Workforce planning exercises can remain frustratingly imprecise despite the people team’s best efforts.
The reason for that is that workforce planning requires bringing together different types of information from both Finance and HR: historical performance data that is measured differently from one department to another, ambiguous information on the current state of the talent market and the company’s workforce, and company-level plans that sometimes span up to five years in the future. However, by trying workforce planning with AI technology, companies might be able to dramatically improve their accuracy.
The best workforce planning practices are not always scalable
In 2010, following the Global Financial Crisis and its impact on the car manufacturing industry, Ford sold Volvo to a Chinese auto company, Geely. Volvo was already facing a few challenges: an indifferent brand, unclear market positioning, and low profit margins. In addition, it now had to become a self-led organization who could draw its own path, after years of executing on Ford’s highly prescriptive strategic guidance.
There were a few challenges with that plan, of course, but the biggest ones were all around talent. Volvo’s culture was not entrepreneurial at all, and a bold entrepreneurial spirit was needed to successfully turn it around. In addition, the company hadn't been preparing for the future, and needed to start hiring the kind of talent that would allow it to become a premium car maker. The CFO and CHRO of the company both quickly realized that they needed a flawless workforce plan to take them through the next three years.
Starting 2011, the two leaders worked closely together to achieve their goals. To build premium cars, they looked for luxury design skills in the fashion industry and among wood craftsmen. They hired sales and marketing people from Google to raise the quality of their customer insights. They found their software engineers in Nokia to build their cars’ navigation and entertainment systems.
They also looked at their existing workforce and realized that they would need to reallocate a large portion of it, and instigate cultural shifts from the ground up in every department. It was a massive undertaking, needing months of careful data gathering and analysis. You can read the whole story in Talent Wins.
Volvo is now doing better than ever. While we can safely say the people planning exercise was successful, the point of the story is that it was an exceptional measure that was needed to turn the company around. Going through that manual process with the same level of granularity and detail for every planning cycle is not scalable. That is where using talent AI for workforce planning can make a difference.
How to improve workforce planning with AI
Why was the Volvo new planning process so impactful? A few reasons were at play here, as the leadership team didn’t want to compete for talent with other car makers who were at the time on better financial standing.
On one hand, they went deeper into the roles needed, and translated them into skills and areas of expertise before starting to look for candidates. On the other hand, the people organization called on its own subjective knowledge of the talent market to find these skills outside of the automotive industry were it usually hired. How much faster and more powerful would the whole workforce planning exercise have been, if those two actions could have been taken over by an algorithm?
Most talent tools today deal in job titles and company names, but the right AI technology can translate large databases of job titles and company names into skill families, industries, or functional expertise. It can also relate financial information and market insights to hiring needs, making workforce planning with AI far more powerful than its current alternative.
Talent data platforms enable better recruiting AI
True talent AI wasn’t possible before, because talent teams did not have reliable datasets to use. Machine learning algorithms, especially, can only be truly successful if they can use large amounts of clean, uniform, up-to-date data to “learn”, so to speak. Otherwise, they will simply spit out inaccurate recommendations and forecasts—we’ve written more about that here.
Now, however, with the emergence of talent data platforms, AI algorithms can produce reliable results, and talent tools can interpret nuanced information, such as how skills relate to each other or to different job families, functions, or industries.
Data platforms exist in other areas of the business already. A data platform is defined as “an integrated technology solution that allows data located in databases to be governed, accessed, and delivered to users, data applications, or other technologies for strategic business purposes.”
Similarly, a Talent Data Platform is the foundational technology of the modern talent acquisition tool stack, and acts as the single source of truth for talent-related information. Done right, it becomes the central database that unifies information across solutions, and surfaces meaningful, reliable data to every other talent tool.
The talent operations function can own more of the workforce planning process
Talent teams have been professionalizing their talent operations function and bringing in practitioners who have the right tool for this kind of job.
Up until now, workforce planning was a disjointed exercise where Finance and HR tried to work together to ensure that there would be people available to execute on the companies immediate and future plans.
Most of the time, Finance would own the strategic aspect of the exercise. By looking at past performance and future strategic plans, it would estimate an approximate number of hires in each department needed to deliver on the company’s 3 year or 5 year plans. The HR organization would try to hit those numbers with the resources assigned, and the handover line between the two functions would vary depending on the company.
With talent operations teams bringing in resource planning and analytics capabilities, the people organization, and talent teams in particular, can own more of that planning process in two different ways: they can go further upstream in the process, and they can bring more granularity to it by expanding the depth of information used for forecasts.
Talent operations professionals have the analytical skills needed to accurately model how talent flows in and out of the company. With access to the right information on planned business initiatives, and some support from the Finance team, they can translate plant openings or geographical expansions into job openings and hiring forecasts. They do not need to rely entirely on the finance function to deliver that part of the workforce planning exercise.
In addition to that, talent operations teams’ analytics and reporting capabilities can quickly surface how much time was needed to hire specific types of roles, and the drivers behind the cost and speed of each type of hire. How is the market for this or that role going to evolve in the next five years? Will it be more expensive because of the location or the skill set required? Can it be fulfilled by a junior recruiter or will it need the capabilities of a more senior team member? They can answer these questions quickly and make workforce planning far more accurate.
In an ideal process of workforce planning with AI, the human part is to decide what factors should be taken into consideration by the algorithm, and to help calibrate the software so its accuracy improves over time. Instead of relying on human memory and creativity to find the right skills in places where the competition isn’t looking, we let the machine surface suggestions, and simply decline the ones that are unsuitable. Over time, the machine will give fewer and fewer unsuitable suggestions. With even more time, AI can even learn to decide which factors should be added or removed from the decision making process.
Implementing this kind of technology and upskilling recruiters to use it takes time, however. That is why People organizations and CHROs need to start looking now into workforce planning with AI if they want to come on top during the next economic cycle.