Max Votek

Votek argues that disconnected tools and workflows leave gains stranded, so businesses need coordinated processes, clear checkpoints, and outcome measures.

Max Votek draws on the history of electric motors: mills saw little output gain until they redesigned factories around the technology. They also describe a 200,000-license AI rollout that stalled before Microsoft shifted to specific sales tasks and peer learning.

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Votek sees people who can connect business teams and technology as essential while tools and models keep changing.

Max Votek describes an architect working with RevOps, procurement, and finance as the slowest link shifts. They argue that forward-deployed engineers translate between those teams and code.

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This is especially important in regulated or mission-critical work.

Max Votek describes ontology grounding as a way to define valid entities, relationships, and constraints. In a separate post, they frame guardrails as the difference between a demo and agents connected to manufacturing or pharma systems.

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They expect value to favor tailored systems and providers accountable for results, while recognizing that complex SaaS can remain essential.

Max Votek describes building a personal CRM with AI tools and argues that consulting firms must move beyond selling faster hours. Their posts contrast software budgets with the larger budgets companies spend on services and outsourced work.

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The piece turns that question into five named criteria and a full score range, letting readers apply the test to their own stack instead of relying on a prediction.

Votek scores 22 enterprise software companies on five dimensions: API openness, agent-native features, data portability, real-time access, and ecosystem flexibility.

• The leaders are Shopify 8.6, Stripe 8.4, Salesforce 8.4, Microsoft 8.4, Databricks 8.0 and Snowflake 8.0.
• Votek ties them together with one trait: each rebuilt its architecture with AI agents treated as first-class users.
• Twelve companies cluster between 6.0 and 7.6, including Oracle at 7.2, Twilio 7.6, HubSpot 7.0 and SAP at 6.6.
• For SAP, Votek singles out data portability at 5/10 as the weak point.
• Veeva scores 6.0 and IQVIA 3.8 even though both sit on life-sciences data, a 2.3x spread from top to bottom.
• Votek says these five dimensions are the same ones the firm evaluates in client work.

For brands planning a campaign

The five dimensions make a ready-made structure for a webinar segment: audience members can score their own vendors live.

• The leader-versus-middle split is a debatable claim worth putting to Votek directly.
• Before repeating the scores, confirm they are Votek's own analysis rather than a republished list.

The piece makes its technical argument through one named study, standard vocabularies and a mechanism readers can follow without a machine-learning background.

Votek contrasts a clinical QA study in which baseline LLMs produced hallucinations in roughly 63% of answers with the same system grounded in an ontology, where the figure fell to 1.7%.

• The argument: the fix is structural, not a better prompt.
• Define which entity types exist, which relationships between them are valid, and which constraints must hold.
• Votek calls this Palantir's actual moat rather than its AI, cites Palantir revenue of about $4.5B for 2025 and guidance of roughly $7.2B for 2026, and lists domain ontologies already used in regulated fields: SNOMED CT and FHIR in healthcare, FIBO in finance.
• Most developers encounter the idea through Neo4j and GraphRAG, which Votek calls a sliver of what it is.

For brands planning a campaign

A strong technical segment for a manufacturing, pharma or financial-services audience.

• The 63% to 1.7% comparison rests on a single cited study; ask for the source before using it in a talk.
• The Palantir revenue figures are public and can be linked.

The piece explains how the bottleneck shifts, an idea under much of Votek's other writing about delivery.

Votek argues the forward-deployed engineer is not a new role: Palantir pioneered it twenty years ago, and enterprise consulting has used architects, solution engineers and transformation leads for decades.

• What changed in 2026 is leverage: a strong Salesforce, SAP or Databricks architect now ships roughly 10x more through agentic coding tools.
• The central mechanism is that the bottleneck moves rather than disappears.
• When code speeds up 10-100x, marketing has not drafted the comms, legal has not reviewed the contract, and the client has not pulled the data.
• The person who sees that in real time works with a different department every day of the week and writes the code that connects them.

For brands planning a campaign

Useful as a counterweight to a replace-headcount narrative on a panel. Present the 10x and 10-100x figures as Votek's assessment, not as measurements.

The experiment lays out a defined system, a stated subscription price and a stated comparison.

Votek describes building a personal CRM from scratch across several sessions in about 20 hours on a $200/month subscription: 47 features, 13 AI-callable tools, a vector knowledge store, LinkedIn enrichment and a Telegram bot interface.

• Asked to estimate the same build for a traditional team, the tool returned 516 hours, $66,400, four months and five people; Votek frames that as roughly an 800x cost reduction.
• The sharper claim: the replacement is better for one user than the SaaS stack it replaced because it is built around one person's context.
• The piece names where this lands first: solo lawyers, small car dealerships, boutique agencies, independent consultants.

For brands planning a campaign

The hours, cost and estimate are self-reported from one project.

• Before repeating them on stage, ask how the traditional-team figure was produced.
• The adoption-first list could anchor a follow-up talk.

The piece supplies the framing behind Votek's implementation writing and a checklist of where implementations break.

Votek borrows the electrification analogy: in the 1890s, mills swapped steam engines for electric motors and output barely moved for thirty years, until factories themselves were redesigned in the 1920s.

• The failure modes Votek lists inside most companies are specific: employees each have their own prompts and habits; outputs never connect into one decision flow; teams drown in generated material until finding signal gets harder; and leadership sees local wins but no system-level growth.
• In Votek's account, institutional AI adds a coordination layer, checkpoints and audit trails, deterministic processes, and models that surface risks instead of agreeing.
• The conclusion: value moves up the stack to organisation design and accountability rather than individual chat sessions.

For brands planning a campaign

Votek's most quotable thesis for a leadership audience. The electricity analogy draws on economic history rather than original research, so attribute it and pair it with the company-side diagnostics, which are Votek's own.

The piece offers a named enterprise case with published numbers rather than a general claim about adoption.

Votek relays Microsoft's published transformation results and leads with the failure: a 200,000-license rollout went nowhere until the sales group abandoned the tool-first approach.

• The team mapped account managers' week, added agents where they touched pipeline analysis, deal preparation and customer research, and used weekly peer sessions to turn experiments into habits.
• Priority-scenario use tripled, revenue per account manager rose 9.4%, close rates were 20% higher, and selected cloud supply-chain workflows became up to 75% faster, with one nine-person product team shipping a first release in 35 days.
• Votek forecasts that serious AI programs will stop reporting seats and monthly active users within two years and start reporting cycle time, conversion and revenue per employee.

For brands planning a campaign

The metrics are Microsoft's published figures, relayed by Votek; link the Microsoft source in any talk that uses them. Label the two-year shift away from seat-based reporting as Votek's prediction.

Votek describes a $1.5B joint venture anchored by Anthropic, Blackstone, Hellman & Friedman and Goldman Sachs, with Apollo, General Atlantic, Leonard Green, GIC and Sequoia joining.

• It is pitched as an AI-native enterprise services business combining consulting, outsourcing and SaaS with early access to Anthropic models.
• The supporting arithmetic comes from Blackstone's Jon Gray: 275 portfolio companies, LLM spending across them up 15x in a year, and labour as a $60T annual cost, where a 15% efficiency gain equals $9T.
• Their conclusion: the competitive set for consultants now includes the labs themselves, PE-owned mid-market companies have been handed a preferred vendor, and speed and ownership of execution are what is left to differentiate on.

For brands planning a campaign

Verify the deal terms, the $1.5B figure and the Blackstone portfolio count against primary reporting before using them live. The tool-versus-outcome split is Votek's recurring argument and works well as a panel prompt.

The post lists scores (Shopify 8.6, Salesforce 8.4, IQVIA 3.8) and names agent initiatives at each company, such as Agentforce 360 and Stripe's Agent Commerce Protocol.

A five-dimension scoring model applied to a named set of 22 companies, with carousel graphics.

The post does not explain the scoring rubric or provide raw data, making the rankings difficult to audit. Ask for both before using them.

For brands planning a campaign

A B2B SaaS company can use this framework as a concrete, data-backed angle for a webinar on agent strategy. Verify the scores and company specifics against current capabilities before publication.

Examples: Source 1

Max Votek cites IBM Consulting's plan to build thousands of digital agents, Anthropic's $1.5B joint venture with private-equity firms, and the gap between AI-driven internal efficiency and client outcomes. Customertimes is described as selling outcomes with AI rather than billing hours.

For brands planning a campaign

This is a strong topic for audiences in professional services or enterprise AI. A webinar could focus on how AI-native delivery models change pricing, client expectations, and the competitive set.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

Posts emphasize coordination layers, deterministic processes, audit trails, and the migration of bottlenecks as code speeds up.

For brands planning a campaign

A B2B audience moving AI from pilot to production would find this relevant. A webinar could explore why speeding up development alone does not deliver business results, with examples from Salesforce implementation and enterprise AI adoption.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

Use cases include quality checks, predictive maintenance, adverse event monitoring, and compliance review. Guardrails are framed as the difference between a demo and a production system.

Pairs technology descriptions (OpenClaw, NemoClaw) with industry-specific workflows and compliance constraints.

For brands planning a campaign

A brand targeting regulated industries can evaluate Max Votek for a webinar on practical, guardrailed AI agents. The specific tool stack and industry scenarios provide concrete talking points.

Examples: Source 1 · Source 2 · Source 3

Examples compare pharma franchises to AI lab revenues and discuss commoditized models versus hardware demand.

Several posts rely on external reports and figures. Ask for the sources and context before using them.

For brands planning a campaign

For a financial or strategic audience, Max Votek offers a market-analysis angle. A webinar could cover the economics of AI infrastructure and application layers; verify their data points before publication.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

Posts stress that buying licenses is easy; changing processes, data trust, and approvals is harder.

For brands planning a campaign

A B2B SaaS or sales-tech brand can use this for a webinar on AI adoption in revenue teams. Verify the specific product details and adoption metrics before publication.

Examples: Source 1 · Source 2 · Source 3 · Source 4

The post cites an open letter from major AI and cloud companies and a $90M bitcoin theft from unreviewed Coldcard firmware. The argument: attackers need one bug; defenders need to find every bug.

For brands planning a campaign

This is a relevant topic for a security-focused or developer audience. A webinar could address the risks of vibe-coded software and practical guardrails for shipping AI-assisted code.

Examples: Source 1

Drawing on a past SAP CRM rollout, they argue that adoption depends more on internal champions—power users, ambassadors, and people who already believe—than on executive mandates. Customertimes is described as spending more time teaching people to use AI they already bought.

For brands planning a campaign

For a brand selling AI tools, this topic offers a practical angle on enablement spending and change management. A webinar could explore how to build internal adoption programs.

Examples: Source 1

They describe Palantir's moat as its ontology layer and connect the approach to regulated industries and standards like SNOMED CT and FHIR.

Uses a before-and-after statistic to contrast prompt engineering with structural grounding.

For brands planning a campaign

This topic would resonate with technical or regulated-industry audiences. A webinar could explore how ontology-grounded AI systems improve auditability and compliance.

Examples: Source 1

They describe companies building custom tools by combining open-source, AI agents, and their own data, and cite OpenClaw and NemoClaw as enterprise-ready infrastructure.

For brands planning a campaign

For a B2B SaaS company, this is a relevant but potentially challenging topic. A webinar could debate when to build versus buy, using real examples and tradeoffs.

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Examples include renegotiating insurance, canceling subscriptions, and AI traffic to retail. The argument: intermediary margins fall while producers and infrastructure win if the agent picks them.

For brands planning a campaign

A retail or e-commerce brand could consider Max Votek for a webinar on agentic commerce and changing consumer behavior. The post cites AI traffic growth of 693% year over year; ask for the source before using the figure.

Examples: Source 1

For a partnership, ask them to clarify their role and formal relationship with the business.

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For client references or details on the terms of this work, ask them directly.

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Jaysingh explains how a shared decision layer can keep customer-service answers consistent across channels; Votek compares Salesforce and Claude integrations, including permissions and implementation choices.

Jaysingh’s examples center on customer experience and CRM decision logic. Votek covers a wider range, from enterprise AI adoption and consulting to software economics.

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Ryan emphasizes data quality, platform complexity, and costs; Votek compares the products’ roles and permissions, as well as build-versus-buy implications.

Ryan focuses particularly on Salesforce marketing operations and migration. Votek covers multiple enterprise platforms and organizational change.

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Bidgood focuses on inconsistent consumer-goods definitions and trusted data; Votek discusses ontologies, guardrails, and governed enterprise systems.

Bidgood focuses on consumer goods, retail, and shared business definitions. Votek covers a wider range of industries and implementation questions.

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In his CRM decision-orchestration post, a refund request could get different answers from chat, voice and text unless the business rules sit in a shared decision layer.

• His Claudeforce analysis adds a useful distinction: permissions govern what an agent may access, while procedures govern how it should act—and procedures need enforcement to be dependable.
• That makes Jaysingh a strong fit for a technical session on CRM architecture and operational guardrails.
• Compared with Max Votek’s broader cross-vendor readiness and implementation themes, Jaysingh offers a more focused argument about decision consistency and control.

The focus is CRM and agent decision-making. For a session spanning AI readiness across software categories, regulated-industry grounding or AI economics, Votek offers broader territory; Jaysingh is the better choice when depth on CRM workflows and enforcement matters more than breadth.

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In his Claudeforce critique, he raises concrete adoption questions: whether an AI assistant can navigate Salesforce’s different data models, how data residency affects EU organizations, and whether combined product and usage costs make the return clear.

• The post also stresses that an integration will not repair poor underlying data.
• That makes Ryan a useful voice for a risk-focused panel or a discussion of readiness before adoption.
• Votek offers more cross-industry frameworks and implementation examples; Ryan’s advantage is a customer-side critique grounded in Salesforce and HubSpot marketing operations.

The strongest fit is marketing operations and CRM adoption, rather than broad agent architecture or regulated-industry AI.

• Max Votek’s other post recommends people in the marketing-operations community, not a technical framework.
• Votek is a stronger choice for a campaign that needs a repeatable cross-vendor evaluation.

Examples: Source 1 · Source 2

In his post on product definitions in CPG, he shows how marketing, supply chain, finance and retail partners can use different definitions of product, customer and margin.

• An agent can then scale that disagreement into unreliable forecasts or trade-spend decisions.
• His post on trusted data and agents sharpens the point: connecting systems is not enough if the business has not agreed on what its data means.
• This is a valuable governance perspective for a CPG-focused campaign; Votek is the broader fit for a session comparing enterprise platforms or implementation approaches.

Bidgood’s two posts focus on consumer goods, CPG and data governance. That specificity suits a vertical audience; a broader B2B software campaign may need a speaker such as Votek to cover cross-vendor readiness and implementation.

Examples: Source 1 · Source 2