The People Side of AI Race

Winning the corporate AI automation race - is it about technology or people?

Let’s think about it. Technology can be the key differentiator when one player can secure exclusive access to some game-changing functionality, and leverage it to win. That is generally not the case with the corporate AI race. Most companies will have access to relatively the same powerful disruptive technology. But some will be faster in adopting it, and thus better positioned to win. Why? The answer is people.

To adapt faster, businesses need to have three key capacities:

1. The ability to differentiate talent, and see the people to bet on. That requires distinguishing different kinds of strengths, which cannot be done by simply looking at the track record, because good results in the “old world” may not directly transpire into the “new world”.

2. The ability to leverage the key talent. That means having an effective process and system that connects strong internal talent with internal opportunities that can be projects or full-time roles. And this process should run on its own, driven by employees and managers, not HR.

3. The ability to engage people in the midst of disruptive change and uncertainty. To do that successfully it is no longer enough to talk to people in townhalls and recognize them with various awards. Companies need to track engagement in real-time at all levels and in all teams and proactively address issues where and when they arise.

Differentiating Talent

The mid-XX century conventional talent differentiation methodology that is still used widely bets on the manager's view and assesses employee behavior and results using the company competency model (behaviors, values, etc.) along with high-level yearly goals as criteria. For our days this approach is too slow, inaccurate, and doesn’t produce enough actionable data. Back when it was created organizations were much more hierarchical, slower-paced, and didn’t have the technology that we now have, along with the quick feedback culture brought up by social media. Now, having multiple project teams, matrix managers, and all sorts of horizontal relationships we simply cannot expect that managers will see the full picture, and often even understand the rapidly evolving skill sets of their direct reports. And, of course, today it is much more likely that we may lose or underutilize our high-potential employees if we don’t have real-time information on them. On top of that, competency models, though very helpful in translating corporate culture, are very far from optimal when it comes to showing different kinds of strengths. Companies normally bulk those competencies (behaviors) all together, and that creates just one dimension to look at. As a result, specific individual strengths are often averaged out.

Why should we care? What are we losing? And does it have anything to do with AI? To answer those questions let’s imagine two companies. Company A promotes and praises only employees who are good at following instructions and providing timely reporting. Company B also promotes such employees but has a separate way of promoting disruptors that can drive drastic improvements. Will the fact that Company B has more than one dimension for talent differentiation play a role when these two compete in the AI automation race? The answer is clear, isn’t it? And, of course, if Company B on top of that has real-time 360 talent data, while Company A only relies on yearly evaluations from managers, that will make Company B’s advantage even more obvious.

Leveraging Talent

It is not enough just to see the key talent. To win the AI automation race your company should leverage the key talent better than competitors. What does that mean practically?

One of the most common ways to categorize employee turnover is to break it into regrettable and non-regrettable. Obviously, not all turnover is bad. When toxic employees or lower performers are replaced by engaged high-performers it is actually good for your organization. But, of course, when strong talent leaves, the company loses money and opportunities. Leveraging strong talent means using its full potential and doing all that can reasonably be done to keep it. That way avoidable regrettable turnover is minimized. But how do we do it?

The short answer is this - you connect strong performers with internal opportunities. To do that you need to create effective internal talent and project marketplaces. You should have processes and systems that enable structured and user-friendly search for talent and for opportunities. In this case, managers who have full-time or project openings will have the talent search tool that allows them to find internal candidates quickly, while employees who want to develop within the company will have a way to distinguish themselves, making their profile more visible and attractive in the search outputs. Such processes and systems create powerful synergies, and in the longer term enable smart career management that boosts company talent capability.

Building and implementing such systems requires a thoughtful approach and a great deal of support from the very top of the organization. On one hand, the system should make sense for employees, meaning that they should clearly see what can be done there, how to do it, and how it can help them get where they want to be. The effort associated with building and maintaining their profile should feel insignificant compared to the opportunities and value that they get back. Thus, employees should themselves want to use the system, without being asked. On the other hand, top managers should champion the tool, nurturing corporate culture that enables talent development. This will require challenging the silos mindset and incentivizing managers to share the talent in their teams and support employees in their longer-term development even when that means letting them go to a different team.

Engaging Talent

Engaging talent is the third key capacity that businesses need to win the AI automation race. Let’s go over the key components of employee engagement, and look at common practices, as well as practices that can get you ahead of the curve.

Engagement is not just about making employees feel good about their work. It’s about making them feel valued, heard, and involved in the company’s mission and goals. It’s about creating an environment where employees are motivated to give their best and feel a sense of ownership and commitment towards their work. And finally, it’s about having employees excited about the path ahead - opportunities to develop their careers within the company.

It is common to improve employee engagement by communicating the company mission and goals, giving updates on strategic priorities and company progress, sharing success stories, recognizing the top performers, conducting engagement surveys 1-2 times a year, and adjusting company practices based on employee feedback. It is also common to use employee training and teambuilding activities as a way to improve employee engagement. 

It is not common yet to track engagement in real-time and have the capability to constantly monitor engagement across all teams. It is also not common to systemically assess managers based on their team’s engagement data. But the ability to do those things effectively, along with the ability to empower employees in building their careers internally, will define the winners in AI adoption. As roles and responsibilities evolve rapidly, employees may feel uncertain or anxious about their future in the organization. This is where real-time engagement tracking comes into play. By monitoring engagement levels across teams and at all levels, companies can identify potential issues before they escalate and take proactive measures to address them.

With the detailed engagement panorama companies can apply more than just a one-size-fits-all approach to improving engagement. They become empowered to make smart and timely talent decisions and focus their resources strategically.

In conclusion, winning the corporate AI race is not just about having the most advanced AI technology. It’s about having the people who can harness that technology effectively. And that requires systemic work in differentiating talent, leveraging talent, and engaging talent. Companies that can do this will be the ones that come out ahead in the AI automation race.