Identify a Problem to Solve

Founded in 1976, HCL Technologies is one of the largest IT consulting firms, with 132,000 employees across 44 countries, and has half the Fortune 500 as customers. The company also has implemented a large number of AI systems.

Here’s what Kalyan Kumar, who is the corporate vice president and global CTO of HCL Technologies, has to say:

• Business leaders need to understand and realize that the adoption of Artificial Intelligence is a journey and not a sprint. It is critical that the people driving AI adoption within an enterprise remain realistic about the timeframe and what AI 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.⁶

It’s great advice. This is why—especially for companies that are starting in the AI journey—it’s essential to take an experimental approach. Think of it as putting together a pilot program—that is, you are in the “crawl and walk phase.”

But when it comes to the AI implementation process, it’s common to get too focused on the different technologies, which are certainly fascinating and powerful. Yet success is far more than just technology; in other words, there must first be a clear business case. So here are some areas to think about when starting out:

• No doubt, decisions in companies are often ad hoc and, well, a matter of guessing! But with AI, you have an opportunity to use data-driven decision-making, which should have more accuracy. Then where in your organization can this have the biggest benefit?
• As seen with Robotic Process Automation (RPA), which we covered in Chapter 5, AI can be extremely effective when handling repetitive and mundane tasks.
• Chatbots can be another way to start out with AI. They are relatively easy to set up and can serve specific use cases, such as customer service. You can learn more about this in Chapter 6.

Andrew Ng, who is the CEO of Landing AI and the former head of Google Brain, has come up with various approaches to think about when identifying what to focus on with your initial AI project:⁷

• Quick Win: A project should take anywhere from 6 to 12 months and must have a high probability of success, which should help provide momentum for more initiatives. Andrew suggests having a couple projects as it increases the odds of getting a win.
• Meaningful: A project does not have to be transformative. But it should have results that help improve the company in a notable way, creating more buy-in for additional AI investments. The value usually comes from lower costs, higher revenues, finding new extensions of the business, or mitigating risks.
• Industry-Specific Focus: This is critical since a successful project will be another factor in boosting buy-in. Thus, if you have a company that sells a subscription service, then an AI system to lessen churn would be a good place to start.
• Data: Do not limit your options based on the amount of data you have. Andrew notes that a successful AI project may have as little as 100 data points. But the data must still be high quality and fairly clean, which are key topics covered in Chapter 2.

When looking at this phase, it is also worth evaluating the “tango” between employees and machines. Keep in mind that this is often missed—and it can have adverse consequences on an AI project. As we’ve seen in this book, AI is great at processing huge amounts of data with little error at great speed. The technology is also excellent with predictions and detecting anomalies. But there are tasks that humans do much better, such as being creative, engaging in abstraction, and understanding concepts.

Note the following example of this from Erik Schluntz, who is the co-founder and CTO at Cobalt Robotics:

• Our security robots are excellent at detecting unusual events in workplace and campus settings, like spotting a person in a dark office with AI-powered thermal-imaging. But one of our human operators then steps in and makes the call of how to respond. Even with all of AI’s potential, it’s still not the best mission-critical option when pitted against constantly changing environmental variables and human unpredictability. Consider the gravity of AI making a mistake in different situations—failing to detect a malicious intruder is much worse than accidentally sounding a false alarm to one of our operators.⁸

Next, make sure you are clear-cut about the KPIs and measure them diligently. For example, if you are developing a custom chatbot for customer service, you might want to measure against metrics like the resolution rate and customer satisfaction.

And finally, you will need to do an IT assessment. If you have mostly legacy systems, then it could be more difficult and expensive to implement AI, even if vendors have APIs and integrations. This means you will need to temper your expectations.

Despite all this, the investments can truly move the needle, even for old-line companies. To see an example of this, consider Symrise, whose roots go back more than 200 years in Germany. As of this writing, the company is a global producer of flavors and fragrances, with over 30,000 products.

A few years ago, Symrise embarked on a major initiative, with the help of IBM, to leverage AI to create new perfumes. The company not only had to retool its existing IT infrastructure but also had to spend considerable time fine-tuning the models. But a big help was that it already had an extensive dataset, which allowed for more precision. Note that even a slight deviation in the mixture of a compound can make a perfume fail.

According to Symrise’s president of Scent and Care, Achim Daub:

• Now our perfumers can work with an AI apprentice by their side, that can analyze thousands of formulas and historical data to identify patterns and predict novel combinations, helping to make them more productive, and accelerate the design process by guiding them toward formulas that have never been seen before.⁹