David C. Edelman and Mark Abraham, co-authors of PERSONALIZED say we all need to skill up for AI readiness in the workplace, fast. Here’s how.
At Harvard Business School, students and professors alike are discussing AI’s transformative impact on business including emerging business models, shifting competitive landscapes, and potential cost structure revolutions.
And while case studies can help learners explore new challengers and pivoting incumbents, astute students delve deeper. In order to prepare for the future of work, young people must question how they should cultivate their personal skill sets for an AI-first world where everyone will have an AI co-pilot at their side.
We all need to increase our AI readiness in the workplace, fast. Here are the top three skills that workers will need to succeed in an AI-driven world—and how to develop these skills yourself.
Top skills
We looked specifically at managers who are succeeding at leading companies, and at people throughout the ranks who are starting to stand out as AI gets more pervasive. In our research, and book, PERSONALIZED: Customer Strategy in the Age of AI, we found three core skills that will distinguish tomorrow’s rising rock stars in an AI-driven workplace:
- Curiosity: The ability to ask an AI sharp questions, and to keep asking questions even when you get what feels like an answer.
- An understanding of data: Knowledge of data sources, gaps, generalizability, and company-specific aspects.
- Unwavering accountability: Taking responsibility for outcomes, scrutinizing AI outputs, considering stakeholder perspectives, assessing risks, and determining appropriate verification methods.
And here is what it takes to develop each of these skills.
Curiosity requires mastering the art of continuous questioning
Asking questions to understand the context of a situation is a natural act for most businesspeople. What questions to ask, how you ask the question, the tone you use, and the way you frame your expectations for an answer always affect the quality of the response you get. So, too, for AI systems.
When interacting with AI chatbots or tools, you’ll invariably receive some form of answer. However, these responses rarely come with quality ratings or clear definitions of what constitutes a high-quality answer. Therefore, persistent questioning becomes crucial. Rephrase your initial question, probe the AI’s information sources, and request alternative perspectives by providing it with a different viewpoint to consider.
For example, asking an advanced and newer AI like Chat GPT 4.5 the following two questions gives very different answers: “How will Search evolve in the future?” versus “How will Search change as more people use LLMs instead of current tools?” The second question gets much more into the details of new Gen AI tools, while the first question generates a list of input and output types that could be used for search.
In our book, we created what we call the “Five Promises of Personalization,” a blueprint for driving growth with AI. The very first Promise, “Empower Me,” is all about asking what the customer wants, so you can design the AI and technology to deliver it.
The best AI apps are designed with this continuous questioning embedded. For example, Duolingo, the language learning app, asks seven simple questions when you begin. It asks not just which language you want to learn and your level, but also why you are learning the language, which kinds of topics you want to be most able to be conversant in, and whether you will be mostly speaking or also writing and reading. The questions are designed purposefully so the curriculum can be adapted with each choice made. The continuous learning doesn’t end there. As you progress in the app, the course material adapts to your knowledge.
In today’s constantly changing business environment, curiosity and constant adaptation to new information will be part of the job in most roles. As a result, managers are increasingly adding curiosity as a required skill in job descriptions.
Become a data-savvy decision maker
Data is everywhere. Data can be found in every function across a company, hidden in interactions and discussions, and generated as teams test new hypotheses and ask questions. Tomorrow’s great managers will be highly attuned to the data they currently have available, potential and new data they can unlock or purchase (third-party data), and data they can create. In order to be a successful leader in the future you are always going to be looking for new sources of inputs to help solve problems, understand complex systems, foster specific actions, and personalize interactions.
Understanding data does not have to be deeply technical, but it does require an understanding of your company’s general systems architecture. So, have a picture of where data comes from, how it is stored, managed, combined, and used. Learning about the quality issues with the data, such as accuracy, timeliness, and completeness can also help you succeed in the future of work.
This rule applies in both B2B as well as B2C environments. For example, a team at an industrial distributor thought about data as a strategic asset and combined data about products and operations with customer data sourced via the call center. This allowed them to come up with better and faster ways for the call center reps to answer incoming questions and also rapidly identify and correct operational issues customers were calling about. AI tools automated this issue identification and also enhanced call rep productivity. Thinking holistically about data across the company is becoming an essential skill for deploying novel solutions like this.
In any business situation, the better the data you bring to bear to address a situation, the higher probability you will have of a successful decision. Business schools inculcate students with the need to understand the exploration of the data available in a case, and to tie that data to the open questions at hand. Consulting firms train associates to start with the questions and work backwards, developing a fishbone of the sub-questions behind the big issues, and the data needed to answer those questions.
With AI, the data becomes even more important. Data is the lifeblood of a company’s intelligence engine. Building your understanding, even at a less technical business level, of the lifecycle of data, the means by which it can be created, and the issues involved in bringing data together, will give you a leg up for opportunities to build new AI-driven capabilities or to push AI tools towards enabling better business performance.
This skill set resonates with the “Know Me” promise of personalization. Just as large retailers capture information from every customer interaction to know their customers better, data-savvy managers must capture and understand data from various sources within their organization. This involves responsible data collection, honoring individual permissions and security, and using this knowledge to make informed decisions. You can leverage AI to gain deeper insights into your business operations, customer needs, and market trends, ultimately leading to more personalized and effective strategies.
Embrace accountability in an AI-driven world
There is no shortage of studies, examples, and articles about the risks of unbridled AI tools. Just as a team leader is accountable for their team’s output, you must take ownership of AI tool outputs. This involves assessing appropriateness, accuracy, biases, and the legitimacy of sources used. Your company’s reputation and your personal brand are at stake.
The principles of good management apply equally in an AI-driven world. Building on the first two skills we discussed, you need to make sure your team is asking the right questions. You need to interrogate how they are answering these questions. You need to assess whether the output makes sense, uses appropriate data, and fits the need at hand. If you are part of a team, and not managing it, you have to look beyond your assigned role to make sure that the team as a whole is moving in the right direction. You have to help in problem solving with others.
But besides taking responsibility for the outputs of AI, great managers will also take responsibility for making the systems better, smarter, more appropriate. This ties to the last and most important promise of personalization, “Delight Me”, the promise that you will constantly improve the customer experience. In the age of AI, companies are now competing on the speed and scale of their test and learn capabilities. Innovators like Netflix and Spotify have transformed the entertainment and music industry respectively because they built AI platforms that rapidly experiment and adjust to customer inputs.
Managers and their teams need to master this skill set to succeed with AI. Just as companies continuously refine experiences through testing to elevate customer satisfaction, managers must take responsibility for refining and improving AI outputs and processes. This involves iterative improvements based on real insights, turning AI interactions into reliable and enduring tools that adapt as needs change. By embracing accountability, enabled by real-time measurement, managers ensure that AI systems not only meet current needs but also evolve to delight customers.
The skills needed for a future AI-world may not be dramatically different from many attributes that shape great business leaders. But the characteristics of AI—how it works, what fuels it, and the risks it presents—sharpen the need for talent that focuses on relentlessly asking smart questions, explores how data can fuel performance, and takes responsibility for the value creation (and the mitigation of value destruction) that AI unlocks.
Are you positioning yourself to develop these three core skills? The time to challenge yourself and begin your AI-powered journey is now. The opportunities for growth are unfolding as we speak.