Repensando o pessoal e a contratação de tecnologia para a era da IA

Rethinking People and Technology Hiring for the Age of AI

June 4, 2024 Roberto Magalhães

Technology leaders will have to rethink the skills and partnerships they develop in the age of AI.

Hiring at technology organizations has solidified the concept of a “knowledge worker,” an employee primarily focused on using their brain to solve problems individually , versus using their physical or managerial talents. Software engineers, technology architects, and digital strategists are different versions of the knowledge worker, each using their skills to collect and synthesize information to produce a result.

With the emergence of natural language AI tools, ChatGPT being one of the most visible, it appears that these types of tools are directly aimed at performing functions similar to those of knowledge workers. While many companies are contemplating changes to their products, customers, and the industry at large, very few are considering how to change their hiring and staffing based on these tools.

From Sole Collaborator to Maestro

One of the biggest promises of AI tools that mimic individual knowledge workers is that dozens of virtual “workers” can be added to your teams for very little cost. This has given rise to speculation that bots will replace entire classes of jobs .

These types of articles generate attention-grabbing headlines, but they miss the main point that technological changes often drive changes in the way we work, rather than creating a zero-sum game in which workers disappear forever. In the case of AI tools, the worker of the future may be copying pages from the workers of the past, rather than being eliminated entirely.

One of the intriguing aspects of the rise of the knowledge worker was the reduction of middle management roles, in which there existed a dedicated class of workers primarily to direct and manage the production of others. Everything from technology to cost-cutting efforts has reduced the need and impact of these middle managers, to the point where the role has been viewed with suspicion.

However, the current crop of AI tools is more like a brilliant new hire or intern than a self-managed knowledge worker. AI tools will make factually erroneous statements with authority and do not have the human capacity to self-assess their competence and use that information to appropriately alert areas where they lack expertise.

Just as humans early in their careers benefit from guidance, mentoring, and supervision, bots will also need experienced humans to oversee their performance. Bots are also extremely task-oriented, performing assigned tasks immediately and quickly. However, they lack any ability to manage their workload beyond current tasks and are unable to break down a complex or ill-defined objective into component tasks.

For example, a relatively simple task like “Give me about 8 slides for next week’s executive presentation,” a task that any mid-level knowledge worker could accomplish with ease, would completely confuse the current generation of bots. A knowledge worker has the context necessary to complete this request and the understanding that there are several distinct tasks required to fulfill this request, most of which could be successfully performed by bots.

These tasks can range from conducting independent research on various industry topics, talking to multiple co-workers about their projects, creating a storyline and writing associated content, and finally assembling that content into slides and sharing for review.

Interestingly, middle management skills that many organizations consider redundant will be extremely relevant in these types of scenarios. You will need a “driver” of sorts to break down these types of tasks into their component parts, assign them to the relevant bots, and then review and integrate their discrete results into a cohesive whole.

The other critical role needed as companies begin to deploy bots will be technicians who can effectively “train” these tools. Most bots today are generalists and trained on commonly available data like web content. While a fleet of low-cost generalists can certainly benefit your organization, exposure to internal data will ultimately yield the greatest benefit. Skills like being able to pull up last year's financials and do a quick year-over-year analysis are perfect for bots, as long as they are trained on your internal data.

This requires specialized technical resources and an understanding of what data is relevant. These roles are in increasing demand and are a great domain for a partner who can provide on-demand resources to fill. Technical partners can provide expertise in training and improving AI-based tools; however, you will still need people who know which data and systems are the right sources for training data.

Finding your drivers

Unfortunately, the trend away from middle management has deemphasized the skills that would help create a successful bot “driver.” The ability to take complex tasks, break them down into component parts, assign them to the right resources, and then reassemble and review the result is often lacking in most individual contributors.

In addition to general management skills, your conductors will also require some degree of subject matter knowledge. Bots lack human “information” when providing incorrect information and will confidently share information that is completely wrong and fail to develop the human trait of humility when corrected.

Ideally, your drivers have enough content knowledge to evaluate the results produced by your bots, or have access to internal or external experts who can examine the results of your team of virtual workers.

This creates a somewhat strange dichotomy, where routine tasks and information can be generated by bots, but in-depth knowledge is required to validate the bots' work, as well as respond to requests that are too complex for bots to complete.

Your drivers will need to manage both emotionless machines and highly specialized experts, managing collaboration and integration between each.

These types of individuals may not currently be on your technology teams, which presents a bit of a challenge. Leaders will be faced with the decision to turn knowledge workers into maestros or look for partners who can provide maestros, teachers, and the bots themselves. Alternatively, capable middle managers who exist outside of traditional technology roles may be well equipped to become AI drivers.

Look for individuals who have had success managing teams of individual contributors and integrating the results of their work, even if that individual is not a deep expert.

For technology leaders at an AI development company, what has made their teams successful in the past – deep knowledge, the ability to work as individual contributors, and minimal supervision or management requirements – is not necessarily the recipe for success in an AI-enabled team. the future.

While many view AI advances as “scary,” leaders will have to determine whether to invest in skilled knowledge workers so they can develop knowledge that exceeds the capabilities of current AI or shift it into a driver role. In all cases, the technology store of the future and the composition of personnel in these roles are poised for significant change.

If you liked this, be sure to check out our other articles on AI.