Five golden rules for ensuring AI lives up to your expectations

As adoption of AI continues to rise, businesses must remember that the secret to success lies in being realistic

AI Business

June 2, 2020

6 Min Read

As adoption of AI continues to rise, businesses must remember that the secret to success lies in being realistic

It’s no surprise that more and more businesses are putting AI at the top of their agendas. From detecting fraud in financial services, to enhancing healthcare and manufacturing, the potential benefits AI offers to revolutionize business processes – and society – are almost endless.

Yet, while enthusiasm about AI is great, over-promising on its success could open up a chasm between expectations of what it will deliver, and the short-term realities.

For one, AI is different to many of the big technology adoptions made by enterprises over the last few years, such as cloud and IoT, as it’s not a ‘plug and play’ offering that works out of the box to bring almost instant benefits. Instead, AI is a journey that can take time.

For example, when using AI for image processing and classification, it would need to be shown hundreds or thousands of images of a certain object just to make the AI model learn how to recognize it. This is perhaps why only 21 percent of business leaders surveyed by McKinsey have embedded AI into multiple business units or functions. Currently, AI is only in its early stages of adoption at most businesses, with the majority still conducting pilot programs, testing the technology or planning to use it going forward.

Five golden rules for AI adoption

As adoption of AI continues to rise, businesses must remember that the secret to success lies in being realistic – not just about what it is capable of, but the time it can take to start making a positive impact. Taking the time to set out a roadmap for AI can play a crucial role in narrowing the gap between expectations and reality within an enterprise. With this in mind, here are five things businesses should bear in mind to avoid over-promising on AI’s success.

1: Strategy first, use cases next

To begin with, business leaders need to realize that the process of adopting AI to enable new business models, drive process automation and achieve cost efficiencies is a journey – not a sprint. To establish a focused AI practice within the enterprise, every business leader should ask their teams to begin by identifying and defining the business problem, as well as challenges that could potentially be solved using AI technologies.

After all, how can you expect to successfully deploy cutting edge applications of AI if you haven’t worked out why you need it? The key is to take a strategic approach and determine which broader areas to invest in before you think about the specific use cases. These could be decided either based on individual needs within the business, or competitive intelligence within the wider market. Implementing AI involves defining the strategy, and then picking the right use cases to address.

2: Choose the tasks wisely

Once you’ve identified a clear AI strategy that meets the needs of employees and the wider business, it’s time to choose which use cases to apply AI to. The key here is to start with the tasks that are the most time consuming and repetitive for human workers. These will offer the fastest business impacts since employees will have more time to spend on complex tasks where they can add the most value.

Once the core ‘mundane’ tasks have been identified, business leaders should then ask their teams to highlight the most pressing use cases they feel would best benefit from AI. That way, organizations can ensure they’re applying AI to the tasks that will deliver the most far-reaching impact in the wider ecosystem.

3: Good data is a foundation for good AI

Effectiveness of any AI model is proportional to the data ingested. Data is the core of any AI algorithm or platform, and it must be supplied in the form that the algorithm understands. Access to high-quality and usable data is a factor that has significant implications for the development of AI. It doesn’t really matter how advanced AI and machine learning algorithms become if they are not able to access the data necessary to perform analysis and generate insights.

Once good, clean data is being gathered, businesses must ensure they have enough of the right data about the process they’re trying to improve or the problem they’re trying to solve. They need to make sure they have enough use cases and that they are capturing all the data variables impacting that use case.

4: Step by step – don’t invest all at once

The hype around AI naturally leads many businesses to feel that they have to jump on the bandwagon and rapidly deploy a multitude of solutions and use cases. The issue with this is that it causes many to rush into projects they haven’t thought through, and which don’t bring tangible business benefits. The solution? Start with a scalable plan with some initial features, and then add on additional capabilities as business needs continue to evolve.

Adopting AI gradually means the most crucial tasks can be enhanced first, with the less important tasks then incorporated later based on time and budget. In line with this, AI implementations should be operated in a continuous feedback and enhancement loop, which ensures desired objectives from AI are incrementally achieved with reduced risk and increased efficiency.

5: Remember that AI is not a silver bullet

Business leaders must bear in mind that AI is not going to entirely transform their business or take away the need for humans altogether. When people discuss AI, opinions are usually polarized into one of the two extreme schools of thought – those who believe that AI will make our lives better, and those who are convinced it will accelerate human irrelevance. It is important to understand that adopting AI will not exclusively bring about either. It will instead do the unglamorous jobs humans don’t want to, freeing us up for more important jobs that will help to accelerate business success.

A realistic outlook

For all things AI, it’s critical that the people driving its adoption within an enterprise remain realistic about the time frame and what it is capable of doing. The relationship between humans and AI is mutually empowering, and any AI implementation may take some time before it starts to make a positive and significant impact on the business. By keeping in mind the five aforementioned considerations, however, business leaders can ensure over-promising on AI is a thing of the past – and that success is a thing of the future.


Kalyan Kumar IS CVP & CTO of IT Services at HCL Technologies.

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