7 Vital Things CEOs Need to Understand about AI

“Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. But only about one in five companies has incorporated AI in some offerings or processes. Less than 39% of all companies have an AI strategy in place. The largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but only half have one.”

The SloanMIT Review: “Reshaping Business with AI”.

Sloan survey respondents rank business issues, such as finding a business case for AI, higher, than technological problems. This data corresponds with from other similar surveys.

According to SloanMIT, companies may face several challenges of adopting AI in their companies, but they may be overcome. A focused AI team can go through all the obstacles to release a pilot, but it helps, if they are backed by a CEO, who understands AI.

Here’s what there is to understand about AI for a C-level manager.

1. How AI works in general

“At their core, algorithms are simple; and beyond the mysterious jargon, the field is quite accessible. For these reasons, executives should be able to develop a functional understanding of the topic.”

“Putting artificial intelligence to work”, BSG

Since executive support is needed to move an AI project forward, it’s crucial for an executive to understand:

  • how AI works in general,
  • which technologies it works with,
  • typical uses cases for AI,
  • nuances of releasing AI into a company.

“Executives need to understand the capabilities and potential value of these building blocks. What is hard but doable today will likely be easy in a few years, and the impossible today may be possible in three to five years.”

“Putting artificial intelligence to work”, BSG

2. The value of AI

“Companies looking to achieve a competitive edge through AI need to work through the implications of machines that can learn, conduct human interactions, and engage in other high-level functions—at unmatched scale and speed.”

“Competing in the age of AI”, BSG

Once execs understand, how AI works, they begin to see, how they can use it in their organization. At this point, they and their AI team can start looking for a business case.

AI has a number of strengths to imbue companies with:

  • AI learns both from the data and from the feedback it receives for its actions,
  • AI works at superhuman speed and scale,
  • AI can work not just with numbers, but also with text, language, speech and video.

“Organizations are still gathering information to inform their AI adoption strategy to facilitate decision making and automate processes. As a result, an AI initiative is most commonly deemed successful when it improves decision making and process efficiency.”

Whit Andrews, Gartner

3. AI works, when there’s a business case

There’s little point in an AI project without a precise way it’ll deliver value to either or both internal or external customers. A business case for AI can be found, when there’s a business problem – likely with heaps of data – where AI’s strengths of data analysis, automation or  prediction/recommendation can be used.

However, BSG says that such approach is oversimplified and recommend looking at AI through the 4 lenses:

  • customer needs: explicit or implicit needs and customer journeys,
  • technological advances: components, platforms, tools,
  • data sources: internal, external, new investments,
  • decomposition of processes

and to evaluate them through the following criteria:

  • richness of data,
  • scale and speed,
  • opportunity for systematic learning.

Once a business case is identified, it helps to clarify:

  • the business AI strategy,
  • the development strategy and requirements,
  • the required data and its sources.

4. AI works, when there’s data

Data is the fuel AI runs on: the more you have access to, the merrier. Without it, AI has nothing to analyze and base predictions on.

That’s why it’s important to:

  • identify sources of data you have access to and can gain access to through purchases, partnerships or negotiations,
  • create and use guidelines to collect data, prepare it and to train AI with it.

“The key is to build an unassailable and advantaged collection of open and closed data sources.”

“Competing in the age of AI”, BSG

Another thing to remember is that, if you want AI to predict failures and you’ve had no or little failures, AI won’t have enough data to analyze to find patterns that caused them and predict the failures.

5. They need to help employees embrace AI in the workplace

“… many people resist AI because of the hype surrounding it, its lack of transparency, their fear of losing control over their work, and the way it disrupts familiar work patterns.”

Brad Power, “How to Get Employees to Stop Worrying and Love AI”, HBR

That’s why, if you use AI in your company, it’s a good idea to show your employees:

  • its value beforehand,
  • where and how it is used,
  • why AI makes particular decisions.

Another way to make sure your employees like AI is to use it to assist them in their job by automating the routine tasks, enabling them to work on more creative, complex tasks. This will require regular communication, education and training.

6. They need to help customers embrace the AI

“…it just takes one tiny miss for the customer to realize they’re engaging instead with a machine, and if they feel duped, you’ll never get them back for self-service, and the fallout could be much worse.”

“AI in the Contact Center: Strategies to Optimize the Mix of Automation and Assisted Service” by Jon Arnold

That’s why it is vital to gain customer trust by:

  • educating them about what AI is and how it works,
  • showing them, how AI is used with their data, and that it is safe,
  • educating them that AI can learn from data on thousands of customers and offer something of value and personalized to each of them,
  • showing them, whether a human or a bot is talking to them,
  • guaranteeing that their data will be accessed only by need to know basis.

7. They need AI talent

Since the top AI and data science talent is scarce and expensive, the only other alternative is to grow AI talent from within. Thanks to numerous quality sources and books available online, that’s easy to do – and it’s possible to consult with some experts as well.

By going through with the first pilot, the AI team will not only find a business case for AI, but will also get its feet wet with the process of integrating AI with your business.

A CEO, who understands what to expect from AI and how to approach it to use in their company, can be of great help to the AI initiative in the company by:

  • initiating the process of AI project research and development,
  • providing insights from the business and client side to come up with a more effective business case,
  • creating an AI team,
  • supporting the AI team within the enterprise,
  • assisting with making the AI pilot a more appealing investment.

Hopefully, you are that CEO or will show this article to him or her, if you are passionate about AI or your product.

You also may want to read 10 more things a manager needs to know about AI from BSG or the complete guide to adopting AI in a contact center.

Image credit:

“„CEO Edition”: 20 Titan V mit 32 GB HBM2 an AI-Innovatoren verteilt” by Marco Verch, copyrighted, CC BY 2.0 license.