Jon Pielak

Strong organic growth signals a repeatable revenue engine rather than a one-time deal bump, which is why buyers reward it. It also tends to produce higher EBITDA multiples and a lower-risk business than acquisition-led growth, with its integration risk.

Jon Pielak lays out five levers funds can apply across a portfolio: sharpen go-to-market (ideal customer profile, value messaging, sales enablement); modernize marketing and demand generation; improve sales productivity through CRM and data-driven processes; expand existing accounts through upsell and cross-sell; and invest in operations that can scale without squeezing margins.

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Legacy industrials such as chemicals and mining tend to compete on price and default to operational improvement, while leaders in those sectors borrow organic-growth models from SaaS and technology. They see the opportunity in that gap.

Jon Pielak points readers to a specific short analysis and singles out page 5 as the point where high and low performers diverge most, while more traditional markets stick to their norms. In a separate post, they highlight a small fund focused on using commercial excellence to unlock growth in stable, legacy sectors where sales and marketing capabilities are underdeveloped.

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Firms respond to growth pressure by hiring go-to-market or commercial leaders into practices whose senior people never wanted to sell.

Jon Pielak notes a pattern across funds' portfolios: professional services has been an active sector, and portfolio companies keep hitting the same hurdles to organic growth.

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The practical task isn't to position as the future; it's to use AI to smooth rough edges for clients today. They describe a response built on go-to-market fidelity at the practice level — sharper positioning, tighter audience segmentation, and highly targeted outreach — so a practice can act like a boutique and counter the impression of large-firm bloat.

Jon Pielak quotes a client saying they don't need a consultant to tell them what AI has already told them is wrong, but a friend to tell them who would be good at executing the idea. The line is the client's, not Pielak's.

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They identify an internal obstacle: pressure to cross-sell and reinforce a corporate brand often conflicts with practice-level positioning.

Jon Pielak says they have been helping several firms test these shifts in what they call GTM Labs — pilot environments designed to rewire organic growth motions without disrupting the broader organization.

• A related model appears in their summary of a Shore Capital Partners podcast with Zachary Nippert, the firm's chief commercial officer.
• Nippert describes a Commercial Center of Excellence built on three pillars — people (hiring, training, mindset), strategy and alignment (go-to-market rigor), and sales process and enablement (consistent, measurable revenue operations) — supported by playbooks, boot camps, go-to-market sessions, and minimum operating standards.
• The pillars, routines, and ambition to be a destination for top sales talent are Nippert's account of Shore's model, as reported by Pielak.

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They hear recurring concerns: firms need a new value proposition that still commands premium rates, sales talent as relationships become more transactional, and less worry about AI positioning.

• In Pielak's view, the real failure is demand generation: firms adopt new positioning and an AI angle but have no working method for building pipeline, because professional-services marketing was built to enable sales rather than create demand.
• Their conclusion: firms recovering from this should rebuild referral networks, evangelists, champions, and partner ecosystems rather than rely on a cleaner AI deck.

Jon Pielak points to a Wall Street Journal article from the previous fall as background and says conditions have compounded since. They single out firms using AI to solve present client problems rather than repositioning as AI consultancies that pivot weekly.

An earlier post catalogs how AI is already changing consulting economics: McKinsey's Lilli, BCG's custom GPTs, Deloitte's internal AI fleets, and KPMG and PwC agent platforms, alongside AI-native boutiques automating pricing, cost savings, slide design, and proposals.

• The post endorses the shift, with takeaways on AI literacy, governance, team structure, and moving from time-based billing toward outcome-driven work.
• A later post is more cautious about firms claiming the AI label without fixing demand.

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The deck speaks the fund's language — revenue, win rate, customer acquisition cost, net retention — and exposes common growth barriers: poor ideal-customer-profile targeting, an inefficient channel mix, a stalled upsell motion, or a misaligned brand. Those are the issues they expect an operating partner to act on.

Jon Pielak shares the structure of a Quarterly Commercial Performance Review deck for a portfolio company and links to a downloadable version. They note that access to strong data of this kind is rare; the example is based on a fictional client, while the structure and insights are real.

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Jon Pielak describes how large consultancies such as McKinsey, BCG, Deloitte, KPMG, and PwC are embedding AI agents into core workflows.

• Examples include McKinsey’s Lilli, BCG teams building thousands of custom GPTs, Deloitte’s internal AI fleets, and AI-native boutiques that compete on pricing, cost savings, and proposals.
• They argue this reshapes consulting delivery: fewer junior staff, more senior-led strategy, and a shift from time-based billing toward outcome-driven models.
• The post also gives practical takeaways for professional-services firms, including AI literacy, governance, tooling, team structures, go-to-market changes, and watching boutique challengers.

For brands planning a campaign

For a private equity operating partner or consultancy, this is a signal to test AI-enabled delivery and pricing assumptions during a diagnostic rather than treating AI as a marketing label. It can frame a pilot GTM Lab around senior-led, outcome-based offers.

Jon Pielak reports that GTM playbook requests have slowed as funds hedge bets and spend less time on organic growth.

• At the same time, founders and partners, especially in professional services and consultancies, face more pressure to sell.
• They list four recurring threads: the need for a new value proposition, the need for sales talent as relationships become more transactional, oversteering on AI, and demand generation becoming a black hole.
• A client quoted in the post says: 'I don't need a consultant to tell me what AI just told me is wrong.
• I need a friend to tell me who'd be good at executing this idea.'

For brands planning a campaign

A diagnostic should probe referral networks, evangelists, champions, and partner ecosystems, not only positioning and AI messaging. This post gives concrete language for that shift.

Jon Pielak says professional services has been active for the funds they work with, and organic growth often hits familiar hurdles.

• Practice leaders know sales must evolve but are busy being billable and managing relationships, so sales and marketing become secondary by default.
• AI has changed the buyer’s first step: clients self-diagnose earlier and arrive with stronger points of view.
• They say what helps is GTM fidelity at the practice level: sharper positioning, tighter audience segmentation, and highly targeted outreach, so practices can operate more like boutiques.
• Internal pressure to cross-sell and reinforce a corporate brand often fights this.
• They also describe helping firms test these shifts inside GTM Labs, pilot environments designed to rewire organic growth motions without disrupting the broader organization.

For brands planning a campaign

Use this as the basis for a pilot GTM Lab plan for one practice, with clear audience, positioning, outreach, and success measures. It also names the internal cross-sell tension a pilot must manage.

Jon Pielak explains why organic growth and commercial excellence are becoming priorities for mega funds.

• They define organic growth as growth from within the company through sales, market expansion, product development, and operational efficiencies, and contrast it with inorganic growth from acquisitions.
• The post lists why it matters for PE-backed businesses: higher valuations, stronger EBITDA multiples, and a lower risk profile.
• It also outlines five ways PE firms can drive organic growth across portfolios: optimize go-to-market strategy, improve marketing and demand generation, enhance sales productivity, expand customer value, and invest in scalable operations.

For brands planning a campaign

A portfolio diagnostic can map a company against the five levers and identify the highest-value gaps. It is a useful frame for a short commercial-motion review.

Jon Pielak summarizes a Shore Capital Partners podcast with Zachary Nippert, Chief Commercial Officer, about commercial excellence.

• They describe Shore’s Commercial Center of Excellence as deploying structured frameworks around talent, strategy, and sales enablement to accelerate organic growth across portfolio companies.
• The post names three pillars: people, including hiring, training, and mindset; strategy and alignment, including go-to-market rigor; and sales process and enablement, including consistent, measurable revenue operations.
• Shore uses playbooks, boot camps, go-to-market sessions, and minimum operating standards to help companies scale faster, and aims for the center to be a destination for top sales talent and a benchmark for organic growth across private equity.

For brands planning a campaign

Use this as a benchmark for designing a commercial center of excellence or a smaller pilot. It gives a PE operating partner a way to compare current talent, GTM, and enablement practices.

Jon Pielak shares what a Quarterly Commercial Performance Review deck typically looks like for a portfolio company when strong data is available.

• The deck speaks the fund’s language, using revenue, win rate, CAC, and net retention, while also revealing common growth blockers such as poor ICP targeting, inefficient channel mix, a stalled upsell motion, or a misaligned brand.
• They note the asset is based on a fictional client but say the structure and insights are real, and link to a downloadable version on the Mavenray resources page.

For brands planning a campaign

This can serve as a template for building a diagnostic readout. It keeps the conversation on fund metrics while surfacing the commercial blockers that would shape a pilot plan.

A useful starting point for industrial portfolio companies and smaller funds seeking to distinguish operational from commercial excellence.

Jon Pielak describes a short read and analysis for anyone applying commercial excellence to a traditional B2B company.

• They say legacy industrials such as chemicals and mining often compete on price and focus on operational rather than commercial excellence, while leaders look to SaaS and tech for data-heavy organic growth models and marketing that drives customer acquisition.
• They point to page 5 of the linked analysis for the most dramatic gaps between high and low performers, and note that more traditional markets tend to stick to their norms.

For brands planning a campaign

For industrial clients, compare commercial focus with operational focus and adapt SaaS-style playbooks carefully. The page-5 gap example can anchor a diagnostic conversation about underused commercial capabilities.

A short entry point into Pielak’s interest in commercial capability gaps rather than follower-count tactics.

Jon Pielak highlights a small fund with a sharp perspective on driving organic growth amid economic headwinds. It focuses on using commercial excellence to unlock growth in stable, legacy sectors where sales and marketing capabilities are often under-developed.

For brands planning a campaign

For a smaller fund or legacy-industry company, frame the diagnostic around sales and marketing capability gaps in stable businesses. This post offers a compact rationale for that approach.

They link it to higher exit valuations, stronger EBITDA multiples, and lower risk than integration-heavy acquisition strategies. The post outlines five portfolio actions: sharpen go-to-market, modernize marketing and demand generation, improve sales productivity, expand existing customer value, and invest in scalable operations.

For brands planning a campaign

A fund or operating partner can use this as a diagnostic for a portfolio commercial review: check whether each company has a structured GTM motion, demand engine, sales process, expansion motion, and scalable operating backbone. For a creator campaign, it supports a short diagnostic or workshop on organic-growth levers rather than a one-off brand-awareness push.

Examples: Source 1

The center uses playbooks, boot camps, GTM sessions, and minimum operating standards to help portfolio companies scale, benchmark one another, and share best practices.

Jon Pielak uses podcast analysis to extract a three-pillar commercial operating model and the routines—playbooks, boot camps, GTM sessions, and standards—that make it repeatable.

The example comes from one fund's approach. Ask whether the same model fits the fund's portfolio before adapting it.

For brands planning a campaign

For a private equity operating partner, this is a model for a pilot GTM Lab: a small set of portfolio companies, shared standards, and measurable revenue operations. A creator or advisor can position an engagement around building the standards and running the first working session, not just giving a presentation.

Examples: Source 1

Founders and partners in professional services and consultancies face more pressure to sell, as AI, geopolitical shocks, and rate cycles disrupt the rain-making machine.

• They identify four recurring threads: a need for a new value proposition, a need for sales talent, oversteering on AI, and demand generation as a black hole.
• In their view, firms that recover rebuild referral networks, evangelists, champions, and partner ecosystems rather than relying only on a cleaner AI deck.

Jon Pielak frames the shift through four recurring conversation threads, contrasting firms repositioning around AI with those using AI to solve current client problems.

Jon Pielak's observation draws on conversations and one WSJ article. Test it against the firm's own pipeline and win-rate data.

For brands planning a campaign

A consulting or professional-services firm can use this as a campaign brief for a demand-generation diagnostic: test whether its positioning still commands premium rates, whether sales is relationship-led or transactional, whether AI work solves a present client problem, and whether pipeline comes from actual demand creation. The practical angle is rebuilding referral and partner ecosystems, not another AI positioning exercise.

Examples: Source 1

The shift points to fewer junior staff, more senior-led strategy, and a move from time-based billing toward outcome-driven models. Their takeaways for professional services firms: invest in AI literacy, governance, and tooling; reevaluate team structures; rethink go-to-market around value and outcomes; and monitor agile AI-first challengers.

Jon Pielak summarizes two articles and adds firm-by-firm examples to show where AI is already changing consulting economics and team design.

The productivity and revenue figures come from reported examples. Ask for measured client results before using them as benchmarks.

For brands planning a campaign

An advisory firm can use this as a demand or positioning campaign around AI-era operating models.

• A useful campaign could focus not on “add AI” but on “change the delivery model”: reduce junior leverage, protect senior judgment, move pricing toward outcomes, and watch boutique competitors.
• A pilot could test one workflow—proposals, pricing, or cost analysis—against a time-based baseline.

Examples: Source 1

AI has changed the buyer's first step: clients self-diagnose earlier and arrive with stronger points of view, shifting the value proposition faster than many firms realize.

• The response they describe is GTM fidelity at the practice level—sharper positioning, tighter audience segmentation, and highly targeted outreach—so practices can operate more like boutiques.
• Internal pressure to cross-sell and reinforce a corporate brand often works against that effort.

Jon Pielak describes GTM Labs as pilot environments designed to rewire organic growth motions without disrupting the broader organization.

Jon Pielak says they are helping firms test this approach. Ask for completed pilot results and conversion data before committing to a broader rollout.

For brands planning a campaign

For a professional-services firm or PE-backed portfolio, a pilot GTM Lab can test practice-level positioning and targeted outreach without disrupting the wider organization. Scope a small pilot around one practice, define the audience and value proposition, and measure whether it produces pipeline before rolling it out firm-wide.

Examples: Source 1

Legacy industrials such as chemicals and mining often compete on price and focus on operational rather than commercial excellence.

• Leaders in those sectors look to SaaS and technology for organic-growth models built on data, customer acquisition marketing, channel, sales enablement, and go-to-market.
• They note that smaller funds often struggle to distinguish operational excellence from commercial excellence, and that the performance gap between high and low performers is especially visible in the analysis.

Jon Pielak recommends a specific short read and highlights the page where high and low performers diverge most.

The observations draw on the linked analysis, not Jon Pielak's own portfolio data. Ask how they apply to the specific sector and fund.

For brands planning a campaign

An operating partner in a legacy-industrial portfolio can use this as an education and diagnostic campaign: separate operational improvements from commercial ones, then apply selected SaaS/tech GTM practices without importing the whole playbook. Start with a commercial-excellence assessment of pricing, channel, sales enablement, and customer acquisition.

Examples: Source 1

The review also surfaces common growth barriers: poor ideal-customer-profile targeting, inefficient channel mix, a stalled upsell motion, or a misaligned brand.

• They note that strong data is rare and offer a downloadable version of the asset.
• The example is based on a fictional client, but the structure and insights are presented as real.

Jon Pielak shows the structure of a fund-language review deck and names the commercial problems it is designed to expose.

The example is fictional, so ask for a client case study before treating it as evidence of outcomes. Jon Pielak notes that access to strong data is rare; ask what data a fund needs to run this review.

For brands planning a campaign

A fund or portfolio company can use this as a template for a commercial diagnostic and recurring review cadence. For a creator campaign, the angle is a practical “bring your data” review: turn fund-level metrics into a short list of commercial constraints and one or two tests for the next quarter.

Examples: Source 1

The focus is on using commercial excellence to unlock growth in stable, legacy sectors where sales and marketing capabilities are often underdeveloped. They argue that the opportunity extends beyond high-growth technology markets: traditional sectors can benefit when commercial motion is built deliberately.

Jon Pielak uses a small fund’s perspective as a lens for applying commercial excellence in stable, legacy sectors.

The post is a brief recommendation of the linked article and does not detail the fund's results.

For brands planning a campaign

For a smaller fund or a legacy-sector portfolio company, start with missing commercial capabilities rather than a broad transformation. A short diagnostic of sales and marketing capability can identify whether the fund needs a center of excellence, a GTM Lab pilot, or a targeted hire.

Examples: Source 1

Ask Pielak to clarify their connection to Mavenray.

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The post credits the podcast as the source; ask whether Pielak has a working relationship with the firm.

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Deva I J. applies that framing to AI adoption and physical AI in factories, warehouses, and field operations, specifying where human judgment, override authority, and accountability have to be designed in.

Deva I J.'s material centres on AI implementation — governance, safety, adoption friction, and robotics near people, products, and equipment — with less focus on pricing, channel, and demand generation. Jon Pielak stays on the commercial side: go-to-market motion, sales capability, account expansion, and pipeline, treating AI mainly as a change in how services are sold and delivered.

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Nick Knight's posts work through the same question Jon Pielak raises about practice-level go-to-market: what structure, targets, and compensation actually reward the behaviour a firm says it wants.

Nick Knight writes from inside the operating company about org design, incentive alignment, product enablement, customer success, and expansion after signature, including buy-and-build integration. Jon Pielak writes more often from the fund's and practice leader's side about portfolio-level commercial programmes and bounded pilots such as GTM Labs.

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Patrick DePeters works through AI and systems modernisation in the finance department, translating recurring savings into EBITDA and enterprise value at a stated multiple — the same value-creation logic Jon Pielak applies to commercial capability.

Patrick DePeters concentrates on the finance function, reporting, reconciliations, forecasting, back-office automation, and where AI lands first and why. Jon Pielak concentrates on revenue-facing work — demand generation, sales productivity, pricing, and customer expansion — rather than finance operations.

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The two posts focus on PE-backed companies managing AI that acts in the physical world and on why AI pilots fail when adoption is not designed into the workflow.

• For diagnostics covering what a system is allowed to do, where human judgment enters, who can override it, and who is accountable when it acts, Deva's material speaks directly to the questions.
• The posts also provide a 30-day toolkit to survey AI usage, remove friction points, and track adoption improvement—a concrete starting point for an operating partner assessing AI readiness.
• For a campaign aimed at AI governance, value creation through automation, or adoption KPIs, this is a closer fit than Jon Pielak's commercial-motion content.

Deva's posts focus on AI and physical AI readiness, adoption, and value creation through technology—not commercial GTM diagnostics, organic growth levers, or a pilot GTM Lab.

• For a buyer assessing a portfolio company's sales and marketing motion, defining a pilot GTM Lab, or running a quarterly commercial performance review, they offer no direct framework.
• The posts are short and omit the linked issue and scorecard.
• Ask Deva for those materials to assess the toolkit's full detail.
• Deva is a specialist alternative for AI adoption, not a general replacement for a GTM advisory engagement.

Examples: Source 1 · Source 2

The posts focus on how structure, targets, and compensation drive behaviour, arguing that incentives focused mainly on new logo acquisition produce poor-fit revenue and weak expansion.

• They also cover account expansion after an acquisition, the role of customer success and RevOps, and the trust-building needed to bring acquired teams and customers into one commercial motion.
• For a PE-backed SaaS or recurring-revenue business—especially after a buy-and-build—Nick's material offers a concrete lens: design incentives, align product, marketing, sales and customer success, and treat expansion as evidence-led growth rather than a cross-sell campaign.
• For a diagnostic into why a portfolio company's expansion motion is stalled, this is more specific than Jon Pielak's posts.

Nick's posts offer operator-level principles about behaviour design, org structure, and customer trust, but do not describe a portfolio-level commercial diagnostic, a pilot GTM Lab, or a fund-facing quarterly review format.

• They also lack a structured checklist or pilot plan a PE operating partner could run directly.
• For a short diagnostic of a portfolio's commercial motion and a ready-to-run pilot, Nick's material supplies useful design principles but not a complete programme.
• Ask Nick for a template or framework before treating the posts as a replacement for Jon Pielak's diagnostic and pilot approach.

Examples: Source 1 · Source 2

The posts describe finance as the beachhead for AI adoption because the work is rules-based, data-rich, repeatable, and measurable.

• One gives a concrete example: rebuilding a ten-person finance department with modern systems and AI agents, cutting about $400K a year in overhead, and translating that recurring saving into roughly $2.4M of enterprise value at a 6x EBITDA multiple.
• For a buyer looking to find EBITDA inside a portfolio company's back office, modernise a manual close, or assess how AI will change finance roles, Patrick's material provides specific patterns and an ROI frame.
• That is a different task from a commercial-motion diagnostic, but within finance transformation it is more directly applicable than Jon Pielak's GTM content.

Patrick's posts focus on finance and AI, not commercial GTM or organic growth.

• They do not cover ideal-customer targeting, demand generation, sales productivity, or a pilot GTM Lab.
• For a buyer diagnosing a portfolio company's commercial motion and proposing a pilot growth plan, the material offers no direct answer.
• The posts are short and include no templates or detailed implementation steps; the finance example is a single anecdote, not a reusable diagnostic framework.
• Ask Patrick for implementation materials before assessing the approach for a specific portfolio company.
• Patrick is a specialist alternative for finance-function value creation, not a substitute for a GTM advisory engagement.

Examples: Source 1 · Source 2