by Chantal Haynes-Curley
When generative AI exploded into the corporate consciousness, initial impressions viewed it
as a productivity enabler, a tool that would drive human capital gains and unparalleled
operational scale. Eager to gain competitive advantage organisations poured investments
into AI infrastructure. However, under pressure to deliver returns to shareholders, attention
turned to treating AI as a mechanism for reducing headcount costs, rather than
a productivity enabler. This leaves HR leaders facing the challenge of how to respond when
the business expects AI adoption to elicit significant headcount reductions.
The answer lies in skills based job architecture (SBJA), a mechanism for calculating and
forecasting human capital advantage where AI can’t compete. It involves moving away
from static job profiles and toward an dynamic internal talent marketplace that
maximises human capability to work alongside automated efficiency. A practice that
streamlines and future proofs resource requirements through data, internal and
external market analysis.
AI Transformation and reduction in Human resource dependency
Many investors are looking at recent AI advancements as a modern gold rush, an
opportunity to increase output while significantly reducing labour costs.
The logic is simple: if AI systems can automate jobs like customer support,
software development and data analysis, investors can expect much higher profit
margins and faster growth. So why then has the move toward AI automation started
to create massive disruption across organisations?
An aggressive pivot away from human capital investment, toward AI infrastructure, has
resulted in AI becoming the leading reason for corporate downsising across the U.S in
recent years. According to data from outplacement firm Challenger, Gray & Christmas,
across the US there are 87,714 AI attributed redundancies year to date, up from 54,836 in
2025. Data from SkillSyncer Layoffs database, which tracks workforce reduction across
technology and adjacent sectors shows AI automation accounts for 55% of global corporate
downsizing events, year to date.
What started out as an aggressive pivot to capitalise on productivity gains from AI
automation, has resulted in what industry observers are calling the “AI Boomerang”
effect.Increasingly companies are realising they acted too quickly in making headcount
reductions,overindexing on AIs ability to manage a business process from end to end,
creating an operational nightmare of hollowed-out enterprises that lack the
human resources required to successfully deploy, optimise and govern the AI agent
workforce they have invested in.
To combat this, a massive course correction is already underway, with recent studies from
the two most prominent technology research and advisory firms, Gartner and Forrester,
showing that 55% of employers already regret their AI driven workforce reductions.
The cost of short sighted automation strategies is compounding; according to Gartner 50%
of companies that replaced operational employees with AI, are now being forced to restaff
those roles under different titles. This costly error is captured in the data from LHH (Adecco
Group) that shows 73% of HR leaders find rehiring lost talent significantly more expensive
than it would have been to redeploy them internally using a talent reallocation strategy.
Another indirect consequence of this knee jerk reaction to AI automation, is that in addition
to the tangible cost of rehiring, short sighted layoffs have potentially eradicated decades of
institutional knowledge,the very information needed to train AI agents. Too many
organisations acted prematurely, before fully understanding the new landscape is an AI
adjacent workforce.
The reality of high speed skills migration
A holistic AI workforce takeover is highly unlikely, what is apparent is that AI is
causing an aggressive, high-speed skills migration, something that HR must immediately
get in front of. Global forecasts from the World Economic Forum, McKinsey,
and Goldman Sachs, predict net positive job growth over coming years. Full substitution
will only sit at around 10–15% of the market by 2030, according to Boston Consulting
Group(BCG), whereas 50–55% of all existing roles will be fundamentally reshaped by
AI automation. AI is reforming jobs not absorbing them
Workforce planning in the age of AI
To maximize AIs ROI for organisations, HR must start managing capability through careful
monitoring of current and future skills.
Traditional workforce planning frameworks most commonly employed today,
are deeply rooted in “Scientific Management” or
“Taylorism”,
which originated in the early 20th century, in response to the structural changes of the
industrial revolution. For almost a century this system has served its purpose, aiding to
forecast human resource requirements to fit rigid corporate structures, within a highly
predictable, slowly evolving macro environment. When generative AI emerged it rendered
this century-old framework of managing talent through the lens of fixed job titles,
static departments and rigid headcount budgets obsolete overnight.
A static job role, tied to traditional job architecture lacks the granularity and
sophistication required to forecast capacity in an AI augmented, AI adjacent workforce,
one where machine capabilities are absorbing tasks at a relentless velocity.
A simple restructured approach is needed; AI automates tasks which are powered by skills.
When a collection of skills within a specific job role gets absorbed by AI, the job itself either
transforms- based on a calculated assessment of the value of remaining skills- or it
dissolves. Therefore a job is no longer viewed as a fixed title tied to a static job description
but a dynamic grouping of skills that can be reconfigured in real time. Strategic workforce
planning is not about counting heads; it’s about mapping and forecasting dynamic skills
requirements.
By deconstructing traditional roles into their underlying skills, through SBJA,
HR gains the data-driven visibility needed to precisely map where AI agents deliver
the highest productivity gains. This approach allows organisations to seamlessly
reallocate human capacity to high-value strategic work, targeted upskilling, training
AI agents and critical knowledge retention. This is what turns automation into a
competitive advantage, rather than a workforce disruption.
Skills based job architecture (SBJA)
Traditional Job architecture limits internal mobility optimisation as it views a
job role in relation to the tasks it performs.Consider how this plays out in
the current landscape, taking customer support operations as an example. Here 70
percent of this role’s tasks are related to routine ticketing, when AI agents automate
routine ticketing the logical step is to make this role redundant. However, as AI
can’t govern itself the organisation faces new unforeseen talent demand for
AI Content managers, and system auditors. What would have been considered a headcount
saving has triggered hidden costs to productivity and headcount budgets.
The extended time to fill for an emerging skill set, recruitment fees, loss of
institutional knowledge all adding up overnight.
This happens because the traditional model does not allow for effective oversight
of what skills customer support agents hold within their arsenal that would enable
a logical move to a redefined role, in this example AI content management.
Workforce planning is restricted to hiring and firing.
SBJA works by identifying all of the underlying skills related to a particular role and
clustering job roles based on shared skills. It evaluates roles not by their functional area,
or the current tasks undertaken, but through the roles underlining skills DNA. Therefore,
when tasks are absorbed through AI, the immediate solution is not to make the role
redundant; it is to isolate the remaining human skills and inject new capability
requirements into the profile. Roles morph in line with changing demands, the organisation
saves on talent acquisition costs,achieves immediate realized ROI, and protects vital
institutional knowledge.
Should SBJA be applied in the example above, the underlying skill DNA of customer
support agents would be easily identifiable as routine troubleshooting, human empathy,
and knowledge base preparation. When AI handles the troubleshooting, HR doesn’t make
the customer support role redundant; they isolate the remaining human skills
(Human empathy and knowledge base prep) and instantly inject a new capability
requirement into their profile, in this case AI Agent orchestration. The role is
redefined in real-time and there is no need to go to market. The job profile transitions
from answering tickets to training, auditing, and governing the AI agent.
The internal talent marketplace
Another significant gain of adopting a SBJA is the mobilisation of an internal talent
marketplace. Traditional Job architecture is structured around job families that are tied to
functional departments ( HR, Finance, Sales, etc) and vertical in nature. It is rare to
migrate talent from one functional area to another.
SBJA organises job families into dynamic groupings of shared skillsets, with soft boundaries
unrelated to functional areas; the commonality is the underlining skills DNA. Talent is not
isolated, capability is visible and easily mapped.
Consider this in the context of front line commercial roles. In a traditional model Account
executives (Sales) and Customer success managers are segmented across two separate
functions, Business development and Client services. A SBJA model recognises that both
roles share core skills, such as commercial negotiation, stakeholder management and
product expertise to name a few, therefore they group these roles under the same
capability/skills cluster.
Using a traditional approach, where the business development function has decided to
deploy AI agents to automate lead generation and initial sales pitches,
it creates a reduction in headcount requirements. This leads to several redundancies
affecting long tenured employees. At the same time the client services team is
experiencing turnover and a growing demand for customer success managers, so HR
responds by launching an expensive time consuming external recruitment search.
This approach is not only costly, it has completely overlooked the ready made,
highly qualified, knowledgeable talent that has been made redundant owing to a lack
of oversight on skills transferability.
Now let’s look at the SBJA approach, as both roles sit within the same capability cluster, HR
can easily map the displaced sales professionals to the resource requirement in client
services. The core skills and product knowledge are identical, the upskilling gap is minimal
and the move represents significant savings to productivity, cost of hire,
redundancy payouts and workflows. The organisation is able to efficiently mobilise
talent across functional boundaries to protect revenue, retain institutional
knowledge and add value.
The changing role of HR as Capability Architect
Forward thinking HR leaders understand that the future of workforce planning is
increasingly more strategic, heavily dependent on forecasting and data analytics.
By organising their workforce around a SBJA model they can leverage data from global
advisory firms such as AON, and Willis Towers Watson to track AI automation velocity
and premium skills ( those emerging and those insulated from AI automation).
Equipped with these insights HR can effectively engage in workforce planning utilising
a build, buy, borrow, bot model that aligns human capital with technological advancements
and capabilities.
For organisations to survive the chaos of AI disruption, HR must keep ahead of the data
rather than relying on historical information to inform future changes, legacy frameworks,
reactive recruitment and headcount planning are entirely unfit for the realities of an AI
augmented workforce. Workforce planning needs to be modelled around a SBJA model and
data. To achieve this, HR must get comfortable working alongside the AI tools that enable
data analytics and to track internal capabilities, identify critical gaps, phase out obsolete
skills and predict emerging needs. HRBPs will assume the role of strategic capability
architects, uniquely positioned to map, mobilise and multiply both AI and human capital.
About the author
Chantal is an experienced HR Professional, content creator and CIPD Qualified HR Business Partner. She has worked across a range of sectors to include Government, INGO, and professional bodies. Experienced in UK and Irish employment Law, her areas of expertise include Human capital strategy, Recruitment and selection, Organisational Culture, Wellbeing and Compliance.
