Building AI First Businesses
The Structural Resilience Metaphor for Business
I read J. E. Gordon’s seminal work, Structures: Or Why Things Don’t Fall Down. It teaches us how materials and buildings endure stress, distribute loads, and stay upright. It struck me (during a long run, as most things do), how historically, we’ve applied these lessons to traditional enterprises by emphasizing redundancy of resources, safety factors, modularity, and adaptability in our org design. Now, Generative AI and agentic tools offer entirely new “materials” for building organizations. This challenges many of our (well at least my own) assumptions about how businesses should be designed and managed.
Several questions arise ( on said long run, I chatted extensively to Open AI using audio and discussing the book’s applicability to business) when we extend structural principles to modern enterprises. First, we need to clarify how fundamental engineering concepts map onto business structures. Next, we should consider what happens when GenAI replaces or significantly augments entire departments. And finally, we must consider how people, and their roles, remain productive and relevant in an ecosystem where AI can handle a growing number of tasks.
Core Structural Principles and Their Business Equivalents. In structural engineering, strength and stiffness refer to a material’s ability to withstand loads without breaking or bending. In business, this becomes the core competencies and brand equity that keep a company from collapsing under competitive pressure. Stress and strain, expressed in structures as σ=F/A reveal how force over a given area leads to potential deformation. Engineers design for strength and flexibility, and often the best way to prevent failure of a structure, is designing in flexibility and use of the right materials. In a corporate context, ‘Business Stress’ arises from market competition and regulatory changes, while ‘area’ corresponds to resources, organizational systems and brand strength.
A safety factor in construction provides a margin to prevent catastrophic failure. In business, this principle manifests as risk management, buffer capital, and contingency strategies - methods designed to absorb unforeseen shocks. Redundancy in buildings typically relies on multiple beams or backup systems. By analogy, diversified revenue streams and cross-trained teams protect businesses from critical single-point failures. Buckling in slender columns warns us that rigid or over-optimized structures can collapse under pressure, much like lean organizations risking implosion if their environment shifts suddenly. Modularity and material efficiency emphasize components that are easy to repair or replace. In a modern enterprise, this translates into agile organizational designs, decentralized teams, and microservices in tech architectures, all of which enable quick pivots in fast moving markets.
Building the ‘Business Structural Engineering’ Paradigm. The Resilience of a business enters the Board meeting as ‘Risk Management’. The board pack covers opportunities and risks, leaving directors and leaders to discuss the level of potential ‘stress’ and what to do about it. To quantify ‘resilience’, we can define a Business Resilience Index (BRI) learning from building engineering methods.
First, we measure Business Stress ‘σb’ borrowing from the structural equation above. Where Forces(F) represents competitive forces and Area (A) represents operational capacity. We can also establish a Business Safety Factor (BSF) by dividing maximum sustainable capacity of our business by its current operational load. e.g. The market wants 10K units of our product but we can only deliver 8K, and are cash constrained. From there, the BRI itself becomes:
In this equation:
BSF (Business Safety Factor) represents financial reserves and risk mitigation.
RI (Redundancy Index) captures the degree to which operations and revenue streams ( products and customers) are diversified.
MF (Modularity Factor) reflects the ability of an organization to reconfigure quickly—often through semi-autonomous teams or flexible processes.
II (Innovation Index) tracks R&D investment and product pipelines.
MAI (Market Adaptability Index) measures speed and effectiveness in adapting to changes.
A high BRI score indicates your company is resilient and can withstand sudden shocks and adapt rather than collapse.
People as the Structural Elements. Despite the focus on processes and strategic moves, people (before GenAI was invented at least) remain the core ‘load-bearing beams’ in most organizations. Leadership and culture serve as the foundation, anchoring the company the way footings anchor a tall building. Specialized experts function as load-bearing beams who hold critical knowledge and sustain high-responsibility tasks. Cross-functional connectors act as reinforcement layers, preventing local problems from spreading. Agile problem-solvers serve as flexible joints, filling gaps and adjusting, even across departments & geographies as needed. Redundant systems appear in backup talent and cross-training, ensuring that if one key individual is unavailable, the entire operation does not fail. Modularity allows orgs to adopt decentralized or semi-autonomous teams that can be reorganized or replaced without upending everything else. This model changes as we consider how GenAI changes traditional roles.
Enter GenAI and Agentic Agents: The New ‘Building Material’. The emergence of GenAI rips up this structural blueprint analogy with people as the key structural design elements. AI systems can substitute for human load-bearers by taking over data entry, customer support, content creation, product development, market research, and routine analytics. Instead of focusing on cross-training large teams, organizations can spin up parallel AI instances for redundancy and failover at a fraction of the cost. This shift also flattens hierarchies, because many tasks performed by middle managers such as horizontal coordination and oversight, can now be automated by agentic AI. The speed of adaptation increases dramatically, as AI-driven processes re-route in real time rather than waiting on monthly or quarterly review cycles. This is not some time in the future, this is now: Example tools enabling this include Open AI’s “Operator”, Lindy.AI, Zapier agents, & our own Quickaction.ai business at Blenheim Chalcot. All are enabling this new paradigm. Right now.
A new emergent risk is ‘AI buckling’ where a single-vendor or single-model dependency leads to complete business failure if the AI goes down or becomes overtaken by a much better model. Moreover, cybersecurity becomes the new safety factor, demanding investments that protect models from hacking and data poisoning. Organizations that ignore these dangers risk building an AI-driven house of cards, with zero protection of the cumulative value created, stable only until a single point of failure topples the entire structure.
Reconciling People and AI. This is the harded one for me to comprehend that there will not be mass role redundancies. In an AI-driven world, some people do remain crucial, and the leverage that top creative talent can give you multiplies; albeit in different capacities. Meta-level strategists (Leadership 2.0) will orchestrate AI workflows, define ethics, and handle work with 5% of the previous team size but 100X the productivity. Creative innovators will ensure new ideas align with brand values and moral standards, an area where AI needs human guidance. System maintainers and redundancy architects will function like civil engineers, safeguarding data integrity and designing fallback strategies. Meanwhile, customers will still value personal rapport and empathy, so relationship builders retain a distinctly human role, even if AI handles day-to-day queries. Face to face services will become premium.
Revising Business Resilience in an AI-Dominated World. We can adapt the original formula above to reflect the growing importance of AI in overall business resilience.
BSF (Business Safety Factor) represents financial reserves and risk mitigation.
RI (Redundancy Index) captures the degree to which operations and revenue streams ( products and customers) are diversified AND the reliability, redundancy and adaptability of AI models.
MF (Modularity Factor) reflects the ability of an organization to reconfigure quickly—often through semi-autonomous teams or flexible processes AND AI Agents that can be quickly deployed or taken offline.
II (Innovation Index) tracks R&D investment and product pipelines AND the speed AI discovers new opportunities.
MAI (Market Adaptability Index) measures speed and effectiveness in adapting to changes AND real time pivots driven by continuous data streams.
Nature’s building principles remain true. The building principles I read remain relevant, but AI transforms people’s roles profoundly. People are not becoming obsolete; they are evolving into roles that emphasize strategy, ethics, and creative insight, with AI handling repetitive tasks. Redundancy, safety factors, and modularity still matter, although organizations will now replicate AI agents or employ multiple vendors rather than simply cross-training people. Risk management involves more than financial buffers-it increasingly includes cybersecurity and model reliability. The pace of innovation speeds up as AI prototypes products and processes at unparalleled rates. Hierarchies will flatten, replaced by agile ‘pods’ or micro-teams supported by AI-driven workflows. Finally, ethical and social considerations become structural vulnerabilities ( e.g. your product could get ‘cancelled’ like a celebrity does on social media). As AI grows more powerful, leaders need to more carefully consider ethical guardrails into their foundational strategies. Not typical for start ups trying to ‘move fast and break things.’
What Might the AI Start Up Business of the Future look like?
If you were starting from scratch, this is what you would consider:
AI as Core Infrastructure. AI isn’t an add-on—it’s the backbone of the business doing 90% of the work, with failover AI models and self-improving AI loops ensuring resilience.
Modular AI Workflows. Business operates like an AI OS, with replaceable AI modules. Instead of departments, we will use modular AI workflows. AI models will specialize in different functions. E.g. HR can use it to select and vet candidates, handle skills assessment and pay negotiations. Each aspect is an AI module, and can be swapped or improved without effecting the whole work flow.
Redundancy & Failover AI. No single AI model should control everything.
Elasticity & Self-Improving AI. AI self-corrects mistakes and continuously improves, this will help companies build moats by training their own version of popular models and accumulate unique data sets too. E.g. AI can automate marketing campaign creation, A/B testing, and performance tracking to improve future campaigns. We are doing this already at our venture Contentive.com.
AI-Driven Strategy Optimization. AI continuously tests, refines, and adapts business models. Bringing AI into management meetings and Board rooms as a colleague is already happening and its collective intelligence should be fed so it builds up the equivalent of a longitudinal medical record over time.
Continuous Evolution. The business must be designed for constant iteration and learning. AI can access and process enormous data pools in real time & suggest pricing improvements, revised business models and new products for development.
The outcome (if you get it right) is a resilient business ( and people) built to adapt, with the best engineering principles from nature and engineering applied. One that scales infinitely, operates autonomously, adapts to real-time market shifts, and never stops evolving.
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