Despite the existence of Artificial Intelligence from several years, organizations have only recently started adopting it within their processes. And major reasons behind this growing trend include a stark reduction in data processing and storage costs and an enhanced level of understanding of the capabilities of AI in business. However, building an enterprise level AI solution is an uphill task; there are several challenges and risks to be faced.
In such a scenario, a Proof of Concept (PoC) can go a long way in establishing the feasibility of a proposed solution. If successful, the PoC can pave the way for accelerated AI adoption, saving the organization much time and mitigating the risk by a large margin.
But before reaching the stage of formulating a PoC, understanding the essential aspects of AI application becomes imperative.
Essentials of AI Application
- Impact on Business KPI – Before implementing an AI solution, business KPIs should be validated thoroughly to ensure that they are in alignment with the technology which is to be used.
- Accuracy & Consistency of Results – As compared to human intelligence, the results derived by your AI solution should be more accurate. The solution should take several factors into account and consult complex algorithms to arrive at the results.
- Application Scalability & Growth – The AI solution should be flexible enough to be scaled without ripple effects. The learning it derives from the data you feed to it should allow it to grow and expand its capabilities.
- Resilience to changes in Data patterns – Since data patterns are subject to frequent changes, the AI solution should be capable of adapting to them with minimum negative impact and loss of productive time.
- Time to Market – The AI solution should allow your business applications to get to production faster than before. Cutting down the time for investigation and improving the accuracy of analysis should lead to quicker time to market.
Development Effort – Your AI application should reduce the development effort and time on part of the experts, allowing them to be more productive.
If you’ve made sure that your proposed AI solution follows the above-mentioned essentials, you can safely take your next step towards its materialization. And to materialize it, establishing a PoC would be a good place to start.
Objectives of PoC
In the business world, a PoC is how organizations demonstrate that a product is viable. The overall objective of a PoC is to find solutions to problems that the business is facing. In this regard, for your AI solution, your PoC should focus on the following objectives:
- Evaluating Use Case Feasibility
You may have envisioned several use cases for your AI solution, but how many of them are actually relevant and feasible? The answer to this question can be ascertained if you construct a PoC based on the minimum viable product.
- Evaluating AI Vendor as a Potential Solution Provider
When building an AI solution for the first time, trying to do everything yourself might not be the greatest idea. You should seek help from experts who have a proven track record. And while selecting one, you should always compare among several providers and evaluate the pros and cons of each before arriving at a decision.
While focusing on the objectives, you should also pay attention to failure factors that often grip some of the best PoCs.
Factors affecting the success rate of PoCs
- Lack of Business Case – If your business use case isn’t aligned with your business KPI, your PoC is bound to fail irrespective of how many features it has.
- Lack of clarity on the expected outcome – If you don’t have a clear picture of the expected behavior of the system you’re building, or of the benchmark results/success thresholds, the PoC would be in a “high risk of failure” zone.
- Enterprise readiness towards POC – For your PoC to succeed, you need to have the supporting infrastructure, resources, and data in place.
- DATA Sufficiency – The data that your PoC is going to take as a baseline should be complete, properly set, and adhering to the design principles.
- Lack of transparency in AI Models – In many cases, the outcomes of PoC cannot be presented directly to stakeholders. This often leads to a non-transparent AI model and can contribute to your PoC’s failure.
- Lack of clarity about expectations from the vendor – If you’ve not explicitly stated the requirements for your solution to the vendor, there are high chances of conflict in the requirements and solution.
- Lack of right evaluation team – If proper evaluation procedures and teams are not in place to verify PoC, there would be an imbalance between expectations and intended solution.
Building a PoC and establishing its success is essential if you hope to build a working, successful product. So you must ensure that you adhere to the guidelines for creating successful PoCs and avoid the general mistakes.