The future of HR isn’t job-based. It’s skills-based.
And SAP SuccessFactors is quietly leading one of the biggest shifts in workforce strategy. Skills are becoming the real currency of the enterprise — powering hiring, development, internal mobility, staffing, and even pay.
With the latest Career & Talent Development + Talent Intelligence Hub, organizations can finally:
🔹 Build a unified skills ontology
🔹 Auto-generate skill profiles for every role
🔹 Map real skills vs. skill gaps
🔹 Recommend learning, mentors, and career paths
🔹 Enable AI-driven talent mobility at scale
This isn’t “HR transformation.”
This is business transformation through skills intelligence.
Companies that move from job-based to skills-based operating models in 2026 will outpace everyone on:
✔ Agility
✔ Workforce planning
✔ Retention
✔ Productivity
✔ Compliance across EU & global markets
Skills are becoming your competitive advantage. SAP SuccessFactors is becoming the engine behind it.
#SAPSuccessFactors#TalentIntelligence#Skills#CareerDevelopment#HXM#HRTech#FutureOfWork
𝗔𝗜 & 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 — 𝗮 𝟲-𝗱𝗮𝘆 𝘀𝗲𝗿𝗶𝗲𝘀 (𝗗𝗮𝘆 𝟭)
I’m running a short 6-day series on concrete ways companies can use 𝗔𝗜 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 - practical, human-centered and immediately actionable.
𝗗𝗮𝘆 𝟭 - 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 𝟭: 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗯𝗲𝗻𝗲𝗳𝗶𝘁 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀
One-size-fits-all benefits don’t reflect reality. People are at different life stages, have varying health needs, and use benefits in very different ways. That’s wasteful and disengaging.
𝗪𝗵𝗮𝘁 𝗔𝗜 𝗱𝗼𝗲𝘀
AI can analyze anonymized usage patterns, survey input, role profiles and life-event signals to create 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝘀 and then recommend benefit packages that actually match needs. This is not about surveillance - it’s about smarter, consented personalization.
𝗖𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲
• Parents automatically receive targeted communications about childcare support, local daycare partners, and flexible scheduling options.
• Early-career hires get nudges toward financial-wellbeing tools and starter retirement education.
• Remote workers receive home-office ergonomic support and virtual mental-health offers.
𝗜𝗺𝗽𝗮𝗰𝘁 & 𝗞𝗣𝗜 𝘁𝗼 𝘁𝗿𝗮𝗰𝗸
• Usage rate of benefits (target +20% within 6 months)
• Employee satisfaction with benefits (pulse scores)
• Reduction in cost-per-utilized-benefit (more value, less waste)
𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝗻𝗲𝘅𝘁 𝘄𝗲𝗲𝗸
Aggregate anonymized benefit-usage data and enrollment records.
Run simple clustering (age, role, family status, utilisation).
Pilot a targeted campaign for one segment (e.g., parents) and measure uptake.
👉 Question for you: Which employee segment in your company would benefit most from targeted offers? Comment below - I’ll share ideas for a pilot.
#employeebenefits#totalrewards#AI#humanresources#hrm
AI Copilots in benefits will fail without infrastructure.
There’s a rush to bolt GPT-like copilots onto HR workflows. But without foundational change, most will deliver superficial gains and lots of customers we talk to are learning this as we speak…
At Ben, we’ve seen firsthand that LLMs are only as useful as the structured data they can reason over.
That’s why we invested heavily in normalising unstructured benefits data, standardising contribution schedules, deduction rules, and eligibility models across fragmented vendors.
Only then could we train models to do what HR teams actually need, for example
- Explain month-on-month cost variance across international teams in natural language
- Predicting enrolment issues before they occur
- Auto-generating queries to carriers with pre-filled claim evidence
This isn’t AI washing.
It’s AI with operational teeth.
$428B Lost Annually: The True Cost of Benefits Confusion in Corporate America
Findings from AI ALPI's study of 5,000+ employees →
The Hidden Leak in Your Bottom Line:
→ Average company loses $3.2M annually through benefits underutilization
→ 82% of benefits programs operate at <60% efficiency
→ For every $1M spent on benefits, $320K goes unused
Beyond Grades - The Reality:
→ Traditional communication methods leave 73% of value on the table
→ AI-powered engagement unlocks 92% of benefits value
→ ROI gap between AI vs. traditional approaches: 3.4x
The Transformation Numbers:
→ 89% reduction in benefits support tickets
→ 3.2x increase in voluntary benefits adoption
→ 74% decrease in HR team bandwidth on benefits queries
→ 4.1x boost in employee financial wellness program participation
Benefits aren't a cost center - they're a value multiplier. But only if your employees can access and understand them
The Multiplier Effect:
Companies using AI for benefits see:
→ 312% higher benefits utilization
→ 2.8x better employee satisfaction scores
→ $2.1M average annual savings for mid-market companies
→ 91% reduction in benefits-related confusion
🔥 Want more breakdowns like this?
Follow along for insights on:
→ Getting started with AI in HR teams
→ Scaling AI adoption across HR functions
→ Building AI competency in HR departments
→ Taking HR AI platforms to enterprise market
→ Developing HR AI products that solve real problems
#HRTech#EmployeeBenefits#AIinHR#FutureofWork#HRInnovation
Why Benefits Need an Operating System — Not Just More Apps
Most benefits solutions are like individual applications: great at one thing, but limited in scope.
You can write a compelling strategy document in Word. But to build the financial model behind it? You need Excel. Each tool excels at its specific job — yet none of them can run the full ecosystem on their own.
That’s where an operating system comes in. It’s the foundational platform that lets all those specialized applications work together seamlessly, reliably, and at scale.
At Alight Solutions, we’re building exactly that for employee benefits: the Benefits Operating System.
It brings together health, retirement, and absence management — not as disconnected point solutions, but as an integrated ecosystem that powers the entire benefits experience.
Instead of juggling multiple vendors, siloed data, and fragmented employee experiences, Alight’s Benefits Operating System creates a single, intelligent platform. It leverages decades of domain expertise, serves tens of millions of users, and uses AI-powered guidance to turn benefits complexity into better outcomes — for both employers and employees.
This operating system approach delivers:
• True integration across the employee lifecycle
• Scalability for complex, evolving organizations
• Deeper insights and personalization
• Reduced costs and administrative friction
• A better, more engaging benefits experience that actually drives retention and wellbeing
In today’s world, benefits aren’t just a cost center — they’re a strategic advantage. But you can’t achieve that with apps alone. You need the right operating system underneath them.
That’s the Alight difference.
What if your Total Rewards system could predict which employee is considering leaving—and automatically adjust their benefits package to address their specific concerns before they even update their resume?
Imagine a system that doesn't just track rewards but actively optimizes them.
One that notices when Sarah in Marketing hasn't used her wellness benefits in months and proactively suggests alternatives better aligned with her life circumstances.
Or one that detects patterns suggesting Alex in Engineering is feeling undervalued and triggers recognition from the right leaders at precisely the right moment.
This isn't science fiction.
It's the promise of agentic AI for Total Rewards—and it's already happening at forward-thinking organizations.
A culture of recognition alone can save a 10,000-employee organization up to $16.1 million annually in turnover costs. Imagine what a fully optimized, AI-powered Total Rewards strategy could do.
While most HR teams are still struggling with fragmented data, standardized packages, and poor employee engagement, a new generation of AI-powered Total Rewards systems is emerging that can transform how we attract, retain, and engage talent.
In this newsletter, I explore:
- The five critical limitations plaguing most Total Rewards systems today
- How these limitations are hurting both your employees AND your bottom line
- Why agentic AI (not just basic automation) is the game-changer HR needs
- Four emerging trends that will transform Total Rewards in the next 3-5 years
- Practical next steps for forward-thinking HR leaders
#TotalRewards#HR#AgenticAI#FutureOfWork#EmployeeExperience#HRTech#AIGuru
Dynamic Skills Intelligence
The Death of Job Descriptions: Welcome to Skills-Based Intelligence 🧠
Static job descriptions are relics of the industrial age. The future belongs to dynamic skills intelligence that evolves in real-time.
Here's the paradigm shift:
📋 Traditional thinking: Fixed roles with predetermined competencies 🚀 AI-powered reality: Living skills profiles that adapt, predict, and optimise continuously
What dynamic skills intelligence delivers:
🎯 Real-time capability mapping - AI tracks not just what people can do, but what they're learning, how quickly they adapt, and where their potential lies
🔄 Intelligent internal mobility - Talent marketplaces that match not just current skills but learning trajectories and hidden capabilities
📊 Predictive skills planning - Anticipating which capabilities will become critical and identifying who can develop them fastest
The transformation for HR is fundamental:
Instead of managing job families, we become skills architects designing pathways for capability evolution.
Instead of generic training programmes, we create AI-curated learning experiences that adapt to individual progress and business needs.
Instead of annual reviews, we provide continuous intelligence about skills development and deployment opportunities.
The organisations building dynamic skills intelligence aren't just filling roles—they're cultivating adaptive capability that responds to market changes at speed.
What skills are becoming currency in your organisation?
#SkillsIntelligence#FutureOfWork#TalentMobility#LearningAndDevelopment#AIinHR
This is a continuation of my last post on creating Skill (markdown) files in Copilot Cowork to create internal role documentation (responsibilities, skills, skill proficiencies, and qualifications by job family, level, and career stage).
Today, I’ll describe the reference files needed for the original skill files to work correctly.
For example, /role-extraction was the skill I created that extracts role information from a SME’s M365 signals, with prompts at each step for the SME to verify or correct what it finds. The SME knows the role deeply and the skill guides the SME to review the outputs, correct any misconceptions, surface gaps early, and package outputs in a consistent format for my team.
The workflow itself has 7 steps
1. Intake (job family, levels, documents to prioritize)
2. Look for M365 patterns in recent files, recent meetings and transcripts, teams chats, and emails. Surface 4-7 recurring work themes and provide evidence
3. Role and level mapping - draft relevant content, referencing the leveling guidance
4. Responsibility generation - draft 2-3 statements per topic, referencing the writing guidance
5. Skill mapping - identify skills necessary for doing the work, referencing our skills library and identify skill proficiencies needed, referencing our skill proficiencies
6. Qualifications - prompt the SME about the minimum a person needs to perform the role at a baseline level (BQs) and the differentiators that would make them more effective (PQs), referencing the qualification guidance
7. Packaging and delivery - name the file {job family - level - SME name - date.xlsx}, referencing the output schema, and directly email it to the internal Talent Architecture team
As you could see in those steps, there were a lot of additional markdown files to reference, including:
/leveling-framework - defines levels and calibration dimensions (scope, autonomy, impact, complexity) across our three career tracks.
/qualifications - includes our template by level for basic and preferred qualifications, with differences for sponsored and non-sponsored roles. It also runs 4 “tests” that all BQ/PQ must pass
/writing-standards - our canonical standards for writing responsibility statements, which includes length, naming guidance, descriptors, drafting cues, DO/DO NOT rules, etc.
/proficiency-scale - describes our 5-point proficiency scale with definitions of each scale point along with description of whether the skill is “needed day one” or can be learned on the job
/output-schema - describes what I want on each of 7 tabs of the excel file
Skill Library (Excel file) - includes our list of ~1000 maintained skills and skill definitions that we use consistently across the enterprise. We typically are looking to attach 12-15 (but no more than 20) skills per job family
Let me know what else would be interesting for me to share about this process or what you’ve learned if you have tried anything similar.
The Most Interesting Thing in #CrEdTech this week ?
💡 The Universal Translator for #Skills is Here
#Interoperability has a paradox: everyone loves standards, so everyone makes their own. ❤️
Brandon Dorman just released a Skills API Translation Service that bridges the gap between the big three: #CASE, #IEEE SCD, and ASN-#CTDL.
Why it matters:
#Policy: Governance usually stalls at "which standard?" This tool suggests that "all of them" is a valid policy if the plumbing is smart enough.
#Tech: By using a FastAPI server to map fields, we move from static spreadsheets to dynamic, "invisible" data flow.
#Practice: Systems become "bilingual," finally closing the gap between sectors. Higher Ed can now ingest K12 skills (CASE) with high fidelity and translate them into workforce-ready signals (SCD/CTDL) as the learner graduates.
Takeaway:
Stop looking for the "perfect" standard. Start building for a polyglot future where translation is a utility, not a barrier.
Question:
Is your skills strategy waiting for a single winner, or are you ready to embrace a multi-standard world?
Check it out: https://lnkd.in/etPet3t5#CrEdTech#Interoperability#DigitalCredentials#SkillsGraph#Instructure#DigitalCredentials#1EdTech#CredentialEngine
Health benefits are getting more expensive. But confusion about them is costing even more.
Employees miss out on care. Employers waste budget. And HR teams are stuck repeating the same conversations every enrollment season.
RELAYTO changes that.
When benefit guides become interactive, personal, and AI-powered, employees finally get what they’re offered and how to use it.
No more "did you read the email?"
Just smarter decisions, clearer understanding, and fewer avoidable costs.
Want to cut healthcare costs without cutting quality?
Start here:
• Turn your benefits guide into a digital experience. Make it searchable, self-guided, always-on.
• Use AI to answer the flood of questions HR gets every open enrollment.
• Track what matters. Who’s reading, clicking, and actually understanding your benefits.
Give every employee the clarity they need, when they need it. Because benefits don’t work if people don’t understand them.
If your team had 1,000 hours back and better data, what could you change?
#EmployeeBenefits