Deva I J. writes Remarkable HX, a newsletter about creating measurable value in private equity-backed businesses. The posts focus on putting AI into business workflows, governing its use, and showing how it affects costs, operations, and enterprise value.
Useful for practical frameworks on AI value, governance, and workflow change in private equity-backed businesses.
Best for
A private equity firm wanting measurable AI value and governance across portfolio companies.
Not for
A consumer electronics brand promoting a fun AI assistant to general consumers.
Key takeaways
AI spend counts only when the process changes.
AI value must show cost, improvement, and evidence.
Governance is a diligence and exit-readiness question.
Check first: Confirm audience roles, engagement data, and sponsor terms before committing.
Main ideas
AI spend counts only when the process changes · AI value has to show its work: cost, improvement, evidence · Governance is a diligence and exit-readiness question
Deva I J writes Remarkable HX, a newsletter about how private equity-backed businesses can turn technology and AI into measurable operating results.
The LinkedIn posts return to a central argument: AI creates value when it changes how work gets done, people adopt it, and leaders can show what improved.
The issues also address AI governance and buyer diligence, extending the operating questions to physical AI such as factory robotics.
Readers interested in the practical link between AI, portfolio-company operations, and enterprise value can find questions and checklists to apply to specific workflows.
Deva’s work connects AI strategy to operational tests rather than treating tool adoption as proof of value.
The posts ask whether an AI-enabled workflow can show its cost, what it improved, and what record it created; they name outcomes such as faster supplier reviews, less rework, and better quality control.
The governance pieces raise questions about data, ownership, human oversight, and audit trails, including what happens when AI acts in the physical world.
The work spans adoption and workflow redesign through diligence and capital allocation, with a recurring focus on measurable results and accountable use.
The posts identify Remarkable HX as the signature work represented here.
Ask Deva whether they have other signature books, shows, businesses, or earlier work.
Deva I J. writes Remarkable HX, a newsletter for private equity-backed companies and the operating partners, CFOs, CIOs, and deal teams around them.
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The recurring argument: AI tools alone create nothing.
Value comes when a business rewires workflows that carry margin, execution speed, and governance, then shows what the work cost, what it improved, and what evidence it produced.
They return often to named anchors—SEGC (Safe, Efficient, Growing, Compliant), MOMENT + MAGIC for the points where people and automation meet, ORAI—and pair each argument with a short working tool such as a 24-hour toolkit, a 30-day plan, a scorecard, or a one-page decision filter.
AI spend counts only when the process changes
Deva I J. argues that AI does not expand margin by itself; rewiring the operating model does.
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The winners are not companies with the most pilots or tools, but those that embed AI, data, and automation in processes that drive margin, execution velocity, governance, customer value, and exit readiness.
A follow-up post adds questions they say leadership teams should ask: Which process is slowing margin improvement?
Where is manual effort still creating drag?
Which decision points should be augmented?
Where should legal and compliance be built in earlier?
What can be rewired and measured within 30 days?
Issue #47 promises a 24-hour toolkit designed to force measurable results in under 30 days. The follow-up post ties the argument to a list of operating questions, not a technology stack.
The first post makes the claim; the later one restates it and turns it into diagnostic questions for operating partners, CIOs, CTOs, CFOs, COOs, and M&A legal leaders.
AI value has to show its work: cost, improvement, evidence
Deva I J. rejects dashboards, hours saved, and the number of agents deployed as proof of AI value.
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They propose testing whether an AI-enabled process can answer three questions: What did the work cost?
What did it improve?
What documentation did it create?
Without evidence of cost, outcome, governance, and documentation, a process is not yet a value-creation system and is not ready to support the enterprise value story.
The posts name operational outcomes, not abstract metrics: fewer supplier-review delays, faster quote turnaround, cleaner contract review, stronger quality control, lower rework, better margin visibility, and clearer diligence evidence.
Governance is a diligence and exit-readiness question
Across several issues, Deva I J. argues that AI has moved from a productivity story to a diligence story.
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Buyers ask where AI is used, what data it touches, who owns the model, how outputs are reviewed, whether bias is tested, whether there is an audit trail, and whether management can show the system is safe, efficient, growing, and compliant.
The posts frame AI without governance as deal risk and governed AI as a source of buyer confidence.
One issue adds a specific control: a human-controlled kill switch.
A family-run manufacturing business illustrates how AI could improve scheduling, quality control, forecasting, procurement, safety, or customer service. The recommendation is to build a one-page AI governance checklist and apply it to every active use case, reviewing data, bias, audit trail, ownership, and business impact.
Deva I J. argues that the harder problem for portfolio companies is no longer access to AI tools, but helping teams use them in the flow of work with the right trust, governance, incentives, workflows, and operating rhythm.
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The posts repeat the formula AI + People + Execution = Realized Value and assign roles: people adopt, managers reinforce, legal and compliance make it safe, finance tracks value, and operating leaders turn it into execution. People are not the resistance, they say, but the route to value.
Issue #63 includes a 30-day toolkit to survey actual AI usage, remove the top three friction points, and track adoption improvement.
Posts on Issue #76 argue that AI that senses, moves, decides, or triggers action near people, products, and equipment shifts the management question from what the technology can do to whether the business can govern it.
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They ask what the system is allowed to do, where human judgment enters, who can override it, what happens when it is uncertain, and who is accountable when it acts. The anchor is MOMENT + MAGIC: identify where people and automation meet, then design cooperation people can trust.
The issue points to a Physical AI Readiness Scorecard for assessing one robotics, automation, or AI-enabled equipment use case in a 2027 value-creation plan. It is aimed at the COO, CIO, CTO, operating partner, or portfolio leader deciding where Physical AI belongs.
Deva I J. ties AI strategy to capital allocation and M&A logic.
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In their framing, markets reward disciplined allocation and defensible execution, not AI ambition.
AI strategy without capital discipline is not a growth strategy, and M&A without capability logic is not value creation.
They recommend tighter portfolio reviews, faster capital reallocation, and acquiring capabilities the business can operationalize, measured against growth, margin, execution velocity, and risk posture.
Issue #45 offers a SEGC Investment Gate, a one-page decision filter that demands proof within 30 days and creates an audit-ready trail. It responds to a private credit liquidity freeze and lenders underwriting AI disruption risk into credit decisions.
Deva I J. argues that AI adoption is not impressive by itself.
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The advantage comes from redesigning work so experts make better decisions faster, more safely, and with stronger evidence—what they call complementary intelligence: human judgment plus AI precision. A related post puts the organizational layer above the AI stack—alignment, execution discipline, shared KPIs, trust, and human judgment—and argues that alignment around the few outcomes that move enterprise value matters more than having more AI solutions.
The posts identify pricing, customer churn, contract review, cybersecurity escalation, inventory tradeoffs, collections, forecasting, and quality assurance as decisions where this matters. The operating partner's question: Is the company aligned around the few outcomes that actually move enterprise value?
This is the clearest statement of the newsletter's core claim and the quickest way for newcomers to see what sets it apart from generic AI commentary.
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The post argues that AI tools alone do not move margin.
In Deva I J.'s framing, the work that does is rebuilding the processes that drive margin, execution speed, governance, and customer value.
It promises a 24-hour toolkit to force measurable results inside 30 days.
A follow-up post repeats the argument and adds the operating questions leadership teams should ask, including which process is slowing margin improvement and where legal and compliance should be built in earlier.
For brands planning a campaign
A private equity firm commissioning a framework on measurable AI operating models can use this as its anchor: the testable claim is that AI spend counts only when the operational process changes, with a diagnostic window of roughly 30 days. Proposed frameworks can be checked against that claim rather than abstract AI maturity language.
Physical AI on factory floors, in warehouses, and on inspection lines is distinct from office AI.
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This post shows how the author frames it for portfolio company operators.
Deva I J. describes AI that senses, moves, decides, or triggers action near people, products, and equipment, and argues that the management question shifts from what the technology can do to whether the business can govern it.
The post lists questions leaders should answer before deployment: what the system is allowed to do, where human judgment enters, who can override it, what happens when it is uncertain, and who is accountable when it acts.
It points to a Physical AI Readiness Scorecard to assess one robotics, automation, or AI-enabled equipment use case for a 2027 value-creation plan.
Companion posts describe the same issue as an operating-system decision and repeat the SEGC framing: safe, efficient, growth enabling, and compliant.
For brands planning a campaign
For a firm assessing AI value creation and governance, this piece extends the governance argument beyond software to capital equipment and shop-floor automation, where override authority and accountability are operational, not procedural. Test a framework against whether it addresses human override, escalation, and uncertainty, not just data use.
This is the author's most direct treatment of AI as a diligence and exit-readiness topic, connecting portfolio company operations to deal value.
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The post says AI is moving from a productivity story to a diligence story, and lists questions a buyer may ask: where AI is used, what data it touches, who owns the model, how outputs are reviewed, whether bias is tested, whether there is an audit trail, and whether management can show the system is safe, efficient, growing, and compliant.
It uses a family-run manufacturing example—scheduling, quality control, forecasting, procurement, safety, and customer service—and argues that AI without governance can create deal risk, while governed AI can build buyer confidence.
A related post for the same issue describes a one-page AI governance checklist applied to every active use case.
For brands planning a campaign
A firm building a measurable AI operating model can treat this as the framework's exit-readiness branch: the observable test is whether a one-page governance checklist exists and is applied to each active use case, with data ownership, bias testing, and audit trail as named fields.
This is the author's clearest statement of how to measure AI value—the central question for any framework on measurable operating models.
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The post sets out three questions for any AI-enabled process: what did it cost to complete the work, what did it improve, and what supporting documentation did it create.
It names concrete operational outcomes rather than dashboard metrics: fewer supplier-review delays, faster quote turnaround, cleaner contract review, stronger quality control, lower rework, better margin visibility, and clearer diligence documentation.
The companion post lists the same criteria and says a process that cannot show cost, outcome, governance, and documentation is not yet a value-creation system.
Both posts tie the argument to SEGC and to human-centered, economically measurable, diligence-ready AI.
For brands planning a campaign
This sets out the measurement backbone for a proposed framework: three questions for each AI-enabled process, measured against named operational outcomes. A firm can require cost, improvement, and documentation fields for each process, and treat failure to answer all three as a disqualifying signal during review.
This post explains why the author treats adoption, not tool access, as the binding constraint—a necessary contrast to the governance and measurement pieces.
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The post says the harder problem for portfolio companies is not access to AI tools but helping teams use them in the flow of work, with the right trust, governance, incentives, processes, and operating rhythm.
It sets out the equation AI + People + Execution = Realized Value and assigns roles: people adopt, managers reinforce, legal and compliance make it safe, finance tracks value, and operating leaders turn it into execution.
The post explains why pilots fail when adoption is not designed into the process, why people are the route to value rather than the resistance, and how the SEGC and ORAI framings support a 30-day toolkit to survey AI usage, remove the top three friction points, and track adoption improvement.
For brands planning a campaign
For a brief on portfolio company AI value creation, this is the framework's adoption half, with a concrete operating rhythm: survey actual AI usage, remove the top three friction points, and track adoption improvement over 30 days.
Here, the author connects AI discipline to capital allocation under pressure, a story private equity firms will recognize from their own deal context.
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The post cites a private credit liquidity freeze as a stress signal and says lenders are underwriting AI disruption risk into credit decisions.
It argues that markets reward disciplined allocation and defensible execution rather than AI ambition, and offers a sub-24-hour toolkit built around a SEGC Investment Gate: a one-page decision filter that requires proof within 30 days and creates an audit-ready trail.
The post is addressed to operating partners and portfolio leadership.
For brands planning a campaign
This gives the framework a capital-allocation entry point rather than leaving it at the process level. A firm could require a one-page investment gate with a 30-day proof requirement and an audit trail for each AI-enabled investment decision.
This post names specific governance artifacts a buyer or diligence team can request, making it a useful checklist source rather than a general argument.
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The post argues that the next competitive advantage may come from proving AI is safe, efficient, growth-enabling, and compliant.
It lists what leaders should be able to show: where AI is used, what decisions it influences, who owns the risk, how humans remain in control, what evidence supports buyer confidence, and where a human-controlled kill switch exists.
It says technology due diligence will ask whether AI can be trusted, governed, explained, and defended, and ties governance to a MAGIC framing — make authenticity generate impact and connection.
For brands planning a campaign
The kill switch and risk-ownership questions are concrete additions a firm can lift into an operating model template: controls that can be shown in a diligence room rather than described in principle.
This post states the author's position on AI's role in expert judgment, the assumption behind the measurement and governance arguments.
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The post says AI adoption is no longer impressive by itself.
The next advantage, it argues, will come not from automating every task but from redesigning work so experts make better decisions faster, more safely, and with stronger evidence.
It names pricing, customer churn, contract review, cybersecurity escalation, inventory tradeoffs, collections, forecasting, and quality assurance as decisions affecting margin, growth, risk, execution velocity, and exit readiness.
It calls the approach complementary intelligence: human judgment plus AI precision plus enterprise value under safety, efficiency, growth, and compliance.
For brands planning a campaign
A framework can use this to set the design principle before the metrics: choose decisions where expert judgment stays central and AI improves the speed and evidence behind them, rather than automating them away.
This is the author's clearest statement that the organizational layer above the AI stack determines whether value appears.
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The post argues that the layer above the AI stack — alignment, execution discipline, shared KPIs, trust, and human judgment — matters more than the technology, and calls it the control plane.
It asks whether a portfolio company is aligned around the few outcomes that move enterprise value, rather than whether it has enough AI solutions.
The post addresses portfolio leaders and operating partners.
For brands planning a campaign
A firm drafting an operating model can make shared KPIs and named outcomes explicit preconditions, keeping the AI stack subordinate to alignment on the metrics that move enterprise value.
AI adoption as an operating discipline, not a tool purchase5
AI governance as a diligence and exit-readiness lever5
Measurable AI operating models and workflow economics4
Physical AI readiness and human-machine cooperation3
Capital allocation discipline and capability-led M&A3
Human expertise multiplication over replacement2
AI adoption as an operating discipline, not a tool purchase
A recurring argument across the posts is that AI does not create value by itself.
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Deva I J frames adoption as the work of people, managers, legal, compliance, finance, and operating leaders.
The formula repeated in several posts is AI + People + Execution = Realized Value.
The posts argue that pilots fail when adoption is not designed into the workflow, and that alignment around a few value-moving outcomes matters more than adding more AI tools.
For brands planning a campaign
For a private equity firm assessing AI value creation, this topic can become a proposed adoption and change-management workstream. A diagnostic can identify where AI sits in workflows, which roles reinforce it, and which shared KPIs track adoption.
AI governance as a diligence and exit-readiness lever
Several posts argue that buyers and diligence teams will ask not just whether a company uses AI, but whether its systems are governed.
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Deva I J lists questions about where AI is used, what data it touches, who owns the model, how outputs are reviewed, whether bias is tested, and what audit trail exists. The posts present governance as a way to create buyer confidence and reduce deal risk.
For brands planning a campaign
The framework can be pitched as a one-page AI governance checklist applied to every active use case. For a portfolio company, the output could be a diligence-ready evidence pack covering data, bias, audit trail, ownership, and business impact.
Measurable AI operating models and workflow economics
Deva I J argues that AI value must show its work.
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The posts reject dashboards, hours saved, and number of agents deployed as proof.
Instead, they ask whether an AI-enabled workflow can show what it cost to complete the work, what it improved, and what proof it created.
Examples include supplier review, quote turnaround, contract review, quality control, rework, margin visibility, and diligence proof.
For brands planning a campaign
This topic supports a measurable AI operating model: pick one workflow, attach cost and outcome metrics, and require governance and proof before calling it a value-creation system. A campaign focused on portfolio AI initiatives can use this as the test for any initiative.
Physical AI readiness and human-machine cooperation
The posts on Physical AI describe systems that sense, move, decide, or trigger action in factories, warehouses, inspection lines, equipment yards, and field operations.
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Deva I J frames this as an operating-system decision, not just a technology decision.
The questions are what the system is allowed to do, where human judgment enters, who can override it, what happens when it is uncertain, and who is accountable.
The recurring anchor is MOMENT + MAGIC: identify where people and automation meet, then design cooperation people can trust.
For brands planning a campaign
For portfolio companies with robotics, automation, or AI-enabled equipment, the posts point to a readiness scorecard for one use case in a 2027 value-creation plan. The plan can include a risk and authority map for physical AI before deployment.
Capital allocation discipline and capability-led M&A
Several posts tie AI and M&A strategy to capital discipline.
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Deva I J writes that markets are not rewarding AI ambition but disciplined allocation and defensible execution.
The posts describe AI strategy without capital discipline as not a growth strategy, and M&A without capability logic as not value creation.
They call for tighter portfolio reviews, faster capital reallocation, and acquiring capabilities the business can actually operationalize.
For brands planning a campaign
This topic can be shaped into a decision gate for AI investments. A brief can ask for a one-page investment filter that forces proof in 30 days and creates an audit-ready trail, using criteria such as growth, margin, execution velocity, and risk posture.
A recurring idea is that AI adoption is not impressive by itself.
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The next advantage comes from redesigning work so experts make better decisions faster, safer, and with stronger evidence.
Deva I J calls this complementary intelligence: human judgment plus AI precision.
The posts list decisions in pricing, customer churn, contract review, cybersecurity escalation, inventory tradeoffs, collections, forecasting, and quality assurance as places where this matters.
For brands planning a campaign
For a PE firm, this frames AI as a way to multiply existing expertise rather than cut headcount. A campaign focused on decision quality and proof in specific expert workflows can use metrics that show faster, safer, and better-supported judgments.
Across the issues, Deva I J repeats a set of named frameworks.
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SEGC stands for Safe, Efficient, Growing, Compliant and appears in nearly every post.
Other anchors include MOMENT + MAGIC for human-automation encounters, ORAI for Opportunity, Relationships, Authenticity, and Innovation, and MMMM for turning transformation into human commitment.
The posts also promote practical formats: 24-hour toolkits, 30-day plans, scorecards, checklists, and one-page decision filters.
Name a framework, attach a short toolkit, and point to one measurable action inside a portfolio company.
For brands planning a campaign
These frameworks give a campaign a consistent language and ready-made working tools. A brief can ask for one named framework to be applied to a specific portfolio company, with a short toolkit that forces action within 24 hours or 30 days.
The posts describe one commercial relationship: they promote Remarkable HX, a subscription newsletter on AI value creation in private equity-backed businesses.
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Written in first person, they describe the newsletter’s contents but do not state who owns or publishes it.
Ask who owns or publishes the newsletter.
No sponsorship disclosures, affiliate arrangements, or client work appear in the posts.
McKinsey, Blue Owl, and Anthropic appear only as sources of cited market news, not as partners.
Remarkable HX
Disclosed partnership
Every post promotes an issue of the Remarkable HX newsletter and links to a subscription page.
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The posts describe topics including AI in operating models, governance, physical AI, and capital discipline, and use first person to describe producing each issue. Ask who owns or publishes the newsletter; the posts do not say.
Deva I J. publishes Remarkable HX, a newsletter for the people running and advising private equity-backed companies.
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One claim runs through almost every issue: AI tools create nothing on their own.
Value arrives only when a business rewires the workflows that carry margin, execution speed, and governance, and can then show what the work cost, what it improved, and what results it produced.
Readers new to this work can start with the issues on AI governance and diligence readiness, follow the thread on adoption and the people side of AI, then move to the Physical AI issues about what changes when machines sense, move, and act near people and equipment.
Expect named frameworks — SEGC (Safe, Efficient, Growing, Compliant) appears in nearly every post — paired with short working tools: a one-page governance checklist, a 30-day plan, a scorecard, or a one-page decision filter.
The posts are issue announcements that summarize the argument and toolkit.
Both write for private equity-backed companies about where AI actually pays off, translating technology work into enterprise value rather than describing tools.
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DePeters turns savings into EBITDA and a multiple, as Deva I J. does. Both test an AI-enabled process against cost, outcome, and results before calling it a value-creation system.
DePeters stays inside the finance function, with concrete implementation examples such as rebuilding a ten-person department and measuring the overhead saved. Deva I J. ranges across governance, adoption, capital allocation, and physical AI, packaging the argument as a recurring newsletter with named frameworks and toolkits rather than a single function's before-and-after.
Both write for a PE-backed audience about how commercial value is actually built rather than acquired, teaching through a chain of related long-form posts that build one argument at a time and close with a question to the reader.
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Knight's point that value is created after the first sale echoes Deva I J.'s point that AI value appears after the pilot, in the workflow.
Knight works on commercial organisation design — ICP discipline, product-market fit, incentives, pipeline quality, and account expansion.
Deva I J. works on AI operating models and governance.
Knight draws on his own commercial pattern-matching; they publish checklists, scorecards, and decision gates in their newsletter.
Both address the operating discipline that sits between a private equity sponsor and a portfolio company, framing their advice as pressure-tests leaders can apply before committing money or headcount.
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Castro's test for a strategic hire and Deva I J.'s test for an AI use case follow the same shape: define the problem, demand measurable impact, and check readiness before acting.
Castro writes from the CFO seat about the finance function and the CEO–CFO relationship, and one of his posts relays another practitioner's principles rather than original frameworks. Deva I J. writes beyond the confines of a single portfolio company, offering a broader, tool-heavy set of ideas spanning governance, adoption, and capital allocation.
The decision these posts support is fit over scale.
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The only measured audience number available is a LinkedIn following of 2,243 as of 8 October 2026.
Deva I J publishes weekly posts promoting Remarkable HX, a newsletter about AI value creation and governance in private equity-backed companies.
The topical alignment is close and specific for campaigns targeting AI value creation and governance in portfolio companies.
The measured audience is small, so treat placements as a targeted senior trade readership rather than a reach buy.
The posts point to a role-defined audience.
Almost every post names its intended readers: operating partners, portfolio CEOs, CFOs, CIOs, CTOs, COOs, strategic finance teams, AI and technology leaders, and M&A counsel.
For example, the physical AI posts address the COO, CIO, CTO or operating partner deciding where robotics and automation belong in a 2027 value-creation plan.
The governance posts frame model ownership, bias checks, audit trails and override authority as diligence-room questions for buyers.
The adoption post treats team productivity and change adoption as value-creation KPIs for operating partners.
Calls to action often ask readers to forward a post to the COO, indicating who Deva I J believes the audience includes, though not who actually reads it.
Several issues appear as repeated announcement posts, so post volume overstates the number of distinct topics.
The posts do not provide platform demographics, engagement counts, newsletter subscriber figures or audience survey results.
A follower count shows a list exists; it does not verify that followers are private equity operating partners, portfolio executives or diligence counsel.
If a campaign depends on seniority, employer type or geography, ask for audience survey results or platform demographic data covering these roles, and recent per-post results on AI governance and value-creation topics at comparable depth and format before committing budget.
LinkedIn followers
2243
LinkedIn profile snapshot for the account that published the reviewed posts · 2026-10-08
Across the 17 posts, every partnership-adjacent mention promotes a Remarkable HX newsletter issue and asks readers to subscribe or forward it to a colleague.
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The posts include no sponsor line-up, media kit, rate card, placement specifications, or partner-terms page, so a buyer has nothing here to price, schedule, or contract against.
They do show the editorial frame a sponsor would be buying into: issues built around named operating tools — SEGC (Safe, Efficient, Growing, Compliant), MOMENT + MAGIC, the 24-hour SEGC × MMMM scorecard, and the Physical AI Readiness Scorecard — and addressed to operating partners, portfolio CEOs, CFOs, CIOs, CTOs, COOs, and M&A counsel at private equity-backed companies.
Sponsor-relevant subject matter includes AI governance and diligence evidence, AI-driven margin and workflow economics, physical AI on factory floors, and M&A capital discipline.
These posts disclose no commercial partnerships or sponsorship offer.
Before building a paid plan, ask Deva I J for their official sponsor or media page, or a firsthand statement of available formats and terms.
Strong subject-matter fit for a commissioned educational campaign, if Deva I J is available. Their posts connect AI adoption to practical operating questions.
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Brief
A private equity firm wants a practical framework for creating measurable AI value and governance across portfolio companies.
Reasons
The posts repeatedly connect AI adoption to workflow redesign, measurable outcomes, and governance.
Issue #75 proposes testing an AI-enabled workflow for cost, improvement, governance, and evidence; Issues #58 and #67 focus on ownership, data, human oversight, and audit trails.
That gives a campaign aimed at operating partners and portfolio company leaders concrete material for an operating-model framework, rather than a generic AI-awareness message.
The posts describe an audience of operating partners and portfolio company leaders, which matches the intended readers.
Recommended angle
Show how to pressure-test one portfolio-company AI workflow before calling it a value-creation initiative: identify the business outcome, measure workflow cost and change, assign risk ownership, and keep evidence suitable for review.
When to choose a specialist
Poor fit on the available information.
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Brief
A consumer electronics brand wants a broad-reach campaign promoting a new AI assistant as a fun, effortless lifestyle product to general consumers.
Reasons
The posts address private equity and portfolio-company operations, with emphasis on adoption, diligence, governance, and measurable business outcomes.
They do not demonstrate a general-consumer audience or a lifestyle product format.
The only audience metric provided is 2,243 LinkedIn followers, which does not establish broad reach or consumer demographics.
Request audience data before selecting a creator.
Better alternative
Consider a creator whose work and documented audience focus on consumer technology or everyday AI use, and request audience and campaign-format evidence before selecting.
A worked campaign example
Campaign angle
From AI pilot to evidence-backed workflow: a portfolio-company test for cost, outcome, governance, and proof.
Hypothetical brief
A private equity firm wants a practical campaign asset for operating partners to assess AI use cases at portfolio companies, with a focus on measurable value and diligence readiness.
Next step
Before planning paid distribution, confirm directly whether Deva I J accepts commissioned work and what formats, terms, and audience data are available. The posts promote Remarkable HX but do not establish a commercial offer.
Worked plan
Use one workflow—such as supplier review or contract review—as the example. Ask the team to record its current cost or delay, define the improvement it expects, name who owns the AI system and who reviews its outputs, and identify the evidence it will keep. Set a 30-day review point and report what changed, what did not, and what governance gaps remain. This angle follows the questions and workflow examples in the Issue #75 posts and the diligence checks in Issues #58 and #67.
Choose Nick Knight when the PE campaign decision turns on commercial execution rather than AI governance: how portfolio companies design incentives, maintain ICP discipline, position products, onboard customers, renew and expand accounts so recurring revenue becomes measurable value.
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The posts speak directly to PE-backed buy-and-build and revenue-quality questions.
The tradeoff is coverage: the posts do not set out AI governance, AI workflow measurement or diligence-ready AI evidence. A firm that needs an operating-model framework for AI use across portfolio companies would need to ask Deva I J or another source for that layer.
Choose Anthony Castro when the buyer task centers on PE CFO leadership and the value-creation plan: when to add headcount, how to pressure-test hire ROI and assess org readiness, and how to build CEO-CFO alignment before treating a hire as the fix.
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That maps to a PE firm deciding who can help portfolio finance leaders make disciplined operating choices.
The posts focus on finance leadership and hiring decisions, not AI governance or measuring AI-enabled workflows. The CEO/CFO post also draws on another CFO's insights, making it less of a standalone AI operating-model argument.
Choose Patrick DePeters when a campaign needs a concrete finance-function AI value-creation story: manual close, rekeyed invoices and hand-built reports; AI agents and workflows replacing repetitive work; and the EBITDA-to-enterprise-value math.
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The $400K savings and 6x multiple example gives the campaign a measurable cost-and-value frame.
The example covers one finance department and one client rebuild, not portfolio-wide AI governance. It is stronger on bottom-line finance automation than on oversight, audit trails and decision rights across a portfolio.
Deva I J.'s 17 LinkedIn posts each announce a Remarkable HX newsletter issue numbered from #44 into the #70s and summarize that issue's argument and toolkit.
These are all the posts discussed here; the complete newsletter text is not included in them, and the posts do not give individual dates or establish anything else about Deva's work or background.
Claims about the work come from what the posts say.
Similar-creator comparisons draw on publicly published posts from Nick Knight, Anthony Castro, and Patrick DePeters.
