# David McCandless

David McCandless helps businesses adopt AI, covering tool selection, data governance, and hands-on team training. McCandless's posts draw on real AI rollout examples, including enterprise procurement and workforce training.

- Profile: https://b2binfluencer.marketing/creators/david-mccandless/
- LinkedIn: https://www.linkedin.com/in/dmccandless
- LinkedIn followers: 5,496
- Topics: artificial-intelligence, data-governance, enterprise-ai-adoption
- Based on 11 dated posts, latest 2026-10-09

## Summary

David McCandless is most useful for grounded explanations of enterprise AI adoption across procurement, data governance, and hands-on team training.

- Best for: A data platform or enterprise AI enablement brand running a thought-leadership series on AI procurement, governance, and training.
- Not for: A security vendor seeking technical launch content with benchmarks, architecture walkthroughs, live demos, and code-level detail.
- Check first: Confirm audience fit and partnership terms, as no rates, deliverables, or sponsorship format are stated.

### Takeaways

- What companies call AI risk is roughly 80 percent procurement friction and 20 percent actual risk.
- The chat assistant is the small layer; governed business data is the real layer.
- State workforce money can help pay to train a team on AI.

## Main ideas

- **What companies call AI "risk" is usually procurement work**: When a business refuses a tool such as Claude, the honest label is mostly paperwork: another data processing agreement, another security review, another afternoon wiring up single sign-on. Microsoft-heavy companies reach for Copilot and Google-heavy companies for Gemini because the contracts, security review and identity configuration are already done, and the vendor relationship already exists—not because those tools won a comparison on safety. Businesses scope and staff a CRM or accounting migration without calling it a philosophical debate, yet adding AI to the same stack gets filed under scary. They acknowledge a new attack surface, then argue that companies can point their existing controls at one more vendor. The costly failures they cite come from people not checking output, something no procurement process prevents. Their stated role sits upstream of the paperwork: discovery and tool comparisons that surface value slipping away.
- **The chat assistant is the small layer; the governed data layer is the real one**: The Copilot-versus-Claude-versus-ChatGPT comparison is real but shallow, they argue. The bigger problem is data trapped in systems that do not talk to each other; the tool for that layer is a data platform, not an assistant. They name Databricks and make three claims: it runs inside the cloud a company has already standardised on and under contracts already signed, so nothing new has to be onboarded; it gives business users joined-up data while IT keeps the entitlements enforced by source systems, so a salesperson sees only their own accounts, down to the row; and, because it sits underneath, switching assistants later requires no rebuilding. The stated payoff is that both camps get what they want, and that the move is safe either way because whichever assistant wins still needs someone to mind the data. The tension is deliberate: the assistant market is contested, but they argue the choice matters less if this layer is right.
- **AI training is a trust problem before it is a technology problem**: Across the field-notes series, the method is to make the room human before the technology arrives. They bring homemade cookies to every session because four hours of new technology goes more easily when people have broken bread with the person teaching them. AI is the topic; trust is the medium. People learn better from someone they have eaten with. The posts also present the training as a family undertaking: baking gives their wife and daughters a way to contribute to the business. Read alongside the funding post, this makes delivery as much a priority as content: the format and the person in the room are part of what is being sold.
- **State workforce money should be paying for AI upskilling**: The same public programmes that have long funded safety certifications, leadership courses and skilled trades can fund AI training, they argue, and too many owners leave the money unclaimed. The programme is funded by a small social charge folded into the unemployment-insurance taxes employers already pay every quarter. A company that has paid in for three years has effectively pre-funded its team's training, and the programme lets it draw that money back down. They add that other states likely run similar schemes. The post credits training provider Elite Training's chief executive, Erin Lasyone, and HiTech for knowing to ask; those endorsements are attributed to the named parties, not presented as neutral coverage.
- **Specific human detail is the thing generic AI content cannot copy**: The reason for launching a series on business lessons from McCandless's family is that, as feeds fill with AI-generated material, what a model cannot invent becomes more valuable: a real person, a real number, an adage actually said on a factory floor at six in the morning. The material is deliberately concrete, and the cast is named: a father described through LinkedIn recommendations as a CIO, two grandfathers with decades in sales and paper-mill management, and one who was a Marine and a serial entrepreneur. The post asks named former colleagues of their father to contribute the sayings they remember, turning the audience into a source for the series. Treat it as announced intent; ask whether installments have been published and how the series has landed.

## Work to read

- [Why a company says “risk” when it means “procurement”](https://www.linkedin.com/posts/dmccandless_ai-procurement-datagovernance-activity-7512870625905016832-sWZh)
- [The chat assistant is the tip of the iceberg; the data layer is the rest](https://www.linkedin.com/posts/dmccandless_databricks-enterpriseai-datagovernance-activity-7514361752143269889-F-La)
- [State workforce money can pay to train a team on AI](https://www.linkedin.com/posts/dmccandless_hitechairolloutfieldnotes-activity-7509251560808464385-t2xa)
- [A planned series on business lessons from family](https://www.linkedin.com/posts/dmccandless_my-dad-just-announced-a-new-position-on-here-activity-7510338416295833600-RO9v)
- [Cookies, and why training rooms need trust before tools](https://www.linkedin.com/posts/dmccandless_hitechairolloutfieldnotes-activity-7508888968143364096-1-Q2)

## Brand relationships

- **HiTech** (disclosed_partnership)
- **Databricks** (disclosed_partnership)
- **Elite Training** (mention)

## Partnerships

David McCandless’s posts show commercial signals, but no stated partnership or sponsorship format. They offer to help companies run discovery and tool bake-offs for AI adoption, promote AI enablement training and a $1,000 discount for clients of select MSP partners including HiTech, and write approvingly about Databricks running inside existing cloud environments. The posts state no payment, sponsorship terms, rates, deliverables, exclusivity, or partnership inquiry route. A brand considering a thought-leadership partnership should ask directly whether the vendor mentions are commercial and what terms apply.

## Alternatives

- Clive Lemmon: Choose Clive Lemmon when the campaign needs a delivered Microsoft Fabric implementation and a training catalogue behind the argument. The Fabric readiness post supplies a dated client story: a governed platform delivered in six weeks for a security services provider running hundreds of sites, with UK and EU data protection and ISO 27001 controls designed in at discovery, and source-system data quality issues found and fixed during the build. The training post lists Microsoft Fabric, Power BI, data engineering, AI, GDPR and cybersecurity programmes, including certification preparation. For a sponsor that must show implementation evidence and a path from content to paid enablement, this is a close fit for the same buyer task as an argument-led series.
- Lucas Hathaway: Choose Lucas Hathaway when the campaign’s centre of gravity is AI risk governance and vendor assessment. The NIST AI RMF post gives a practical starting sequence: benchmark against NIST AI RMF or CRI’s Financial Services AI Risk Management Framework, write policies for how teams actually use AI, run a repeatable AI risk assessment inside the existing risk process, add AI items to vendor security reviews, and update incident-response playbooks for models acting on their own or giving confidently wrong answers. The vendor-assessment post goes further: send a discovery email to critical and high vendors asking what AI is in the product, demand notice before new AI features are enabled on the tenant, and keep a dated record of what was asked, answered and decided. That is specific, checkable governance detail for buyers who need to show examiners they have asked the right questions.
- Jeff Willinger: Choose Jeff Willinger when the campaign needs a Microsoft 365 Copilot adoption and Center of Excellence case study with measured outcomes and a partner-ecosystem angle. The AI Center of Excellence post describes a $1.2B health and life sciences organisation with 400+ Copilot licences, no formal governance model and inconsistent usage, then lays out a 90-day phased model: vision and strategy, enablement infrastructure, policies and security, user training, business/IT alignment, and usage analytics. It reports proposal creation time down 28%, executive prep time down 35%, 14 internal workflows automated, and Copilot expansion approved within six months. The job announcement adds two decades in the Microsoft ecosystem, Microsoft MVP years, and a focus on Modern Work, Copilot, Power Platform, Dynamics and Microsoft co-sell. For a sponsor that needs a Microsoft-anchored transformation story with numbers, this directly addresses the same corporate AI enablement brief.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/