How to Prepare for Enterprise AI Adoption?
The probability is pretty high that you may have been already heard of probable use cases of AI and the potential it holds for the enterprises. According to Gartner AI adoption has increased by 300 % in 2018 alone. The giants like Google are pledging millions of dollars to tackle the AI challenge. It seems clear now that AI can provide robust ROI.
It is due to this acceptance that AI is slated to grow by 270 % more in the coming years.
Enterprise AI Adoption Guidelines:
In fact, AI could transform how businesses operate. But for this complete makeover to be realized, the enterprise has to adhere to certain basic guiding principles to reap the full benefits.
- The first step is to get a clear understanding of what is AI?AI is much larger than the chatbots all of us have experienced on the phone and home devices. Business decision-makers must acquire a clear sense of the potential of AI to come up with a relevant use case that is right for their business.
AI is an umbrella term which covers, data analysis, collection, applying machine learning or deep learning, and coming up with relevant applications. Once the stakeholders can see the lay of the land it will be easier for them to come up with a use case that can be aligned with their vision.
- An important next step would be to carry out an extensive competitor analysis. Every industry has multiple segments and a variety of players. These competitors maybe at different levels of maturity with regards to AI adoption. Analyzing the moves the competition is making will help you understand the possible areas you could focus on.
These two steps will help you zero in on the likely use cases for AI. This will depend upon your specific needs and on the maturity of the organization. Choosing the right use case is of primary importance as that will have a massive impact on the ROI. AI use cases will vary according to the industry.
For the automobile industry, it might be predictive maintenance whereas for an e-commerce company a recommendation engine might be the need of the hour. Similarly, a chatbot guiding the customer through self-service options might be another potential application.
That done, mandatory due diligence or a current state assessment must follow. This assessment will identify the current state of the data, how it is being used, the gaps, the best possible ways to go ahead and design the collection, management, and how to effectively and economically achieve the analysis of data. This assessment will also help you decide the tools and technologies that can be used to make the organization AI-ready.
This is an important stage in driving the strategy. Based on the gaps and the requirements that emerge, the company must analyze whether they have the capability in the house or will have to onboard an outside partner with the required skill set necessary for creating the AI. Obviously, this step has tremendous implications on budgets and business strategy. Selecting the right partner becomes an important decision point in the AI journey.
Once done with the make or buy decision and after having on-boarded the right partner, the groundwork must begin. The primary task at this stage is to build a scalable data foundation, whether on-premise or in the cloud. The data collation pipeline has to be set up. Tools and technologies have got to be aligned. A robust data foundation is a mandatory first step for setting forth on the AI/ML journey.
It is great to think big but start small. This means that first carrying out a small POC to see whether you get the desired result and whether the system is effective or not. Look for limited applications at this stage. For eg. apply the recommendation engine for a particular category rather than going full throttle.
Similarly, monitor one aspect of the engine health of an automobile, and see the system’s efficacy in spotting anomalies. Once the kinks are ironed out over the course of the limited scope pilot, the AI implementation can spread to different areas.
It’s clear that that the effectiveness of an AI / ML system improves as more and more data is fed into the system. Interestingly, this effectiveness does not hit a plateau but improves exponentially as it gets new data to train on. This suggests that it is imperative to ensure the system undergoes continuous improvements and updates.
Last but not least, think about the organization. Most organizations are reluctant to change. Hence it becomes important to drive AI adoption as a top-down approach. The push from the top management and their committed involvement becomes mandatory.
This is a critical aspect as it is this commitment that will convince the rest of the workforce to align with a new digital culture powered by AI.