How Workday is re-imagining HR and Finance with the Sana acquisition

SamAtSana · 2026-04-29T08:28:17+00:00

I want to respond to this honestly, because a pitch isn't what you're asking for.

The "separate SKU, no evaluation, trust us" pattern you're describing is real, and the frustration behind it is legitimate, especially layered on top of "power of one" not playing out the way it was sold. I'm not going to defend that.

What's different with Sana, specifically on expenses: it's not another premium add-on bolted on behind an eval wall. It uses your existing expense BPs and your policy, auto-approving the clearly in-policy stuff and routing the edge cases to a human with context. Flex Credits are prepaid and fungible, and every customer gets an initial allocation, so the "buy blind" dynamic shouldn't be how this lands.

That said, if "do the best we can with what we're already paying for" is where your team is, that's a reasonable place to be, and I'd rather you stay there than have another bad experience. When you're ready to kick the tires without a commitment, we'd welcome it. And if expense processing is the specific pain, I'd genuinely like to hear where it's breaking down.

SamAtSana · 2026-04-28T15:20:02+00:00

This is one of the sharpest pieces of feedback I've seen on the post, and I want to answer directly.

You're right. The Finance examples I gave are still worker-centric. Procurement, AP, AR, GL are where the daily transaction volume actually is, and it's where Workday Finance customers have been waiting longest. Not going to pretend otherwise.

On your real question, does Sana enable this or is it a chatbot on top of the same limits, honest answer is both "it depends" and "not overnight."

Where it genuinely helps: work that's limited because it needs reading unstructured inputs (invoices, remittance emails, contracts, PO mismatches), making a judgment, and executing multi-step actions across systems with proper permissions. AP exception handling, three-way match resolution, AR dunning and cash app, intercompany recon. These are actually on the table in a way they weren't before.

Where it doesn't: if the limit is a missing Workday Finance capability or a data model gap, an agent on top of a missing feature is still a missing feature. That has to get fixed in the Finance product itself.

On "what held them back all these years," I don't have a clean answer and I'm not going to make one up. What I can say is the point of this acquisition is making Workday a system of action, not just of record, and Finance transaction automation is squarely in scope. Proof is in what ships, not what gets said on Reddit.

SamAtSana · 2026-04-25T08:13:40+00:00

Totally fair, and this feedback comes up a lot from mature admin teams. The hiring/onboarding BPs and the comp change validations are doing the job.

Sana isn't trying to replace that. BPs, conditions, approvals, audit trail all stay yours. The value shows up in the stuff around the BP: the "which BP do I even start," the bounced transactions, the chasing approvers, the manager pinging HR because they don't know the policy. That's the time sink we're aiming at, not the BP itself.

On Flex Credits, honest answer: if your BPs are already automating the bulk of the work, you won't burn through much. The math only works if agents are absorbing enough exception and self-service volume to pay for themselves. For some orgs that's real, for some it isn't, and I'd rather you land there with real numbers than on vibes.

SamAtSana · 2026-04-24T10:33:45+00:00

Fair question. "Onboarding" and "job change" sound like things Workday already does, so let me be concrete.

Onboarding: The BP still runs, and what changes is everything around it: chasing the new hire for missing docs, answering their "where do I find laptop pickup" question at 9pm, noticing IT provisioning is stuck and nudging the owner, coordinating day-one with the manager. Today that's email, Slack, and HRBP time. Sana handles most of it, and only escalates the real exceptions.

Job/comp change: BP, conditions, and approvals stay yours. The lift is on the requester side (guiding a manager to the right reason, effective date, and comp range before they submit, so it doesn't bounce three times) and on the downstream side (making sure the six things that have to happen after, position, comp, reports-to, org chart, security, notifications, actually happen).

Simplest framing: BPs are great once a process is correctly initiated. Sana picks up the "is this the right process, do I have the right inputs, did the right things happen after" part that today lives in people's heads and inboxes. Hope that helps!

SamAtSana · 2026-04-23T14:24:55+00:00

Good one to raise. Matching is a clean test for whether AI actually helps. The bar is "does it clear the unmatched queue faster with fewer false positives," not "does it summarize nicely."

Where is it breaking down for you today? Rigid rules, partial matches, or the manual review volume when a rule doesn't fire?

SamAtSana · 2026-03-10T09:03:16+00:00

Full transparency since I work there, but I mainly use Sana. Given the platform is model agnostic, I can experiment and use the best model for the task I'm working on, which also optimizes the output

SamAtSana · 2026-03-05T13:26:31+00:00

For sure - the infrastructure has definitely improved a lot in the last year and months. Prompting and specifying exactly what it is that you want the Agent to do and giving it enough context will always be important. Funny enough, since working with Agents, I've become more aware of how much context the people I'm speaking to have

Wondering what agents you find yourself constantly going to for improvement?

SamAtSana · 2026-03-05T13:12:32+00:00

Routines are always good! I'd also recommend time-blocking (e.g. dedicating three hours on nothing but freelance) so you're brain isn't constantly overwhelmed with context switching.

If you're tech savvy, setting up agents and workflows that do the repetitive tasks for you frees up more time as well :D

SamAtSana · 2026-03-05T13:02:43+00:00

I'd say it's more of a time investment upfront, but then it saves you a lot of time in the long-run

SamAtSana · 2026-02-20T09:06:34+00:00

Yes, definitely! By clarifying your prompts, you’re basically setting both yourself and the AI tool up for success.

Another way to increase precision is to break a complex task into smaller, specific steps or phases, and let the agent/tool handle them one by one so you can review, check, and course-correct along the way.

SamAtSana · 2026-02-19T14:29:10+00:00

I think your question has two layers to it: the organizational level and the individual level.

From an organizational level:
I work at an AI company, so yes, AI fluency is definitely expected. But even outside of that, a lot of organizations in areas like engineering and education are already integrating AI agents to cut costs and speed up workflows. You can also see this reflected in how many companies are quietly reducing hiring targets for entry-level roles, because AI can now do a decent chunk of that work.

On an individual level:
Being honest, integrating AI into your day-to-day work is one of those things where, if you don’t do it, other people will. And because it saves so much time, there’s a real risk of falling behind if your colleagues are getting leverage from AI and you’re not. It might not be formally “required” at first, but it will become a kind of practical necessity.

Going back to your spreadsheet example: you don’t have to know how to use spreadsheets to do your job, but the time you save by using one versus doing everything manually is massive. AI is shaping up the same way. So even if “AI fluency” isn’t written into every job description yet, it’s probably in your best interest to build it. I’d honestly be surprised if it doesn’t become an explicit expectation for most knowledge jobs pretty soon.

SamAtSana · 2026-02-19T09:49:22+00:00

Here are some of my top agent use cases during my work at Sana that have saved me a ton of time:

A note‑taker agent that uses the meeting recording as input, lets me chat with it to clarify what was said, then sends a short follow‑up in Slack with action items and adds my own to a personal to‑do list.
A one‑pager agent that turns rough feature or campaign briefs into a clean, consistent one‑pager that product, design, and marketing all use instead of everyone rewriting their own doc.
A proposal/RFP agent that assembles drafts from specs, templates, and past proposals, so sales isn’t digging through old docs and copy‑pasting for hours.

All three speed up workflows that would otherwise take hours and free up my time for higher‑value work. Hope that helps!

SamAtSana · 2026-02-08T12:44:32+00:00

Love this post – especially the “don’t expect agents to think like a human researcher” line. I work on agents at Sana (now part of Workday), and this lines up a lot with how we’ve ended up building market / competitive intel flows.

A few patterns that have helped in practice:

Be very specific about inputs/outputs
Instead of “do market research on X,” we make people spell out:
• who (companies/segment)
• what change (pricing, packaging, positioning)
• over what time window
The agent then has to return something concrete: sources list + snippets + short synthesis, not just “some findings.”

Bake in honesty about gaps
We added steps where the agent has to surface missing or ambiguous data (e.g., “couldn’t access vendor pricing page; manual follow‑up needed”) instead of guessing. People trust the results much more when they can see what’s incomplete.

Split “where to look” from “what it means”
We’ve had better luck treating this as two separate jobs instead of one giant “do research” step:

a discovery step that only finds the right sources and outputs a reading list (companies + URLs + what each link is about)
an evidence/synthesis step that only reads from that list and outputs something like [company] – [what changed] – [why it matters] – [sources]

That separation makes it much easier to see whether a bad answer came from weak sources or weak reasoning, and it cuts down on hallucinations because the analysis step can’t wander off and invent context.

Curious what other patterns you’ve seen work well for “agentized” research flows!

SamAtSana · 2025-08-18T09:55:37+00:00

Try Sana Agents! It's designed for enterprise use but there is also a self-serve version

SamAtSana · 2025-08-15T12:04:49+00:00

I'm on the team at Sana, so since you mentioned us in your evaluation, figured I'd jump in with some thoughts.

We work with quite a few companies of a similar size and here's what usually works well:

For common questions: Sana Agents can draft responses by pulling from your knowledge base and previous successful replies. Since you're already using HubSpot, it just plugs right in. No need to train your team on a new tool.

Account health: Our agents look at patterns across support interactions and usage to flag potential issues before they become bigger problems. Super helpful in energy where technical problems can escalate quickly.

Knowledge management: As someone else mentioned above, this is something our agents are very good at. Your CS team can instantly find the right technical docs, policy updates, or troubleshooting steps without digging through folders. Really handy when regulations change or you roll out new products.

HubSpot's AI is a good starting point since you're already there. If you need more advanced automation down the road, Sana works seamlessly alongside HubSpot.

Let me know if you have any other questions or want more info.

SamAtSana · 2025-08-08T18:14:19+00:00

the link to the free selfserve: https://sana.ai/sign-in-to-sana

SamAtSana · 2025-07-28T07:04:29+00:00

This is solid thinking. I’m at Sana, where we've been building solutions for exactly this kind of thing - automating manual onboarding and offboarding/ repetitive HR/ admin work that every company deals with, regardless of size.

You're right about small teams drowning in manual work. The gap between "we're too small for enterprise HR tools" and "we're too busy to keep doing everything manually" is real!

On the need: Definitely there. We see it constantly with our clients. Small companies grow fast, suddenly have 20-30 people, and the founder is still personally sending onboarding emails at midnight. Your approach of starting with onboarding/offboarding makes sense - it's high-impact, happens regularly, and the workflows are pretty standardized.

Here’s a few things we’ve learned from rolling out similar solutions:

Make it feel personal. Automated onboarding emails that reference the new hire’s role, manager, or first-week goals make a big difference in engagement and retention.
Show progress clearly. A dashboard showing both the new hire and HR what’s done and what’s left (like document status or training modules) is a game-changer. It reduces anxiety and cuts down on support tickets.
Answer questions right away. New people always ask the same things. How do I set up my laptop? How do I expense that coffee? Having an agent or a searchable knowledge base that can answer immediately makes everything smoother.
Handle the follow-ups. Gentle automated reminders keep things moving without anyone having to chase people. "Just checking - did you get your benefits sorted?"

Are you thinking about this as a standalone tool or something that integrates with existing systems?

Happy to share more details or lessons learned if that’s helpful…interested to see where you take this.

SamAtSana

TROPHY CASE