AI upskilling in the enterprise: the C-suite mandate decides it

The Businessolver study: 300 executives, 1,000 employees

Businessolver published the eleventh edition of the State of Workplace Empathy on 30 September 2026, the annual research that surveys 300 C-suite executives and 1,000 employees. The central finding ties AI upskilling in the enterprise to the mandate written at the top. Executives who name cost cutting as the main motivation for AI are half as likely to put training among their priorities^[1].

The sample is self-declared, the method is a survey with self-reporting, the collection belongs to 2026.

The useful reading comes from the order of the factors: the motivation stated at the top precedes the budget, and the budget precedes the classroom. Whoever designs AI as a lever for headcount reduction builds a portfolio consistent with that intention. Training people stays outside that portfolio in half the cases.

Three coordinates confine the reach of the figure to the perimeter of the respondents. Half as likely describes an association inside the sample, beyond a cause-and-effect relationship. The direction of the signal stays readable: the AI mandate written at the top arrives before any training plan.

The stated mandate becomes an investment portfolio

Executives who keep headcount out of the mandate invest in predictive analytics at more than double the rate of colleagues focused on costs.

The original Businessolver release^[2] reports two further gaps in the same direction. Fifteen points on time savings and productivity. Fourteen points on support for reducing people's administrative load.

Three aligned distances describe a structure, beyond a passing mood.

Predictive analytics serves to show where skills are missing before the gap becomes a delivery delay. A leadership team oriented towards cutting buys less visibility on that point. The decision about people therefore arrives with less information exactly where it weighs most.

The investment portfolio tells the mandate better than any public statement. A board that wants to know the organisation's real position on AI reads the spending lines, in the order in which they were approved.

Twenty-five points between those who see the risk and those who fund it

The most informative gap runs across the functions at the top. Among CIOs and CTOs, 88% fear the technology will outpace internal systems or people's skills. Among finance chiefs that share falls to 63%.

Twenty-five points of distance on the same risk, inside the same team. That figure belongs on a board agenda.

Whoever governs the technology observes every day the distance between the tool and the skill of the person using it. Whoever governs capital observes it through a twelve-month plan, where personnel cost appears before skills risk. The two readings enter the same meeting and produce two different budgets.

Alignment between these two functions is worth more than any training programme launched downstream.

The twenty-five-point gap is where this organisation's readiness lives.

Empathy read as an obstacle: 30% against 19%

The study also measures an attitude, beyond a budget. Almost one executive in three (30%) among those with a headcount-centred agenda sees organisational empathy as an obstacle to their business goals.

Among executives with other priorities for AI, that share falls to 19%.

Eleven points separate two ways of reading the same word.

In this research empathy works as a process variable: it describes how far a decision accounts for the conditions of the people receiving it. A leadership team that files it as a brake tends to compress the timeline of the transition. The transition stays the phase where new skills are formed.

The 30% who see an obstacle and the 19% who see a working condition represent two distinct populations, with limited overlap. They sit at the same table and sign the same industrial plan.

The figure on people: 1.5 times

On the people side the research reports a measurable figure. Those who receive adequate training report career progression, confidence and optimism up to 1.5 times more often than the others^[3].

The verb counts: the measure is self-reported, collected across 1,000 employees inside the same survey. A perception indicator, to read alongside performance indicators.

Even as perception the signal has operational value. Confidence and optimism predict the willingness to use a new tool in breadth, beyond a single task. Adoption lives inside that willingness, and readiness lives one step earlier.

For a CFO this is the most interesting line of the study: training produces effects that are measured on the retention and the progression of qualified people. Two items that carry a known cost, when they are missing.

Capacity and capability: two distinct decisions

The counter-argument deserves room.

A leadership team under pressure on margins has legitimate reasons to revisit the size of its headcount, and technology offers a credible way to do it.

The study speaks to the sequence, beyond the choice. Reducing productive capacity and building skills stay two distinct decisions, and the second requires an investment that the first frees up only in part.

Sony SungChu, chief AI officer at Businessolver, ties the value to people: AI produces results when skills and confidence arrive before the tool. Whoever compresses capacity and defers capability puts at risk exactly the productivity gains they were chasing.

This dynamic already has a documented precedent: AI introduced inside untouched processes generates passive supervision, a work-design defect. Cutting decided before redesign produces the same outcome, on a wider scale.

What the organisations closing the gap do

The organisations closing this gap share observable behaviours.

• They state the AI mandate in writing, with the relative weight between efficiency and skills development
• They align CIO and CFO on the same measure of skills risk, before the budget discussion
• They convert internal talent towards the new roles, alongside external recruitment

External recruitment stays useful for the skills the organisation lacks entirely. Internal mobility covers the rest, at a lower cost and with knowledge of the context already acquired.

Converting internal talent costs less than a dismissal followed by a rehire, and it requires a mobility infrastructure that few organisations possess today. Those that possess it turn a reorganisation into a skills transition.

The first behaviour costs a meeting. The second costs an alignment of metrics between two functions. The third costs a system, and produces the most visible effect on the retention of qualified people.

This evidence reaches four tables with four different questions.

• CEO: which AI motivation is stated to the board, and which budget line confirms it
• CHRO: how much of the training plan covers the roles exposed to redesign
• CFO: what documented return people development brings, measured on progression and retention
• Talent and Compensation Committee: which human capital metric enters quarterly reporting

One design question closes the picture, with an answer that sits in the accounts. How much of this organisation's AI budget funds people's skills, and how much funds the replacement of tasks? The ratio between those two figures measures readiness better than any statement of intent.