Evan Shapiro writes about helping businesses use AI with shared, reliable company data and definitions. Their posts focus on making AI useful for business decisions, rather than leaving it as a tool used by a few individuals.
Most useful for explaining why AI seats and licences do not produce trusted company-wide use.
Best for
B2B SaaS AI assistant or analytics brand considering a paid talk or advisory session.
Not for
Consumer app or retail brand needing paid social video with guaranteed mass reach.
Key takeaways
One power user’s AI is not a company capability.
Paid AI seats go unused because answers can’t be traced, not because access is missing.
Fix the decision problem before buying infrastructure.
Check first: Confirm recent audience data, fees, availability, and any competing interest with MIRA.
Main ideas
One power user’s AI is not a company capability · Paid AI seats go unused because answers can’t be traced, not because access is missing · Fix the decision problem before buying infrastructure
Evan Shapiro writes about making AI useful across an organisation, not just for a few employees.
In three LinkedIn posts, the recurring problem is that businesses already have data but struggle to get answers people can trust and check.
They contrast an individual connecting AI to a CRM with a company-wide capability built on shared sources and definitions, illustrate the trust problem with Microsoft Copilot, and apply the same argument to the operational decisions of a football club.
Follow their work for practical ways to examine whether AI answers are consistent, traceable and connected to real business decisions.
Shapiro makes an abstract AI-adoption argument concrete.
One post shows how differing risk scores can collide in a board meeting; another turns the gap between broad Copilot access and trusted use into a ten-question consistency test.
The football-club post extends the argument beyond SaaS: before discussing infrastructure, identify where decisions are difficult, time is lost, and answers are trapped in finance, CRM, ticketing or spreadsheets.
Together, these examples give readers both a useful distinction—individual AI use versus shared organisational capability—and questions they can apply to their own operations.
Evan Shapiro’s recurring argument is that AI’s value in a business comes from shared, agreed data and definitions, not individual competence or software distribution.
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They distinguish general AI, which one person uses as a tool, from organisational AI, which a company owns as a capability, and frame adoption as a trust problem rather than an access problem.
Their practical priority is to identify the decision that is hard to make or the answer that is unreachable before buying infrastructure.
They often close with a question or short test for readers to try in their own operations.
One power user’s AI is not a company capability
Evan Shapiro argues that general AI is a tool an individual uses, while organisational AI is a capability a company owns.
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The difference becomes clear as soon as more than one person relies on it.
In the SaaS businesses they describe, a capable user in revenue operations or sales leadership connects AI to the CRM and automates their own reporting, while the rest of the team still reads dashboards and waits for answers.
From the outside, the company looks like an early adopter, but the capability lives in one person’s account.
The problem compounds as use spreads: different people’s AI computes the same quantity using different definitions, and nobody notices until the numbers disagree in front of a board.
Their proposed fix is to let anyone ask a question and draw answers from agreed sources and shared definitions, so two people asking the same thing get the same number.
An AI computes a risk score one way and a colleague’s AI computes it another; neither person knows the definitions differ until the numbers collide in a board meeting. Shapiro closes the post by asking readers where AI capability in their business actually lives: in the company or in a few people’s personal accounts.
Paid AI seats go unused because answers can’t be traced, not because access is missing
Shapiro separates distribution from adoption.
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Copilot already sits inside Word, Excel, Outlook and Teams, with nothing to install, yet the post reports that fewer than 4.5% of those roughly 450 million seats pay for it, and that only 20 to 30% of paying users reportedly use it in a given week.
Their explanation is a verifiability gap: AI confidently answers a question about the business, someone asks where the number came from, no source can be shown, and the tool quietly becomes something used for drafting emails.
In this account, general AI queries data without understanding the business.
Software that draws on the CRM, call recordings and management accounts — and can show where an answer came from — survives a board meeting.
The post offers a test anyone can run in an afternoon: ask your AI the same question about your business ten times and count how many different answers come back.
It also quotes Microsoft’s Copilot chief, as reported, telling his team the product has to "earn the right to exist".
The usage figures and quotation come from Evan Shapiro’s post, not first-hand measurement.
This post turns the positioning argument into a named adoption case. It is the only one of the three that names Evan Shapiro’s own business, MIRA, and its focus on business performance rather than guesses.
Fix the decision problem before buying infrastructure
Reporting from Reading Football Club’s AI summit, Shapiro argues that organisations, football clubs included, are not short of data: it already sits in the finance system, the CRM, ticketing and the spreadsheet someone updates every Monday morning.
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The hard part is identifying where time is being lost, which decisions are difficult to make, and where an answer exists but takes a day of pulling reports to reach.
Once that is clear, the infrastructure conversation becomes focused because you know what you are building for.
The panel Shapiro singles out, "How the biggest companies are investing in football", featured speakers from NVIDIA and HUMAIN and centred on the infrastructure needed to run AI at scale — a view presented alongside their own, not as their position.
A club that cannot identify which decisions are slow to support is buying AI infrastructure without a target.
A club that knows its Monday spreadsheet ritual is the bottleneck knows what it is building for.
The post thanks Joe Jacobson and Stuart Fenton for hosting and credits the speakers and attendees without a promotional hook.
Shapiro applies the same argument made about SaaS businesses to sport and operations, extending the organisational-AI idea beyond software buyers. The post breaks off partway through, so ask for the remarks that followed the Manchester City reference if they matter to your assessment.
It defines the individual-versus-organisational distinction, offers a test case (two risk scores, one board meeting) readers can check against their own company, and frames the question a company should answer before buying anything else.
This post lays out the split that the others build on.
One capable user, usually in revenue operations or sales leadership, connects AI to the CRM and automates their own reporting while everyone else still reads dashboards and waits for answers.
From outside, the company looks like an early adopter; inside, the capability sits in one person’s account.
The failure mode is concrete: one AI computes a risk score one way, a colleague’s AI computes it another way, and nobody notices the definitions differ until the numbers collide in a board meeting.
The proposed answer is shared, agreed sources and definitions, so two people asking the same question get the same number.
The post closes by asking readers where their AI capability actually lives and carries the hashtags "#GeneralAI #OrganisationalAI #SaaS #SearchMIRA".
For brands planning a campaign
A natural opening for a B2B SaaS talk or advisory session on AI positioning. The closing question works as an audience diagnostic, and the SaaS hashtag makes the post directly quotable for software buyers.
This turns the positioning argument into evidence and a practical test.
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A newcomer gets the adoption paradox (distribution without trust), the specific failure behind it and a takeaway they can run in an afternoon.
A post about adoption, not access.
Copilot already sits inside Word, Excel, Outlook and Teams, so nobody had to install it, yet almost nobody pays.
The post says Microsoft’s own Copilot chief reportedly told his team the product has to "earn the right to exist".
The explanation is trust: ask the AI a question about your business and it answers confidently, but ask where the number came from and there is no source to check.
You go back to whoever owns the spreadsheet, and the AI becomes something you use to draft emails.
The post attributes a figure of 20 to 30 percent weekly use among paying users to reports and to operators the author talks to, then names the author’s own business, MIRA, and its focus on business performance rather than guesses.
It ends with a test anyone can run: ask your AI the same question about your business ten times and count how many different answers you get.
For brands planning a campaign
Strong material for the middle of a talk: a distribution-versus-trust story, a named platform example and the ten-answer test as an audience exercise. The usage percentages come from reports rather than first-party data, and the post ends with the author’s commercial pitch.
Read this to see the argument travel beyond software.
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For buyers unsure whether it applies outside SaaS, the post shows it used with ticketing, CRM and finance data at a sports organisation, and captures what Evan Shapiro says on a panel stage.
A field note from a football club’s AI summit, carrying the same argument into sport.
The observation: people picture AI in football as on-field decisions, while sound off-field decision making often makes a club successful; Manchester City is cited as a case of what can go wrong off the field.
The panel that stayed with the author, "How the biggest companies are investing in football", featured speakers from NVIDIA and HUMAIN and focused on infrastructure for running AI at scale.
The counterpoint: most organisations, football clubs included, are not short of data.
It already sits in the finance system, the CRM, ticketing and the spreadsheet someone updates every Monday morning.
The hard part is finding where time is actually lost, which decisions are difficult and where the answer already exists but takes a day of report-pulling to reach.
Work that out first, and the infrastructure conversation becomes focused.
The post thanks Joe Jacobson and Stuart Fenton.
For brands planning a campaign
Works as a live-event anecdote and a credibility signal that Evan presents in public, not just online.
The "not short of data, short of reachable answers" framing suits retail, sport and operations audiences, not just software.
Ask for the full remarks after the Manchester City line; the post cuts off there.
Format: short operator posts that end with a question or test3
General AI versus organisational AI: one power user is not a capability1
Why paid AI seats go unused: the Copilot adoption case1
AI in sport: settle the decisions before buying the infrastructure1
General AI versus organisational AI: one power user is not a capability
The clearest recurring argument in the posts is the split between general AI, which Shapiro describes as a tool an individual uses, and organisational AI, which they describe as a capability a company owns.
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In SaaS businesses, they say, one person in RevOps or sales leadership connects AI to the CRM and automates their own reporting, while the rest of the team still reads dashboards and waits for answers.
The company looks like an early adopter, but the capability lives in one person’s account.
The problem worsens as use spreads: one person’s AI computes a risk score one way, a colleague’s computes it another, and nobody notices the definitions differ until the numbers collide in a board meeting.
The proposed fix is to let anyone ask a question and get answers from agreed sources and shared definitions, so two people asking the same question get the same number.
Defining a product category against the adjacent state it replaces, then naming that state’s failure mode in operational detail.
For brands planning a campaign
This is the strongest positioning argument for a talk or advisory session: sell the shift from scattered individual AI use to company-owned AI with shared definitions.
Lead with the buyer-side symptom: one power user and a team waiting on dashboards.
It gives an AI product team a ready-made frame and a concrete failure story for customer conversations.
Why paid AI seats go unused: the Copilot adoption case
In the Microsoft Copilot post, Shapiro contrasts distribution with adoption.
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Copilot sits inside Word, Excel, Outlook and Teams in front of 450 million seats, yet fewer than 4.5% pay for it, and reports cited in the post put weekly use among paying users at 20 to 30%.
The post attributes this to a trust and verifiability gap: the AI gives a confident answer about the business, someone asks where the number came from, no source can be shown, and the tool quietly becomes something used for drafting emails.
General AI is described as querying your data without understanding your business.
The alternative is software that draws on the CRM, call recordings and management accounts and can show where an answer came from, so it holds up in a board meeting.
For brands planning a campaign
Use this as the adoption-risk half of the narrative: seats, licences and distribution do not produce usage, and verifiability moves people from occasional drafting to recurring, decision-grade use. The post’s closing test — ask your AI the same business question ten times and count the different answers — is a short, portable diagnostic a brand can put in front of prospects.
AI in sport: settle the decisions before buying the infrastructure
Reporting from Reading Football Club’s AI summit, Shapiro argues that clubs are not short of data: it already sits in the finance system, the CRM, ticketing and a spreadsheet someone updates every Monday.
Read more
The harder work is finding where time is lost, which decisions are difficult to make, and where an answer exists but takes a day of report-pulling to reach. Once that is clear, the infrastructure conversation — the angle taken by the NVIDIA and HUMAIN speakers on the panel — becomes more focused, because you know what you are building for.
Reordering a technology conversation so the decision problem is settled before infrastructure, with a single sector example to make the point land.
For brands planning a campaign
Shows a discovery-first advisory approach that transfers beyond sport: diagnose decisions and bottlenecks before recommending tooling.
That suits sessions for operations or finance leaders who already own data and are sceptical of another platform pitch.
Sport and venue businesses are a credible vertical for the organisational AI story.
Format: short operator posts that end with a question or test
All three posts follow the same shape: a few short paragraphs, one concrete contrast or statistic, a plain-language explanation of why it matters, and a closing question about the reader’s own operation, such as where the AI capability in a business actually lives or how many different answers the same question produces.
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The sports post is closer to event commentary, opening with the summit and thanking the people met there. Throughout, the argument focuses on business decisions and adoption behaviour rather than model or tooling detail.
Writing each post as a self-contained diagnostic that gives the reader an action, not a conclusion.
Only the Copilot post names a business connection: Shapiro describes helping business leaders through MIRA. The other two are educational, and the sports post credits individuals and organisations without a promotional hook. Ask for examples of commercial integration if that is part of the brief.
For brands planning a campaign
Expect a partner who can produce short, self-contained posts that close with a question or testable exercise.
They can be reused as campaign assets and are built for comment-thread conversation rather than long-form thought leadership.
For video, long-form or a case study, ask for a specific format plan rather than assuming those formats from these posts.
Their posts present MIRA as a business relationship, promoting it in the first person and tagging it with #SearchMIRA.
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Microsoft Copilot, Reading Football Club, NVIDIA, HUMAIN, and Manchester City appear as subjects of commentary or event context, not as business relationships.
MIRA
Disclosed partnership
Writing about why Microsoft Copilot adoption stalls, Evan Shapiro says: "At MIRA we help business leaders see the REALITY of their business performance, not guesses or fabrications, and the practical steps to improve it through data-driven decisions."
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A second post on general versus organizational AI carries the hashtag #SearchMIRA.
The posts promote the service in the first person.
Ask whether they own MIRA, work there, or have another arrangement.
Evan Shapiro writes short LinkedIn posts about where AI value comes from inside a business.
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The starting point is the split between general AI, which one person uses as a tool, and organisational AI, which a company owns as a shared capability.
One example is a power user in revenue operations who has connected AI to the CRM while the rest of the team still waits on dashboards.
A post on Microsoft Copilot adds that roughly 450 million seats and reported low paid usage show access is not adoption, and that answers nobody can trace get quietly abandoned.
A post from a football club AI summit argues that organisations should find the slow decision or unreachable answer before buying infrastructure.
Across all three, the format is consistent: a compact post, one concrete contrast or statistic, and a closing question or short test readers can apply to their own operation.
The tags below group the themes.
For more on grounded AI in finance, consolidation across multiple ERPs, tool-specific how-to lists for recruiters, or AI in golf and hospitality businesses, the creators listed under similar creators offer adjacent ground with a different sector focus or commercial posture.
Wilkinson writes about putting AI to work inside a real operating business, not at the level of models.
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Like Shapiro, they treat the human side of system change as the real blocker: a post on selling CRM into golf businesses says the biggest obstacle is not the software but fear of trying something new and the learning curve. A post on rebuilding golf club websites argues that AI coding tools make bespoke design, SEO and content work affordable for clubs once stuck on the same templates—a narrower version of their point that capability, not access, changes outcomes.
Wilkinson focuses on websites and CRM for golf clubs and holiday-park groups, closing with client names, project results and an invitation to get in touch. Shapiro stays a step back from tooling and product, writes for business leaders across sectors, and ends by asking readers about their own operation rather than making an offer.
The FinanceOS launch post says a chatbot attached to any ticketing system is feeling its way in the dark unless grounded in the latest finance data.
A post on one group running several ERPs concludes that reliable, audit-ready AI output needs data that is properly consolidated, mapped and enriched with financial context—the same claim Shapiro makes about answers coming from agreed sources and shared definitions, so two people asking the same question get the same number.
Both tell the story through a named customer and a concrete operational detail rather than a general claim.
Gurfinkel writes as the voice of a named finance platform, using product launches, customer stories and sign-up links to make the point.
The domain is the CFO's office and the systems around it.
Shapiro names no product, offers sector-neutral diagnosis, and closes with a question or a test rather than a launch.
Jones answers the practical version of Shapiro's question—how a team gets value from AI instead of leaving it in scattered individual accounts—with numbered moves readers can use at work, from loading an account's documents once and reusing them to turning one intake call into five deliverables.
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The post draws the same line Shapiro does between drafting and deciding: AI does the heavy lifting; people make the call.
Jones writes tool-specific how-to content for staffing and recruiting firms, using lists and emoji, with a named advisory firm and free-session registrations attached.
The same post appears on both the LinkedIn and Instagram accounts.
Shapiro stays tool-agnostic, addresses business leaders across sectors, and closes with a question about the reader's own operation instead of a promotion.
Evan Shapiro had 3,717 LinkedIn followers on 11 October 2026.
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Three posts published between 13 August and 29 September 2026 address organizational AI capability, Microsoft Copilot adoption, and AI operations at a football club.
They speak to business leaders and operators seeking AI answers they can verify and take into a board meeting—a plausible thematic fit for a B2B SaaS campaign targeting product, marketing, or RevOps decision-makers.
But the available information does not establish whether those decision-makers are in the audience or where they are based.
Ask Shapiro for recent results from comparable organizational AI and Copilot posts, such as impressions, unique viewers, or an audience breakdown, and for any survey data on audience job function, seniority, or company size.
Until then, the follower count is the only measured audience figure, and it indicates reach, not composition.
The three posts contain no sponsorship offer, rate card, media kit or published partnership terms.
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The only commercial activity described is Evan Shapiro’s own business, MIRA, which they present as helping business leaders see the reality of their performance through data-driven decisions rather than guesswork.
When the posts invite a response, it’s tied to that business: readers are asked how AI capability lives inside their company or what a club is actually trying to decide.
They don’t invite paid placements, sponsored posts or brand collaborations.
A buyer should ask Shapiro directly how to open a partnership conversation.
Partnerships would also touch on the same territory the writing covers: AI adoption, shared definitions and trusted answers inside a company, and the operational decisions that make AI infrastructure worth funding.
Strong fit for a paid talk or advisory session. The reviewed writing already addresses this brief, and the material transfers to a stage without rewriting.
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Brief
A B2B SaaS company selling an AI assistant or analytics product wants to explain adoption more clearly: why seats and licences do not produce use, and what makes an answer trusted enough to act on. It is considering Evan Shapiro for a paid talk or advisory session on moving customers from scattered individual AI use to shared organisational AI capability.
Reasons
The core argument is the brief. Evan Shapiro distinguishes general AI, a tool an individual uses, from organisational AI, a capability a company owns. The operational symptom is clear: one person in revenue operations or sales leadership connects AI to the CRM and automates their own reporting, while the rest of the team still reads dashboards and waits for answers. The company looks like an early adopter, but the capability lives in one person's account.
The adoption evidence is already packaged. The Microsoft Copilot post contrasts distribution with use: Copilot is inside Word, Excel, Outlook and Teams in front of 450 million seats; fewer than 4.5% pay, and 20 to 30% of paying users use it weekly, according to reports. The post attributes the failure to verifiability: a confident answer about the business falls apart at the question "where did that number come from?", so the tool quietly becomes something used for drafting emails.
The same post offers a portable closing exercise: ask your AI the same business question ten times and count how many different answers you get. It turns the argument into something the room does rather than hears.
The risk-score collision is a ready-made failure story. Two people's AI tools compute a risk score differently, and nobody notices that the definitions differ until the numbers collide in a board meeting. It is a specific, checkable scene rather than a generic claim about data quality.
The argument travels beyond software. The Reading Football Club summit post applies the same reasoning to ticketing, CRM and finance data at a sports organisation, giving a SaaS client a second setting for the same story. It also shows Evan Shapiro presenting in public, not only posting.
The posts end with questions about the reader's own operation, a good fit for a session built around audience participation rather than a scripted keynote.
Caveats
Topic fit is plausible, but audience fit is unverified. The only measured audience figure is a LinkedIn following of 3,717, observed on 11 October 2026. There are no demographic details, per-post engagement breakdowns or survey results showing job function, seniority or company size. Ask for recent audience data before booking.
The usage percentages come from reports, not first-party measurement, so present them as reported figures.
No sponsorship offer, rate card, media kit, published partnership terms or official inquiry route appear in the posts. Ask directly about fees, availability and commercial formats; the absence of published terms does not establish whether Evan Shapiro takes commercial work.
The commercial interest named in the posts is Evan Shapiro's own business, MIRA. If a client's product overlaps with MIRA's, raise competing interests before booking.
The third post's text breaks after the Manchester City line, so the remarks that follow are unavailable here.
When to choose a specialist
Reject. The mismatch is in the format and audience, not the quality of the writing.
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Brief
A consumer app or retail brand wants a paid social campaign built on short-form video and product placement, booked against a guaranteed reach figure and a mass consumer audience.
Reasons
Nothing in the posts is video. All three sources are written LinkedIn posts: two argument pieces and one event note. A video title or written post does not establish on-camera work. If the campaign needs short-form video, ask for examples.
The only measured audience figure is 3,717 LinkedIn followers, with no engagement data attached. That indicates reach among a business audience but cannot support a guaranteed-reach consumer campaign.
The writing is pitched at operators and business leaders making decisions they have to defend — AI capability, board meetings, CRM and management accounts. That is a different register from consumer product placement and would likely read as off-topic to a consumer audience.
The posts list no sponsorship formats, rate card or inquiry route. Ask directly about the offer before planning a consumer placement; the absence of published terms does not establish whether Evan Shapiro takes commercial work.
The posts integrate Evan Shapiro's own company, MIRA, but show no third-party brand placement. The ability to deliver one is untested.
If the brief is instead a paid talk to a consumer-marketing audience, the fit is arguable but unproven. It rests on the same operator-level AI argument and needs a specific format plan, not an assumption carried over from the written posts.
A worked campaign example
Before booking
Ask for recent results from comparable appearances or posts: impressions, unique viewers, or an audience breakdown for the organisational AI and Copilot material. Also ask whether any survey documents job function, seniority or company size.
Ask whether the client's product competes with MIRA and how any conflict would be handled.
Agree on the format explicitly: the posts are written posts, alongside one event appearance. If the session needs video, ask for examples.
Settle fees, availability and the inquiry route directly; no terms are published.
Brief
B2B SaaS company, AI assistant or analytics product seeking a 45-minute paid session for prospects and customers, plus an advisory follow-up, on moving from individual AI use to trusted organisational AI.
Carries into
Turn the ten-question test into the campaign asset: a short diagnostic clients can send to prospects or run in a webinar. Use the closing question as the landing-page headline and sales-qualification prompt. Make the risk-score story the objection-handling line for prospects who say their team already uses AI.
Run of show
0–5 minutes: The power-user trap. One person in revenue operations or sales leadership connects AI to the CRM and automates reporting; everyone else still reads dashboards and waits for answers. The capability lives in an individual account, not in the company.
5–15 minutes: Why distribution is not adoption. Copilot sits inside Word, Excel, Outlook and Teams in front of 450 million seats; fewer than 4.5% pay, and reports attribute 20 to 30% weekly use to paying users. Present these as reported figures, not first-party data.
15–25 minutes: The trust failure, told as a scene. AI gives a confident answer about the business. Someone asks where the number came from; no source can be shown, so you go back to whoever owns the spreadsheet. Two colleagues' tools compute the same risk score differently, and nobody notices until the numbers collide in a board meeting.
25–35 minutes: Live test. Ask the room to put the same business question to their own AI ten times and count the different answers. Compare results on the spot. This is the asset that survives the session.
35–45 minutes: What changes the answer? Shared, agreed sources and definitions ensure two people asking the same question get the same number. Close with the question raised at the opening: where does the AI capability in your business live — in the company or in a few people's accounts?
Advisory follow-up: two working sessions mapping where time is actually lost and which decisions are hard to make at the client, followed by a focused conversation about infrastructure. The football club material supplies the framing: most organisations are not short of data — it sits in finance, CRM, ticketing and a spreadsheet updated every Monday. The hard part is reaching an answer that already exists.
Session angle
“Where does your AI capability actually live?” Open with a split the audience already feels: one power user has automated their own reporting, and the company calls itself an early adopter. Show why that breaks, then give the room a test and a fix.
Why it works
Each segment draws on details in the posts and can be traced back to them: the organisational-versus-general split, the Copilot adoption figures, the risk-score collision, the ten-question test and the decisions-before-infrastructure point. The slide deck can be built from the posts without commissioning new research.
A better choice when the client sells to clubs, hospitality or small service operators and the session needs to explain everyday software adoption rather than enterprise AI strategy.
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One post explains why a CRM rollout was hard: the blocker was the team's fear of a new way of working and the learning curve, not the software.
It also describes the training material built to help people through it.
The other shows AI cutting the cost of bespoke website work, with build time down to weeks, sites search-optimised from the start and connected to a CRM, and posts written in the client's own tone.
For a small-team audience, that's closer to their reality than a talk about company-wide AI capability.
The change-management detail comes from doing the work, not from a framework.
The setting is narrow.
Evan Shapiro's posts do not cover multi-team or enterprise AI, data governance, or the trust problem of where an answer came from—central issues for a campaign aimed at those topics.
The two posts offer no reach or engagement figures.
Ask for those, and speaking information, before making a guarantee-led booking.
The work also serves James Wilkinson's commercial interests in CRM and website products, so a client in the same field should expect a vendor's point of view.
A better choice when the buyer is the office of the CFO and the AI story turns on financial data being consolidated, mapped and auditable.
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The writing makes the same trust point as Evan Shapiro—that an answer is only usable if it is grounded in real numbers—but grounds it in specifics: several ERPs in one group, some local books kept by outside firms, and the difference between an AI tool stitching data together when the mappings are wrong and an output that is reliable and audit-ready.
One post describes a finance-native ticketing system where each request carries its budget, commitments and forecast impact, with permissions keeping AP, AR, Payroll and FP&A data apart, and quotes the finance lead at the company involved.
That gives a client a named, checkable example close to their own data problem.
The posts read as product marketing for Datarails and FinanceOS, and name those products, so the perspective is a vendor's rather than an independent operator's. The subject is finance; a B2B SaaS audience that is not selling into finance would get less from it than from Evan Shapiro's across-the-board adoption argument.
A better choice when the session is a hands-on enablement workshop for a functional team—recruiters or back-office staff—rather than a positioning talk for leadership.
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The material is close to a ready run-of-show: five named Claude uses, each a concrete move, from giving every client account its own project so past job specs and rate cards stay loaded, to turning one intake call into five deliverables, bottling a top performer's writing style into a reusable skill, connecting tools so a brief is waiting on Monday morning, and keeping a human on the hiring decision. The same post advertises two free live sessions on Copilot and Claude, direct evidence of training being delivered to this audience.
Evan Shapiro's Instagram and LinkedIn posts use the same text, so this is effectively one piece.
The content offers tips, tool instructions and a registration call to action; it does not analyse why adoption stalls, address provenance or trust, or make the case for company-wide AI capability—the argument a campaign aimed at that need requires.
A figure quoted in the post about job descriptions drawing more applicants is stated without a source; ask for one before using it.
This profile draws on three LinkedIn posts dated 13 August, 19 August and 29 September 2026, covering organisational AI, Microsoft Copilot adoption and an AI summit at Reading Football Club.
The summit post’s available text ends during its discussion of off-field decision-making; conclusions here are limited to the material available.
