Sarah Toomey leads procurement within Levelpath’s finance team. Toomey has built and run procurement functions at technology companies and writes about procurement processes, AI automation, and supplier relationships.
Useful for procurement and finance software campaigns that need a practitioner explaining process-first AI adoption.
Best for
Enterprise procurement or finance software vendor wanting sharper messaging for AI-assisted buying.
Not for
Consumer or lifestyle brand seeking broad reach, product reviews, or aesthetic creator content.
Key takeaways
Change the process before buying procurement software, or new tools disappoint.
AI can handle contract scanning, bid comparison, and intake triage.
Automation raises the value of supplier relationships, negotiation, and human judgment.
Check first: Confirm whether they offer creator partnerships, terms, rates, and audience analytics.
Main ideas
Change the process before you buy the tool · Automation makes relationship work more valuable, not less · Speed and rigor are not opposites — buyers need visibility
Audience
2,577 LinkedIn followers · 5 LinkedIn posts, 1 July to 10 September 2026 Posts published between 1 July and 10 September 2026
Sarah Toomey leads procurement within Levelpath’s finance team and writes about how procurement teams can adopt AI without losing the process controls and human judgment that matter.
Their posts connect practical automation—such as routing requests, checking policy, and triaging intake—to supplier relationships, negotiation, and faster decisions.
Follow their work for a practitioner’s perspective on changing procurement processes before adding software, and balancing buying speed with visibility into risk and commitments.
Toomey links technology adoption to the work and processes around it.
They argue that new procurement software disappoints when teams leave outdated processes and policies untouched; AI works better when teams first decide how the job should change.
Their posts make the point concrete with examples of tasks agents can handle, including contract scanning, bid comparison, and intake triage, then explain how people can use the time saved: to build supplier relationships, negotiate, and exercise judgment.
They also argue that speed is no reason to skip rigor: visibility helps teams move faster while still understanding what they are approving.
Sarah Toomey writes about procurement from the buyer's side of the table.
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They say they fell into sourcing more than 15 years ago, have built and run procurement functions across technology companies, and now oversee procurement inside Levelpath's finance team. Their posts argue that tools and automation pay off only when the underlying work changes first, and that procurement professionals' value shifts toward supplier relationships, negotiation and judgment as routine tasks move to agents.
Change the process before you buy the tool
Sarah Toomey argues that procurement software succeeds or fails on what happens before the purchase.
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Teams that get results from new tools change how they work first; teams that buy good software and see no improvement usually run it on top of unchanged processes and policies. They apply the same test to AI: the technology works only as well as the process it supports, so teams should rethink and update the job before choosing a tool built for the new version.
The post contrasts teams that think through the job, identify what needs updating and then bring in a tool built for the new process with teams that buy great tools but feel let down because the software is new and the underlying processes and policies are not.
Automation makes relationship work more valuable, not less
As AI takes over contract scanning, bid comparison and intake triage, Sarah Toomey expects human procurement work to become more valuable, not redundant.
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What remains includes building supplier relationships to secure better deals, turning vendors into partners through negotiation and making judgment calls about where a deal lands.
They describe finance as the function that helps the business reach a yes faster, not a checkpoint that slows it down.
Automation lets them spend more of the day on those relationships and decisions.
Introducing Levelpath's Ranger, they describe an autonomous source-to-pay platform that routes requests, pulls supplier and contract data, checks policy and moves work toward a decision on its own.
That gives procurement more time for relationships, negotiations and decisions requiring judgment.
A later post names the automated tasks — scanning contracts, comparing bids and triaging intake — and says leading teams are shifting their time toward supplier and negotiation work.
Speed and rigor are not opposites — buyers need visibility
Sarah Toomey argues that buying AI is often the slowest and most expensive purchase an enterprise makes, even as businesses want new tools live immediately.
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Security reviews, more stakeholders and longer cycles are nothing new, they say; those steps used to be called rigor.
What changed is that businesses under pressure now treat rigor as a bottleneck.
The teams pulling ahead are not skipping steps: they build visibility so they can keep pace with the business while knowing exactly what they signed up for.
The post argues that leading teams do both: they move at the business's speed and keep track of what they approved. Sarah Toomey points readers to Stan Garber's separate take on how Levelpath approaches the problem.
AI-native as a foundation, with the process expert building the automation
Sarah Toomey treats an AI-native approach as a foundation, not a feature bolted onto existing software.
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In keeping with their process-first view, they highlight that anyone can build a procurement agent in plain language in Levelpath, with no technical background required. That lets the people who know the process best automate it: the tool meets the workflow expert rather than requiring them to become a developer.
The Ranger launch post says AI-native is not a feature to bolt on but the foundation. A short follow-up says Taylor makes this look easy by building a procurement agent in plain language in Levelpath, with no technical background required.
This post explains what Sarah Toomey is working on now and why procurement AI is central to it.
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It offers newcomers the current product context and the work experience behind it.
Sarah Toomey says they fell into sourcing more than 15 years ago, built and ran procurement functions at technology companies, and now oversee procurement within the finance team at Levelpath.
The post introduces Ranger, Levelpath's autonomous source-to-pay platform.
They describe it as routing requests, pulling supplier and contract data, checking policy and moving work toward a decision autonomously.
AI-native, they say, is a foundation, not a feature; the goal is to free procurement teams to focus on relationships, negotiations and judgment calls.
For brands planning a campaign
For procurement AI adoption messaging, this is the clearest source for an account-led angle: show AI handling intake, data gathering and policy checks, freeing people to focus on supplier relationships and negotiations. The post supports a message about pairing autonomy with human judgment, not autonomy alone.
This post explains why procurement becomes a bottleneck when businesses want AI quickly.
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It lays out Sarah Toomey's speed-versus-rigor argument and how they frame enterprise buying friction.
Sarah Toomey writes that buying AI is often the slowest and most expensive purchase an enterprise makes, even as businesses want AI live immediately.
Security reviews, more stakeholders and longer cycles are not new, they say; the same steps used to be called rigor.
What changed is that businesses under pressure now treat that rigor as a bottleneck.
In their view, teams moving ahead are not skipping steps but building visibility to keep pace while knowing what they signed up for.
The post points to Stan Garber's take on how Levelpath thinks about this.
For brands planning a campaign
Make the tension between speed and rigor a core theme. Rather than promising faster purchasing alone, show how visibility lets procurement teams move at business speed without losing control of what they approve.
This post explains what makes new procurement software succeed or fail.
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It makes the case that process change must come before tool adoption.
Sarah Toomey says the teams they have seen succeed with new procurement software all changed how they worked first.
They have also watched teams buy good tools and feel let down when nothing improved because the underlying process and policies stayed the same.
The best adopters, in their account, think through the job, identify what needs updating and then bring in a tool built for that new version.
AI works the same way: the technology only works as well as the process it is layered onto.
For brands planning a campaign
This directly supports a process-first automation message. Ask prospects to map the job and update policies, then position AI as the layer that makes the new process scalable—not as a fix for an unchanged process.
This post explains how Sarah Toomey expects AI to change procurement work and which human tasks they believe will grow more valuable as automation expands.
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Sarah Toomey writes that AI is taking on more procurement tasks every month, including scanning contracts, comparing bids and triaging intake.
As agents take on that work, they argue, human work becomes more valuable: supplier relationships that earn better deals, negotiations that turn a vendor into a partner, and judgment calls that decide where a deal lands.
Leading procurement teams are moving fast to spend their time on those tasks.
They say this lets finance help the business get to yes faster rather than act as a checkpoint that slows it down.
For brands planning a campaign
Position AI as an enabler of relationship-led procurement. Highlight contract scanning, bid comparison and intake triage as automated work, then show the payoff in supplier negotiations and faster business decisions.
This short post shows how easily someone close to the process can build automation in Levelpath, with no technical background required.
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Sarah Toomey says that at Levelpath, anyone can build a procurement agent in plain language without a technical background, putting automation in the hands of the people who know the process best. They point to Taylor as making it look easy.
For brands planning a campaign
Use this as a practical proof point for process-first adoption. It supports messaging that the process owner, not an engineer, can turn procurement knowledge into an agent—potentially narrowing the gap between identifying a problem and automating it.
Sarah Toomey argues that new procurement software pays off only when the underlying work, policies, and job design change first.
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One post describes teams that bought good tools but felt let down because nothing improved: the software was new, but the process underneath was not.
The best adopters rethink the job, identify what needs updating, then choose a tool built for that new version.
They apply the same logic to AI: the technology works only as well as the process it is layered onto.
Contrasts teams that changed their process with teams that changed only their software, then applies the same test to AI adoption.
For brands planning a campaign
A procurement AI campaign can use a process-first angle to build credibility. Lead with process and policy redesign before making automation claims, and give buyers a way to assess whether their process is ready for an agent.
Sarah Toomey introduced Levelpath's Ranger as an autonomous source-to-pay platform.
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The post says Ranger routes requests, pulls supplier and contract data, checks policy, and moves work toward a decision autonomously.
They describe AI-native as a foundation rather than a feature bolted on, framing the result as more procurement time for relationships, negotiations, and judgment calls.
A separate post notes that AI is taking on more procurement tasks every month, including scanning contracts, comparing bids, and triaging intake.
For brands planning a campaign
Campaign messages can focus on autonomous procurement reducing routine intake, contract, and bid work. The strongest angle explains where the agent stops and human judgment takes over, rather than presenting automation as a total replacement.
Across the posts, Toomey argues that automation makes human procurement work more valuable, not less.
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Supplier relationships that earn a better deal, negotiations that turn a vendor into a partner, and judgment calls that decide where a deal lands are the work that remains. They say automation gives them more of the day for those relationships and decisions, and describe finance as the function that gets the business to yes faster rather than the checkpoint that slows it down.
Names the tasks AI takes over, then contrasts them with the relationship and negotiation tasks that remain human.
Ask which procurement decisions Toomey reserves for humans and how Levelpath's agent routes those decisions.
For brands planning a campaign
A campaign can position procurement AI as a way to shift expert time toward negotiation and supplier strategy. Useful content would show the handoff between automated intake or analysis and the human decision, with concrete examples of what changes for a procurement team.
Sarah Toomey argues that buying AI is often the slowest and most expensive purchase an enterprise makes, making friction reduction a priority.
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The post says security reviews, more stakeholders, and longer cycles are not new; they used to be called rigor.
What changed is a business that wants AI live yesterday and treats that rigor like a bottleneck.
The teams pulling ahead are not skipping steps, according to the post.
They build visibility so they can keep pace with the business while knowing exactly what they signed up for.
For brands planning a campaign
A campaign can address the tension between speed and control directly. Offer content on visibility, review tracking, and clear commitments that let procurement move quickly without treating security and stakeholder review as optional.
Toomey highlights that anyone at Levelpath can build a procurement agent in plain language, with no technical background required.
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The people who know the process best can automate it. This fits the process-first theme: the tool should meet the process expert rather than require them to become a developer.
Ask for a demonstration of an agent build, its limits, and the governance steps.
For brands planning a campaign
Enablement content can target procurement process owners, not engineers. A demo or guide showing a process expert describing an agent in plain language would match the approach Toomey describes.
Sarah Toomey’s posts document one brand relationship: employment at Levelpath, the source-to-pay software company.
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They say they oversee procurement within Levelpath’s finance team and have built and run procurement functions across technology companies. Their posts also promote Levelpath’s products, including the launch of Ranger, which they describe as Levelpath’s Autonomous Source-to-Pay platform, and the company’s position on buying AI.
Levelpath
Employment
Sarah Toomey says they oversee procurement within Levelpath’s finance team, after building and running procurement functions across technology companies.
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In the same post, they introduced Ranger, which they describe as Levelpath’s Autonomous Source-to-Pay platform, and said it was the platform they had been waiting for.
They also point readers to a message from Alex and Stan and a product demonstration.
In later posts, they promoted Levelpath’s view on buying AI, linking to a take shared by Stan Garber, and highlighted that anyone can build a procurement agent in plain language inside Levelpath.
Their posts speak for the company, and they describe their role as being inside the business.
Sarah Toomey's posts build one argument about how procurement technology actually lands.
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Start with the case that teams win with new procurement software only after they change how they work, then follow it into Levelpath's Ranger launch and the claim that an AI-native foundation is not a feature bolted on.
From there, the posts narrow to where the human work goes: contract scanning, bid comparison, and intake triage move to agents, while supplier relationships, negotiation, and judgment calls about where a deal lands become the reason procurement time matters.
A separate thread takes the buyer's side of enterprise AI purchasing, arguing that speed and rigor fail together unless a team can see what it approved.
The posts appear as short LinkedIn updates, connecting these positions across recurring themes.
Both write about software and AI purchasing from inside the function that has to control it.
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Barnard describes the same failure Sarah Toomey names in process terms: at 500 people, no process sat between someone needing a tool and the company paying for it, so a $5k approval stalled while a $50k commitment went on a corporate card. He also asks how AI spend is tracked and optimized, the finance-side counterpart of Toomey's argument that buyers need visibility into what they approved.
Barnard writes as a CFO about cost visibility, routing low-risk requests automatically, and optimizing AI spend across providers.
Sarah Toomey writes from the procurement buyer's side, where the question is whether the process and policies underneath a tool are ready for it.
The payoff is stronger supplier relationships and negotiation rather than spend control.
Kumar makes the same move Sarah Toomey makes about procurement: he separates the part of a commercial function that AI has largely solved from the part it has not.
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His five functions of product discovery, ending in the merchandising judgment most vendors skip, parallel Toomey's split between automated contract scanning and bid comparison and the human relationship and judgment work that remains. Both also insist the new technology is not the old discipline in a new bottle.
Kumar's subject is consumer-facing: retail and DTC brands, AI visibility across ChatGPT and Gemini, and conversational shopping agents. Sarah Toomey focuses on enterprise procurement, including supplier relationships, negotiations, and source-to-pay workflows, rather than consumer discovery or AI search surfaces.
Oztan shares Sarah Toomey's core procurement concern: information asymmetry between buyers and suppliers, and the value of giving buyers objective data before they negotiate.
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His example of a supplier requesting a 15% raw material increase, answered with producer price indices and a should-cost model, reflects Toomey's argument that procurement people should spend their time on the negotiations that turn a vendor into a partner. Both also tie procurement improvement to changing incentives upstream rather than adding later approval steps.
Oztan's work is narrower on cost: modeling cost during design so engineering decisions carry financial weight, and justifying or challenging supplier price increases. Sarah Toomey covers the wider procurement function, process-first tool adoption, AI-native source-to-pay, and where human judgment stays in the loop.
Sarah Toomey’s audience case rests on topical overlap, not measured audience data.
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The available material gives one reach figure—a LinkedIn following of 2,577 recorded on 8 October 2026—and five LinkedIn posts published between 1 July and 10 September 2026 about procurement, AI adoption and supplier relationships.
The posts target procurement and finance practitioners evaluating AI-assisted buying, but do not establish who reads them, where readers are based, their seniority or the companies they work for.
For an enterprise software campaign about procurement AI adoption, Toomey is a plausible test placement for a procurement-interested LinkedIn audience, but the evidence does not support a larger targeted buy.
Before scaling, ask for recent platform analytics or audience survey results covering job function, seniority, geography and company size, plus performance data for sponsored posts on comparable procurement AI and supplier relationship topics at similar length and format.
LinkedIn followers
2,577
LinkedIn profile follower count recorded in the account evidence · 2026-10-08
Posts published between 1 July and 10 September 2026
No partnership or sponsorship program is described in Sarah Toomey's posts.
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They write as a procurement practitioner working inside Levelpath's finance team, and the commercial content is tied to that employer: announcing Levelpath's Ranger source-to-pay platform, sharing a colleague's take on AI buying cycles, and linking to Levelpath pages.
That is an employer relationship, not a creator partnership offer with published formats, terms, or rates.
Any collaboration would need to be negotiated individually rather than selected from a listed package.
Ask directly about deliverables, pricing, disclosure practice, availability, and how to inquire about partnerships.
Brands should also expect their public activity to sit close to Levelpath's product narrative rather than run as independent sponsored placements.
A campaign aimed at procurement and finance practitioners who are being asked to move faster: an enterprise procurement or finance software vendor wants sharper messaging for AI-assisted buying.
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It needs a practitioner who can explain process-first automation and why supplier relationships still carry the deal.
This is a strong fit on topic and argument, not audience scale.
Across their posts, Toomey makes four claims that map directly onto the campaign need: new procurement software pays off only when processes and policies change first; AI is taking over contract scanning, bid comparison and intake triage, which raises the value of negotiation and supplier judgment; buying AI is slow because security reviews and stakeholder cycles are seen as bottlenecks rather than rigor, and the answer is visibility, not skipped steps; and a process owner, not an engineer, can describe an agent in plain language.
Those are usable message pillars the campaign can adopt almost as written.
They also come with a practitioner biography: more than 15 years in sourcing, procurement functions built and run across technology companies, and now overseeing procurement inside a finance team at Levelpath.
For a vendor selling into procurement, that combination of lived buying experience and a clear process-first thesis is the reason to shortlist this creator.
Constraints to plan around.
First, reach: the LinkedIn account shows a following of 2,577 as of 8 October 2026, making this a test-placement or niche-audience buy, not a scale play.
Second, topic overlap is clear, but audience composition is not: their posts do not state followers’ job functions, seniority, geography or company size.
If a campaign needs verified procurement-leader titles, ask for analytics before committing.
Third, employer alignment: the product-led posts are tied to Levelpath (the Ranger launch, the plain-language agent build).
A vendor that competes with Levelpath should treat that as a conflict; a vendor adjacent to it, or one selling the underlying process discipline, can still use the material.
Fourth, ask for partnership formats, rates and disclosure practices; their posts do not specify them.
Where a vendor should use this creator: mid-funnel content arguing for redesigning intake, policy and approval before buying automation, with the creator’s own framing as the spine of the piece.
When to choose a specialist
A campaign aimed at broad awareness for a consumer or lifestyle brand through creator content: high-volume reach, product-review or aesthetic formats, an audience of general shoppers, and a media kit with rates, past placements and disclosure history.
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This is a poor fit, and the mismatch is structural rather than a matter of degree.
Nothing in their posts is consumer-facing: the material is written for procurement and finance practitioners and covers enterprise buying, security review, supplier negotiation and procurement agents.
The posts offer no creator-style formats — no reviews, unboxings or visual-led content — and no audience composition data showing who actually reads them.
The only reach figure given is a LinkedIn following of 2,577, which will not support a broad-reach awareness target.
A brand wanting scale and consumer appeal would be buying a procurement practitioner’s professional commentary and hoping it converts into shopper attention.
That is a different job from the one this content does.
The same conclusion applies to two related needs.
A vendor looking for a neutral, independent product reviewer is a poor fit: the product posts sit inside an employer narrative, not a review practice.
A campaign that needs sponsorship availability confirmed up front, with published formats and terms, is also a poor fit; ask for their sponsor policy, rate card and partnership inquiry route.
What would change the assessment: analytics showing a consumer or mixed audience, or creator-format work outside the employer context.
Until then, route this campaign to a creator with consumer-format examples and measured reach rather than trying to reshape this one.
A worked campaign example
Worked angle for a campaign aimed at procurement and finance practitioners: “Process first, agent second — a procurement AI adoption message built on the order of operations.”
Positioning.
Use Toomey as the practitioner voice arguing that automation amplifies a process rather than fixing one.
The campaign line is not “buy AI” but “change the work, then let the agent scale it.”
Format. One long-form LinkedIn piece from the creator, roughly 700–900 words, with three concrete examples drawn from their own framing, plus a short follow-up post and a two-page buyer worksheet the reader can complete before any vendor conversation.
Structure of the piece.
Open with the failure pattern: teams buy good tools and feel let down because the process and policies underneath stayed the same.
Move to the diagnostic — what the job actually is, and which approvals, intake steps and policy checks need updating before an agent touches them.
Then show where the agent stops: it routes requests, pulls supplier and contract data, and checks policy; the supplier relationship and the negotiation that decides where a deal lands stay with a person.
Close on the speed-versus-rigor argument: the teams getting ahead reduce friction by building visibility into what they approved, not by skipping security reviews and stakeholder cycles.
Points their posts make. The process-first argument, which the campaign can quote or paraphrase; the observation that AI handles contract scanning, bid comparison and intake triage while relationships and negotiation grow more valuable; the claim that buying AI is often the slowest and most expensive enterprise purchase, with visibility as the fix; and the plain-language agent build, where a process expert rather than an engineer automates the task they know best.
Buyer worksheet.
Three columns — the step as it runs today, what must change before an agent takes it over, and what stays with a person.
The reader fills one row per step in intake-to-signature, then uses the completed sheet as the agenda for an internal review.
Calls to action. A downloadable worksheet plus a demo of the agent handling one named step, so the reader can see the automation boundary rather than read a claim about it.
Measurement.
Treat this as a mid-funnel, topic-led test: content engagement and worksheet downloads as the primary read; qualified procurement or finance conversations as the secondary read.
Do not judge it on raw impressions, given the following size stated in their posts.
Before booking: confirm whether the employer relationship creates a conflict for the vendor, ask for follower composition and past post performance on comparable procurement AI topics, and agree in writing how employer and sponsor content will be labelled.
Choose Matt Barnard over Sarah Toomey when the campaign is aimed at the finance side of the same decision — the CFO or finance leader who has to answer what the company spends on AI and whether the approval process is holding the business back.
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His posts approach procurement from the finance seat: a first-person account of how the approval model broke between 50 and 500 people, why adding more gates only moves the bottleneck to a committee, and what replaced it — low-risk requests routed and approved automatically, high-value contracts flagged with the context to decide fast, and compliance checks built into the workflow rather than added after the fact.
A second post tackles the same problem from the spend side, arguing that a finance leader's job is not to shrink the AI number but to know what drives it and where it can be optimised — something that cannot be answered from a bill read after the event.
For a vendor whose product story is spend visibility or approval logic embedded in the workflow, Barnard is a more direct match than a process-and-supplier-relationships argument.
You give up the procurement practitioner's framing.
Barnard's argument is about cost governance and control, not supplier relationships or negotiation, and the AI thread in his material is about seeing and managing AI spend rather than adopting AI into a sourcing process.
The concrete examples are organisational-scale stories and a vendor product narrative, so the same employer-conflict check that applies to Toomey applies here.
Choose Vinod Kumar when the campaign is aimed at retail, ecommerce or merchandising rather than enterprise procurement.
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His work covers a neighbouring question — how an AI assistant should decide what a shopper sees — from the merchandising seat.
One post breaks product discovery into five functions: answering a question, retrieving products, reading shopper intent, deciding what gets shown, and carrying the merchant's accumulated judgment.
His argument is that most vendors build the first two or three and call the job done, while the ranking and curation decisions are where the value sits.
A second post reports an AI-visibility audit that found one brand surfacing consistently in ChatGPT and largely missed in Gemini across tested shopping queries.
It separates two questions most vendors collapse: whether a brand is mentioned, and what the AI says about it.
For a vendor selling a conversational agent whose real value lies in deciding what appears rather than in the chat layer, Kumar names the problem more precisely than a process-first procurement argument.
The reader changes with the creator.
His audience is merchandising and ecommerce leaders, so an enterprise procurement or finance software vendor is buying adjacency rather than a line to its buying centre.
The framing also needs translation: 'answering a question is not the same as merchandising a store' maps onto intake triage versus decision routing only by analogy.
The audits, platform comparisons and product names in the posts are retail examples, not procurement proof, so claims would need to be re-sourced rather than reused.
Choose Ali Oztan when supplier negotiation and cost data are the focus rather than the adoption process.
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One post takes a single procurement problem and shows the mechanism: a supplier requests a 15% price increase; the information asymmetry behind the request gives way to a counter based on identifying the cost drivers in a part, comparing the request against relevant producer price indices and a should-cost model, and giving the buyer objective data before negotiating.
The second post explains why that cost work is skipped in the first place, tracing it to incentives: design engineers are rewarded for speed, so cost and lead time are optimised later, when changing anything is expensive.
Cost modelling happens only if it is fast enough to fit the design cycle.
For a vendor whose product supports negotiation, should-cost or supplier cost analysis, Oztan's material sits closer to the product than Toomey's process-first argument.
You lose the intake, policy and workflow angle.
Oztan's posts sit at the design and cost stage of the buying cycle, so a vendor selling adoption or process automation would be borrowing a negotiation story rather than describing its own product.
The second post speaks to engineering and procurement readers together, which may not match a finance-led buying centre.
Its connection to an employer product also makes an employer-conflict check relevant.