Modern corporate executives need to be adept at politics and communication if they are to fully realize the benefits of emerging technologies.
With Big Data and AI reshaping the corporate landscape, it’s becoming increasingly likely that all executives will need to make use of such tools in order to further their own organizations and, by extension, their careers. Yet doing so is intimidating for most leaders, and this is sometimes worsened by job descriptions for leadership jobs connected to data, analytics, or AI that have laundry lists of abilities. To successfully drive business results using data and AI, many of these descriptions fail to grasp the basics.
When tackling AI/Big Data for the first time, analytical thinking is crucial, but many more traditional leadership abilities are also required. Being able to prioritize well and recognize value levers, as well as being able to work well in an uncertain environment, are also essential. Leaders that are able to combine a business mindset with their knowledge of data and AI tend to have the most effect. In addition to being data-driven, today’s business executives need to be politicians and communicators to fully leverage data and AI to increase revenue, productivity, and innovation, and to effectively articulate and defend the value they bring to the table.
Of course, familiarity with current and developing data and AI technologies is also necessary for success, while advanced technical skill in every area is not required. To understand the potential benefits, costs, and dangers of implementation, to keep up with pertinent trends, and to motivate data science and engineering teams, you must have a solid grounding in the requisite technical information. On the other hand, it is unrealistic to expect to know everything.
Whilst there is much to do to ready yourself and your organization to exploit these technologies, we describe three critical measures to implement if you want to be a Big Data and AI leader.
Discover your “why”
It’s crucial to have a sense of the CEO and executive board’s long-term goals for data and AI before getting started. The question is why they desire to establish a data capability despite their dedication being so crucial. Why? Because they saw it in the paper. A wrong response. Because experts recommended it? The wrongdoing continues. Just because they see their rivals doing it, right? That doesn’t make it right.
Knowing the answer to the “big why” issue is critical because it clarifies the organization’s long-term goals and positions data and AI investments as means to an end rather than as isolated features. Dr. Walsh, the first worldwide Chief Data & AI Officer at Vodafone, set out to clearly define the firm’s future by asking, “Why is it that individuals cannot live without their phone for two minutes, yet readily switch carriers, departing the same company that made that phone come to life?” Knowing this why helped her team and their partners to zero in on the data-driven prospects that would make their firm as vital to their customers’ lives as the gadgets they linked for them.
Consider the quality of the data collected by your company. Consider issues such, “Do we have the data necessary to answer the questions that are essential to our vision?” How easily digestible and well-structured is our data? If you don’t answer these issues early on, you can spend the first three years of your mandate cleaning up data instead of delivering any value at all. If you don’t have access to reliable data, you can’t make anything of worth.
Form a group
Hiring top-tier technical talent is a common strategy used by businesses as they strive to become data- and AI-ready. Having the right technical skills is crucial, but the data’s impact may be constrained by stakeholders’ and partners’ lack of interest or ability to participate. Investing in the training of employees at all levels of the organization is equally crucial, and is often the most efficient and effective use of resources. You may, for instance, find and educate sales, marketing, and operations professionals on how to recognize and pursue opportunities and formulate questions that lead to effective solutions for business challenges using data and AI. This strengthens internal buy-in and may be more effective than recruiting a large army of highly skilled technical experts who, without active allies, would struggle to provide economic value.
There comes a time in every business when it’s essential to train employees to think critically and act on facts. Businesses are beginning to see this, and they are making significant investments in employee upskilling as a result. As an added bonus, the most valuable data and AI teams have a wide range of backgrounds represented on them, from analytics and engineering to philosophy and law grads and even self-taught specialists who have never attended college. There is one thing they should share, though: a strong constitution. The capacity to overcome obstacles is crucial to success in change management.
Establish a plan of action
Determining the “how” of your data and AI endeavor is the next step after answering the “why” and “who” questions, and time is always of the essence. There are still just too many issues to be adequately addressed by a single organization, notwithstanding the scope of its stated aim.
The good news about data is that you can do a lot with it, but the bad news is that you can do a lot with it as well. The discipline and concentration required to produce business goals is substantial. The top-down vision and “why” are essential, but there also has to be a method for translating those broad strokes into specific objectives that a group of analysts, data scientists, and engineers can work toward. If improving customer satisfaction is the overarching objective, then the best strategy for increasing that statistic must be determined. Do you need to work on improving the user experience? On how to better serve customers? If that’s the case, then maybe it’d be smart to work on creating chatbots, improved product suggestions, or more specific forms of advertising.
It’s tempting to try to find answers to all of an organization’s challenges at once. Quick thinking and early value creation are essential, but so is a commitment to learning more about the issues at hand and discussing them with the people who can help you the most. This guarantees that the appropriate action is taken in response to the appropriate problem.
You may avoid the temptation to pursue data and AI technologies for their wow factor rather than the value they will offer if you have a clear picture of the most pressing issues facing your firm.
With challenges that have been properly recognized, characterized, and scoped to back up your “why,” what order should you approach them in? The apparent solution is to assess prospects based on the value they’re expected to give and the likelihood of their success, and then select those that are most likely to deliver the largest return. The availability and quality of the essential data are also crucial factors in technical feasibility analysis. Collaboration with stakeholders and partners who are well-defined in their goals, actively involved, and willing to test new approaches is crucial for the project’s feasibility. Data can expose difficult facts, therefore it helps if they are honest and value-oriented.
The time it will take to provide that value is also an important factor to consider. People’s attention spans are short, thus data and AI solutions that gain traction and provide value rapidly should be prioritized. In many businesses, the most momentum is gained and the most skeptics are won over by the simplest straightforward applications. Instead of jumping at the most promising prospects presented by advances in artificial intelligence and machine learning, Morgan Stanley’s Chief Data & Analytics Officer, McMillan, prioritized improving stakeholders’ access to fundamental data like sales numbers (ML). McMillan gained a great deal of trust, buy-in, and support to pursue ambitious initiatives leveraging cutting-edge AI and ML technologies by doubling down on teaching younger staff in data visualisation and data warehouse access. You won’t earn praise for making things more difficult than they need to be, but you will for contributing something of worth.