The ground is moving under our feet, and that’s exactly why this conversation matters. We sit down with Sandy Carter to unpack a practical path through the AI hype.

We explore how to drive real business results with AI by starting with outcomes, clean data, and human-first change. We dig into trust, identity, GEO vs SEO, and the surprising future where marketers sell to both people and agents.

• linking AI to revenue, cost, and risk
• treating data quality and governance as a core asset
• communicating change to earn trust and adoption
• using blockchain for verification and provenance
• handling deepfakes and identity with proof
• humans plus machines and agent-driven choices
• marketing to robots, agents, and households
• shifting from SEO to GEO for discovery
• Reddit and Wikipedia as LLM signal sources
• tracking UTMs from generative engines
• staying sane with first principles in fast cycles
• keeping “human always” as the differentiator

AI as a Force Multiplier, Not a Magic Solution

AI is not a magic trick; it is a force multiplier that only works as well as the business goals and data behind it. The most effective teams start by defining a measurable outcome, then select AI as the method, not the mission. Sandy Carter's core point hits hard: AI projects fail when they are framed as tech-first instead of outcome-first.

Leaders who obsess over models and features while ignoring change management set themselves up for resistance and rework. Think of AI like any transformation—pilot toward a clear KPI, align incentives, and integrate with current workflows. When the hype fades, the teams that map AI to revenue, cost, and risk will keep compounding gains.

Start with Outcomes, Not Technology

The most common mistake in AI implementation is leading with the technology rather than the business problem. Before selecting models or platforms, define what success looks like in concrete terms. Are you trying to reduce customer service response time by 40%? Increase lead conversion rates by 15%? Cut operational costs by 25%? These measurable targets give your AI initiative direction and accountability.

Change Management Is Half the Battle

Technical excellence means nothing if your organization resists adoption. Successful AI projects allocate significant resources to training, communication, and workflow integration. Identify champions within each affected team, create feedback loops for continuous improvement, and demonstrate quick wins to build momentum. The technology may take months to deploy, but the cultural shift requires ongoing attention.

Data Quality Determines AI Performance

Data is the true governor of AI performance. You cannot optimize what you cannot observe, and you cannot infer what you did not collect. The sales-loss example shows the trap: without structured reasons for loss, an agent has nothing to learn from. Marketers and operators need systematic capture, taxonomies, and governance so feedback loops become assets.

Building Feedback Loops That Learn

Consider a sales team using AI to prioritize leads. If the system only sees which deals close but never learns why others fail, it cannot improve its predictions. Structured loss reasons, competitor analysis, and objection categories transform raw outcomes into training data. This requires discipline: standardized fields, mandatory documentation, and regular audits to ensure quality.

The Human Element in Data Strategy

The people side matters as much as the pipelines. Projects collapse when they spend months on tech and a week on communication. Earn trust with transparent intent, privacy protections, and clear user benefits. Teach stakeholders what is measured, what is not, and how insights will be used. Human insight remains the strategic edge: empathy, context, and creativity steer AI toward value.

Blockchain and Trust in the Age of Synthetic Media

Trust is breaking under the weight of synthetic media, which is why blockchain's verification power is resurfacing as a practical partner to AI. The point is not decentralization for its own sake, but verifiable identity, provenance, and ownership. When the public doubts both fake photos and real ones, cryptographic proof becomes a user experience feature.

Provenance as a Competitive Advantage

Organizations that can verify the authenticity of their content, products, or credentials will stand out in a world drowning in synthetic output. Blockchain-based certificates can prove that a photo came from a specific camera at a specific time, that a document originated from an official source, or that a credential reflects real achievement. This verification layer becomes part of brand value.

Strategic Data Exposure Decisions

That same trust lens extends to data exposure. Some brands will block scraping to protect IP; others will open selective surfaces to be discoverable in LLMs. The strategic question is portfolio-based: which assets should remain private, and which should be designed to flow into answer engines with the right context and links?

From SEO to GEO: Optimizing for Answer Engines

Discovery itself is shifting from SEO to GEO—generative engine optimization. Keywords matter less; prompts, entities, and authority cues matter more. Executives increasingly ask an LLM for "the top five" options and stop there. To appear in that short list, marketers need structured, credible signals where models forage.

Where AI Models Look for Answers

High-quality Reddit threads, a current Wikipedia page, technical documentation, product comparisons, and first-party benchmarks all serve as training ground and retrieval sources for large language models. Content must anticipate questions, not just rank for terms. Structure matters: clear headings, direct answers to common questions, comparison tables, and authoritative citations all improve your chances of being surfaced.

Measuring GEO Performance

Measurement evolves too: tag links from answer engines, track UTM patterns from tools like ChatGPT and Perplexity, and compare GEO-driven visits against organic search. Expect uneven behavior across models; what works for Gemini may not move Grok. Set up distinct tracking for each major AI platform and monitor which types of queries drive traffic from each source.

Marketing to Humans and Agents

Humans plus machines is no longer theoretical—agents collaborate, robots learn by watching, and household workflows quietly adapt. That raises novel marketing questions: if a humanoid chooses Coke over Pepsi while setting your table, who is the buyer—the person or the agent? Brands will market to both, optimizing for human preference and agent defaults.

Physical Spaces Adapting to Robots

Homes may change for robots as door widths and layouts adapt, just as websites adapted for mobile. Safety and culture training become design inputs, because robots imitate environments. This creates opportunities for companies that design spaces, furniture, and products with both human and robotic users in mind.

New Roles for the AI-Robot Era

This calls for new roles: robot behavior trainers who teach appropriate decision-making, agent experience designers who optimize digital assistant interactions, and provenance product managers who ensure authenticity and trust throughout automated systems. These positions blend technical understanding with human psychology and brand strategy.

The Durable Playbook

Through every shift, the durable playbook remains: align to outcomes, protect trust, design for discoverability, and keep people at the center. AI amplifies existing strategy—it does not replace the fundamentals of understanding your customer, delivering value, and building relationships. Organizations that remember this will navigate technological change successfully, while those chasing tools without purpose will cycle through expensive disappointments.

The winners in the AI era will not be those with the most sophisticated models, but those who deploy technology in service of clear goals, grounded in quality data, and guided by human judgment.

Sandy's latest book: AI First, Human Always: Embracing a New Mindset for the Era of Superintelligence