AI is already influencing how work gets done, how organisations operate, and how value is created. Yet, for many businesses, conversations about AI still focus on tools, pilots and experimentation rather than the underlying talent strategy required to make it work.

This is the disconnect where risk creeps in.

As Sarah Blanchard, Head of Talent Advisory at Solve, puts it:

“The critical question isn’t whether to adopt AI. It’s whether the organisation’s talent strategy is designed to capitalise on it responsibly and at scale.”

In 2026 and beyond, AI won’t automatically replace people, but it’ll expose organisations whose talent strategies are built on outdated assumptions.

The real impact of AI is structural, not just technical

Much of what first took over the public discourse around AI centred on job displacement: Will roles disappear? Will machines replace humans?

While those questions matter, they distracted many from a more immediate issue: AI is changing the structure of work itself.

Tasks are being unbundled from roles, and skills are becoming more fluid and transferrable. Work that once required full-time roles can now be automated, augmented, or redistributed across technology, partners, and alternative talent models.

Organisations that still rely on static role definitions and headcount plans are finding it increasingly difficult to adapt.

“Traditional workforce models assume stability,” Sarah explains. “But AI and automation are fundamentally breaking those assumptions.”

This is why modern talent advisory services focus less on roles and more on capability, and on how that capability can be mobilised as conditions continue to inevitably change.

Why AI readiness is really a talent issue

Many organisations are feeling the pressure to “do something” with AI, and without the right talent foundations, AI initiatives often stall, create unintended risk, or fail to scale.

More than just the technology, AI readiness requires:

• The right skills to design, manage, and govern AI-enabled work
• Leaders who can make informed decisions in ambiguous environments
• Clear accountability for how AI is used
• Workforce models that support flexibility rather than rigidity

“Adoption alone doesn’t create value,” Sarah notes. “Capability does.”

This is where integrated talent advisory becomes critical in helping organisations align AI ambition with workforce capability, leadership readiness, and governance.

Governance matters more than ever

As organisations explore the new possibilities with AI, governance is often treated as an afterthought with policies retroactively written up as tools are already in use. Measures of success remain vague, and accountability continues to be unclear.

This creates operational, reputational, and ethical exposure, and Sarah sees it regularly when speaking with senior leaders:

“They know they should be doing something with AI, but they don’t always know exactly what that should be.”

A strong talent strategy helps close that gap by clarifying:

• Which capabilities are required to use AI responsibly
• Where decision-making authority sits
• How risk is managed alongside innovation

Without this foundation, AI can magnify existing weaknesses rather than delivering meaningful advantage.

Capability is the planning unit, not just job titles

One of the biggest mistakes organisations make when planning for AI is getting too wrapped up in trying to predict future roles.

Many AI-related roles don’t exist yet, and some will evolve faster than standard job descriptions can keep up. As a result, workforce planning around job titles alone is a losing game.

Instead, future-ready organisations focus on:

• Core capabilities that underpin value creation
• Skills that can evolve as technology changes
• Transferable capability across different contexts

“How do you plan for roles that don’t exist yet?” Sarah asks. “You plan for capability.”

This shift also supports smarter decisions about whether capability should be built internally, sourced from the market, accessed via partners, or supported through a contingent workforce. Understanding what contingent workforce is in this context is less about short-term resourcing, and more about strategic flexibility.

Leadership capability is the hidden constraint

Technology rarely fails on its own and, more often than not, AI initiatives stall because leaders aren’t equipped to manage new ways of working.

Leading hybrid, blended, and AI-enabled teams requires different skills: decision-making in uncertainty, accountability for workforce impact, and comfort with continuous change.

Sarah describes leadership capability as a “hidden constraint”:

“Sometimes workforce change fails not because of technology or change management, but because leaders lack the capability to lead in an AI-enabled operating model.”

Without leadership readiness, even will-designed AI strategies struggle to deliver value, and retention risks increase as workforce expectations go unmet.

AI will reward the prepared and expose the rest

AI itself isn’t the threat many workers and organisations fear. The real threat is attempting to layer AI onto talent strategies that were designed for a different era.

Organisations with capability-led talent strategies will use AI to enhance productivity, create flexibility, and unlock new sources of value. Those without will find that AI simply highlights existing gaps in skills, governance, and leadership.

As Sarah puts it:

“The organisations that succeed won’t be the ones who adopt AI fastest, but the ones whose talent strategy allows them to use it effectively.”

AI won’t replace your workforce, but it will make very clear whether your talent strategy is fit for the future.

Want to discuss where you’re at with your business’ talent strategy? We’re happy to chat.