing on app design to create an intuitive interface, and ensuring data privacy and security compliance. All require substantial financial commitments. But you want the end product to be worth every penny, right? That’s where the AI PoC steps in.
With an alarming 50% of AI projects facing failure, having the safety net that an AI proof of concept (AI PoC) offers is crucial. By building an AI PoC, you can enhance your project, steer clear of potential pitfalls, and avoid wasting significant time and resources. Interestingly, companies with artificial intelligence experience successfully transition only 53% of their AI PoCs into full production. It becomes evident now that partnering with a seasoned development team is paramount when working on your prototype.
In this article, we’ll highlight AI POC benefits and show the steps to significantly increase the likelihood of transforming your AI PoC into a full-scale, impactful solution. At Unicsoft, we pursue the field-tested approach to AI PoC development, so you can learn to spot pitfalls and choose a software development provider who can truly deliver.
Why is an AI proof of concept crucial to the success of your project?
An AI PoC is a prototype demonstrating that a proposed AI solution works, putting its feasibility to the test. You get to observe how a smaller version of the actual solution performs, determine its benefits and limitations, and validate the concept.
Deploying a PoC is crucial to the success of your AI development project because it lets you:
Although building an AI PoC is an important part of many development projects, you’ll find cases where it’s possible to skip the process. We’ll explore those scenarios next.
When is it OK to skip creating an AI PoC?
Here are some scenarios that give you the leeway to forego an AI PoC.
- You’ve laid out and thoroughly documented your innovative approach in both theoretical and practical terms.
- You’re building a solution that’s already an industry standard or has been proven technically feasible.
- Your development team has built very similar solutions before.
Now which scenarios make an AI PoC inescapable. We’ll talk about them in the next section.
When is it absolutely critical to develop an AI PoC?
How do you determine whether you need an AI PoC? You need to ask yourself if any of the following situations describe your case. If any of them are true, a PoC is absolutely necessary.
- You’re working on a novel idea that, to this point, has only been discussed at the executive level and not among technical specialists.
- Your development team is unsure of the technical feasibility of the concept (e.g., is implantation possible? Can you build the software with available technical resources?).
- You need to secure buy-in from key stakeholders or investors.
Do you relate to the situations that make an AI PoC necessary? If yes, you’ll have to deploy one, even if you find it daunting. In the next section, we’ll talk about the benefits of creating an AI PoC.
What are the benefits of proving your AI concept?
From preventing costly failures to setting the stage for a successful development process, an AI PoC brings benefits that make it well worth the effort. Here are the top AI proof of concept advantages.
Minimize business risks
Creating a prototype of the proposed AI solution provides clarity on your organization’s capabilities and resources. It forces you to carefully consider your expectations and evaluate your understanding of AI, its practical applications, and the challenges you might face.
A PoC can help you come to terms with your AI readiness (or lack thereof) and lets you terminate a project if you determine it’s doomed to fail (which avoids wasting time and resources). It can also help you recalibrate the solution to include measures that mitigate any risks you identify, which increases the likelihood of your project’s success.
Test the technology stack
Building a prototype lets you put your chosen technology stack to the test, verifying that your development team has everything they need to implement the desired functionalities. It also helps evaluate the different features you plan to include in your AI software. Early on, developers can check for technical issues and project ambiguities.
Prepare to scale from PoC to production
An AI PoC lets you streamline your objectives and plan for complex data scenarios as you prepare to scale the solution. You’ll gain insights that help you reimagine workflows, determine the proper infrastructure, and prepare to deal with constant changes in data quality and availability.
Hopefully, you’re now convinced that you need a PoC, so let’s talk about how to build one.
What are the steps to deploying an AI PoC?
For the successful deployment of an AI PoC, you need to be methodical. The following tips discuss how to build an AI PoC.
Clearly define your goals
You need to be clear about your goals. Take the time to articulate and map out the problems you’re trying to solve. It helps to consider what other companies in your industry are doing with AI (a solution similar to the one you want to build may already exist). You must also clearly define what business impact you’re trying to achieve and confirm that it’s well worth the investment.
Plan data selection and preparation
Now that you’ve clearly defined your goals, it’s time to think about the data you’ll need to train your AI algorithm. You may mine data from within your business, leverage open-source data, acquire datasets from third-party providers, or hire a specialist to scrape data from many sources.
Next, you must examine the properties of the data you’ll use for AI modeling. You need a data scientist to effectively screen the data, which involves checking for errors, reviewing the distribution of variables, adding missing data, and organizing the data fields.
Once you have your datasets sorted, you need to randomly divide them into a training, validation, and testing set.
Develop and test your model
This is the stage where you choose an AI algorithm to learn from your training dataset and train your model. You’ll have to choose the most appropriate algorithm or write one to solve the problem at hand, then develop a model with suitable parameterization.
The next step is the validation phase, where you verify that the solution meets the specified requirements to test the model. The training phase follows. Here, you’ll assess how well the model makes predictions based on different data. You’ll also examine the algorithm’s logical steps and compare them with your business teams’ knowledge stream.
Make iterative improvements to the AI PoC
If the assessment reveals that your PoC does not meet the evaluation criteria, you can iterate to create a solution that does measure up.
The insights you gain from the first version of the PoC can help you make modifications to the ML algorithm. Measure its performance after every iteration and make further adjustments as necessary. Consider additional variables, such as different cloud service models and processors.
Scale up the AI PoC
If the AI PoC’s performance is sufficient, you can start scaling it. Here are some ways to go about this.
- Scale the infrastructure. Review your existing infrastructure and explore how to expand its processing power and storage capacity to optimize performance and prevent bottlenecks at full capacity.
- Optimize the AI based on PoC performance. Over time, you’ll gain more insights into the solution and find more ways to finetune it. You can train new models, improve data labeling, and expand its inference capabilities.
- Scale out to other business cases. Maximize the use of your system by exploring other use cases. For instance, you may have designed the solution for predictive maintenance in manufacturing but find that its predictive model could be just as useful for marketing tasks.
How long does it take to implement a PoC?
The time it takes to fully develop an AI PoC varies. Creating a PoC is meant to consume fewer resources than the whole project and can generally be completed in a matter of weeks. However, the PoC implementation period can extend over many months if the team you choose to work with is not experienced and competent.
Assess the PoC’s potential to generate business value
To streamline the POC implementation process, avoiding unnecessary delays, it’s essential to evaluate whether the proof of concept meets your expectations. Test it against key performance indicators (KPIs), such as customer satisfaction and mean time to repair (how long it takes to fix a problem). At Unicsoft, we use the following evaluation criteria:
- Accuracy. The AI system must deliver results that are reasonably accurate and repeatable.
- Completeness. The system should leverage all data resources.
- Timeliness. Insights must be delivered right when the users need them.
- Scalability. The solution must function even when the data surges or accumulates.
- Compatibility. Integration with third-party databases should be enabled.
- Flexibility. The solution must be adaptable to data and model changes.
- Engineering. Debugging the trained model should be a straightforward process.
- Bias. The solution should be checked for unconscious biases that often occur because people choose and don’t always extensively check the data that the algorithms run.
- Causality. The model must provide correct and explainable inferences.
- Safety. Users must be able to count on the results an AI system returns.
It also helps to compare task performance before and after PoC deployment. Check whether the PoC reduces time spent on tasks or the number of errors. You might also gather user feedback, conduct a cost-benefit analysis, or compare the PoC’s performance with that of other existing AI products across the industry.
So, how can you increase the chances of success and reduce the chance of failure with proof of concept for artificial intelligence projects? It’s always smart to work with a software development company that has extensive experience developing AI prototypes.
The Unicsoft Experience
Look at the way we did at Unicsoft and offer the approach that makes the AI PoC process a breeze for our clients.
- We do all the heavy lifting. We take charge of the entire process, ensuring that the work progresses. And we are listening carefully to your requests and suggestions.
- We offer guidance at every step. We’re here to ensure that you don’t embark on the journey blindly. We’ll walk you through each stage, from conceptual design to data preparation, to scaling the model.
- We ensure stress-free legal compliance. We make it our responsibility to ensure you don’t get into legal hot water. Count on us to keep your datasets and AI systems secure and compliant with privacy and security regulations, such as GDPR and the EU AI Act recommendations.
- We deliver reliable outcomes. We set KPIs, draw a roadmap, and document every step forward.
- We keep the project efficient and low-cost. We leverage lean methodology and low-code tools to complete a working prototype in a matter of weeks. You won’t have to put up with ballooning costs resulting from PoC development that lasts for months.
- We don’t compromise when it comes to quality. Unicsoft has earned the ISO 25010 certification. This means you can count on us to maintain a quality control system that covers functional suitability, performance efficiency, reliability, maintainability, security, portability, compatibility, and usability.
In the next section, we’ll talk about how we helped a couple of our clients validate their AI solution. Here are the examples.
AI-Powered Image Recognition for Detecting Skin Disease
A European startup looking to develop an AI solution for skin disease detection worked with Unicsoft to build an AI prototype.
Our R&D team leveraged computer vision algorithms to develop a prototype for the Skin Diagnostic Model. We conducted in-depth research and analysis to support the project and thoroughly documented the estimated time and resources required to scale the PoC into production.
Our dedicated team promptly delivered a fully functional AI prototype that met the client’s initial requirements. The solution is capable of processing thousands of images within seconds, providing accurate early diagnosis of skin disorders.
AI Solution for Assessing Credit Risk Based on Climate Change Factors
YAPU Solutions, a FinTech startup for farmers, wanted to augment the credit lifecycle management system we built for them with AI-powered modules that enhance financial institutions’ risk management and operational efficiency.
Their main goal was to manage credit assessment and default in a manner that accounts for specific risks, such as climate-related risks. They wanted an AI solution that could predict a farmer’s probability of defaulting on their debt based on multiple factors, including nature-related risks, location, crop cultivation skills, and soil quality.
Unicsoft has completed a feasibility study that provides a comprehensive report on data use cases. We’ve determined the types of data required and the best algorithms to train the models. The project is ongoing and actively developing.
Conclusion
Embarking on an AI development project without first securing a proof of concept puts you at risk of squandering a huge investment. As long as you work with a competent development team, a PoC in artificial intelligence projects will result in more savings, relative to the cost, in the long run. Reach out to us if you want to kick off your own PoC development.
FAQ
Here are the common questions we hear about AI PoCs:
Do you really need to implement a PoC for all AI projects?
You need to build a proof of concept for artificial intelligence solution if you’re working on an idea that’s entirely new for your development team and stakeholders. You need a prototype to test out its technical feasibility and secure buy-in.
What steps are usually followed in the PoC development of an AI concept?
Development teams usually go through the steps that include defining goals, planning data selection and preparation, developing and testing the model, making iterative improvements, and scaling up the PoC. Clear articulation of problems, thoughtful data handling, model development and validation, and iterative adjustments ensure a successful PoC. If the evaluation criteria aren’t met, iterations help refine the solution. Scaling involves infrastructure optimization, AI finetuning, and exploring other use cases.
What is the role of an AI PoC in the product or solution development process?
An AI PoC determines the technical feasibility of an idea and reveals gaps in knowledge, data, or other areas to improve your chance of developing a successful AI product.
How long does the development of a PoC for an AI project take?
Depending on the competency and efficiency of the development team, the process could run from a few weeks to several months.
