TL;DR
Core thesis: Technology doesn’t just enable HR to work differently. It shapes what HR can conceive. The “resistant to change” narrative blames people for tool constraints.
The analogy: The 1986 Ford Taurus wasn’t curvy because Ford decided to be trendy. It was curvy because CAD software finally allowed designers to compute curves. For 25 years, HR technology (ATS, HRIS, workforce planning tools) was built on relational databases that demanded angular, structured thinking: keywords, job codes, headcount, funnels.
The shift: AI-native platforms (Eightfold, Gloat, Phenom) and talent intelligence providers (Lightcast, Revelio, Draup) now enable inference, adjacency, and semantic understanding. This is HR’s “curved surface moment.”
The reframe: HR culture isn’t resistant to change. It’s been shaped by its tools. Give recruiters technology that understands candidates as whole people, and they’ll start treating candidates as whole people. Culture follows capability.
The call to action:
Pay attention to what technology makes thinkable, not just what it does
Become a demanding customer (ask: what assumptions are baked into this architecture?)
Partner with vendors who build with you, not just for you
The punchline: Next time someone says HR needs a culture change before it can transform, ask them what tools they’re using. The answer might explain more than the culture ever could.
In 1985, Ford was weeks away from bankruptcy. The company had lost $3 billion in the preceding years, its product line was stale, and Japanese automakers were eating its lunch. The solution wasn’t a strategy consultant or a new CEO. It was CAD software.
The 1986 Ford Taurus arrived with curves. Not gentle curves, but full aerodynamic swoops that earned it the nickname “jelly bean.” Motor Trend called it “the shape of tomorrow.” Critics compared it to a flying potato. It didn’t matter. The car sold over 200,000 units in its first year and single-handedly pulled Ford back from the brink.
Here’s the thing most people miss about the Taurus: Ford didn’t suddenly decide to make curvy cars. The technology enabled them to think in curves for the first time.
Throughout the 1970s and early 1980s, automotive CAD software could handle straight lines, angles, boxes. That’s what the computers of the era could compute. So that’s what designers designed. The boxy muscle cars of the 70s, the angular sedans of the early 80s. These weren’t purely aesthetic choices. They were what the tools could produce.
When 3D modelling software evolved in the mid-1980s to handle complex surface geometry, designers like Jack Telnack at Ford could finally explore what they’d been imagining. The DN5 project team used 90% computer-generated body panels on the Taurus, something unthinkable just five years earlier. The rounded shape wasn’t a trend. It was what happened when constraints lifted.
This phenomenon repeated across every design discipline through the 1990s. The term “blobjects” was coined by designer Steven Skov Holt in 1993 to describe the organic, fluid shapes suddenly appearing everywhere. Designers like Karim Rashid and Ross Lovegrove built entire careers on curves that earlier tools simply couldn’t render. The Apple iMac G3 in 1998, with its translucent, teardrop-shaped case, became the defining aesthetic of the dot-com era. Jony Ive’s design team at Apple used emerging CAD capabilities to sculpt forms that would have been impossible to prototype a decade earlier.
The principle is clear: technology doesn’t just enable execution. It shapes conception. What we can model, we can imagine. What we can compute, we can conceive.
And nowhere is this principle more visible than in the evolution of HR technology.
The Database Logic of Talent Acquisition
The first applicant tracking systems emerged in the late 1990s, built on relational databases. This is important. Understanding the ATS means understanding what a database requires.
Databases need structured fields. They need discrete categories. They need entries that can be compared using boolean logic. Is this field true or false? Does this value match that value? These are the questions a 1998 database could answer.
So the ATS demanded structure. And recruiters provided it.
A job requisition became a set of requirements: degree (yes/no), years of experience (number), specific skills (list), job titles (exact match). A candidate became a collection of parsed attributes: name, email, work history broken into discrete roles, education parsed into institution and qualification.
The technology didn’t ask “is this person capable of doing this job?” It asked “does this candidate record match this requisition record?” The first question requires judgment. The second requires keyword matching.
Resumes that didn’t include exact keywords were overlooked, even if the candidate was qualified. Job seekers learned to tailor their applications not to communicate their value, but to survive algorithmic filtering. The funnel metaphor took hold: applications in, conversion rates out, efficiency measured by speed and volume.
None of this was inevitable. It was the database demanding structure, and humans conforming to that demand.
Consider what the early ATS couldn’t do:
Parse “managed a 5-member DevOps team” as evidence of leadership
Recognise that proficiency in C++ might transfer to embedded systems work
Understand that someone with “Google Sheets scripting” experience might handle “Excel macros”
Infer skills from project descriptions rather than explicit skill lists
Evaluate whether someone’s career arc suggested they could grow into a role
These aren’t capability gaps. They’re category differences. The database could only compare what it could categorise. Everything else was invisible.
The Frictionless Flood
Then the internet made applying easy. Too easy.
The 1990s brought online job boards. Monster launched in 1999. CareerBuilder in 1995. Suddenly candidates could search and apply for jobs from their home computers. No more printing resumes, addressing envelopes, buying stamps. The application process that once took an afternoon now took minutes.
By the mid-2000s, “one-click apply” had arrived. LinkedIn, Indeed, and others let candidates submit applications with a single button press, their profile data auto-populating application forms. The friction that had previously limited application volume evaporated almost entirely.
The numbers exploded. A job posting that might have attracted dozens of postal applications now received hundreds or thousands of digital submissions. Corporate careers pages became firehoses. Recruiters who once managed stacks of paper now faced overwhelming digital queues.
This should have been a triumph. More candidates meant more choice, better matches, stronger talent pipelines. The technology had democratised access. Anyone could apply anywhere.
But the ATS wasn’t built for this. Remember: it was a database designed to store and filter. Its matching logic was crude. Keyword search. Boolean operators. Exact matches on structured fields. These tools worked adequately when volumes were manageable. They broke down catastrophically when volumes exploded.
Recruiters responded the only way they could: by adding filters. More required fields. More knockout questions. More automated screening layers. The goal wasn’t better evaluation. It was volume reduction. Get the pile down to something a human could actually review.
The candidate experience became collateral damage. Applications disappeared into what became known as the “black hole.” Candidates applied, received automated acknowledgments, then heard nothing. Ever. Research consistently showed that the majority of applicants never received any human response to their submissions. The systems were designed to process records, not to communicate with people.
The cruel irony is that frictionless application created friction-full hiring. The easier it became to apply, the harder it became to get hired. Candidates responded by applying to more positions, further increasing volume, further overwhelming systems, further degrading the experience. A vicious cycle with no obvious exit.
Job seekers learned to game the system. Resume optimisation became its own cottage industry. Candidates stuffed keywords into white text, reformatted their experience to match ATS parsing expectations, applied strategic repetition of exact phrases from job descriptions. The human being crafted a document designed to satisfy a machine’s pattern matching, hoping to survive long enough to reach a human reviewer.
Meanwhile, qualified candidates fell through the cracks. The ATS couldn’t recognise capability that wasn’t expressed in its expected vocabulary. Strong candidates with non-traditional backgrounds, career changers, people who described their experience in narrative rather than keyword-dense bullet points. All filtered out. Not because they couldn’t do the job. Because the database couldn’t see them.
The technology that was supposed to expand access to opportunity became a bottleneck that narrowed it. The tool designed to help recruiters manage volume instead created volume it couldn’t meaningfully evaluate. The system built to match candidates to jobs instead optimised for rejecting candidates at scale.
This was the state of play by the late 2010s. A fundamentally broken paradigm, straining under volumes it was never designed to handle, using matching logic that couldn’t recognise capability, creating experiences that frustrated everyone involved. Candidates hated it. Recruiters hated it. Hiring managers hated it. But it was what the technology permitted, so it was what existed.
The same logic extended throughout HR technology. Workforce planning became spreadsheet exercises with headcount as the unit of measure. Career paths became ladder rungs, discrete steps between job codes. Performance reviews became rating systems where complex human contributions were compressed into a single number.
The tools shaped the thinking. And for twenty-five years, HR thinking remained angular.
The Blobjects Arrive in HR Tech
Something changed around 2019-2020. The technology shifted, and with it, what became thinkable.
Talent management insight platforms like Eightfold AI, Gloat, and Phenom emerged with a fundamentally different technical architecture. Instead of relational databases comparing structured fields, they used machine learning models trained on hundreds of millions of career trajectories. Instead of keyword matching, they used semantic inference.
Simultaneously, a new category of talent intelligence providers emerged, focused on external labour market data. Companies like Lightcast, Revelio Labs, Talent Neuron, Draup, Claro, and Textkernel began offering deep insights into skills demand, compensation benchmarks, talent availability, and competitive intelligence. For the first time, HR could see beyond their own applicant pool to understand the broader market dynamics shaping talent strategy.
But the shift goes deeper than new vendors and new data sources. The fundamental paradigm of software design is changing, and HR technology is being swept along.
Software design in 2025-2026 is experiencing what designers call a “customer-first revolution.” The core shift is from reactive, static interfaces to anticipatory, hyper-personalised, and proactive experiences. Instead of providing a tool and waiting for users to figure it out, modern software anticipates needs, adapts to context, and integrates seamlessly into the user’s workflow.
Consider what this means in practice. Hyper-personalisation via AI means interfaces that learn user habits and preferences, customising layouts and workflows for each individual rather than forcing everyone through identical screens. Proactive service means systems that identify potential issues and address them before the user even notices, rather than waiting for support tickets. Conversational and multimodal interfaces mean users can interact via whatever method suits their context: typing for precision, voice for speed, gesture for spatial navigation.
The old HR technology paradigm was the opposite: static dashboards, reactive support, one-size-fits-all interfaces, mode-locked interaction. The recruiter adapted to the system. The candidate filled in whatever forms the system demanded. The hiring manager navigated whatever workflow the vendor had designed.
The new paradigm inverts this relationship. The system adapts to the user.
This is HR’s curved surface moment.
The tools can now handle complexity that earlier systems couldn’t compute.
Josh Bersin, the industry analyst, puts it directly: these systems don’t just look at words and matches. They can predict an individual’s capabilities, identify adjacent skills, and infer potential from patterns rather than explicit declarations. Eightfold’s algorithm, for example, trains on data from successful candidates to identify candidates with similar attributes. It learns to recognise capability even when it isn’t stated.
Skills inference means the system can recognise that someone who has done data pipeline work probably understands SQL, even if their resume doesn’t list “SQL” as a keyword. Adjacent skill mapping means understanding that machine learning and statistics overlap, that project management and stakeholder coordination relate, that customer support experience builds capabilities relevant to account management.
The shift goes beyond matching. Modern AI can parse unstructured text, understanding context and meaning rather than just pattern-matching keywords. Resume parsing has evolved from keyword filters to semantic understanding. Today’s AI recognises roles, responsibilities, achievements, and inferred skills even when they aren’t explicitly stated. According to researchers working on LLM-based recruitment tools, these systems can identify transferable skills like proficiency in C++ from experience with embedded systems, even if not explicitly stated on the resume.
Meanwhile, talent intelligence platforms provide the external context that internal systems always lacked. What skills are emerging in your industry? Where is talent concentrated geographically? How do your compensation packages compare to market? What’s the realistic supply of candidates with specific capability combinations? These questions were previously answered by gut feel and occasional salary surveys. Now they’re answered by data covering hundreds of millions of professional profiles and job postings.
Consider what becomes possible:
Instead of skills taxonomies: skill inference and adjacency mapping
Instead of candidate screening: candidate understanding
Instead of workforce planning as spreadsheet: dynamic scenario modelling informed by real labour market data
Instead of static dashboards: adaptive interfaces that anticipate what each user needs
Instead of the recruiter as process operator: the recruiter as talent advisor
The technology permits a more organic conception of talent. Just as the Ford Taurus team could finally design the aerodynamic shapes they’d imagined, HR leaders can finally conceptualise talent management in terms that match human reality rather than database constraints.
For candidates, the shift is equally significant. Instead of optimising resumes for machines and hoping to survive algorithmic filtering, they can present their actual capabilities and be evaluated for genuine potential. The career changer whose experience doesn’t fit neat categories can finally be seen. The non-traditional candidate whose narrative tells a compelling story can finally be heard. The technology that once made candidates invisible can now make them visible in ways that weren’t possible before.
The Scramble to Acquire Curves
The market has noticed. And the response tells you everything about how fundamental this shift is.
Workday acquired HiredScore in early 2024, then announced plans to acquire Paradox later that year. SAP moved to acquire SmartRecruiters. Cornerstone bought SkyHive and TaleSpin. Bullhorn acquired Textkernel. The pattern is unmistakable: legacy HCM vendors whose original architectures were not AI-based are buying their way into the new paradigm.
This is the equivalent of a 1980s car manufacturer acquiring a CAD software company because their engineers couldn’t design curves with existing tools. The acquisitions are an admission that the old architecture can’t do what the new era requires.
Bersin observes that as talent management insight vendors grow, they start to deliver platforms that threaten the traditional HCM “system of record” model. If your employees, candidates, alumni, and prospects all sit in an AI-native platform like Eightfold or Gloat, the legacy HRIS starts to look like a tactical payroll system. The curved surface swallows the angular box.
According to industry surveys, AI adoption in recruiting surged from 26% of organisations in 2024 to 43% in 2025. The “agentic AI” language is everywhere at HR Tech conferences: autonomous systems that run processes end-to-end rather than just assisting with discrete tasks. Startups are pushing toward fully autonomous recruiting agents that handle entire hiring processes with minimal human input.
The technology is reshaping what’s possible so rapidly that vendors are struggling to keep pace. Which raises a question: who should be driving this evolution?
The Customer HR Never Was
Here’s an uncomfortable truth: HR has historically been a passive technology customer.
For decades, HR departments accepted what vendors offered. The ATS worked a certain way, so recruiting worked that way. The HRIS had certain fields, so those became the data model. Workforce planning tools produced certain outputs, so those outputs defined what planning meant. HR adapted to technology rather than demanding technology adapt to HR.
This passivity made sense when HR technology was a back-office concern, when the systems existed primarily for compliance and record-keeping. But talent has become a strategic differentiator. The quality of hiring, the effectiveness of development, the intelligence of workforce planning: these now determine competitive outcomes. HR technology isn’t administrative infrastructure anymore. It’s strategic capability.
The shift demands a different relationship between HR and its vendors.
Look at what’s happening in software design more broadly. The best technology companies are moving from demographic-based personalisation to hyper-personalisation, treating each user as a “segment of one.” They’re shifting from reactive support models (submit a ticket and wait) to proactive service that anticipates needs before users articulate them. They’re building adaptive, multimodal interfaces that meet users where they are, rather than forcing users to adapt to rigid workflows.
This is what modern, customer-first software looks like. And HR should be demanding it.
But there’s also a pragmatic counter-trend worth noting. After years of hyper-complex microservices and AI-everything implementations, many technology teams are returning to simplicity. They’re re-evaluating whether complexity actually serves users or just impresses investors. They’re choosing modular architectures that perform better and cost less. They’re focusing AI on specific, high-value tasks rather than sprinkling it everywhere for marketing purposes.
This pragmatic turn matters for HR technology buyers. The question isn’t “does this vendor have AI?” Everyone has AI now, or claims to. The question is “does this vendor’s AI solve real problems in ways that actually work?” Flashy demos and impressive feature lists mean nothing if the underlying architecture creates more friction than it removes.
HR leaders need to become more demanding customers. Not demanding in the sense of complaining about bugs or requesting features, but demanding in the sense of articulating what the function actually needs to accomplish and refusing to accept tools that constrain rather than enable strategic thinking. When a vendor’s architecture forces you into reductive models of talent, that’s not a limitation to work around. It’s a reason to find a different vendor.
This means asking different questions during procurement. Not just “what does this system do?” but “what does this system make thinkable?” Not just “how does this integrate with our existing stack?” but “does integrating with our existing stack preserve the limitations we’re trying to escape?” Not just “what’s the implementation timeline?” but “what assumptions about talent and work are baked into this architecture?”
It also means demanding genuine customer-first design. Does this system adapt to how our recruiters actually work, or does it force them into predetermined workflows? Does it anticipate needs and surface insights proactively, or does it wait passively for queries? Does it provide a unified experience across touchpoints, or does it fragment the user journey across disconnected modules? Can our candidates interact naturally, or must they navigate forms designed for database ingestion?
Vendors, for their part, need to stay closer to the actual problems HR is trying to solve. The acquisition frenzy suggests that many legacy vendors lost touch with how fundamentally the requirements were shifting. They kept optimising for efficiency within the old paradigm while customers needed tools for an entirely different paradigm. By the time they noticed, they had to buy their way back into relevance.
The best vendor relationships will be genuine partnerships where HR articulates strategic needs and vendors innovate to meet them. Where customer feedback shapes product roadmaps. Where the people building the tools understand the work the tools need to support.
External talent intelligence providers like Lightcast, Revelio Labs, Draup, and Claro are already demonstrating what this looks like. They’re building products in close collaboration with talent strategy teams, creating labour market intelligence that informs strategic decisions rather than just populating dashboards. Workforce planning platforms like Orgvue are co-developing scenario modelling capabilities with the HR leaders who need to run those scenarios.
This is what mature technology partnership looks like. HR brings the strategic problems. Vendors bring the technical capability. Together, they expand what’s thinkable.
But it requires HR to show up as an equal partner, not a passive recipient. The tools will shape the thinking either way. The question is whether HR will have a voice in determining what those tools make possible.
The Cultural Fallacy
Here’s the insight that matters for HR leaders: the conventional narrative gets causation backwards.
The standard story goes like this: HR culture is resistant to change. HR professionals are stuck in their ways, clinging to outdated practices. Digital transformation requires cultural change first, then technology will follow.
But what if it’s the opposite? What if HR hasn’t been resistant to change. It’s been shaped by its tools?
The funnel mentality isn’t a cultural failing. It’s what you get when your primary technology is a relational database with boolean search. The fixation on job titles rather than skills isn’t lack of strategic vision. It’s what you can measure when your system demands structured fields. The impersonal candidate experience isn’t lack of care. It’s what happens when your technology can only process applications as records to be filtered.
HR professionals have been designing jelly beans in a world that could only render boxes.
This reframe changes the transformation conversation entirely. The question isn’t “how do we change HR culture to become more strategic?” The question is “what does HR look like when the technology no longer demands reductive structure?”
The Ford Taurus designers didn’t need their mindsets shifted before they could imagine curved cars. They needed tools that could compute curves. Once they had those tools, the designs followed naturally.
The same dynamic is playing out in HR. Talent management insight platforms don’t just enable better hiring. They reshape how recruiters conceptualise their work. Skills-based hiring isn’t a philosophy to be adopted. It’s what becomes natural when your tools can actually work with skills rather than job titles. Internal mobility programmes flourish when the technology can match employees to opportunities they might not have considered based on capability inference rather than job code matching.
This is not to say that culture doesn’t matter. It absolutely does. But culture follows capability more often than it precedes it. Give recruiters tools that can understand candidates as whole people rather than keyword bundles, and watch how quickly they start treating candidates as whole people.
What Technology Makes Thinkable Now
To see what this means concretely, consider how a single business challenge plays out differently under each paradigm.
A manufacturing company needs to staff a new facility. Under the old paradigm, this triggers a requisition process. HR creates job descriptions with specific requirements. Those requirements get posted. Applications flow in. The ATS filters for keyword matches. Recruiters review what survives. The process optimises for filling predefined boxes.
Under the new paradigm, the same business need triggers a fundamentally different process.
Talent intelligence from providers like Lightcast reveals the labour market reality around the proposed facility location: what skills are available locally, what the competitive landscape looks like, what compensation expectations exist. This external data shapes the conversation before a single requisition is written.
The talent management insight platform then looks at the company’s existing workforce. It infers capabilities from work history, identifies employees who could develop into needed roles, surfaces internal candidates who might not have applied but whose capability profiles suggest fit. The rigid boundary between external hiring and internal mobility dissolves.
When external sourcing begins, the system doesn’t just match keywords. It understands that someone with automotive manufacturing experience probably has transferable capabilities relevant to the new facility’s needs, even if the specific terminology differs. It recognises that a candidate’s progression from line worker to team lead to shift supervisor suggests leadership capability, even if “leadership” never appears on their resume.
Workforce planning tools like Orgvue run scenarios: what if attrition is higher than expected? What if the facility ramp-up accelerates? What capabilities create bottlenecks? The static headcount plan becomes a dynamic model that adapts as conditions change.
Throughout this process, conversational AI handles candidate communication. Instead of form submissions disappearing into a black hole, candidates interact with systems that can answer questions, gather information through dialogue, and provide meaningful status updates. The experience feels less like applying to a database and more like engaging with an organisation.
This isn’t hypothetical. Companies are already operating this way. Foley, an 84-year-old heavy equipment dealer, used Eightfold’s platform to address a common challenge: finding skilled diesel mechanics in a tight labour market. Instead of hoping qualified candidates would appear, they identified service technicians who, with the right development path, could grow into diesel mechanic roles. The platform helped them create personalised upskilling journeys and connect hiring strategy to workforce development. They found talent they couldn’t see before, because the technology made that talent visible.
None of this required the HR team to first adopt a new philosophy. The technology made these approaches possible. Once possible, they became obvious. Once obvious, they became practice.
This is the pattern across every dimension of HR work. Inference replaces declaration: systems recognise capability from context rather than requiring explicit keyword statements. Adjacency replaces matching: the question shifts from “does this candidate have the exact qualifications?” to “does this candidate have capabilities that could serve this need?” Narrative replaces record: career stories become evaluable as evidence of capability, not just searchable as data fields. Scenarios replace plans: workforce planning becomes simulation rather than spreadsheet. Conversation replaces form: candidate interaction becomes dialogue rather than submission.
The shift is comprehensive. And it’s being enabled by technology, not mandated by strategy.
The Design Principle
There’s a lesson here that extends beyond any specific technology or trend.
When we evaluate HR technology, we typically ask: what can this tool do? What features does it have? How does it improve efficiency?
These are reasonable questions. But they miss the deeper one: what does this technology make thinkable?
The early ATS made it thinkable to process thousands of applications. That was the stated capability. But it also made it thinkable to reduce candidates to keyword bundles, to treat hiring as a conversion funnel, to optimise for volume over insight. Those were the unstated implications.
Talent management insight platforms make it thinkable to consider capability beyond credentials, to value potential alongside performance, to see internal mobility as an algorithmic matching problem rather than a political negotiation. Talent intelligence platforms make it thinkable to ground workforce strategy in labour market reality rather than internal assumptions. AI agents make it thinkable to automate not just tasks but judgments, to have systems take autonomous action in recruiting processes.
The technology shapes the imagination. And the imagination shapes the culture.
This isn’t to suggest the new paradigm is without risks. AI systems can encode bias, automate poor judgment at scale, or create new forms of exclusion. The tools that make inference possible can also make mistakes invisible. Algorithms trained on historical data can perpetuate historical inequities. Systems that claim to see potential can instead see proxies for privilege.
Part of being a demanding customer is insisting on transparency, auditability, and human oversight. The goal isn’t to replace human judgment with algorithmic judgment. It’s to expand what human judgment can accomplish while remaining accountable for the outcomes.
This is why HR leaders should pay attention to architectural choices, not just feature lists. The underlying technical paradigm determines what becomes easy to conceptualise and what remains difficult to imagine. A system built on relational databases will always push toward categorisation and exactness. A system built on machine learning will naturally embrace inference and probability. A system built on conversational AI will tend toward dialogue and adaptation.
Ford didn’t set out to revolutionise car design. They bought CAD software that could handle curves, and the revolution followed. HR isn’t setting out to abandon the talent funnel. But organisations adopting AI-native talent platforms may find the funnel concept quietly disappearing, replaced by something more organic, more continuous, more human.
What Comes Next
The evolution doesn’t stop here. The current generation of AI tools is already being superseded by more capable systems. Large language models continue to improve. Reasoning capabilities are expanding. The gap between AI capability and human judgment continues to narrow for many tasks.
The arc bends toward technology that can handle increasing complexity. And as it does, the concepts we use to think about talent will continue to evolve.
Skills might give way to capability patterns. Career paths might give way to career spaces. Workforce planning might give way to workforce dynamics. The static models of the database era will increasingly seem as antiquated as the boxy cars of the early 1980s.
Nobody looks at a modern sedan and consciously sees the Ford Taurus’s influence. The curves are simply how cars look now. The aerodynamic principles that seemed revolutionary in 1986 became invisible through ubiquity. But every designer working today operates within a possibility space that the Taurus helped create.
The same will happen in HR. In ten years, skills-based hiring won’t be a “transformation initiative.” It will just be hiring. Workforce planning informed by real labour market intelligence won’t be “advanced analytics.” It will just be planning. The AI-native approaches that seem novel today will become the unremarkable baseline.
What won’t change is the underlying principle. The tools shape the thinking. They always have. They always will.
For HR leaders navigating this transition, the practical advice is threefold.
First, pay attention to the tools. Understand what they make easy and what they make hard. Recognise that adopting new technology isn’t just about efficiency gains. It’s about expanding what’s thinkable.
Second, become a demanding customer. Don’t accept tools that constrain strategic thinking. Ask vendors what assumptions are baked into their architecture. Insist on technology that enables the work HR needs to do, not technology that forces HR to work within arbitrary limitations.
Third, engage with vendors as partners. The best tools will emerge from genuine collaboration between the people who understand talent strategy and the people who can build the technology. Neither side has the complete picture alone.
The Ford Taurus team didn’t know they were about to reshape American automotive design. They were just using new CAD software to solve a business problem. The shapes that emerged from that software changed everything.
The same dynamic is unfolding in HR right now. The technology has changed. The shapes are emerging. And the next time someone tells you HR needs a culture change before it can transform, ask them what tools they’re using.
The answer might explain more than the culture ever could.
References
AWOL Trends. (2015). Blobjects. http://awoltrends.com/2015/01/blobjects/
Artnet News. (2024). Artcore: When Buildings Became Blobs. https://news.artnet.com/art-world/artcore-blobitecture-architecture-2527525
Bersin, J. (2024). Workday To Acquire HiredScore, A Potential Shakeup In HR Technology. Josh Bersin Company. https://joshbersin.com/2024/03/workday-to-acquire-hiredscore-a-potential-shakeup-in-hr-technology/
Bersin, J. (2024). Enterprise Talent Intelligence Has Arrived. Josh Bersin Company. https://joshbersin.com/2024/05/enterprise-talent-intelligence-has-arrived-new-research-available/
Brandon Hall Group. (2024). Eightfold AI is Changing the Game in Talent Intelligence. https://brandonhall.com/eightfold-ai-is-changing-the-game-in-talent-intelligence-heres-what-you-need-to-know/
ClearCompany. (2024). The Evolution of Applicant Tracking Systems and Expert ATS Software Predictions. https://blog.clearcompany.com/the-evolution-of-applicant-tracking-systems-and-expert-ats-software-predictions
Digital Trends. (2019). Jony Ive’s Greatest Achievement at Apple Wasn’t the iPhone. It Was the iMac. https://www.digitaltrends.com/computing/jony-ives-greatest-achievement-was-the-imac/
Eightfold AI. Talent Intelligence Platform.
https://eightfold.ai/
Ford Authority. (2025). 1986 Ford Taurus Team’s Decisions Were Revolutionary. https://fordauthority.com/2025/01/1986-ford-taurus-teams-decisions-were-revolutionary-video/
Grokipedia. Blobject. https://grokipedia.com/page/Blobject
HeroHunt.ai. (2025). AI Adoption in Recruiting: 2025 Year in Review. https://www.herohunt.ai/blog/ai-adoption-in-recruiting-2025-year-in-review
HeroHunt.ai. (2025). AI Recruitment 2025: The Extremely In-Depth Expert Guide. https://www.herohunt.ai/blog/ai-recruitment-2025-the-extremely-in-depth-expert-guide-10k-words
HR Executive. (2024). 4 Trends Shaping Talent Intelligence in 2024. https://hrexecutive.com/4-trends-shaping-talent-intelligence-in-2024/
HR Executive. (2025). HR Tech Consolidation: Workday Acquires AI Recruiting Tool Paradox. https://hrexecutive.com/workday-to-acquire-paradox-as-hr-tech-consolidation-ramps-up/
Lightcast. Labor Market Analytics. https://lightcast.io/
Lo, F.P.-W. et al. (2025). AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening. arXiv. https://arxiv.org/html/2504.02870v1
MotorBiscuit. (2023). The 1986 Ford Taurus Is the Single Most Important Vehicle Design of the Past 50 Years. https://www.motorbiscuit.com/ford-taurus-is-single-most-important-vehicle-design-of-past-50-years-convince-me-im-wrong/
OnBlick. The Evolution of Applicant Tracking System: A Historical Perspective. https://www.onblick.com/blogs/the-evolution-of-applicant-tracking-system-a-historical-perspective
Orgvue. Workforce Planning and Analytics. https://www.orgvue.com/
Panna.ai. (2025). AI-Powered Hiring in 2025: How LLMs and Semantic Tech Are Transforming Recruitment. https://www.panna.ai/recruit/blogs/ai-powered-hiring/
Recruitify. (2024). The Evolution of Applicant Tracking Systems (ATS): From Manual Processes to AI-Powered Recruitment. https://www.recruitify.ai/blog/en/the-evolution-of-applicant-tracking-systems-ats-from-manual-processes-to-ai-powered-recruitment/
Revelio Labs. Workforce Intelligence. https://www.reveliolabs.com/
SAP. What is an Applicant Tracking System (ATS)? https://www.sap.com/products/hcm/recruiting-software/what-is-an-applicant-tracking-system.html
Scan2CAD. (2024). How CAD Has Evolved Since 1982 — Past, Present & Future. https://www.scan2cad.com/blog/cad/cad-evolved-since-1982/
SlashGear. (2024). Why The 1986 Ford Taurus Was Far More Important Than You Realize. https://www.slashgear.com/1540015/1986-ford-taurus-more-important-than-you-realize/
The Henry Ford. (2021). Mimi Vandermolen and the 1986 Ford Taurus. https://www.thehenryford.org/explore/blog/mimi-vandermolen-and-the-1986-ford-taurus
The Henry Ford. Apple iMac G3 Personal Computer, 1998-1999. https://www.thehenryford.org/collections-and-research/digital-collections/artifact/442867/
The Josh Bersin Company & Eightfold AI. (2024). Enterprise Talent Intelligence: Applying Skills Technology and AI at Work. https://eightfold.ai/wp-content/uploads/Enterprise-Talent-Intelligence-Applying-Skills-Technology-and-AI-at-Work-Josh-Bersin.pdf
Wikipedia. Ford Taurus (first generation). https://en.wikipedia.org/wiki/Ford_Taurus_(first_generation)
Wikipedia. History of CAD software. https://en.wikipedia.org/wiki/History_of_CAD_software
Wikipedia. iMac G3. https://en.wikipedia.org/wiki/IMac_G3
Workday Newsroom. (2024). Workday Announces Intent to Acquire HiredScore. https://newsroom.workday.com/2024-02-26-Workday-Announces-Intent-to-Acquire-HiredScore
This. This is what I’ve. been waiting for - technology has finally caught up and will enable companies to see how “Skills might give way to capability patterns. Career paths might give way to career spaces. Workforce planning might give way to workforce dynamics”. So excited for the potential to really guide companies and their people to be their most productive while continuing to grow.