Analytics & Workforce Planning Software Buyer’s Guide for HR & Recruiting

Who This Guide Is For

You need this guide if you are one of the following:

1) VP/Director of People Analytics

Pain points:

• Your HR data is scattered across HRIS, ATS, payroll, performance, and finance systems
• Executives want answers in hours, not weeks
• You are tired of building “one-off” dashboards that go stale in 30 days
• You need to prove impact on retention, hiring, DEIB, and headcount planning

2) Head of Workforce Planning / Strategic Workforce Planning

Pain points:

• Finance and HR use different headcount numbers
• Scenario planning takes too long and relies on spreadsheets
• You cannot reliably model org design, hiring plans, or restructure impacts
• Leaders keep asking for “what if” answers you cannot produce quickly

3) CHRO / Chief People Officer

Pain points:

• You need an evidence-based workforce strategy, not instinct and anecdotes
• Leadership expects a single source of truth for talent, headcount, and skills
• You need to connect people decisions to business outcomes
• You want something that scales beyond your internal analytics team

4) HRIS / HR Operations Leader

Pain points:

• Reporting requests consume too much of your team’s time
• Data quality problems break confidence in every dashboard
• Your current tools are either too basic or too complex
• You need self-service analytics without creating governance chaos

Who should not read this guide

This is not for buyers who:

• Only need basic HR reporting from an HRIS
• Want scheduling/time-and-attendance software only, with no planning or analytics ambition
• Don’t have access to clean employee data or executive sponsorship
• Are buying a standalone tool just to “look modern” without a use case

If your organization cannot name a decision this software will improve, stop here.

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What Analytics & Workforce Planning Software Does

At a high level, this software helps HR, finance, and business leaders understand the workforce, forecast what comes next, and make better decisions faster.

It typically does four things:

• Unifies data from HRIS, ATS, payroll, performance, finance, and sometimes productivity systems
• Surfaces metrics like headcount, attrition, internal mobility, hiring funnel performance, diversity, compensation, and span of control
• Models scenarios such as hiring freezes, reorganizations, growth plans, and skill gaps
• Supports action through alerts, recommendations, approvals, and sometimes AI-driven insights

Before vs. After

The business case: what this is worth

A decent workforce planning platform can pay for itself quickly if it changes even one big decision.

Example:

A 5,000-employee company has:

• Average fully loaded labor cost: $95,000/year
• Annual voluntary turnover: 18%
• Cost of replacement per employee: conservatively 30%–50% of salary
• 900 annual exits

If better analytics reduces regrettable attrition by just 1 percentage point:

• 50 fewer exits
• Assume $38,000 replacement cost each
• Annual savings: ~$1.9M

Now add:

• Avoiding 10 unnecessary hires or backfills: $950K
• Reducing recruiter/HR analyst time by 1,500 hours/year at $50/hour: $75K
• Preventing one bad org redesign or over-hire cycle: often six figures or more

The point: this software does not need to “solve HR.” It only needs to improve a few high-cost decisions.

What it is not

Do not confuse this category with:

• Core HRIS: stores employee records, but usually lacks real planning depth
• ATS: tracks applicants, but rarely gives strategic workforce insight
• Workforce management/time & attendance: handles shifts, time, and compliance, but not necessarily strategic planning
• BI tools only: flexible, but require too much data modeling and governance to serve HR well
• Performance or engagement suites: may include analytics, but usually not planning-grade modeling

Where buyers usually get it wrong

The biggest mistake is buying a “people analytics” platform when what you actually need is:

• headcount planning
• org design
• skills intelligence
• workforce optimization
• or operational labor planning

Those are related, but not interchangeable.

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Market Landscape 2026

This market is no longer a simple “HR dashboards” category. It has split into five distinct product families, and buyers who ignore that reality usually buy the wrong thing.

1) Enterprise people analytics platforms

These are the broadest offerings. They unify data, create dashboards, and increasingly use AI to surface insights.

Examples:

• Visier People
• ThoughtSpot
• IBM SPSS Modeler
• Workday AI
• Sage People
• ClearCompany HR Analytics and Reporting
• Crunchr
• Breathe HR Reporting & Analytics

These are usually the right fit when the buyer wants:

• executive dashboards
• workforce metrics
• retention analysis
• hiring funnel reporting
• self-service analytics

2) Strategic workforce planning and org design

These tools focus on future-state modeling, not just reporting.

Examples:

• ChartHop Headcount Planning
• ChartHop
• Crunchr Workforce Planning
• Orgvue
• Henshaw AI by Orgvue
• Dayforce Strategic Workforce Planning (formerly Agentnoon)
• Humaans Headcount Planning
• PredictiveHR Analytics Platform

These are the strongest fit when you need:

• scenario planning
• budget-to-headcount alignment
• restructure planning
• spans and layers analysis
• org chart modeling
• approval workflows

3) Talent intelligence and skills platforms

These focus on matching people to roles and surfacing skills across hiring and internal mobility.

Examples:

• Eightfold Talent Intelligence Platform
• Eightfold Workforce Exchange
• Beamery
• Job Architectures by Beamery
• Fuel50 Insights
• 365Talents API-First Solution
• Hibob Ltd. Succession Planning
• AI-Powered Solutions by Paylocity

These are strongest when the buyer cares about:

• skills inference
• internal mobility
• talent marketplace
• succession planning
• candidate-to-role matching
• talent rediscovery

4) Workforce management and labor optimization

These tools are more operational and are often better for hourly, distributed, or regulated workforces.

Examples:

• ATOSS Workforce Management
• WorkForce Software
• Verint Desktop and Process Analytics
• TeamSense Employee Call-In Solution
• HappyRobot AI Platform

These are best for:

• scheduling
• time and attendance
• labor compliance
• staffing optimization
• process/activity analytics
• operational throughput

5) Adjacent or niche analytics tools

Some products in the directory are analytics-adjacent, not pure workforce planning platforms, but they still matter in buyer evaluations.

Examples:

• Equilar Data Feeds: useful for executive compensation and benchmarking
• Diversio: DEI analytics and inclusion insights
• Guusto Employee Appreciation Gift Cards: not analytics software, but often part of retention programs
• Empuls Employee Retention Software: engagement and recognition with retention impact
• VialtoExplore: workforce compliance and mobility intelligence for global workforces
• KeepActive Workforce Analytics: analytics-focused but narrower
• PredictiveHR: smaller, more niche predictive analytics
• IBM SPSS Modeler: data science platform, not HR-specific
• ThoughtSpot: BI/analytics platform that can be configured for HR
• Workday AI: embedded intelligence in a suite, not a standalone planning specialist

What changed in 2026

AI moved from “nice demo” to product expectation

Almost every serious vendor now claims AI:

• natural-language querying
• insight generation
• scenario recommendations
• skills inference
• automated classification
• anomaly detection

But here is the catch: AI is not the same as value.

The question is not “Does it have AI?” The question is:

• Does the AI work on your data?
• Can it explain its answer?
• Can you trust it for headcount or compensation decisions?
• Does it reduce analyst labor, or just create more polished noise?

Buyers are demanding faster time to value

A platform that takes 9 months to show useful insight is losing to one that can deliver:

• a first dashboard in 2–6 weeks
• a reliable headcount model in 4–8 weeks
• usable scenario planning in 6–12 weeks

The market is bifurcating between suites and specialists

There are two buying motions now:

Suite buyers

• choose Workday, Sage, Paylocity, Dayforce, or similar
• want a lower-integration, lower-risk path
• accept less flexibility for faster deployment

Specialist buyers

• choose Visier, ChartHop, Orgvue, Crunchr, Eightfold, Beamery, ThoughtSpot, or ATOSS
• want deeper capability
• will tolerate more implementation work
• often already have a core HRIS and need a better layer on top

How the vendor landscape is structured

Where the 36 products fit

Here is the practical reading of the directory:

• Most mature analytics specialists: Visier People, Crunchr, ThoughtSpot, IBM SPSS Modeler
• Most compelling planning/org design tools: ChartHop Headcount Planning, Orgvue, Dayforce Strategic Workforce Planning, Henshaw AI, Humaans Headcount Planning
• Best talent intelligence layer: Eightfold Talent Intelligence Platform, Beamery, Fuel50, 365Talents
• Best operational labor planning: ATOSS, WorkForce Software, Verint
• Best suite-embedded options: Workday AI, Sage People, Paylocity
• Best for smaller or more cost-conscious teams: ClearCompany, Breathe, Humaans, KeepActive, PredictiveHR

Direct advice

If your core problem is “We cannot forecast future headcount or restructure impact”, do not start with a generic BI tool. If your core problem is “We need recruiters and managers to find skills internally”, do not start with a pure dashboard product. If your core problem is “We need labor scheduling and compliance across shifts”, do not buy a people analytics suite.

Pick the category that matches the decision you are trying to improve.

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The 8 Things That Matter Most

1) Data integration and model quality — 20%

Why it's critical: Bad data makes every dashboard wrong. Workforce planning is only as good as the identity resolution, job architecture, hierarchy logic, and historical data you feed it.

What to evaluate:

• HRIS, ATS, payroll, finance, and learning integrations
• Prebuilt connectors vs custom ETL
• How the vendor handles employee identity matching
• Support for historical snapshots and effective dating
• Ability to normalize titles, departments, locations, and cost centers

What good looks like:

• Prebuilt integrations to major HRIS platforms
• Clean historical trends without manual rebuilds
• Clear data dictionary and governed metric definitions
• Deduplication and hierarchy logic you can audit

Red flag:

• Vendor says “we integrate with everything” but has vague connector detail
• You need to export CSVs every week to keep the model alive
• Metrics change depending on who built the report

Ask the vendor:

• Which systems have native, production-grade integrations?
• How do you handle historical changes in department, manager, and job data?
• What data cleanup is required before go-live?

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2) Scenario planning and modeling depth — 18%

Why it's critical: Planning tools should help you answer “what if” questions before you commit money or people.

What to evaluate:

• Hiring, promotion, transfer, and layoff scenarios
• Budget impact by role, org, and location
• Multi-scenario comparison
• Versioning and approval workflows
• Collaboration between HR, finance, and business leaders

What good looks like:

• Real-time recalculation when assumptions change
• Multiple scenarios side by side
• Clear assumptions and audit trail
• Org-level and enterprise-level views

Red flag:

• Scenario planning is just a spreadsheet with prettier charts
• No version control
• You cannot model timing, cost, or role replacement logic

Ask the vendor:

• Can we model multiple future states at once?
• Can finance and HR work from the same assumptions?
• How fast does the model refresh when data changes?

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3) Workforce insight quality — 15%

Why it's critical: Pretty dashboards are not enough. You need answers that change decisions.

What to evaluate:

• Retention, hiring, DEIB, compensation, performance, and mobility analytics
• Root-cause analysis, not just descriptive reporting
• AI-generated insights and anomaly detection
• Benchmarking capability

What good looks like:

• Insight suggestions tied to actual workforce drivers
• Alerts that highlight meaningful changes
• Ability to slice metrics by org, location, function, tenure, and level
• Standard and custom dashboards

Red flag:

• The product only tells you what happened, never why
• AI insights are generic or unexplainable
• You can’t drill into subgroups

Ask the vendor:

• What insight types are automated vs manually configured?
• Can users drill from enterprise metrics to individual records?
• How do you avoid false positives in AI-driven alerts?

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4) Skills intelligence and talent mobility — 12%

Why it's critical: The market is moving toward skills-based workforce planning. If your software cannot see skills, it cannot help you future-proof labor supply.

What to evaluate:

• Skills inference from profiles, roles, learning, and performance
• Internal mobility and talent marketplace features
• Role architecture and capability mapping
• Succession planning support
• Candidate-to-role matching

What good looks like:

• Skills graph or skills ontology
• Recommendations for internal candidates or development paths
• Clear role families and job architecture support
• Connections to hiring and learning systems

Red flag:

• “Skills” means a free-text field on employee profiles
• No governance on how skills are inferred
• No tie between skills and actual workforce plans

Ask the vendor:

• How are skills identified, validated, and updated?
• Can we align skills to roles and future org needs?
• Does the platform support internal mobility use cases?

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5) Usability for business leaders — 10%

Why it's critical: If only analysts can use it, adoption will stall. Workforce planning needs HR, finance, and business leaders to actually interact with the system.

What to evaluate:

• Dashboard clarity
• Natural-language querying
• Executive-friendly views
• Commenting, sharing, and collaboration
• Mobile or browser accessibility

What good looks like:

• Managers can answer simple questions without training
• Leaders can filter and compare views without a ticket
• The interface supports non-technical users
• Visualizations are clean and decision-oriented

Red flag:

• Every meaningful task requires SQL or vendor support
• Users need a 3-hour training to answer basic questions
• The UI is cluttered with too many filters and tabs

Ask the vendor:

• What can a manager do without help from HR analytics?
• How much training is required for non-technical users?
• Can users create their own views safely?

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6) Security, privacy, and compliance — 10%

Why it's critical: People data is sensitive. Workforce platforms often contain compensation, performance, demographic, and sometimes health-adjacent data.

What to evaluate:

• SOC 2, ISO 27001, GDPR, CCPA, and region-specific controls
• Role-based access controls and row-level security
• Audit logs
• Data residency and retention policy
• AI governance and model transparency

What good looks like:

• Strong certifications
• Fine-grained permissions
• Auditability of changes and access
• Clear statement on how AI uses your data

Red flag:

• No formal security certifications
• Weak role restrictions
• Vague answers about AI training or data retention

Ask the vendor:

• Is customer data used to train shared models?
• Can we restrict access by region, department, and role?
• What audit logs are available out of the box?

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7) Implementation effort and data operations — 8%

Why it's critical: The best product in the world fails if implementation takes 9 months and requires a mini data engineering team.

What to evaluate:

• Typical time to first dashboard
• Data mapping requirements
• Vendor-led vs customer-led setup
• Ongoing admin workload
• Support for changing org structures

What good looks like:

• First value in 4–8 weeks for a standard use case
• Clear implementation checklist
• Low maintenance after go-live
• Strong documentation

Red flag:

• “Implementation” means endless custom work
• You need a consultant to make every data change
• No plan for quarterly data refresh

Ask the vendor:

• What do most customers complete in the first 30 days?
• Who owns data mapping and validation?
• How much ongoing admin time is typical per month?

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8) Pricing clarity and total cost of ownership — 7%

Why it's critical: Analytics software often looks affordable until you add modules, services, and integrations.

What to evaluate:

• Subscription model
• Per employee, per user, or per module pricing
• Implementation fees
• Add-on costs
• Renewal increases

What good looks like:

• Transparent package structure
• Clear module boundaries
• Known professional services costs
• Predictable renewal terms

Red flag:

• Pricing is hidden behind sales calls
• Essential features are gated behind expensive tiers
• Costs rise sharply at renewal

Ask the vendor:

• What is included in base pricing?
• What triggers price increases?
• What recurring services fees should we expect?

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Use-Case Matching: Which Type of Buyer Are You?

1) Growth-stage company buyer

Typical profile: 500–5,000 employees, fast hiring, evolving org structure, small HR team, lean analytics resources.

What matters most:

• Fast implementation
• Easy-to-understand dashboards
• Headcount planning and budget alignment
• Manager usability
• Moderate pricing

Prioritize:

• ChartHop
• Crunchr
• Humaans Headcount Planning
• ClearCompany HR Analytics and Reporting
• ThoughtSpot if you already have strong data infrastructure

Avoid:

• Overbuying enterprise data science platforms
• Tools that need a full-time analyst to operate
• Deep planning suites if you only need reporting

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2) Enterprise buyer with complex planning needs

Typical profile: 10,000+ employees, multiple geographies, matrix orgs, heavy finance partnership, lots of legacy data.

What matters most:

• Governance
• Scale
• Security
• Scenario planning depth
• Role-based access
• Auditability

Prioritize:

• Visier People
• Orgvue
• Dayforce Strategic Workforce Planning
• Workday AI
• IBM SPSS Modeler
• Eightfold Talent Intelligence Platform
• WorkForce Software for operational labor environments

Avoid:

• Lightweight reporting tools pretending to be planning systems
• Products without strong permissions or hierarchy logic
• Vendors that cannot explain data lineage

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3) Industry-specific operational buyer

Typical profile: Retail, manufacturing, healthcare, contact center, logistics, or similar labor-intensive environment.

What matters most:

• Labor forecasting
• Scheduling
• Compliance
• Time and attendance
• Absence management
• Real-time workforce visibility

Prioritize:

• ATOSS Workforce Management
• WorkForce Software
• Verint Desktop and Process Analytics
• TeamSense
• Sometimes HappyRobot for workflow automation use cases

Avoid:

• Generic people analytics tools that do not understand labor operations
• Planning systems without shift or time data
• HR-only products that ignore frontline operations

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4) Budget-conscious HR analytics buyer

Typical profile: Smaller HR team, need clear ROI, limited implementation bandwidth, likely replacing spreadsheets.

What matters most:

• Low lift
• Standard dashboards
• Quick wins
• Transparent pricing
• Basic planning or reporting, not a giant suite

Prioritize:

• Breathe HR Reporting & Analytics
• ClearCompany
• PredictiveHR
• KeepActive Workforce Analytics
• Humaans if you already use it as core HR infrastructure

Avoid:

• Multi-year enterprise commitments before proving value
• Buying advanced AI features you won’t use
• Tools requiring a data warehouse just to get started

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Vendor Evaluation Questions

Features

• What are your core use cases: reporting, forecasting, scenario planning, skills, or labor optimization?
• Which analytics are prebuilt vs custom?
• Can we model headcount, budget, and org changes in the same workflow?
• Do you support historical trend analysis with effective-dated data?
• Can users drill from summary metrics to underlying employee records?
• Do you provide role-based dashboards for HR, finance, executives, and managers?
• How do you handle skills inference and role mapping?
• What AI features are available, and what do they actually automate?

Integration

• Which HRIS, ATS, payroll, and finance systems have native connectors?
• How often do integrations refresh data?
• Can you support flat-file imports if no API exists?
• Do you support multi-entity, multi-country, or multi-legal-entity structures?
• How do you manage data mapping, field normalization, and historical changes?
• Can we bring in external benchmarks or compensation data?

Security

• What certifications do you hold today?
• Do you support row-level and field-level security?
• Can access be segmented by region, department, and manager hierarchy?
• Is customer data used to train AI models?
• What audit logs are available for data access and scenario changes?
• Do you support data residency requirements?

Pricing

• What is included in the base subscription?
• What costs extra: modules, seats, data volumes, integrations, implementation?
• Are there annual usage minimums?
• What happens to pricing at renewal?
• Are professional services mandatory?
• Can we buy only the modules we need?

Implementation

• What is the typical time to first value?
• What internal resources do we need to dedicate?
• How much data cleanup is usually required before go-live?
• What does the implementation plan look like week by week?
• Who owns data quality after launch?
• How do you support changes in org structure or new business units?

Support and adoption

• What training do managers and executives receive?
• Is customer success included or extra?
• What does support response time look like?
• How do you help drive adoption after go-live?

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Common Mistakes to Avoid

Mistake #1: Buying dashboards when you need decision support

If your problem is headcount planning or restructuring, basic reporting will not solve it.

Consequence: You still end up in spreadsheets for the hard questions.

Do instead: Define the decisions you need to improve before evaluating software.

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Mistake #2: Overestimating your data readiness

Most companies think their data is cleaner than it is.

Consequence: Implementation drags, trust drops, and reports become politically contested.

Do instead: Budget 2–4 weeks for data cleanup at minimum, longer if HR, finance, and ATS data disagree.

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Mistake #3: Ignoring finance

If finance does not trust the model, the software becomes an HR-only toy.

Consequence: No budget alignment, no scenario adoption, no executive credibility.

Do instead: Bring finance into evaluation from day one.

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Mistake #4: Prioritizing AI demos over actual workflows

AI demos are often polished but shallow.

Consequence: You buy a flashy interface that does not fit how planning works.

Do instead: Test real tasks:

• “Show me turnover by manager and tenure”
• “Model a 10% hiring freeze”
• “Compare two org structures with cost impact”

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Mistake #5: Choosing a tool that is too flexible for your maturity

A highly flexible platform can be a trap if your team lacks analytics discipline.

Consequence: Too much customization, too much governance overhead.

Do instead: Pick the simplest tool that handles your core use case well.

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Mistake #6: Forgetting who will maintain it

Many teams buy a platform and assume it will run itself.

Consequence: Dashboards decay, metrics drift, admins get buried.

Do instead: Assign a named owner for:

• data refresh
• metric governance
• user support
• change management

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Mistake #7: Not validating permissions early

Workforce data is sensitive. A bad permissions model creates real risk.

Consequence: Confidential data exposure, legal issues, executive distrust.

Do instead: Test access rules in the demo using real org scenarios.

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Mistake #8: Buying too much too soon

This category is modular for a reason.

Consequence: You pay for features that sit unused for 12 months.

Do instead: Start with one or two high-value use cases:

• headcount planning
• retention analysis
• executive reporting
• internal mobility

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Evaluation Timeline & Process

6-Week evaluation plan

Weighted evaluation matrix template

Use this template to compare vendors consistently.

Scoring guidance:

• 5 = excellent, low risk, proven
• 4 = strong with minor gaps
• 3 = acceptable but imperfect
• 2 = material concerns
• 1 = poor fit

What a strong evaluation looks like

A good process ends with:

• one clear business sponsor
• one agreed data model
• one or two primary use cases
• a realistic implementation plan
• a measurable expected outcome

If the evaluation ends with “let’s buy the one with the nicest dashboard,” you did it wrong.

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Implementation Reality Check

Typical implementation timelines by company size

Key success factors

• Clean hierarchy data
• Named business owner
• Active finance partnership
• Stable metric definitions
• A narrow first use case
• Clear access controls
• Regular refresh cadence

What vendors won’t tell you

1) Your first model will probably be wrong in subtle ways

Not disastrously wrong, but wrong enough to create arguments.

2) Data cleanup is not optional

Budget 2–4 weeks minimum just to reconcile titles, managers, departments, and location logic.

3) Stakeholder alignment takes longer than software setup

The tool is often ready before the business is.

4) Adoption fails when the output is too generic

If leaders see dashboards they could have guessed, they stop logging in.

5) Maintenance is a real operating cost

Expect ongoing effort for:

• data refresh
• new org structures
• metric governance
• user support
• change requests

Practical implementation advice

Start with one clear use case:

• “How do we reduce regrettable attrition in sales?”
• “Can we plan 2026 headcount against budget?”
• “Where are the critical skill gaps in engineering?”
• “How do we improve internal mobility?”

Then expand only after the first use case proves value.

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Red Flags — When to Walk Away

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How This Guide Was Created

This guide is based on public product information, vendor-reported capabilities, category analysis across analytics, workforce planning, talent intelligence, and workforce management software, and buyer-pattern synthesis for HR and recruiting teams.

Review date: September 2026 Update frequency: Quarterly, or sooner if major product/category changes occur

Independence disclaimer: commercial.ai is an independent software directory. This guide is written to help buyers evaluate the category objectively and does not represent endorsement of any specific vendor. Product capabilities, pricing, certifications, and packaging may change; buyers should verify details directly with vendors during procurement.

Analytics & Workforce Planning solutions in the catalogue (30)

• 365Talents by 365Talents — Skills that serve your ambition.
• API-First Solution by 365Talents — 365Talents unveils API-First solution for tailored talent experiences
• Ask ChartHop by ChartHop
• ATOSS Workforce Management by ATOSS Software AG — The Intelligent Workforce Operating System for Enterprise Organizations
• Beeline AI by Beeline — Powerful enough to transform hiring and ethical enough to protect it
• Breathe Reporting & Analytics by Breathe — Use our HR Analytics Software to uncover data-driven insights to improve your HR processes
• ChartHop by ChartHop — Your org changed this morning. Your spreadsheet didn't.
• ClearCompany People Analytics by ClearCompany — Understand what’s happening across your workforce
• Crunchr by Crunchr — See the signals. Shape what’s next.
• Data Feeds by Equilar — Custom Data Feeds & API: Have It Your Way
• Dayforce Strategic Workforce Planning (formerly Agentnoon) by Agentnoon — Strategic workforce planning for agile organizations
• Embedded Workforce Insights by Beamery
• Five9 Workforce Engagement by five9 — Empower your agents to deliver extraordinary customer experiences.
• FOUNT Global SaaS Platform by FOUNT Global — A workflow intelligence platform that runs inside the LLM you already use.
• Fuel50 Insights by Fuel50 — Transform Talent Data into Strategic Clarity
• Guusto Employee Gift Cards by Guusto — Employee gift cards for any budget
• Headcount Planning by ChartHop — Make plans with real-time data, securely.
• Henshaw AI by Orgvue — Henshaw Roles and Henshaw Assistant accelerate data analysis and insights for rapid organizational design and workforce planning outcomes
• Humaans Headcount Planning by Humaans — Plan today the organisation of tomorrow.
• IBM SPSS Modeler by IBM — Drive ROI and accelerate time to value with an intuitive, drag-and-drop data science tool
• Job Architectures by Beamery
• Lattice Analytics by Lattice — Turn people data into people decisions
• Orgvue by Orgvue — Plan and design with clarity and control
• PredictiveHR Analytics Platform by PredictiveHR — Data-Driven Insights + Practitioner Experience Empowers Tomorrow’s HR Pros.
• TechWolf by TechWolf — Skills, work and market intelligence in one data layer
• ThoughtSpot by ThoughtSpot — Unified platform to power trusted insights across analytics, applications, and workflows.
• Verint Desktop and Process Analytics by Verint — Transform desktop activity into business outcomes
• Verint Workforce Management (WFM) by Verint — AI-Powered Workforce Management Software for the Hybrid Workforce
• Visier People by Visier — Go from instinct to impact with AI-powered people analytics.
• Workday AI by Workday — AI that truly knows your business.