Why a proof of concept is one of the key factors for an AI-based project success in the enterprise data world?
Artificial Intelligence (AI) is one of the most important business opportunities for 2019. While there are many definitions of AI (and even if many still don’t have t a clear idea about the range of AI technologies we encounter when talking about AI), we can all agree that AI is changing our lives. It’s essentially a technology that simulates capabilities that we normally associate with the human brain, and it can a variety of issues such as process automation, customer interaction and information intelligence.
The POC: A Taste of Artificial Intelligence
As with other technologies, any business considering AI should undergo a proof of concept (PoC) approach to get started from concept to deployment. Let’s take the case of text analytics for enterprise AI where, Gartner, recommends that data and analytics leaders dealing with text analytics based on AI technologies should “conduct flash or quick proofs of concept to test a vendor’s capability with a small sample of data to narrow down the options, then plan for a full ontology of domain-specific data for a comprehensive evaluation of the tools’ capabilities” (“Market Guide for Text Analytics”, November 2018).
Why you need a PoC
Implementing a text analytics AI-based solution doesn’t have to be long and complicated. Choosing the right partner for your projects is key to ensure that you’re starting your AI journey with solid forethought, planning, expertise and best practices. This way, you’ll be positioned to implement the solution best suited for your needs and place you ahead of the competition without challenging your expense budget.
A proof of concept can provide a concrete demonstration of the capability of AI technology—using real output—to solve your real problem. It’s the best way to understand whether a use case can really achieve positive benefits from that specific application. Essentially, it can determine if that AI solution will be successful or not. To get started, it’s important to identify the problems you need to solve, and to be sure that what you’re asking of the technology is realistic: some issues may not be suited for an AI-based technology. In addition to considering the problems you’d like to solve, you’ll also want to focus on the internal skills that your teams may need. Your partner vendor should be able to provide you with the guidance you need by working with you to evaluate with you the scope of your AI projects.
Today, implementing a project that can rapidly deliver business value and demonstrate relevant business impacts is mandatory. For emerging technologies such as AI, a PoC is generally the most cost-effective path to demonstrate quick wins and build confidence.
A PoC allows you to quickly compare different solutions and to test a vendor’s capability. Vendors who promote a PoC approach will accelerate your AI-based solution benchmark, and you’ll want to work with an AI technology partner that can provide experience and guidance.
Some of the main factors in a successful AI-based project are proper analysis and effective implementation: a proof of concept can help a business understand if an existing process has 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:
- Test the technologies and methodologies to be used
- Deliver more immediate and concrete value
- Quickly compare different solutions and approaches
- Gain experience, skills and confidence in AI
What’s next?
A PoC is a “closed” but working solution to help an organization understand whether an AI-based project can be successful or not.
The next step will be creation of a detailed approach and a plan for implementation, including cost estimates, timelines and a high-level work estimate of what will be required to take the PoC to the next step and begin building the real, successful project.
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