Chris Goumas writes about managing Workday HRIS teams and systems, with a focus on practical delivery challenges for small and mid-market organizations. Chris Goumas's posts cover team capacity, system maintenance, AI adoption, and how to test new tools safely.
Chris Goumas is most useful for practical Workday HRIS operations, team capacity, and grounded AI adoption for mid-market teams.
Best for
A Workday-focused HR technology consultancy seeking thought leadership on HRIS delivery and AI agent adoption.
Not for
A competing HCM platform arguing mid-market companies should replace Workday.
Key takeaways
Approving time off and covering the work are two different decisions.
Faster ticket closure can hide declining team capability.
Run a narrow AI pilot instead of waiting for data to be ready.
Check first: Ask for recent LinkedIn audience data and results for comparable posts, including roles, seniority, and company size.
Main ideas
Approving time off and covering the work are two different decisions · Faster ticket closure can hide declining team capability · Run a narrow AI pilot instead of waiting for data to be "ready"
Chris Goumas writes about the practical work of running Workday HRIS for small and mid-market organizations. The posts focus on pressures that larger, better-resourced teams may be able to absorb: overloaded teams, aging system configurations, coverage gaps, and the effort required to adopt AI safely.
Goumas turns broad technology and staffing debates into specific operational choices.
For AI, one post proposes a narrow, supervised pilot and traces wrong answers to causes such as outdated policies or missing exceptions; another warns that faster ticket closure can conceal lost skills or unchecked errors.
Elsewhere, they separate approving time off from actually covering essential work, and argue that Workday teams should be included before plans are promised.
These examples make the work useful to readers weighing what to fix, test, or staff next—not just what new technology to buy.
They also show a willingness to revise a position: Goumas describes moving from “fix the data first” toward using a controlled AI pilot to identify specific data problems.
Readers interested in Workday operations, mid-market team constraints, and grounded AI adoption can follow the posts for concrete scenarios and practical tests.
Goumas writes for the directors and small teams who run Workday at companies without the headcount of a large enterprise.
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Their recurring argument is that the constraint is rarely the software itself.
It is coverage, capacity, and the ability to verify what the system and its new AI agents actually do.
Goumas pushes back on two easy answers—hustling harder and buying more modules—and keeps returning to the same habits: name a backup, run a small controlled test, and make anything new prove what it adds.
They are direct about changing their position on sequencing data cleanup against AI testing, and write from the perspective of someone who has watched mid-market HRIS teams get assignments their staffing cannot absorb.
Approving time off and covering the work are two different decisions
Goumas argues that teams called "small and mighty" often run on heroics that hide key-person risk.
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Executives read long hours as commitment, but a team that catches every ball inches from the floor is one absence, resignation, or bad release window away from an outage nobody budgeted for.
Goumas's fixes are deliberate rather than motivational: one owner and one backup for the things that cannot wait two weeks, a one-page runbook, a locked intake process, and a partner already cleared through security and procurement before an emergency.
Hustle is not a plan, and heroics are a poor measure of whether a system is healthy.
Chris Goumas describes an HRIS lead who became the glue across payroll, integrations, and security, worked 100-hour weeks, and negotiated an exit while the company was still interviewing.
In another case, a newly hired HRIS director lost a payroll subject-matter expert with four years of institutional knowledge and no backup, and was still answering tickets.
They also spoke with three people who were the only HRIS person at their company: a tech startup, a manufacturer, and a private utility.
All three raised time off before Goumas asked: booked PTO turns into remote work when payroll asks whether they will be around for the Tuesday run and benefits says the carrier file can only be fixed next Thursday.
Faster ticket closure can hide declining team capability
Goumas frames AI adoption around four separate risks, each with a different response.
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Never-skilling is when a person never learned a skill because AI did the task for them.
Deskilling is when someone had the skill and reliance let it fade.
Mis-skilling is when an AI error gets learned as fact.
Automation bias is when someone trusts a plausible answer because checking it takes time.
A scorecard built only on faster ticket closure can count declining capability as success, Goumas argues; rewarding the analyst who catches a bad recommendation matters more than rewarding speed.
A junior analyst follows AI instructions to change a business process and closes the ticket, but cannot explain the condition rule, approval routing, or security implications.
An experienced analyst who used to trace integration failures through logs and mappings later struggles to diagnose one without the suggested fix.
In the mis-skilling example, a chatbot claims removing an approval step also removes the approver's access to employee data.
The analyst accepts it, puts it in the runbook, and teaches the next hire; an AI error becomes "how Workday works."
Run a narrow AI pilot instead of waiting for data to be "ready"
Goumas says client conversations changed their position.
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They had been in the camp that wanted the data foundation fixed before anything was built on top of it.
Their objection is that "get the data ready" gives a small team no definition of done: how much, which records, for what purpose, and what counts as ready are all left open.
Goumas proposes the reverse in some situations: a controlled pilot with one narrow use case, a small group of testers, approved sources, people who can verify answers, and no automated changes.
The pilot then produces specific, traceable problems the team can fix.
Goumas pictures a three-person HRIS team already handling payroll deadlines, a broken integration, an open enrollment cycle, and a daily ticket backlog.
A policy assistant gets asked "How much time off can I carry over?" ten different ways; the wrong answers get traced to an outdated policy, a missing country-specific exception, the wrong employee location, or the AI failing to find information the team was sure was in Workday.
Each cause has a different fix.
Goumas says plainly that this differs from a few weeks earlier, when they argued for fixing data before building on it.
Agents inherit your tenant, so someone has to own the consequences
Goumas argues that AI agents show up inside your Workday tenant on the release schedule whether you ask for them or not, inheriting your security model, business processes, and approvals.
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That inheritance is both the advantage and the risk: if security groups are a mess, an agent inherits the mess and moves faster than any person could.
They ask who checks what agents actually do and say the workforce consequences of AI belong to HR, not an IT tech-stack review.
On adding outside agent platforms, Goumas puts the burden of proof on the added platform: use delivered capability first, and be clear that eligible external-agent calls can count against your Workday API entitlement, so paying another vendor does not cover the Workday consumption.
Chris Goumas describes executives asking Copilot to design a Workday solution for surveying employees and routing follow-ups to managers, then forwarding the confident answer to HRIS with "Please build this," with no check on whether the company licenses the required products or what the configuration might break.
On the vendor side, Chris Goumas reads Gartner's first Emerging Market Quadrant for AI Agents in HR, where Workday was named a Market Shaper alongside Oracle, SAP, and ServiceNow; Chris Goumas points out that the same report warns about pricing predictability, lock-in, and deploying agents only if your team can test and audit them.
A home address change is Chris Goumas's example of a task Workday's own Self-Service Agent already supports, and a poor reason to stand up a second agent platform.
Most Workday tenants have outgrown their operating model, not broken
Goumas says the honest diagnosis is usually that a tenant was architected for the company that launched it and nobody resourced the operating model to evolve.
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Job architecture goes untouched since go-live, security roles get patched on top of patches, business processes still describe a company that has since had layoffs, reorgs, or acquisitions, and data quality slips quietly.
Their prescription is a reset in sequence: audit job architecture, clean up security roles, rebuild business processes, fix data quality at the source, then build the four or five reporting hubs and KPIs leadership will actually use.
Foundation first, new SKUs second, adoption metrics third.
Goumas is skeptical of adding modules to a base that was never maintained.
Goumas describes a CHRO who wants to discuss Sana, Extend Pro, and augmenting Workday Recruiting with another tool while the foundation is unbuilt, the C-suite is already losing faith in the data, and the business is routing around the system. They tie the conversation to the 2017 to 2023 go-live wave now hitting renewal, and suggest telling executives that the company did not fail at Workday; it fell behind and lacked the resources to catch up.
The platform is moving faster than mid-market teams can absorb
Goumas argues that Workday's platform expansion and the headcount available to absorb it are diverging.
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Large enterprises can assign dedicated Extend teams, integration architects, and data engineers, while mid-market and smaller companies may have one studio integrations person already buried in EIBs, release testing, and a ticket backlog.
Goumas calls that difference a canyon rather than a skills gap, and warns that it widens with each release.
Their advice to a mid-market HRIS director is not to chase the whole platform: understand what Workday Build is, catalog the current state, pick one small use case, be honest about the bottleneck, and watch the Flex Credits consumption model because it changes the budget math.
Goumas contrasts Walmart or Citi, which they say have people whose entire job is evaluating new Workday capabilities, with a 2,500-employee company that may have one Studio integrations person who has never used Extend. Reviewing the 26R1 release, they wanted to find real improvements for smaller customers and found roughly five they considered usable, naming Worker Communication via SMS, job profile visibility control, dependent effective dating, an onboarding plans task, and a correct change job feature; the rest aimed at large-enterprise scenarios.
When choosing an AMS partner, hourly rate is not the cost
Goumas argues that shopping managed services by hourly rate is a trap: the cheap rate often comes with junior consultants who take longer, unfamiliar assignments where someone learns your configuration on your billable time, and pod rotation that makes you explain your setup every time.
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They say to measure total billable hours to resolution, real experience in your specific modules, how long the team stays together, and whether the same people can carry their knowledge into project work with no ramp-up. In Goumas's framing, the best model is the same senior consultants on your modules, because the learning stays with your team and your budget absorbs the result rather than the hours.
Goumas compares a junior consultant who spends eight hours researching an advanced compensation ticket at $169 per hour, about $1,352, against a senior specialist who fixes the same ticket in 3.5 hours at $189 per hour, about $662. They advise HRIS leaders to ask who actually answers their tickets, what their real experience is in the relevant modules, how many billable hours resolution takes, how long team members stay, and whether that same team can run a project.
Explains what it means when one person is the entire HRIS function—and why approved time off does not automatically create coverage.
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Goumas describes the “HRIS One & Only” as the only HRIS person at a startup, manufacturer, or utility.
Approved PTO turns into remote work when payroll asks whether they will be around for the Tuesday run, benefits reports a failing carrier file and a vendor who can only meet next Thursday, and finance needs a headcount pull on Monday.
They separate two decisions: approving the time and covering it.
For teams of three or four, they suggest naming a second person for the few things that cannot wait two weeks and cross-training.
For the One & Only, they recommend getting a partner through security and procurement before an emergency, not during one.
For brands planning a campaign
This is a strong entry point for a partnership on fractional HRIS coverage or staff augmentation for mid-market Workday teams. Show a named-backup model and specific coverage triggers, not generic “we help when you are busy” messaging.
Shows newcomers how AI can erode HRIS capability even as ticket metrics improve.
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Goumas names four risks: never-skilling, where a junior analyst follows AI instructions to change a business process but cannot explain the condition rule, approval routing, or security implications; deskilling, where an experienced analyst stops tracing integration failures and later struggles to diagnose one independently; mis-skilling, where an AI error becomes documented as “how Workday works”; and automation bias, where someone trusts a plausible answer because checking it takes time.
Their practical response: supervised practice without AI, periodic diagnosis before seeing AI's recommendation, checking the source and tenant, and making verification easier.
They argue that a scorecard focused only on faster ticket closure can count declining capability as success.
For brands planning a campaign
Supports a thought-leadership partnership on AI agent adoption, stressing supervised practice, independent verification, and capability metrics. It makes a concrete case against measuring AI success only by faster ticket closure.
Shows how Workday work gets promised before the HRIS team can check licensing, dependencies, and risk.
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Goumas describes executives asking Copilot to design Workday solutions, then forwarding the confident answer to HRIS with “Please build this.”
No one checked whether the company licenses the required Workday products, whether prerequisites exist, or what the configuration might break.
They also describe functional teams making Workday-dependent plans without HRIS in the room, so by the time HRIS sees the requirements, expectations and dates are already set.
Their fix is a shared backlog where each function identifies priorities and leadership ranks them against the capacity of the team doing the work.
AI makes this more urgent because it can make an unvalidated idea sound implementation-ready.
For brands planning a campaign
A useful campaign angle: bring HRIS into planning and AI governance. A partner can offer operating-model workshops or planning content that shows how to validate AI-generated Workday ideas before making commitments.
Explains why the cheapest Workday AMS hourly rate can mean a higher total cost—and what to ask instead.
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Goumas compares a $169/hour junior consultant who spends eight hours researching an advanced compensation ticket with a senior specialist who fixes it in 3.5 hours at $189/hour.
They call out the “speed at all costs” trap, “next up” assignments where someone learns your configuration on your dime, and “pod rotation” where you explain your setup every time.
Their recommended questions: who actually answers tickets, their real experience in your modules, billable hours to resolution, team retention, and whether the same team can run a project.
Their preferred model is the same senior consultants for your specific modules, with full knowledge transfer.
For brands planning a campaign
A Workday AMS delivery partnership can build on Goumas's “total cost and transferred learning” argument rather than competing on hourly rates.
Offers a practical, test-first alternative to waiting until data is “ready” before trying AI.
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Goumas says they changed their mind about “fix the foundation before building AI” after client conversations.
They picture a three-person HRIS team told to get data ready while handling payroll deadlines, broken integrations, an open enrollment cycle, and a daily ticket backlog.
Their alternative is a controlled pilot: one narrow use case, a small group of testers, approved sources, people who can verify answers, and no automated changes.
In a policy assistant example, the team asks a time-off carryover question ten different ways, finds wrong answers, and traces whether the cause is an outdated policy, a missing country exception, the wrong employee location, or the AI not finding information.
That gives the team a specific problem to fix instead of an open-ended data-cleaning project.
For brands planning a campaign
A strong fit for a campaign on AI pilots for mid-market Workday teams. Show narrow use cases, verification, and tracing as practical ways to understand data quality.
Frames the Workday platform complexity gap between large enterprises and mid-market HRIS teams.
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Goumas could not clearly explain Workday Build, despite working in the Workday world daily.
They looked it up and described it as a unified developer platform bringing together Extend, Orchestrate, a Flowise Agent Builder, Data Cloud, AI development tools, analytics, and a developer copilot.
They contrast Walmart or Citi, which have dedicated Extend teams and integration architects, with a 2,500-employee company that may have one Studio integrations person buried in EIBs, release testing, and tickets.
They call the difference “not a skills gap.
That's a canyon.”
Their advice for a mid-market HRIS director: know what Workday Build is, catalog the current state, pick one small use case, be honest about the bottleneck, and watch Flex Credits because they change budget math.
For brands planning a campaign
A strong educational partnership angle on Workday Build and AI agents for under-resourced mid-market teams. Avoid enterprise-only messaging and focus on small, intentional steps.
Introduces Goumas's "Workday Reset" argument for companies facing renewal with a shaky foundation.
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Goumas says most Workday tenants are not broken; they have outgrown the operating model that launched them.
They list job architecture untouched since go-live, security patched on top of patches, business processes built for a company that no longer exists, and slipping data quality.
When CHROs want to discuss new SKUs, Extend Pro, or adding recruiting tools, they ask whether the company wants to bolt more onto a foundation that has not been resourced to evolve.
Their Workday Reset sequence starts with the foundation: audit job architecture, clean up security roles, rebuild business processes, fix data quality at the source, then build reporting hubs and real KPIs.
New SKUs and adoption metrics come second and third.
For brands planning a campaign
Useful for a renewal or reset offer. Contrast adding more Workday modules with fixing the existing tenant, using Goumas's audit and rebuild sequence as the practical agenda.
Small and mighty HRIS teams, coverage and key-person risk4
Agent governance and the HRIS seat at the planning table3
Controlled AI pilots before large data-cleanup programs2
Mid-market capability gap and release value2
AI skill erosion in HRIS teams1
Workday reset: fix the foundation before adding new products1
AMS partner economics: total cost over hourly rate1
Small and mighty HRIS teams, coverage and key-person risk
Goumas writes repeatedly about HRIS teams that are understaffed but expected to absorb payroll, integrations, security, benefits and release work.
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They frame PTO and parental leave as coverage problems, not scheduling problems, and warn that heroics hide key-person risk. They argue for deliberate redundancy, runbooks, named backups and pre-vetted outside support—not more hustle.
Uses direct address to “Dir HRIS,” short client or colleague scenarios and numbered coverage steps.
For brands planning a campaign
A thought-leadership partnership can build on Chris Goumas’s coverage-planning framing with practical resources on backup coverage, staff augmentation and fractional leadership for mid-market HRIS teams.
Controlled AI pilots before large data-cleanup programs
Goumas argues that a narrow, controlled AI pilot can reveal where data is wrong faster than an open-ended data-quality project for small HRIS teams.
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They recommend approved sources, a small tester group, no automated changes, and tracing wrong answers to outdated policy, missed exceptions or location errors. In a related post, they apply the same burden-of-proof logic to adding a second agent platform when Workday already supports the task.
Chris Goumas says they changed their position on data-first sequencing. The exception is a controlled AI pilot, not skipping data quality.
For brands planning a campaign
Sponsor content can translate Chris Goumas’s pilot design into a repeatable evaluation template for Workday AI agents, including test cases, escalation paths and cost checks against Flex Credits and API entitlements.
Chris Goumas frames AI adoption around four risks: never-skilling, deskilling, mis-skilling and automation bias.
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Each risk gets a concrete Workday example and a different response, from supervised practice to independent diagnosis to correcting documentation. The core point: faster ticket closure can mask declining team capability.
Four-part risk framework with Workday scenarios and separate corrective actions.
For brands planning a campaign
A partnership can support a practical series or workshop on AI-era HRIS capability, measurement and verification—especially content that rewards analysts for catching bad AI recommendations.
Agent governance and the HRIS seat at the planning table
Goumas argues that agents inherit Workday security, business processes and approvals, so bad security or an unmanaged tenant becomes a faster risk.
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They question who checks what agents do and warn that HRIS teams are often left out until implementation deadlines, after Copilot-generated answers have set expectations. They position HR, not IT alone, as accountable for AI’s workforce consequences.
Chris Goumas is not anti-Workday: they say to look at Workday first, except where another vendor has already shown real ROI.
For brands planning a campaign
This is a strong opening for thought leadership on agent governance, audit responsibility and bringing HRIS into AI planning before commitments are made.
Workday reset: fix the foundation before adding new products
They describe many Workday tenants as outgrowing the operating model that launched them, with stale job architecture, patched security roles, outdated business processes and slipping data quality.
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Their prescription is a reset: audit job architecture, clean security, rebuild business processes, fix data at the source and build trusted reporting before buying new modules. They are skeptical of bolting new SKUs onto a foundation that was never resourced to evolve.
For brands planning a campaign
A partner can use this as the spine of a mid-market Workday health-check campaign, with assessments and sequenced remediation offers tied to Goumas’s foundation-first argument.
Goumas argues that Workday Build—Extend, Orchestrate, Flowise agents, Data Cloud and related tools—is arriving faster than small and mid-market HRIS teams can absorb.
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They contrast enterprises with dedicated platform teams against a two-person HRIS team still handling EIBs, release testing and ticket backlogs. They also review 26R1 as thin for smaller customers and list the few features they consider useful.
Platform inventory, org-capacity comparison, release-note review and a short action list for mid-market directors.
Chris Goumas does not argue for switching platforms; they dismiss Oracle, SuccessFactors, UKG and Ceridian as alternatives in their 26R1 review.
For brands planning a campaign
A brand can position thought leadership around realistic mid-market adoption paths, release-value analysis and managed support that closes the capability gap without pretending the team can absorb everything.
AMS partner economics: total cost over hourly rate
Goumas challenges HRIS leaders who choose AMS partners by hourly rate, then spend extra on junior consultants, rotating pods, and repeated explanations.
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They argue that billable hours to resolution, module-specific seniority, team stability, and the ability to carry knowledge into project work matter more than the lowest rate. The post includes cost math and questions to ask before renewing or selecting a partner.
Cost comparison, named failure patterns, and buyer questions.
For brands planning a campaign
A partnership can build an AMS selection guide or webinar around Goumas’s total-cost framing, especially for mid-market teams weighing senior continuity against cheap hourly rates.
Chris Goumas writes about Workday HRIS teams and systems.
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Their posts disclose no sponsorship, employment, client, affiliate, investment, or owned-business relationship with any company. Workday is the platform they analyze and the practical subject of their advice, not a disclosed commercial partner.
Workday
Mention
Chris Goumas writes about Workday as the HRIS platform their audience operates: release reviews, AI agent rollout, data quality, AMS support models, and team capacity.
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They explain Workday features and commercial mechanics such as Flex Credits and external-agent API entitlements, but disclose no employment, sponsorship, or client relationship with Workday.
Chris Goumas writes about the people side of running Workday: small HRIS teams asked to handle payroll, integrations, security, benefits, and every new release with one or two people.
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Start with the coverage and key-person-risk posts for team structure, the AI-agent posts for what changes when assistants and agents inherit a tenant, and the managed-services post for how consulting and support contracts are priced.
Their posts are practical and scenario-driven, usually built around a client conversation or a specific ticket, and end with a short list of next steps.
They are willing to say they changed their own position, and argue against buying more modules before fixing the foundation.
Read their work for the operational view of Workday, rather than product announcements or vendor marketing.
WorkdayHRISHRIS team capacity and coverageAI agents in HR technologyWorkday release analysisAMS and consulting vendor selectionLinkedIn text postsNumbered action listsHRIS directors and managersMid-market and smaller employers
Both write for the person who owns Workday and People Systems at a company without a large platform team, drawing on the work of cleaning up and stabilizing live tenants.
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They frame the problem as one of governance and foundation, not features, and use specific scenarios from real cleanup work to make the case—for example, a CHRO who would rather fund new features than the months of governance work needed to make them stick.
Mojahedi concentrates on the tenant itself: conflicting reports, unexplained configuration, integrations still running into work the business has replaced, and the damage undisciplined automation can do to that foundation.
Goumas covers a wider field: team capacity, PTO and parental-leave coverage, vendor pricing, and the HR-versus-IT question of who owns an agent’s consequences.
Read Mojahedi for a diagnostic view of a messy tenant; read Goumas for the staffing and buying decisions around it.
Goumas also writes about AI agents moving into enterprise systems of record and the commercial terms attached to them.
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Both look past the demo to the bill, naming per-agent pricing, API entitlements, consumption charges, and vendor lock-in. Blonski’s argument that rigid, domain-specific data models and consultant-heavy stacks drive the real cost of legacy SaaS overlaps with Goumas’s skepticism about adding another platform to a Workday tenant.
Blonski writes as a vendor-side advocate for replacing legacy SaaS outright, naming Salesforce, ServiceNow, SAP, and Workday as platforms to cut and pointing to his own platform as the replacement.
Goumas stays within the Workday ecosystem and argues for the opposite default: use delivered Workday capabilities first, treat Workday as the system that owns the transaction, and make any additional platform prove its value.
For readers weighing a switch, they offer useful counterweights, not the same recommendation.
Kaufman writes about AI agents taking on real back-office work rather than answering prompts, and the controls and ongoing costs that come with it.
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Both ask who checks the output, how to change direction later, and what a platform’s role in daily operations commits you to. Kaufman’s questions about the economics of running an agent—including model choice, context size, and how many calls a process needs—are the finance-side version of the governance question Goumas raises about HRIS.
Kaufman focuses on Finance and the CFO’s buying decision: token consumption, segregation of duties, audit trails, and a financial data graph.
He writes on behalf of a finance AI platform.
Goumas focuses on HR and the HRIS team, with attention to tenant configuration, security groups, business processes, and the analyst who has to verify an answer before payroll runs.
They are complementary, not interchangeable: read Kaufman for money and controls, Goumas for people and the tenant.
Chris Goumas’s posts align closely with a Workday-focused thought-leadership campaign for HRIS leaders, especially teams at small and mid-market organizations.
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Dated examples cover AI agent adoption, system readiness, team capacity and safe implementation. Before committing, ask for recent LinkedIn audience data and results for comparable posts, including audience roles, seniority and company size.
No partnership or sponsorship offer appears in Chris Goumas’s posts.
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Their public posts offer vendor-agnostic analysis of Workday HRIS operations, not branded placements or published ad formats; none carries a sponsorship disclosure, rate card, media kit or partner offer.
Commercial references describe how Workday AMS and staff-augmentation firms operate — junior versus senior consultants, pod rotation, billed hours to resolution, knowledge transfer, pre-vetting a partner through security and procurement, and no-retainer ad hoc support — and read as first-hand commentary on the consulting market, not an offer.
One post invites readers to “compare notes” on a Workday reset, a conversational prompt rather than a published service or partnership route.
Buyers planning a paid thought-leadership partnership should confirm terms directly with Goumas; the posts do not verify a format, deliverable or rate.
A Workday-focused HR technology consultancy seeking a thought-leadership partnership on HRIS delivery and AI agent adoption for mid-market teams is the closest thing to a native fit in Chris Goumas’s posts.
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They write to the director or manager running Workday HRIS at a small or mid-market company, and their recurring arguments give a consultancy a usable spine instead of a generic platform pitch.
Approving time off is not the same as covering it, so name a backup and pre-vet a partner before the emergency.
Run a narrow, controlled AI pilot before an open-ended data cleanup, and trace each wrong answer to its cause.
Assume agents inherit your security model, business processes and approvals, which means someone has to test and audit them.
Reset the foundation — job architecture, security roles, business processes, data at the source, trusted reporting — before bolting on new SKUs.
Judge an AMS partner on billable hours to resolution, module-specific seniority and knowledge transfer rather than the lowest hourly rate.
Each argument converts naturally into an asset for a thought-leadership partnership: a coverage worksheet, a test-case template for an agent pilot, a foundation audit agenda or questions to ask an AMS provider.
Their framing of the mid-market capability gap and the Flex Credits budget question also gives the consultancy a mid-market narrative that enterprise-vendor messaging usually misses.
The fit is subject-matter fit; ask for audience size and seniority data before treating it as a reach opportunity.
Goumas’s post inviting readers to compare notes on a Workday reset is a conversational opener, not a published service.
Agree on deliverables, scope and rates directly rather than assuming them from the posts.
When to choose a specialist
A competing HCM platform buying a paid launch series that argues mid-market companies should replace Workday is a bad fit for specific reasons.
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The message collides with Chris Goumas’s published position: their answer to a shaky tenant is a Workday reset, not a switch.
They dismiss Oracle Fusion, SuccessFactors, UKG and Ceridian as alternatives and tell readers not to wait for a migration to fix their problems.
Their stated default for any second platform is to use the delivered Workday capability first and make the additional platform prove what it adds.
They also argue that recent release value has skewed toward Fortune 50 use cases while smaller customers get little, so an enterprise-oriented replacement pitch would push a scope they openly criticize.
There’s also no documented sponsorship format, deliverable or rate; the commercial passages describe how AMS and staff-augmentation firms behave, not an offer.
Before planning paid placement, ask what format and deliverables are available, what they cost, and for audience composition, seniority and engagement data to assess whether impressions or demo requests can be planned and measured.
The same checks apply to any brand seeking paid reach or performance metrics rather than a subject-matter association with a named practitioner.
A worked campaign example
Take the reader’s need — a Workday consultancy seeking a thought-leadership partnership on HRIS delivery and AI agent adoption for mid-market teams — and build a three-part sequence aimed at a two- or three-person HRIS team, with a single call to action at the end.
Part one, coverage before capability: open on the One & Only problem, where an approved week of leave turns into remote work because payroll still needs the Tuesday run and the carrier file only has one vendor slot.
Walk readers through naming one owner and one backup for payroll, time, benefits carriers, identity and security, then provide a coverage worksheet they can fill in during a team meeting.
Part two, pilot before cleanup: lay out a controlled pilot — one narrow use case, a small tester group, approved sources, people who can verify answers, no automated changes — and work through a concrete example: ask a time-off carryover question ten different ways, then trace each wrong answer to an outdated policy, a missed country exception or the wrong employee location before fixing the cause and retesting.
The test-case template gives the consultancy a serviceable idea by turning a pilot into something a small team can repeat.
Part three, total cost over hourly rate: compare a junior consultant burning eight hours at a low rate with a senior specialist finishing in a few hours at a higher one, then list questions to ask an AMS provider — who actually answers the tickets, their real experience in your modules, billable hours to resolution, team retention, and whether the same team can run a project.
Close with one measurable step: a coverage and readiness review, with the worksheets as the entry offer and the pilot template as the reason to book.
Before committing, ask for the audience’s actual roles, seniority and company size; performance data for comparable posts; and the partnership terms, including format and rate.
The advisory tone alone does not establish a proven reach channel.
Examples such as conflicting reports, unexplained configuration and integrations that no longer serve the business give diagnostic conversations a concrete starting point. The governance story also makes the case for protecting time to keep fixes from eroding.
This work stays close to Workday cleanup and governance. Goumas’s posts range across team capacity, vendor support, release choices and AI adoption, offering more angles for a broader HRIS thought-leadership program.
Choose Blonski’s perspective for a campaign making the case for replacing legacy enterprise software rather than improving a Workday environment.
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His posts argue that separate, domain-specific AI stacks create cost and complexity, and frame AI as a way to reduce operational friction without reducing the workforce.
That replacement argument differs from Goumas’s advice to reset Workday before adding new products and make additional platforms prove their value. Blonski’s examples focus on enterprise architecture and software economics, not practical steps for a small HRIS team.
Kaufman’s examples concern finance processes and Safebooks, not Workday configuration or HRIS team capacity. Goumas is more directly relevant to a campaign aimed at HRIS practitioners; Kaufman is a better fit when the campaign centers on finance buyers and agent deployment.
This profile covers 14 LinkedIn posts by Chris Goumas, read end to end rather than summarized from titles or snippets.
The public account listing showed 18,654 followers on 8 October 2026; the posts do not say how many people engage with them or describe audience composition or seniority.
The posts reference Workday release cycles from 26R1 through 27R1 but carry no publication dates, so the sections describe recurring subjects and arguments rather than a timeline.
Chris Goumas's posts do not mention a signature book, show, business or career change.
Three comparison creators — Seena Mojahedi, David Blonski and Ahikam Kaufman — are each represented by two posts dated May to October 2026.
Gaps that remain open: the posts do not independently confirm employers, clients or credentials named in them; no sponsorship, rate or media-kit information is published in the posts here; and Goumas's posts have no publishing chronology.
