Aligning Skills, Tasks, & AI For Better Workforce Decisions
Artificial intelligence is reshaping the world of work faster than most organizations can adapt. Automation is accelerating, but clarity is lagging behind.
Beamery’s new research, Inside the Human-Machine Economy, reveals that while 99% of C-suite leaders say their organizations are automating tasks, nearly half admit they struggle to decide which tasks to automate – and over a third of employees think their company is missing bigger opportunities to do so.
It’s a stark finding, and one that highlights a growing challenge for leaders in the age of AI: automation is easy to start, but hard to steer. Without a clear understanding of the work being done across teams, automation decisions risk being guided by instinct rather than evidence. The result is diminishing returns from even the most sophisticated technology.
This is where task intelligence – the ability to map, analyze, and connect the work people actually do – becomes essential.
Confidence Without Clarity
Ninety-nine percent of C-suite leaders surveyed by Beamery said they are confident they’ve made the right workforce decisions around AI adoption. Yet most admit they lack a detailed view of how work really happens inside their organizations.
Only 29% say they assess task duplication across roles when deciding what to automate, and fewer than half consider the potential for time savings or process simplicity.
In other words, companies are automating tasks – but not always the right ones. And when AI is applied without clear visibility into workflows, it can easily amplify inefficiency rather than eliminate it.
The Critical Importance Of Skills & Tasks Data
For years, organizations have focused on skills as the key to better workforce planning. Understanding what employees know, and can do, has been vital for reskilling, hiring, and career development. But as AI takes on more “work”, a skills-based approach alone no longer provides enough insight.
AI doesn’t do jobs – it performs tasks. To plan effectively for a blended workforce of humans and intelligent agents, organizations need to understand work at the task level: who does what, how often, and how each activity connects to business outcomes.
Task intelligence fills that gap. It provides leaders with visibility into the work happening across functions, allowing them to see where duplication exists, where automation could make the greatest impact, and where human expertise adds the most value.
When combined with skills data, this creates a complete, dynamic picture of both supply (the skills available) and demand (the work that needs to be done).
This is no small shift. It moves workforce planning from static job descriptions to a living model of how value is created – one that evolves as technology, markets, and capabilities change.
The Enterprise Challenge: Coordination At Scale
Independent AI pilots often begin with good intentions. They promise quick wins and create enthusiasm within teams. But when every department runs its own experiments, the result is tool sprawl and fragmented insight.
Research from MIT shows that 95% of corporate AI initiatives deliver zero measurable return. The solution isn’t to slow down, but to connect efforts. Enterprise-scale AI adoption requires coordination: ensuring that automation initiatives reinforce each other rather than compete. That means connecting the dots between people, skills, and tasks across the organization so leaders can prioritize the right opportunities.
Beamery’s Workforce Intelligence Suite supports this coordination by helping leaders visualize work and make evidence-based automation choices. By linking skills and task data to business performance, it reveals where AI can create the most enterprise value – not just the quickest local gain.
From Gut Feel To Data-Driven Decision
Without accurate data, even experienced leaders rely on instinct. But instincts can be incorrect, especially in fast-changing environments where job roles evolve faster than reporting structures.
More than a third (37%) of HR leaders we surveyed said that not having enough clarity on what people actually do day to day was a top barrier their organization faces in preparing the workforce for AI.
By combining validated skills and task intelligence, organizations can shift from assumptions to insight. They can see not just who their people are, but what they do, how they work, and where they could be redeployed as automation changes demand.
This clarity unlocks smarter decisions around:
- Automation: Targeting repetitive or duplicated tasks to free people for higher-value work.
- Reskilling: Identifying where human capability should evolve as AI reshapes workflows.
- Redeployment: Moving employees into new roles where their skills can be applied more effectively.
The result is a more agile organization – one that can continuously reallocate human and digital capacity as business priorities shift.
Aligning Technology With Strategy
Technology should serve strategy, not the other way around. Deloitte research shows that 42% of failed tech investments stem from unrealistic business cases or insufficient data. The same applies to AI. Before choosing a platform or pilot, leaders should ask: What are we trying to achieve?
With a connected, data-rich understanding of their workforce, leaders can prioritize automation efforts that truly matter: those that align with cost pressures, strategic goals, and human potential.
For example, task intelligence can reveal overlapping workflows across departments, allowing leaders to streamline processes before automating them. It can show where AI augmentation would increase accuracy or speed, and where human creativity or judgment remains irreplaceable.
This kind of evidence-based decision-making turns AI from an experimental add-on into a coordinated source of competitive advantage.
The Path To Precision
Organizations that align skills, tasks, and AI gain more than efficiency – they gain foresight. They can anticipate how work will evolve, prepare employees for the shift, and ensure every automation investment creates measurable value.
It’s this visibility that transforms workforce transformation from a guessing game into a science. Leaders can predict not only where automation will save time or cost, but how it will reshape roles, unlock innovation, and strengthen resilience.
The future belongs to those who automate with intent – guided by data, aligned across the enterprise, and grounded in a clear understanding of human capability.