How to start learning at work

How to start learning at work

There is a paradox at the heart of new AI technologies.

On the one hand, they make almost everything more efficient. Amazon CEO Andy Jassy reports that Amazon has already done it saved $260 million and 4,500 developer-years of work in software upgrades alone. Klarna, a customer service company, claims its AI assistant handles two-thirds of customer service conversations and does the work of 700 full-time agents. A carefully controlled study of BCG consultants showed increases in both volume and speed of work for a range of consulting tasks. Artists, photographers, authors and filmmakers also become more productive and creative.

Given the economics of these technologies, companies will have no choice but to adopt them. And professionals will have no choice but to learn how to use them.

But there is a downside. The use of these technologies can also inhibit the development of important job-related skills. While technologies in theory hold the promise of freeing up time for iteration and learning, in practice people often use them to do a task quickly and at a level of quality that is “good enough”. The ease of use of technologies actually encourages this.

Flipping the script in schools

The dilemma may be most apparent in schools. Students use ChatGPT to write papers, teachers use other generative AI tools to catch cheating, and a game of cat and mouse ensues. Students avoid both work and learning. Teachers face an existential question: What’s the point?

What if, instead of using new tools in an old context, we could flip the script and use them to dramatically increase learning? There is evidence that this may be possible.

In 1984, a set of studies at the University of Chicago under the guidance of Benjamin Bloom demonstrated that average the student taught through a mastery learning process performed one standard deviation above the average student in a typical classroom. Learning mastery is not rocket science; they simply try to make sure each student understands the basic skills before moving on to the skills that build on them. But it takes time.

Studies have also found that tutoring students have performed two standard deviations above average student in a class. This means that the average student who was tutored performed better than 97% of the students in a class. This is an amazing effect.

These studies led Bloom to begin the search for “group instruction methods as effective as one-on-one tutoring.” AI tutors may be the answer. Many such tutors are already available, covering topics from philosophy to Python programming. I’ve used several and was amazed at how good the base models are. Khan Academy has already begun efforts to integrate tutors into its programs. The promise is training at scale, something only the wealthy can afford today.

Flipping the script in business

What is the business equivalent of this phenomenon? In 2023, according to According to the 2023 Training Industry Report, companies spent more than $100 billion on training (including their employees’ time spent on training). Much industry training mirrors classroom training (or, worse, is simply online slideware). How might new approaches using AI tutorials improve both the efficiency and effectiveness of this training?

The design would definitely depend on the subject of interest. Tutors focused on imparting practical knowledge could be customized to the student’s skill level based on the principle of learning by mastery. Other topics—those related to interpersonal relationships or cultural awareness, for example—require deeper understanding or even behavioral changes. They could benefit even more from the advantages of these systems. These could allow employees to challenge, explore and question the material through interaction with the tutor. Although most corporations today focus on using generative AI to make employees more efficient, the big payoff may be in making them more efficient.

The challenge goes beyond individual employee learning. AI and robotics technologies can automate work to a degree that inhibits learning for the next generation of professionals. In his book, qualification code, Matt Beane gives the example of robotic surgery systems. The use of these robots by senior surgeons removes the need for the surgeon to rely on residents to assist with operations, which prevents them from gaining the necessary hands-on experience. One manufacturer of such a system has developed a mentoring mode, which allows a student to perform the surgery while a surgical mentor observes. The surgeon retains the ability to jump if necessary. However, the mentoring capacity is rarely used. Beane explores the implications of this for learning in many contexts. A rethinking of working practices is needed to support skills development, one that incorporates not only the need for short-term efficiency but also the long-term need for highly skilled people.

Managerial recommendations

So what should executives in this AI-enabled world do to start learning in their organizations?

◦ First, look to use AI to make internal training courses more effective; it moves from online slideware to AI tutors – many of which can be developed in-house.

◦ Second, make learning a meaningful goal for every employee; too often, development is viewed as a secondary goal, one that employees can reach when time permits; this mindset causes learning to be undervalued, which is dangerous in a time of rapid change.

◦ Third, don’t fall into the trap of maximizing productivity extraction from AI at the expense of learning; give employees the time they need for continued growth.

◦ Finally, encourage those in engineering and IT to think about the consequences of their design decisions on the learning of those affected; it can be difficult to fully account for the implications of automation – many consequences are surprises that only appear during use – but if issues are raised and discussed during design, there is a better chance of avoiding a blunder.

These steps require discipline because they require reinvesting some of the AI ​​savings into employee development. But an increased focus on learning will likely be a requirement of any new world that emerges as AI continues to permeate every aspect of our jobs. It is best to start changing your mindset now.