Which Jobs Will AI Impact?

The workplace is being disrupted by Artificial Intelligence. A 2019 report by McKinsey Global Institute projects that by 2030, up to 39 million jobs in the US will be displaced by AI. What is the nature of this displacement? In the shadow of AI, how can workers make sure they stay employed and organizations make sure they have the skills they need?

First let’s talk about what AI does for an organization, how it’s used. Recall that today’s AI uses massive artificial neural networks, trained with massive amounts of data. The ‘superpower’ these systems bring to the table is their ability to recognize very complex patterns in digital data. AIs are essentially pattern recognizers. And they are a good fit to today’s world, much of which is represented online as digital data: images, video, business reports, news, financial transactions, customer preferences, ….

AI’s ability to recognize patterns can do lots of useful work. For example AI can be used to augment or replace a human worker’s eyes, ears, brain, or even arms and legs in performing tasks such as:

  • Industrial inspection, inventory management and warehousing, farming, security, transportation, and elder care
  • Data entry, customer service, market analysis, travel booking, legal document review, and sentiment analysis
  • Personal assistance, language translation, news and weather reporting, image captioning, and document summarization
  • Crime pattern detection, materials and drug discovery, credit checking and fraud detection, business lead generation, and business forecasting
  • Warehousing, factory assembly, taxi service, delivery service, and long-haul trucking.

Where does this leave us humans?

A short answer: as with other workplace revolutions such as steam engines, mass production, computers, or the internet, some jobs will disappear, new jobs will appear, and many jobs will morph into something different.

AI will tend to replace human labor in jobs that involve routine mental or physical work. For example the 2019 McKinsey report projects that the number of office support jobs in the US will decline from 21 million in 2017 to 18 million in 2030 (an 11% loss), while the workforce will grow 9% over the same period for all jobs in the categories analyzed. Factory jobs, over the same period, are expected to decline by 5%.

Strong job growth 2017 – 2030 is expected in occupational categories that emphasize human relations (health care, 36%; business and legal professionals, 20%; education and training, 18%), as well as in STEM professions (37%).

Of course in a growing economy there can be job growth even in job categories where AI will replace a lot of human labor. For example, McKinsey estimates that 25% of today’s work in customer service and sales will be replaceable by AI by 2030; however the number of jobs in this occupational category will still grow by 10% during that time.

Across all job categories, McKinsey estimates that 25% of human labor expended in 2017 will be replaceable by AI by 2030. This replacement potential ranges from 10% to 39% of the current workforce for every job category. This means few of us will be far from AI’s impact.

How can workers and organizations get ready for the AI transition? A 2019 MIT Sloan School report highlights the need for employers and workers to create and maximize the motivation to learn and adapt over their lifetimes. In a previous blog post I address the need for organizations to adopt an “all-in” approach to AI, integrating organization, IT, and operations.

At the individual level, many workers will need to become familiar with and learn to work with AI. Although AI technology is being developed through the work of specialized researchers with advanced university degrees, the AI research community has made learning about and using the technology surprisingly accessible.

For example Google, Amazon, and Apple all offer free cloud-based environments where workers can learn about AI, and develop and run business tools, without an extensive AI background. A 2019 Northeastern University/Gallup study found that organizations are increasingly developing the internal AI skills they need by training existing staff or through non-degreed interns.

Yes, AI is disrupting the workplace. It is accelerating automation. Workers and organizations must adapt and learn, and then adapt and learn again. But this challenge is accompanied by unparalleled opportunity.

So, how is it going so far? Interesting data point: by analyzing job postings, ZipRecruiter estimates that AI created about three times as many jobs as it destroyed in 2018.

How Will AI Impact Organizations?

We can think of AI as software that helps organizations more effectively reach their goals, e.g., by reducing costs and increasing revenues.

Gaining benefits from AI, or any other innovative technology, requires organizational change. New strategies. New job descriptions. New workflows. New org charts. Training.

What makes AI different? Are the challenges faced by organizations adopting AI different from those encountered in adopting other software innovations?

After all, using computers to revolutionize organizations is nothing new. IBM developed the SABRE reservation database for American Airlines back in 1964, replacing manual file cards with a system that could handle 83,000 reservation requests. Pretty disruptive!

So how is AI changing organizations? Let’s take an example – the financial industry. AI’s ability to find patterns in mountains of data can help financial organizations:

  • Make more accurate and objective credit decisions by accounting for more complex relationships among a wider variety of factors
  • Improve risk management using forecasts based on learning patterns in high volumes of historical and real-time data
  • Quickly identify fraud by matching continuously monitored data to learned behavioral patterns
  • Improve investment performance by rapidly digesting current events, monitoring and learning market patterns, and making fast investment decisions
  • Personalize banking with smart chatbots and customized financial advice
  • Automate processes to read documents, verify data, and generate reports.

To make these improvements using AI, a financial organization needs to undertake the sort of activities needed to introduce any new software into their operations and products, such as:

  • Establish strategic priorities and budgets
  • Clarify and communicate objectives and plans with stakeholders
  • Work with software developers/vendors/users to establish and carry out software/system development projects
  • Create/modify procedures and organizations to take advantage of the new software
  • Hire, train, retrain the workforce as needed
  • Monitor results and adapt as required.

What are the special challenges AI brings to these activities?

The first challenge is AI’s high profile. Managers feel compelled to catch the wave of the future, and workers fear they will lose their jobs. As a consequence:

  • Managers may undertake AI projects with unrealistic expectations. AI can be extremely effective, however only when there is access to large volumes of data relevant to an operational role that truly benefits the organization
  • Employees essential to successful adoption of the new systems may stand in the way or quit if they see AI as a threat.

Clearly due diligence is required in the first case, and effective employee engagement in the second.

A second challenge is an “all or nothing” aspect of AI. To reap the benefits of AI, the core AI technology must be fully integrated with an organization’s IT infrastructure and business operations. Notice in the financial organization example above, how many aspects of the organization could be affected by AI. To successfully integrate AI, an organization must be “all in”. To do this requires particularly high levels of communication, investment, and cross-organizational participation.

A third challenge is that with successful adoption of AI, the requirement for personal growth and change is pervasive, up and down the organization. Leaders, engineers, and operators all need to learn and embrace the changes brought about by AI. For many, this can be an exciting opportunity for career growth and more fulfilling jobs. Others will mourn the lost relevance of hard-won experience. The organization must be prepared to invest in training, re-training, and professional development. The more AI takes over routine data gathering and analysis, the more important ‘soft’ skills will be to every worker.

Finally, a fourth challenge is that even a very capable AI can produce unintended results. For example, although AI-based analysis can lend objectivity to credit decisions, training AIs using historical data can promulgate past biases. Also, when highly-trained AIs encounter situations they have never seen before, the results can be unpredictable. This means AIs need human supervisors, and these supervisors are dealing with a whole new kind of employee!

Next: What kinds of jobs will AI impact?

Five Ways Engineers Struggle to Become Managers

Engineers can make great managers. They have highly-developed problem solving skills, and they have mastered knowledge needed to lead technology-based organizations. But the road from engineering to management can be surprisingly rocky. Management requires ways of behaving and thinking that can seem contrary to hard-learned engineering values. This can lead to teams of engineers, frustrated by managers who can’t manage, and newly-promoted managers who find themselves in positions of bewildering stress. 

Engineers understand that management is different from engineering. New managers learn they need to set objectives, delegate, give feedback, handle finances, etc. The concepts are easy, make sense, and engineers can convincingly articulate them. But engineers frequently struggle with applying them. Why? 

One reason is that engineers try to manage while still thinking like an engineer. They follow the checklist of good management practices, but they approach management with deeply-held engineering values. An engineer who wants to manage must not only learn new skills, but also learn to look at things from a new perspective.  Here are five areas where mindset can torpedo even the best engineer’s attempt to manage: 

  • Identity: What is My Job? An engineer is focused on product, tools, and technical expertise. A manager must focus on people – roles, relationships, organization. 
  • Independence: How do I do my job? Engineers treasure independent thinking and personally coming up with the best idea or solution. A manager needs to treasure effective teamwork and success of the group.
  • Aesthetics: What does excellence look like? Excellent engineering is elegant, flawless, uncompromising, and efficient. Excellent management often compromises, trading performance for affordability, efficiency for buy-in, perfection in the face of resource constraints.
  • Influence: How do I work with others? Engineers seek unambiguous, data-driven interactions with others, to communicate and resolve issues. Managers engage in rich personal interactions to bridge barriers to communication, pool multiple perspectives, explore ambiguity, and achieve consensus.
  • Learning: How do I develop as a professional? An engineer’s growth is driven by expanding explicit, technical knowledge in an area, so the engineer can operate there without making mistakes.  Managers become good managers by managing, making mistakes, and adding to their base of tacit knowledge. 

It’s not that the engineering mindset is not useful to managers. An engineering background can give a manager a real advantage. And of course, like managers, engineers need to relate to people and learn from experience. However, an engineer stepping into management needs to know that his or her job involves not just new duties, but new ways of thinking. And these new ways of thinking are going to be especially hard to learn, because they can grate against engineering sensibilities. Engineers becoming managers need to honor their engineering instincts, but recognize their limitations and make sure they don’t get in the way.