The Digital Distance Framework for AI Leaders

Why proximity decisions determine whether AI amplifies your leadership or undermines it

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Most leaders think they’re using AI to get closer to results, but they may be creating gaps they can’t see. In fact, leaders make 15-20 AI proximity decisions weekly without realizing it.

Picture this: You’re standing in your kitchen, watching a master chef prepare a meal. Every cut, every seasoning choice, every timing decision comes from years of experience and every discussion comes from a human connection and emotional base. Now imagine replacing that chef with a perfectly programmed robot that can replicate the recipe flawlessly. The food might taste identical, but something fundamental has been lost. The intuition. The ability to adapt when the tomatoes aren’t quite ripe. The wisdom that comes from a thousand small failures. The relationship to others.

This is what Eric Barberio calls the Digital Distance. Eric, is a colleague and an alumnus of Said School of Business at Oxford and a member of Oxonian Ventures, a VC fund focused on investment, mentorship, and support for Oxford University sourced startups. As a twenty-plus year executive in finance, operations, and management consulting, and specialized in AI ecosystems with experience across startups and Fortune 500 companies, Eric introduced me to this concept that’s been reshaping how I think about AI integration.

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As leaders, we’re making proximity decisions about AI integration every single day, often without realizing it. We’re choosing when to let AI handle tasks and when to keep them human. But here’s what’s happening: we may be accidentally creating two perilous gaps. First, we’re putting digital barriers between ourselves and the people we serve. Second, we’re outsourcing the very thinking that makes us effective leaders in the first place.

The answer isn’t to avoid AI or to embrace it completely. Generative AI and AI in all of its forms is not a panacea. It’s about understanding proximity. Some AI decisions should bring us closer to our mission and our people. Others should stay at a strategic distance. The difference between these two approaches will determine whether AI amplifies your leadership impact or quietly undermines it.

In this article, you’ll discover:

• How to evaluate every AI decision using Eric’s Digital Distance Spectrum Framework

• Four complete business model examples that show proximity in action

• The hidden workforce development crisis that most leaders miss

• When to keep AI close versus when to create strategic distance

• A systematic approach to protecting both relationships and expertise

The Digital Distance Framework

The Digital Distance Framework cuts through the complexity with precision. Eric’s insights from mapping the evolution of AI ecosystems across organizations and applying the fundamentals ingrained in business transformation in companies of every size reveal something crucial: every AI decision you make as a leader now falls somewhere on two critical spectrums.

Digital Distance is about the technology-rooted gaps that AI can create for humans.

Automation and autonomy aren’t new, but the pervasive impact of AI, when not designed carefully, may separate human connection to customers and coworkers in unintended ways. Another gap develops where overreliance on “reasoning” or analytical output creates cognitive separation from our own expertise, knowledge retention, and unique perspectives.

Every leadership decision about AI integration falls somewhere on these two axes. The horizontal axis represents Customer Digital Distance - how far your AI implementation takes you from direct customer and stakeholder connection. The vertical axis represents Cognitive Digital Distance - the degree to which AI handles your thinking and expertise.

Understanding how to solve problems and transform operations at a task-by-task level using AI technologies can proactively avoid these digital distances or embrace them where strategically viable. Let’s examine each spectrum through complete business model examples.

Four Business Models That Show Digital Distance in Action

Source: E Barberio

Quadrant 1: Human-Delivered & Human-Driven (Low AI Cognition, Low Customer Autonomy)

The Concierge Primary-Care Practice Model

This is where human expertise and connection remain central. Think of a concierge medical practice where patients pay membership fees for highly personalized, relationship-based care. Phone scheduling happens through real people. In-person exams are conducted by clinicians who make all diagnoses and treatment plans without AI assistance.

The business model may use AI tools for diagnostic research support, but these don’t significantly impact the human-delivered work where reliance on decision-making is outsourced. The value proposition rests entirely on the depth of human relationships and clinical judgment.

Task Example: Nurse Triage by Phone

• The nurse manually assesses symptoms, prioritizes urgency, and books appointments

• Every decision involves human reasoning and personal interaction

• AI stays completely in the background, if present at all

This quadrant works when your competitive advantage comes from human expertise, trust, and personal relationships that can’t be replicated by machines.

Quadrant 2: Autonomy of Routine Tasks (Low AI Cognition, High Customer Autonomy)

The Self-Service Restaurant Model

Here, customers interact primarily with technology, but the “intelligence” behind the system is mostly rules-based rather than cognitive AI. Picture a quick-service restaurant with self-order kiosks integrated with customer profiles and order history. The value proposition focuses on faster throughput, fewer order errors, and lower labor costs for ordering.

Guests place orders through kiosks or apps following deterministic menus, options, and promotional rules. The system expands food service automation while reducing human interactions, but without complex AI reasoning.

Task Example: Airline Self Check-In Kiosk

• Passengers scan ID, select seats, and print boarding passes

• High interaction autonomy with minimal AI reasoning required

• Process follows clear, predictable rules

This quadrant excels for standardized processes where consistency matters more than creativity, and where customers value speed and control over personal interaction.

Quadrant 3: Outsourcing Cognition to AI (High AI Cognition, Low Customer Autonomy)

The AI Underwriting Co-Pilot Model

This is where AI handles complex reasoning while humans maintain customer relationships. Consider a B2B SaaS platform that provides AI underwriting support for commercial insurance carriers. The AI ingests submissions, prior losses, and third-party data to propose coverage terms, pricing recommendations, and appetite flags.

The reasoning - risk scoring, pricing recommendations, exclusions - gets delegated to AI systems that can mimic increasing levels of underwriting skills and experience. But brokers and clients still interact with human underwriters who review, interpret, and negotiate based on AI recommendations.

Task Example: Quote Prep Recommendation

• AI summarizes submissions, scores risk drivers, and proposes pricing terms

• Human underwriter reviews AI analysis and negotiates with the broker

• High cognitive outsourcing combined with human-led customer interaction

This quadrant works when you need to scale expertise rapidly while maintaining the trust and nuanced judgment that comes from human relationships.

Quadrant 4: High Autonomy, Decreased Human Dependency (High AI Cognition, High Customer Autonomy)

The Fully Digital Robo-Advisor Model

Here, both cognition and customer interaction become automated, creating minimal human dependency. Picture a robo-advisor with an AI financial agent that provides 24/7 tailored advice, instant onboarding, and continuous optimization.

The AI converses to understand goals and risk tolerance, selects portfolios, rebalances investments, handles tax-loss harvesting, and answers service questions - all without human intervention by default. The business model delivers predictive analytics in real-time, blended with customer insights for a fully autonomous investment strategy and portfolio management.

Task Example: Autonomous IRA Setup & Funding Plan

• AI agent gathers goals and risk tolerance independently

• Selects target allocation and opens accounts automatically

• Schedules monthly transfers and only escalates to humans when needed

This quadrant succeeds when standardized expertise can be systematized and when customers prefer convenience and cost savings over personal relationships.

The Hidden Cost Most Leaders Miss

Here’s what Eric’s framework reveals that most leaders don’t see coming: the workforce development crisis.

As AI systems are expanded to greater levels of decision-making and customer engagement, the next generation of workers may lack the depth of exposure their predecessors had. One emerging concern is the reduced opportunity for new professionals to gain experiential knowledge.

Eric noted “I’ve seen this personally in my eleven years in management consulting. It’s becoming easy to see a path that could automate significant amounts of work from project plan development to data analysis that many interns or early career consultants would typically perform.”

If the rate of automation reduces reliance on humans for this work, two things happen:

1. Existing team members may over-rely on AI outputs, gaining new knowledge at a slower or declining rate

2. New people entering the consulting workforce may not get properly trained, lacking hands-on exposure to the tasks and trial-and-error iterations that build the knowledge needed to replace seasoned consultants.

Additionally, automating customer interactions risks weakening what sociologist Allison Pugh terms “Connective Labor” - the undeniable human effort involved in building trust and empathy that creates lasting relationships.

Research shows that cognitive retention is markedly different (lower) for groups depending entirely on AI output compared to those using their own reasoning first. We’re not just changing how work gets done. We’re changing how expertise gets built. And we could be changing the amount of human interaction at the same time.

The Proximity Decision Framework: When to Stay Close vs. Create Distance

The most effective leaders aren’t asking “Should we use AI?” They’re asking “Where should AI stay close, and where should it keep its distance?”

When AI Should Stay Close (Skill Amplification)

The lower-left quadrant represents the sweet spot for many business models: human-driven delivery with focused cognitive support. This is where AI amplifies without replacing.

As Eric notes, when you reach a crossroad of completely autonomizing a cognitive task, remember: humans can make errors, and an AI system can make errors too (context, conflicting rules, spontaneity, things generative AI does not do well). But the reaction, the adjustment, trust and emotional embodiment of how to proceed is likely best left with humans after weighing the significance and nuances inherent in a given task or decision.

AI works best when it stays close during:

• Complex problem-solving where human judgment interprets AI analysis

• Strategic planning where AI provides data but humans provide wisdom

• Customer relationship building where AI offers insights, but humans create connection

• Skill development where AI supports learning rather than replacing staff

When AI Should Stay Distant (Strategic Autonomy)

The upper-right quadrant shows where full automation makes sense: routine tasks with high degrees of autonomy and decreased human dependency.

Eric’s insight: “Well-scripted routine tasks” are prime candidates for distant AI implementation.

AI distance may be best for:

• Standardized processes with clear inputs and outputs

• High-volume data processing where consistency matters more than creativity

• Routine customer inquiries that follow predictable patterns

• Administrative tasks that don’t build competitive advantage

The key is intentional placement on both spectrums, not accidental drift toward either extreme.

Leading Through Digital Distance

The most dangerous digital distance is the one you don’t see coming.

Every AI decision you make creates proximity. The question isn’t whether AI will change how you lead, but whether you’ll lead those changes intentionally.

McKinsey’s research on AI implementation shows that productivity gains of 30-45% are possible in customer service when human oversight maintains the right balance with automation. The companies seeing these results aren’t eliminating human touchpoints - they’re optimizing them strategically using frameworks like the Digital Distance model.

This isn’t about being anti-AI or pro-human. It’s about being pro-mission. Your mission deserves the best of both human wisdom and AI capability, deployed with the kind of intentionality that creates lasting impact.

The leaders who master digital distance don’t let AI happen to them. They happen to AI, with frameworks that protect what matters most while amplifying what serves their people best.

💡 Copy-Paste Prompt:

I’m a [leader type] evaluating digital distance in my organization. Based on the following process or task: [INSERT SPECIFIC TASK/PROCESS], help me create an evaluation that includes:

Clear proximity recommendations using the Digital Distance Framework (close vs. distant AI integration)

Specific risks to watch for on both the customer and cognitive spectrums

Implementation phases that protect both relationships and expertise

Success metrics to measure digital distance impact

Then, use Infinite Prompting. Generate 15 artifacts that help you deeply explore the topic, build momentum, and uncover insights I may have missed. Each artifact should build on the previous one and include:

A distinct reasoning style, framework, or paradigm

Deep exploration and rigor

Connections to real-world applications or implications

Format the output in clear sections with bullets and label each part. Use emotionally intelligent, strategic, and faith-aware language. Keep it practical.

If You Only Remember This

• Every AI decision creates proximity - use Eric’s Digital Distance Framework to make these choices intentionally rather than accidentally

• Four quadrants require different strategies - human-delivered, routine autonomy, cognitive outsourcing, and full automation. Each serves a different purpose

• The biggest risk is workforce erosion - loss of expertise, development, and connective labor happens gradually, then suddenly

• Strategic boundaries create better outcomes - knowing when NOT to use AI is as important as knowing when to use it

• Your mission guides proximity - every AI decision should move you closer to your core purpose, not further from the people you serve

How are you currently evaluating digital distance in your leadership decisions?

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