The New Operating Model: Why CEOs Must Adopt Algorithmic Thinking

Severin Sorensen
Jun 13
4 min read

For decades, businesses were modeled like machines: linear, process-driven, and built to run with mechanical precision. But in today’s world of real-time data, intelligent automation, and rapid iteration, this metaphor is breaking down. The winners in today’s economy don’t operate like factories. They operate like algorithms.

That may sound like a stretch, but consider this: Netflix refines its product based on real-time viewer data. Amazon tweaks its logistics and product mix based on user behavior patterns. Tesla pushes updates to its cars like a software company, learning from every mile driven.

What these companies have in common is algorithmic thinking. Their leaders treat their businesses not as rigid machines, but as living systems—fed by inputs, governed by decision rules, shaped by feedback, and designed to learn.

What It Means to Treat Your Business Like an Algorithm

Algorithms are step-by-step processes for solving problems. They:

Take inputs (data, signals, resources),
Apply a logic layer (rules, heuristics, models), and
Produce outputs (actions, decisions, results).

In a business context, the mapping looks like this:

Inputs: Customer insights, capital, human talent, culture, operational data
Logic Layer: Your strategy, decision-making frameworks, policies, and AI systems
Feedback Loops: KPIs, dashboards, performance reviews, customer satisfaction, AI model tuning
Iterations: Quarterly reviews, product updates, org redesigns, hiring pivots

Thinking algorithmically means designing your business to process information and continuously improve—not just to run routines.

Five Benefits of Algorithmic Thinking for CEOs

When CEOs adopt algorithmic thinking, they unlock a set of strategic advantages that drive clarity, speed, and scalability across the business. Specific benefits of algorithmic thinking include:

Faster, Smarter Decision-Making: Treating your business like an algorithm forces clarity around decision inputs and logic. This makes your team faster and more aligned. It also allows you to test assumptions, simulate scenarios, and remove bottlenecks.
Adaptability in Fast-Changing Markets: The best algorithms adapt. They retrain with new data, spot anomalies, and evolve. When your business is structured with feedback loops and responsive logic, you can shift faster than competitors.
Scalability Without Chaos: Manual decisions don’t scale. Algorithmic systems do. When your operations are modeled with clear rules, automations, and checkpoints, you can grow without losing control or clarity.
Clearer Accountability: Algorithms expose performance. You know which inputs are working, where the logic is flawed, and what outcomes need tuning. This leads to better metrics, clearer ownership, and sharper performance reviews.
Stronger AI Integration: AI thrives in structured, feedback-rich environments. If your business already behaves like an algorithm, you can apply AI to optimize specific functions. If not, AI will only amplify inefficiencies.

The Cost of Ignoring Algorithmic Thinking

Organizations that fail to adopt algorithmic thinking incur compounding costs across multiple dimensions. Consider the following:

Lost Growth and Competitive Ground: Top-performing companies deploy AI and data use cases at a rate four times higher than others, generating financial returns up to five times greater (Baltassis, 2024). This growing disparity shows how early movers are accelerating away from the rest—making inaction a competitive liability.
Poor ROI from AI Initiatives: Almost half of CIOs report that AI investments haven’t met expectations (Wilkinson, 2024). This often stems from implementing AI without proper data readiness or strategic clarity. High-maturity companies, by contrast, are seeing nearly five times the growth and outperform peers by over 15%—a gap set to widen by 2026 (Accenture, 2024).
Macro-Level Missed Opportunity: At the national scale, AI adoption could double GDP growth in developed economies by 2035 (WSJ, n.d.). This illustrates the broader economic upside—and the corresponding opportunity cost at the company level—for those not embracing algorithmic transformation.
Operational Inefficiencies and Wasted Resources: A McKinsey case study found that an insurance firm saved 50% in costs and halved the timeline by using “data products” instead of traditional approaches. This highlights how smarter, AI-powered operations extract more value, faster (Tavakoli, 2025).

Top 5 Considerations When Treating Your Business Like an Algorithm

Treating your business like an algorithm is powerful, but it requires thoughtful design. These five considerations help ensure your systems remain effective, ethical, and adaptable as they scale:

Stay Iterative, Not Rigid: Treat your logic like code: version-controlled, regularly tested, and open to improvement. Algorithms must evolve.
Balance Efficiency with Adaptability: Optimization is seductive—but beware of squeezing out slack, innovation, or redundancy that protects you during disruption.
Keep the Human Loop Alive: Your culture, values, and coaching infrastructure must remain strong. AI and automation can’t replace emotional intelligence or ethical oversight.
Audit for Bias in Inputs and Rules: Garbage in, garbage out. Ensure your data sources, assumptions, and logic don’t reflect outdated or exclusionary patterns.
Make the Logic Visible: If your team can’t see or question the algorithm behind decisions, you lose transparency and accountability. Surface the logic to foster trust and innovation.

How to Start: A CEO's Algorithm Audit

Ask yourself:

What are my most important business inputs?
Are our decision-making rules visible, or locked in people’s heads?
Where are feedback loops missing or broken?
Are our KPIs driving surface outcomes or system health?
How often do we revise our assumptions and processes?

Final Thought: You Already Think This Way (Sort Of)

If you’re a strategic CEO, you already think in cause and effect, in systems and processes. Algorithmic thinking is just a sharper lens—a way to make your business more responsive, intelligent, and future-ready.

In a world where AI is reshaping competition, the best leaders won’t just use algorithms. They’ll build companies that behave like them.

References

Accenture. (2024). Going for growth Navigating the great value migration in the age of AI. https://www.accenture.com/content/dam/accenture/final/accenture-com/document-3/Accenture-Going-for-Growth.pdf

Baltassis, E., Quarta, L., Yassine Khendek, Fernández, M., Miguel Monedero Rubio, Sylvain Duranton, Lukic, V., & Schuuring, M. (2024, September 16). Leaders in Data and AI Are Racing Away from the Pack. BCG Global. https://www.bcg.com/publications/2024/leaders-in-data-ai-racing-away-from-pack

Tavakoli, A., Holger Harreis, Kayvaun Rowshankish, & Klemens Hjartar. (2025, April 23). The missing data link: Five practical lessons to scale your data products. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-missing-data-link-five-practical-lessons-to-scale-your-data-products

Wilkinson, L. (2024, October 21). Gartner sounds alarm on AI cost, data challenges. CIO Dive. https://www.ciodive.com/news/gartner-symposium-keynote-AI/730486/

WSJ. (n.d.). Why AI Is the Future of Growth. WSJ Custom Studios. https://partners.wsj.com/accenture/breaking-through/ai-future-growth/