Ray Hill shares practical perspectives on modernizing finance operations, using process intelligence to find recurring work and improve outcomes across areas such as order-to-cash, accounts payable, and financial planning.
853 followersAI in financeBased on 15 posts · latest Oct 2026
Ray Hill is useful for finance operations talks that connect process data to practical decisions in order-to-cash, accounts payable, record-to-report, and FP&A.
Best for
A finance transformation consultancy or CFO roundtable needing practitioner-led speaking and co-produced LinkedIn content.
Not for
A software vendor wanting guaranteed reach, lead volume, or a product-led webinar as the central proof point.
Key takeaways
Finance modernization starts with the work and its constraints, not with AI.
Automating order-to-cash is not the same as making it perform.
Process mining exposes variation hidden inside a process and what a single measure misses.
Check first: Confirm whether Ray Hill offers sponsorship formats, rates, audience data, and approval terms in writing.
Main ideas
Finance modernization starts with the work and its constraints, not with AI · Automating order-to-cash is not the same as making it perform · Process mining exposes how much variation hides inside a process — and what a single measure misses
Ray Hill shares practical perspectives on finance modernization and process intelligence, especially how recurring problems in order-to-cash, accounts payable, record-to-report, and FP&A can point to deeper issues in how work is designed.
Across the reviewed LinkedIn posts, they argue that finance improvement should start with the work and the business result—not with adopting AI for its own sake.
The posts also describe their work through Nexact Solutions LLC.
Readers may find their work useful for concrete questions about recurring exceptions, process variation, and what a business should change once those patterns are visible.
Hill connects process data to the decisions people must make about the work.
In one example, analysis identifies strategic customers who pay late; a colleague explains that recurring disputes follow shipments that arrive late.
Both the KPI and the operational explanation matter, but they lead to different decisions.
Other posts trace repeated invoice corrections, close investigations, billing disputes, and forecast explanations back to unresolved decisions or missing information.
This makes their perspective more than a general case for automation: it asks where work returns, what causes it, and which business result a change should improve.
The posts also describe a repeatable approach—identify the desired result, follow the handoffs, find constraints, and build a case that can be tested.
Ray Hill writes about finance modernization as a discipline that starts with the work itself — where it repeats, where it breaks, and what constrains it — and only then asks where AI, automation, and process intelligence belong.
Read more
Across the posts, the priorities are diagnosing recurring work across order-to-cash, source-to-pay, record-to-report, and FP&A; turning process data into a decision about what the business will actually change; and proving each change with a business case before moving to the next one.
Finance modernization starts with the work and its constraints, not with AI
Ray Hill argues that most discussion of AI in finance is commentary about the topic rather than the work itself, and that finance modernization is a discipline rather than a technology project.
Read more
The starting point is how a finance function's data is structured, layered, moved through the business, and shaped by the variation in how work actually gets done. Applied technology comes later, once the function and its constraints are understood.
A post opens with the writer's exhaustion at empty AI talk, then draws a line between "the thing" and "the thing about the thing." The remedy is reading material on finance modernization, data intelligence layering, and the work underneath both.
A later installment defines the vocabulary more carefully: modernization is the continuing work of moving finance's business contribution forward, while transformation is the deliberate redesign needed when the next level requires something more substantial. The terms are often used interchangeably; Ray Hill separates them.
Automating order-to-cash is not the same as making it perform
Ray Hill argues that AI can automate much of order-to-cash execution without improving process performance; the real question is where the work goes wrong.
Read more
The alternative is to ask where cash is delayed, where customer commitments break, where people repeatedly intervene, and where exceptions are created upstream and managed downstream.
Each exception signals how the operating model is performing, rather than presenting an isolated automation opportunity.
The objective is a business result — faster cash flow, fewer exceptions, stronger customer commitments, less unnecessary manual intervention — not more AI.
The post names the failure points it looks for in order-to-cash: credit blocks, re-ATP, fulfillment mismatches, billing errors, disputes, collections, and manual workarounds. Its approach combines process knowledge with process intelligence, decision intelligence, data, and AI-enabled execution to find constraints and design interventions around measurable outcomes.
Process mining exposes how much variation hides inside a process — and what a single measure misses
Ray Hill describes drilling into a process and finding that what first looks orderly becomes dozens, hundreds, or thousands of ways the work actually gets done.
Read more
Data can reveal a pattern, but the people closest to the work often explain it; the hard part is deciding what the business is prepared to change once the full story is visible. A single KPI and a frontline account can both be true without being the same conversation.
Machine learning clusters late- and short-paying customers in order-to-cash and finds that many are among the most strategic clients.
That raises a decision, not an answer: exclude them from the measure, change their terms, challenge the relationship, fix the fulfillment problems behind their disputes, or accept the behavior because of the broader economics.
A colleague named Carla says shipments are rarely on time, so the same disputes recur every month.
Give every finance stream a result it owes, then build a business case you can prove
Ray Hill assigns each part of finance a next result rather than a generic improvement: source-to-pay should protect critical supply and put cash to work, order-to-cash should convert profitable commitments into predictable cash, record-to-report should surface material shifts while leaders can still act, and FP&A should move capital and capacity as conditions change.
Read more
The described method — a finance modernization workbench — follows the handoffs, finds what constrains the result, scopes the change, and builds a business case with a clear way to prove it. Once a result is proven, it becomes the starting point for the next business case.
The workbench is presented with a question about where finance needs to move the needle next, suggesting the sequence is meant to be applied stream by stream rather than as one large program.
The recurring-work framing appears alongside it: accounts payable work traces back to unresolved purchasing decisions, record-to-report to unexplained balances, order-to-cash to order and fulfillment exceptions, and FP&A to fragmented evidence and unclear assumptions. A seven-page perspective follows the expected process in each schematic into the clarification, investigation, and rework underneath it.
Constraints and measurable contribution decide which AI ideas deserve investment
Ray Hill's answer to the abundance of AI possibilities is constraints-based value engineering: rather than inventorying technology, determine which possibilities deserve investment and what must change around them to produce value.
Read more
They describe studying the work, watching decisions and handoffs, and measuring business contribution; those observations shape the goal, the solution, and the next move. The practice draws on the Theory of Constraints and process reengineering.
A post maps four finance areas to the unresolved decisions underneath the work: unresolved purchasing decisions become invoice-processing work, unexplained balances create recurring investigations and adjustments, order and fulfillment exceptions become billing disputes and collection problems, and fragmented evidence consumes time needed for decisions.
The thinking is deliberately unfinished and open to challenge
Ray Hill frames the writing as working in the open — sometimes thinking out loud — and invites readers to bring their operational reality, challenge the assumptions, and sharpen the next move.
Read more
The post ends with a question rather than a conclusion.
The material also holds a tension: it treats AI skeptically when discussed without the underlying work, yet presents AI as a reason to revisit what finance could anticipate, decide, and deliver, and as something that expands what is possible.
Its value depends on whether the work underneath has been understood first.
A post asks which finance decision the reader would rethink and what business result would make the change worthwhile. It describes the installment as covering the art of the possible across source-to-pay and procure-to-pay, order-to-cash, record-to-report, and FP&A.
Across the posts, the posture shifts from diagnosis toward collaboration: earlier items explain how the work is studied, while this one explicitly presents the writing as shared thinking to be tested against a reader's own operating reality.
This is the fastest way to see where Ray Hill stands in the crowded AI-in-finance conversation.
Read more
If you read one item, read this for the stance: AI is the last step, not the first, and the real subject is process variation and data structure.
Ray Hill opens by saying they are exhausted by empty AI talk, then draws a line between commentary about AI in finance and finance modernization as a discipline.
In this telling, the discipline does not start with AI.
It starts with understanding the function and its constraints: how data is structured, how it is layered, how it moves through the business, and how it reflects the variation in how work actually gets done.
The post closes by pointing readers to Nexact Solutions material on finance modernization and data intelligence layering.
For brands planning a campaign
A useful framing piece for a collaboration built around substance over hype. A firm proposing a speaking engagement on process intelligence could borrow this framing, but would need to provide the underlying examples separately: the post asserts a position rather than walking through a method.
It shows the working method in one place: a named process area, the symptoms Hill looks for, and the outcome the work is aimed at.
Read more
Readers looking for what process intelligence means in practice get a concrete list rather than a definition.
The argument is that AI can automate much of order-to-cash execution, but that does not make the process perform better.
Ray Hill replaces the automation question with diagnostic ones: where does cash actually get delayed, where do customer commitments break, where are people repeatedly intervening, and where are exceptions created upstream and managed downstream.
The post names the specific failure points they look for — credit blocks, re-ATP, fulfillment mismatches, billing errors, disputes, collections, manual workarounds — and treats each as a signal about how the operating model is performing rather than as a standalone automation opportunity.
The stated approach combines process knowledge with process intelligence, decision intelligence, and data- and AI-enabled execution, with the objective framed as faster cash flow, fewer exceptions, stronger customer commitments, and less unnecessary manual intervention.
For brands planning a campaign
A strong anchor for a session on order-to-cash or finance operations, because the symptoms are specific enough to argue with.
A campaign could take the four diagnostic questions and run a working session against a buyer's own O2C data.
The post supplies the questions, not the analysis.
It shows the analysis, the contradiction it surfaces, and the decision it forces in a short narrative rather than a method description.
Ray Hill describes drilling into a process: the process explorer starts out looking orderly, and the more you zoom in, the more the apparent single process becomes dozens, hundreds, or thousands of ways the work actually gets done.
The post then uses a concrete case.
Clustering late- and short-paying customers in order-to-cash turns up many of the most strategic clients, raising an awkward question instead of offering an answer: do you exclude them from the measure, change their terms, challenge the relationship, fix the fulfillment problems behind their disputes, or accept the behavior for the broader economics?
The post sets a KPI statement — late payment — against the account of a person closest to the work, Carla, who says shipments are rarely on time, so the same disputes recur every month.
Both statements may be true, Hill writes, but they are not the same conversation: data reveals the pattern, the people closest to the work explain it, and the hard part is deciding what the business is prepared to change once the full story is visible.
For brands planning a campaign
A good piece to point a content partner toward if the goal is to show judgment rather than tooling.
It could seed a case-study format in which a client's own data reveals an inconvenient pattern, with the post supplying a template for the uncomfortable decision conversation.
The post names neither the client nor the tooling, so any published adaptation would need new, permissioned examples.
It answers the obvious next question after the diagnosis: what does Hill actually look at, and in what order?
Read more
The four-area mapping is a compact inventory of where recurring work is created, useful whether or not you accept the underlying constraints framing.
This post starts from the observation that finance modernization begins with understanding why the same work keeps coming back: the invoice that needs another correction, the reconciliation item carried into another close, the customer deduction that sends collections back through the order history, the forecast that needs another round of explanations before anyone can act.
Ray Hill maps four areas to the unresolved decisions underneath the work — accounts payable, where unresolved purchasing decisions become invoice processing work; record to report, where unexplained balances create recurring investigations and adjustments; order to cash, where order and fulfillment exceptions become billing disputes and collection problems; and FP&A, where fragmented evidence and unclear assumptions consume time needed for decisions.
The post describes a seven-page perspective in which each schematic follows the expected process into the clarification, investigation, and rework underneath it and identifies where Hill would look first.
It closes with Theory of Constraints and process reengineering as the bodies of knowledge behind the practice.
For brands planning a campaign
The four-area map is the most reusable asset here — it could structure a workshop, webinar, or written collaboration. The seven-page perspective is not included, so a partner should obtain and read the schematics before building on the claim that they identify where to look first.
This is the clearest statement of the operating model: each finance area gets a result it owes, and the work is a business case that can be proved and then repeated.
Read more
Readers who want to know how Hill sequences an engagement should start here.
Ray Hill defines finance modernization as the discipline of moving the finance function's business contribution forward, then assigns each stream a next result: in source-to-pay, critical supply protected and cash put to work; in order-to-cash, profitable commitments converted to predictable cash; in record-to-report, material shifts seen while leaders can still act; in FP&A, capital and capacity moved as the evidence changes.
The post describes the Finance Modernization Workbench as following the handoffs, finding what constrains the result, scoping the change, and building a business case with a clear way to prove it.
The proven result then becomes the starting point for the next business case.
For brands planning a campaign
The strongest fit for a consulting-firm audience, since it reads as a method statement a firm could compare against its own.
It also suggests a natural collaboration: a session or series on building provable business cases stream by stream.
The Workbench is described but not shown, and the post gives no client evidence or metrics; ask for evidence that the mechanism works.
It clears up vocabulary that the other posts assume and shows Hill's posture toward an audience: open working notes and an explicit invitation to push back.
Read more
Useful for judging tone before approaching them.
Ray Hill separates two terms that are often used interchangeably.
Modernization is the continuing work of moving finance's business contribution forward; transformation is the deliberate redesign needed when the next level requires something more substantial.
The post frames the writing itself as thinking in the open — some of it thinking out loud — and invites readers to bring their operational reality and challenge the assumptions.
This installment covers the art of the possible across source-to-pay and procure-to-pay, order-to-cash, record-to-report, and FP&A, and positions constraints-based value engineering as the way to decide which possibilities deserve investment and what must change to produce value.
It ends by asking readers which finance decision they would rethink and what business result would make the change worthwhile.
For brands planning a campaign
Good for a co-created format such as a roundtable or open-question series, since the piece already models that style. A firm weighing a speaking engagement can see the discussion-led approach; a firm seeking polished thought leadership should note that the post presents itself as unfinished thinking rather than a settled position.
Practical formats: diagnostic posts, process schematics, and an open playbook6
Finance modernization as a discipline, not an AI-first project3
Constraints-based value engineering and measuring business contribution2
Engineering order-to-cash rather than automating it1
Process mining exposes variation and the limits of a single KPI1
A finance modernization workbench: goals, handoffs, constraints, proof1
Finance modernization as a discipline, not an AI-first project
Ray Hill argues that finance modernization starts with understanding why the same work keeps coming back: an invoice needing another correction, a reconciliation item carried into another close, a customer deduction sending collections back through order history, or a forecast needing another round of explanations.
Read more
The work distinguishes surface-level AI talk from the discipline underneath. Modernization is the ongoing work of advancing finance’s business contribution; transformation is the deliberate redesign required when the next level demands more substantial change.
Uses recurring-work examples across finance streams and schematics that trace expected processes into the clarification, investigation, and rework beneath them. Distinguishes modernization from transformation.
For brands planning a campaign
A content collaboration or speaking engagement should start with operational diagnosis, not AI hype. The strongest angle maps where repeated work is created and revisited across accounts payable, record to report, order to cash, and FP&A, then shows where AI or automation fits.
Engineering order-to-cash rather than automating it
Ray Hill says AI can automate much of order-to-cash execution, but that does not mean it will improve process performance.
Read more
The bigger opportunity is to start with the work itself: where cash is delayed, customer commitments break, people repeatedly intervene, and exceptions are created upstream and managed downstream.
Examples include credit blocks, re-ATP, fulfillment mismatches, billing errors, disputes, collections, and manual workarounds.
The approach combines process knowledge with process intelligence, decision intelligence, data, and AI-enabled execution to identify constraints and engineer interventions around measurable business outcomes.
Combines process knowledge with process intelligence, decision intelligence, data, and AI-enabled execution. Treats exceptions as signals about operating-model performance, not just automation opportunities.
For brands planning a campaign
For a finance transformation firm, a practical session can focus on exception patterns and cash-flow outcomes rather than tool demonstrations. Order-to-cash offers a concrete diagnostic area: trace the exception, name the constraint, and define the business result the change should deliver.
Process mining exposes variation and the limits of a single KPI
A closer look at a process reveals more variation: what first looks orderly becomes dozens, hundreds, or thousands of ways the work actually gets done.
Read more
Ray Hill gives an order-to-cash example in which machine learning clusters late- and short-paying customers and finds that many are strategic clients.
A KPI says late payment; a colleague says shipments are rarely on time and disputes recur every month.
Both statements may be true, but they are not the same conversation.
Data reveals the pattern; people closest to the work explain it.
The challenge is deciding what the business is prepared to change.
Combines process mining, process explorer views, machine learning clustering, and frontline explanations. The method contrasts a metric with the operational story behind it.
Ray Hill notes that if the answer were easy, someone probably would have fixed it already.
For brands planning a campaign
A speaking engagement can turn this into a live exercise: choose an order-to-cash KPI, separate the data pattern from the human explanation, and ask what the business will actually change. This positions process intelligence as a decision aid, not just a reporting layer.
A finance modernization workbench: goals, handoffs, constraints, proof
Ray Hill describes a workbench approach that starts with the result each finance stream needs to deliver next.
Read more
In source-to-pay, that may mean protecting critical supply and putting cash to work.
In order-to-cash, it means converting profitable commitments to predictable cash.
In record to report, it means spotting material shifts while leaders can still act.
In FP&A, it means moving capital and capacity as evidence changes.
The Nexact Solutions Finance Modernization Workbench follows the handoffs, finds what constrains the result, scopes the change, and builds a business case with a clear way to prove it.
Once proven, that result becomes the starting point for the next business case.
Follows handoffs, identifies constraints, scopes change, and builds a provable business case. Ties each finance stream to a specific business result.
For brands planning a campaign
A workshop can take the form of a workbench session: choose one finance stream, trace its handoffs, identify the constraint, and define a measurable business case. It gives a brand a concrete format for process intelligence content that ends in a decision, not just a diagnosis.
Constraints-based value engineering and measuring business contribution
Ray Hill links AI to a broader question: what finance could anticipate, decide, and deliver.
Read more
Constraints-based value engineering helps determine which possibilities deserve investment and what must change to produce value.
Their approach studies the work, observes decisions and handoffs, and measures business contribution.
Those observations shape the goal, the solution, and the next move.
Related thinking draws on the Theory of Constraints and process reengineering.
Applies constraints-based value engineering, Theory of Constraints, and process reengineering to finance work. The method measures decisions and handoffs to shape the next change.
For brands planning a campaign
A content collaboration can explore how to choose among AI and automation ideas based on constraints and measurable contribution. The useful angle is selection and prioritization, not a technology inventory.
Practical formats: diagnostic posts, process schematics, and an open playbook
Across Ray Hill's posts, they use direct problem lists, rhetorical questions, KPI anecdotes, and named finance process areas.
Read more
The posts cover accounts payable, record to report, order to cash, FP&A, source-to-pay, procure-to-pay, and shared services. One describes a seven-page perspective with schematics; another says the playbook is being opened on the Nexact Solutions page; a third describes posting research, lessons, and thinking out loud as an invitation to engage.
Uses short diagnostic posts, process-area categories, rhetorical questions, and process schematics. The format invites readers to bring operational reality and challenge assumptions.
For brands planning a campaign
A brand can consider Ray Hill for explainer content, a LinkedIn series, or speaking that translates process intelligence into finance-specific diagnostics. The material suits practitioner education and decision framing more than pure AI thought leadership.
The posts consistently point to one commercial relationship: Nexact Solutions LLC, the finance-operations practice behind the material.
Read more
Ray Hill writes as part of that practice (“At Nexact Solutions LLC we study...”, “our Finance Modernization Workbench”), promotes its perspective documents, and invites readers to bring problems to it.
The posts do not state whether they own the firm or work there; ask for their role.
The posts carry no publication dates, and their account was observed on 8 October 2026.
Nexact Solutions LLC
Disclosed partnership
Hill writes as part of Nexact Solutions, presenting the firm’s thinking on finance modernization and its Finance Modernization Workbench, and closing posts with an invitation to bring problems to the practice.
Read more
The posts use “we” and “our practice” but do not say whether Hill owns or works for the business; ask for their role.
Ray Hill offers a practitioner's route into finance modernization: start with a finance process, find why the same work keeps returning, then decide where AI or automation belongs.
Read more
Explore the work through its process areas—order-to-cash, source-to-pay, record-to-report, and FP&A—and look for the recurring diagnostic: what creates the exception, what constrains the result, and what business case would prove a change. The posts are short and often end with a question, making them useful prompts for team discussion.
Finance modernizationProcess intelligenceProcess miningTheory of ConstraintsOrder to cashSource to payRecord to reportFP&AFinance transformationAI in financeLinkedIn postsFinance leadersFinance operations teams
Victoria Shcherbakova
Writes about finance transformation as continuous change, not an occasional project, and about what finance teams can sustainably absorb.
Read more
That overlaps with Ray Hill's argument that modernization must fit the function and its constraints, not just the technology.
Victoria Shcherbakova keeps the frame on change capacity, fatigue, trust, and leadership culture. Ray Hill's posts focus on process-level diagnosis across order-to-cash, source-to-pay, record-to-report, and FP&A, using constraints and provable business cases.
Both explore how finance operations can use AI to support decisions, not just record transactions.
Read more
Pranil C. Pawar argues that AI should free leaders to apply judgment and flag payment, vendor, and collection risks before they hit cash flow—a perspective close to Ray Hill's decision-intelligence and exception framing.
Pranil C.
Pawar writes from a platform and product angle—OptimAR, accounts payable and receivable controls, fraud detection—with a focus on what AI can do.
Ray Hill starts with the process and its constraints, then decides where AI or automation belongs.
Both examine how AI changes the architecture of finance and operations work.
Read more
Tino Lancellotti describes AI taking over the operations layer while ERP becomes the system of record, and uses agentic AI to connect customer data across service, contracts, and finance. Ray Hill likewise asks where AI and automation belong in finance work and what needs to change around them.
Tino Lancellotti focuses more on enterprise architecture, ERP consolidation, and customer-success workflows. Ray Hill focuses more on finance process streams, recurring work, and constraints-based value engineering.
The only measured audience figure available is 853 LinkedIn followers on Ray Hill’s profile, recorded 8 October 2026.
Read more
That’s a small, focused following, making a speaking slot or co-produced post a more realistic placement than a campaign built for reach at scale.
The posts target people working in order-to-cash, process intelligence, record-to-report and FP&A, with practitioner-to-practitioner commentary rather than broad-market coverage.
Before committing, ask for documented audience data on job role and seniority, plus recent engagement results for comparable topics and formats.
LinkedIn followers
853
LinkedIn profile linked from this page · 2026-10-08
Ray Hill publishes finance-transformation thinking on behalf of Nexact Solutions LLC, with a consultative engagement style rather than a packaged creator sponsorship.
Read more
Posts invite readers to bring a recurring problem to the team, discuss where AI and process intelligence fit in a finance operating model, or explore the firm's finance-modernization material.
These are openings for working conversations about finance operations, not published sponsor offers.
The posts don't specify sponsorship formats, rates, deliverables, exclusivity terms, audience guarantees, or approval timelines, and no official partnership inquiry page or media kit appears alongside them.
Before committing to a content collaboration or speaking engagement, ask directly about scope, compensation, review process, and disclosure expectations.
The subject matter includes order-to-cash, source-to-pay, record-to-report, FP&A, process mining, and constraints-based value engineering, with an emphasis on evidence-led, plain-language discussion over surface-level AI commentary.
A co-authored perspective, recorded working session, or finance-operations speaking slot may fit better than a standard product-placement sponsorship; confirm available formats directly.
A finance transformation consultancy wants a 45-minute paid speaking slot at a client roundtable plus one co-produced LinkedIn piece aimed at CFOs and finance-operations leaders. Success is measured in booked follow-up conversations with attendees, not impressions.
Reasons
The campaign's buyer and Ray Hill's subject matter line up without translation.
Their posts use the vocabulary a finance-operations audience knows: order-to-cash, source-to-pay, record-to-report, FP&A, shared services.
The session can draw directly on published material rather than an abstract campaign request.
The strongest asset for this format is the four-area map of recurring work: unresolved purchasing decisions turning into invoice processing in accounts payable, unexplained balances creating repeat investigations in record to report, order and fulfilment exceptions becoming billing disputes and collection problems in order to cash, and fragmented evidence consuming decision time in FP&A. That map gives the session a spine and attendees something to match against their own close.
The order-to-cash material adds diagnostic questions that make a 45-minute slot practical instead of inspirational: where does cash get delayed, where do customer commitments break, where are people repeatedly intervening, where are exceptions created upstream and managed downstream?
The late-payment example is the most useful single piece for a live session because it stages a conflict rather than a conclusion: a KPI says a customer pays late, the person closest to the work says shipments are rarely on time and the same disputes recur monthly, and both can be true.
It ends with a decision the room has to argue about, which makes a roundtable worth attending.
There is also a clear reason the consultancy is not simply buying a generic AI keynote.
Hill's position is that AI is not the starting point and that selecting which automation ideas deserve investment is a constraints-and-value question.
A firm selling finance transformation can use that as a credible, non-promotional frame for its services.
The fit is topical, not a reach buy: Hill's LinkedIn profile lists 853 followers. Before committing, settle scope, fee, review rights and disclosure directly, and obtain the seven-page finance modernization perspective and the Finance Modernization Workbench, which are referenced in the posts but not included there, if the session will depend on them.
When to choose a specialist
Poor fit as written.
Read more
Brief
A process-intelligence software vendor wants a sponsored thought-leadership series and a product-led webinar in which its platform is the central proof point, tied to guaranteed reach targets (for example, 100,000 impressions), a hard number of qualified leads, and a named customer ROI story with metrics and quotable executives.
Reasons
Hill's posts do not describe sponsorship formats, rates, deliverables, exclusivity terms, audience guarantees or approval timelines, and no media kit or partnership inquiry page appears. The vendor should first confirm whether Hill can offer these commitments and agree on scope, compensation, approval rights and disclosure in writing.
The reach target exceeds the only measured audience figure available: 853 LinkedIn followers as of the 8 October 2026 account snapshot. A 100,000-impression guarantee would have to come from paid distribution or syndication arranged by the vendor, not Hill's own audience; the posts show no campaign-style distribution.
The product-led framing conflicts with Hill's position.
The posts argue that AI can automate much of order-to-cash execution without making the process perform better, that finance modernization does not start with AI, and that the useful work is understanding process variation, handoffs and constraints first.
A webinar led by the vendor's platform inverts that argument.
For an audience that follows this material, the reversal could undermine the co-produced content's credibility.
The posts provide no proof for a named ROI story: no client is named and no metrics are given.
They reference two artifacts—the seven-page perspective and the Finance Modernization Workbench—that are not included in the posts.
Obtain and review them before deciding whether they support a case study with quotable executives.
The posts also say nothing about video production, so confirm whether Hill can meet the webinar's production standard.
The posts provide no demographic or role data. To substantiate lead quality by job title or seniority, ask for a platform export, engagement data or audience survey.
To make the campaign viable, reframe the series as practitioner education where the vendor's platform is one option among several; drop guaranteed-impression and guaranteed-lead terms; agree on disclosure language and a named accountable author or reviewer; obtain engagement results on comparable topics before committing budget; and confirm deliverables, approval rights and compensation in writing.
A worked campaign example
Angle
Run the roundtable as a working session on recurring finance work, not a talk about AI. Promise that each attendee will leave having traced one piece of work that keeps coming back and named the decision it exposes. The consultancy hosts a diagnostic method; Ray Hill supplies the map and questions as the practitioner.
Deliverables
A 45-minute session, a one-page handout carrying the four-area map and the order-to-cash diagnostic questions, one co-produced LinkedIn post built from the session's recurring examples and published under agreed attribution, and a short follow-up prompt sent to attendees asking which decision they intend to revisit. Attendees keep the handout; the consultancy keeps the follow-up conversation.
Measurement
Booked follow-up conversations with attendees, one commitment per attendee, and the count of exit tickets naming a specific decision and business result. Record impressions, but do not treat them as the goal.
Sequence
1. Open with the stance rather than a biography: finance modernization does not start with AI; it starts with how data is structured and how work actually varies. Spend two minutes, then move on. 2. Show the four-area map of recurring work and ask each attendee to pick one item in their own cycle: an invoice back for correction, a reconciliation carried into another close, a deduction that sends collections back through order history, or a forecast that needs another round of explanations. 3. Have each attendee trace where that item re-enters the process and who currently owns the decision. 4. Run the late-payment example as a live disagreement: the payment KPI says one thing, the colleague closest to the work says shipments are rarely on time and the disputes repeat monthly, and the room has to decide what the business would actually change. 5. Close with the question Hill uses to end the study post: which finance decision would you rethink, and what business result would make the change worthwhile? Each attendee writes one sentence as their exit ticket.
What to confirm first
Confirm the exact deliverable and session length, compensation, who reviews and approves the co-produced post, disclosure wording, and whether the seven-page perspective or Workbench material can be shared and cited. If those artifacts are unavailable, build the session only from the published examples above.
Choose Victoria Shcherbakova when the roundtable's real subject is change fatigue rather than process detail.
Read more
The two posts reviewed argue that finance is no longer transforming occasionally but operating in permanent change, that transformation fatigue is not only a wellbeing issue but a business risk affecting trust, productivity, adoption and value creation, and that the future finance function will be defined by how much change it can absorb.
For a consultancy whose follow-up offer is helping a finance organisation sustain a transformation, that framing gives the room a shared problem to name and a reason to keep talking after the session.
It also suits a conference or client event where the audience's complaint is change overload rather than missing process intelligence.
Ray Hill's posts offer little operational detail to make a working session concrete: they don't address order-to-cash handoffs, show how exception rework returns through the close, or give attendees diagnostic questions to apply to their own cycle.
The session would focus on capacity, routines and adoption, leaving the consultancy to supply the process-level content.
The co-produced LinkedIn piece would argue for absorbing change rather than offer a method for finding where finance work repeats.
Ask the consultancy to bring a process-level method if the session needs to deliver one.
Pawar when the need is finance controls and anomaly detection rather than process intelligence across the whole function.
Their posts argue that as AI becomes better at producing answers, leadership shifts from finding information to applying judgment.
They use the line that fraud rarely starts with a large transaction but with one exception nobody notices to ask whether accounts payable and receivable merely process transactions or actively protect the business.
That is a sharper angle for a CFO audience worried about duplicated invoices, unauthorised vendor changes, delayed collections and unusual transactions, and it pairs with a controls, audit or risk follow-up offer rather than a process-redesign offer.
Ray Hill's material lacks the four-stream map and process-variation argument that anchor it.
There is no handoff analysis, no account of how purchasing decisions become invoice work or how unexplained balances create repeat investigations, and no live disagreement example such as the late-paying customer whose shipments are rarely on time.
One post is built around what a finance platform could flag and carries a product name as a hashtag, bringing a vendor-adjacent frame to the roundtable.
Ask the consultancy to supply a neutral method if the session needs to avoid that framing.
Choose Tino Lancellotti when the audience is enterprise architecture and transformation leadership rather than finance operations.
Read more
Their ERP post argues that AI is not replacing ERP systems but replacing the workflow layer built on top of them, and lists five changes to how a consolidation programme should be designed: keep consolidating the system of record, stop building workflows inside the ERP, add an AI orchestration layer, invest in master data and semantic modelling, and architect for AI-native operations.
Their second post extends that to agentic AI joining contract, service, finance and customer data to reach long-tail accounts, including data federation and a single customer identifier.
A consultancy selling an AI-native architecture story can build a session on that split between the system of record and the system of work, and the co-produced piece can take a position CIOs and transformation leads will argue with.
The material is written for people who own architecture and programme decisions, not for controllers and shared-services leads who recognise a reconciliation carried into another close.
The posts make recommendations and set out an agenda for leaders, leaving less room for the audience to diagnose its own recurring work.
The second post is structured as an outline with headings and bolded steps rather than evidence from a specific finance cycle.
The session would focus on where AI should sit, not which finance decision needs an owner.
Ask the consultancy to bring a specific finance-cycle example if the session needs that operational detail.
Six complete LinkedIn posts from Ray Hill cover finance modernization, process intelligence, process mining, order-to-cash, source-to-pay, record-to-report, and FP&A.
The posts do not include publication dates, so they are grouped by theme rather than presented as a dated sequence.
The LinkedIn follower count was observed on 8 October 2026 and was 853.
Two artifacts the posts refer to—a seven-page finance modernization perspective and the Finance Modernization Workbench—are not described here beyond Ray Hill's own descriptions.
The similar-creator comparisons draw on two posts each from Victoria Shcherbakova, Pranil C.
Pawar, and Tino Lancellotti, published between February and June 2026.
The posts here provide no information about books, shows, video, speaking history, client evidence, or earlier career material, and no sponsorship or client relationship beyond Nexact Solutions LLC appears in them.