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How Traditional Enterprises Risk Becoming Mere Inputs to Big Tech

Are the days of mighty industrial titans numbered? Will entire sectors simply become feedstock for the Al value chains of big tech? Complacency in the face of disruptive innovation will determine the answers.

Dr Lisa created in partnership with Microsoft Designer (Copilot)

Artificial intelligence (AI) promises to propel innovation to new heights. Yet, it also presents a critical paradox: traditional companies risk becoming only raw materials in the AI-driven operations dominated by big tech giants. Bottom line, once-dominant enterprises who don’t master the new AI-infused world face a painful future, while those who embrace and harness AI will excel.

In this blog, I tackle this paradox by examining the impacts of AI on manufacturing, energy, and healthcare, as demonstrative industries. I discuss how all sectors risk becoming mere suppliers to big tech's AI ecosystems and offer strategic solutions to avoid this fate.

The AI Talent and Innovation Gap

The race to develop and commercialize AI is concentrating talent, data, and computational resources in a handful of big tech titans like Google, Microsoft, Amazon, and Meta. This concentration of AI prowess is creating an innovation gap that legacy companies across sectors – manufacturing, energy, healthcare, finance, and more – are struggling to bridge.

Enterprises that fail to keep pace risk ceding the high ground of AI innovation to big tech. In this scenario, traditional companies will find themselves reduced to being suppliers of raw materials, data, and other commoditized inputs to the AI value chains controlled by the tech giants. Their role will be peripheral, rather than being leaders and innovators in their own right.

The Paradox in Action

As AI reshapes the market, traditional powerhouses in every industry are facing a stark reality. Once leaders in their fields, they now risk becoming mere suppliers in the AI-driven ecosystems dominated by big tech companies. Below, I explore hypothetical industry situations of how these giants can leverage technology prowess to reduce legacy industries into basic input providers.



Envision a future where traditional automakers have been disrupted by big tech companies and their "software-defined vehicles." These tech giants have gained mastery over Al-driven vehicle design, manufacturing, and distribution through vertically integrated platforms.

A once-mighty automotive company finds itself merely supplying raw materials and components that feed into the tech titans' highly automated micro-factories. Their century of engineering prowess and skilled workforce is now devalued and commoditized.

Al Technologies Used by Big Tech

  • Advanced Robotics andAutomation: Big tech companies utilize robotics for automated manufacturing processes that are faster, more precise, and less costly than traditional methods.

  • Machine Learning Algorithms:These are employed to optimize the production lines and supply chains, reducing waste and downtime.

  • Predictive Maintenance: Al systems analyze data from equipment to predict failures before they happen, significantly reducing maintenance costs and improving efficiency.

Impact on Traditional Manufacturing

These technologies enable big tech to produce higher quality products at lower costs and with greater speed, making traditional manufacturing methods outdated and less efficient, thereby diminishing the market share and relevance of traditional companies.

Value Proposition Before Disruption

  • Core strengths in automotive R&D, design, and manufacturing expertise

  • Competitive advantage from proprietary processes and experienced workforce

  • End-to-end control of the vehicle production value chain

Value Proposition After Disruption

  • Relegated to a supplier of raw materials and commoditized inputs

  • Ceded control over high-value activities like product design and distribution

  • Legacy processes and institutional knowledge no longer differentiated



Picture a world where big tech deployments of Al, robotics, and autonomous systems have optimized energy production and distribution into a highly efficient, vertically integrated ecosystem.

A longtime energy titan that once led in upstream oil and gas exploration and extraction finds its operational expertise obsoleted by automation. It has been reduced to a mere supplier of hydrocarbons feeding the tech giants' distributed micro-refineries.

Al Technologies Used by Big Tech

  • Al-Driven Exploration Tools:These tools use data analytics and machine learning to identify potential oil and gas sites more accurately and quickly than traditional geological methods.

  • Optimization Algorithms for Resource Extraction: Al algorithms analyze various operational data points to enhance the efficiency of extraction processes.

  • Smart Grid Technology: Al manages energy distribution, improving efficiency and integrating diverse energy sources seamlessly, including renewable energy.

Impact on Traditional Energy Companies

By enhancing efficiency and reducing costs, these Al technologies allow big tech to control the energy value chain more effectively. Traditional companies, without similar Al capabilities, risk becoming mere suppliers, providing raw materials to more technologically advanced entities.

Value Proposition Before Disruption

  • Value derived from controlling the upstream energy value chain

  • Leveraged proprietary engineering capabilities and scarce resource access

  • Competitive advantage through specialized exploration and production skills

Value Proposition After Disruption

  • Reduced to a commoditized hydrocarbon extraction and supply role

  • Lost control of midstream/ downstream to tech's integrated ecosystem

  • Legacy human expertise like roughneck skills no longer relevant



Imagine a healthcare landscape where big tech companies have deployed Al-powered "digital hospital" platforms that dictate clinical workflows, diagnosis, and treatment planning across integrated health systems.

A renowned hospital system finds its physicians' decades of training and patient care expertise subjugated to following Al-generated protocols. They are degraded to mere data entry clerks, feeding the tech giants' Al healthcare engines with medical data inputs.

Al Technologies Used by Big Tech

  • Al in Diagnostics: Deep learning models are used to diagnose diseases from imaging data more accurately and much faster than human radiologists.

  • Personalized Medicine: Al algorithms analyze patient data, including genetics, to tailor treatments to individual patients, improving outcomes.

  • Virtual Health Assistants: These Al systems manage routine health inquiries and monitor chronic conditions, reducing the need for frequent physical hospital visits.

Impact on Traditional Healthcare Providers

Big tech's use of Al in healthcare not only streamlines operations but also enhances patient outcomes, making traditional methods seem less effective. Providers that fail to integrate such technologies risk becoming relegated to executing backend, non-patient-facing tasks or simply acting as data collection points.

Value Proposition Before Disruption

  • Delivered personalized care through doctor-patient relationships

  • Competitive advantage from experienced medical staff and reputational value

  • Led innovation in treatment quality and cutting-edge medicine

Value Proposition After Disruption

  • Reduced to a data source providing feedstock for tech's Al clinical solutions

  • Doctors' expertise de-valued, becoming task nodes executing Al directives

  • Lost autonomy over care decisions and stifled ability to innovate

In each case, technological prowess shifted - reducing legacy companies to low-value input suppliers while big tech controls innovation.

The Path Forward

For boards and leaders, actively confronting the AI paradox is critical to prevent their businesses from becoming obsolete. Here are key strategies to consider:

1) Investing in AI Talent Acquisition and Development

To build and maintain a competitive edge in AI, companies need skilled professionals who can develop, implement, and optimize AI technologies. Investing in AI talent ensures that your company can innovate internally rather than rely on external technologies.

2) Forming Strategic Partnerships and Acquisitions to Access AI Capabilities

Partnerships and acquisitions provide immediate access to advanced AI technologies and expertise that may take years to develop in-house. This can accelerate your AI initiatives and help integrate cutting-edge AI solutions into your business operations.

3) Leveraging AI-as-a-Service Offerings from Cloud Providers

AI-as-a-Service (AIaaS) allows companies to use state-of-the-art AI tools without the need for extensive infrastructure investment. This makes AI accessible and scalable, enabling businesses to test and deploy AI solutions with lower risk and investment.

4) Participating in Open-Source AI Communities and Initiatives

Engaging with open-source AI projects can help your teams stay at the forefront of AI developments. It facilitates collaboration with global innovators and researchers, which can inspire new ideas and bring fresh perspectives to your AI strategies.

5) Developing Industry-Specific AI Solutions that Leverage Domain Expertise

Custom AI solutions that are tailored to your industry's specific needs can significantly enhance operational efficiency and effectiveness. This approach ensures that the solutions are directly applicable and beneficial to your core business processes.

6) Upskilling and Reskilling the Workforce to Work Alongside AI Systems

As AI technologies become integral to business operations, the workforce must be trained to work effectively with these new systems. Upskilling ensures that your employees are not only comfortable using AI tools but are also able to enhance their productivity and innovation capabilities through AI.


By embracing these strategies, enterprises can steer towards sustainable innovation and prevent the commoditization of their businesses in an environment where big tech's dominance in AI innovation poses a continuous threat.


The AI revolution is both an opportunity for growth and a serious threat to those who are unprepared. If you're feeling daunted by what seems like inevitable risk and a gap in your AI knowledge, take heart. Those who actively engage with this challenge are the ones who will flourish in an AI-driven landscape. I fully concur with the assessment that, "the shift towards becoming mere suppliers is a risk, but not an inevitability, as companies can adapt and evolve to integrate new technologies into their operations."

As leaders, we have the opportunity to create the future. Now, perhaps more than ever, it's essential to act decisively. "Wait and see" is simply not an option.

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