Hello everyone, and welcome back to the Cognixia podcast! Every week, we bring you new insights into emerging technologies.
We have an absolutely game-changing episode for you today, and we can barely contain our enthusiasm! So, grab your favorite beverage, settle into your comfiest chair, and prepare to dive headfirst into one of the most misunderstood yet critical distinctions in modern technology!
Picture this: you walk into a boardroom tomorrow morning, and the executives are excitedly discussing their new AI strategy. Half the room is talking about implementing chatbots and automation tools, while the other half is debating whether to build their own machine learning algorithms from scratch. Meanwhile, the IT department is frantically trying to figure out which approach will actually deliver results versus which will drain resources into a technological black hole. Well, this scenario is playing out in conference rooms across every industry, every single day! The fundamental misunderstanding between ‘Doing AI’ and ‘Using AI’ is creating massive inefficiencies, wasted investments, and strategic missteps that could determine which companies thrive in the next decade and which become cautionary tales of misguided technological ambition!
Today, we are unpacking this critical distinction and exploring why understanding the difference between these two approaches is not just important — it is absolutely essential for survival in our rapidly evolving digital landscape. We will dive deep into what each approach actually means, examine real-world implications across different industries, and discuss why the next 2-3 years represent the most crucial period for organizations to get this distinction right. Trust us, by the end of this episode, you will understand why this seemingly simple conceptual difference has become the defining factor separating successful AI adoption from expensive technological disasters!
Let us start by establishing what we mean when we talk about ‘Doing AI’ versus ‘Using AI’, because these terms get thrown around with alarming imprecision in business discussions, and the confusion is costing organizations millions of dollars in misdirected efforts.
‘Doing AI’ refers to the fundamental research, development, and creation of artificial intelligence systems from the ground up. This involves building neural networks, training custom models, developing proprietary algorithms, and creating entirely new AI capabilities. Companies that are ‘Doing AI’ are essentially becoming AI research laboratories, investing heavily in data scientists, machine learning engineers, computational infrastructure, and the lengthy experimentation processes required to push the boundaries of what artificial intelligence can accomplish.
‘Using AI’, on the other hand, involves leveraging existing AI technologies, platforms, and services to solve specific business problems or enhance operational capabilities. This approach focuses on integration, application, and optimization of proven AI solutions rather than fundamental research and development. Organizations ‘Using AI’ are strategic consumers of AI technology, applying these powerful tools to drive efficiency, improve decision-making, and create competitive advantages without necessarily understanding or developing the underlying algorithmic complexities.
But here is where things get fascinating — and where most organizations make their first critical mistake. The choice between ‘Doing AI’ and ‘Using AI’ is not just a technical decision; it is a strategic imperative that fundamentally shapes resource allocation, competitive positioning, and long-term business viability. The companies that choose the wrong approach for their specific circumstances, industry dynamics, and organizational capabilities often find themselves either burning through capital on unnecessary research projects or falling behind competitors who leverage AI more effectively.
The misconception that every organization needs to ‘Do AI’ to remain competitive has created one of the most expensive technological fallacies of our time. We are witnessing countless companies attempting to build proprietary AI capabilities when they should be focusing on the intelligent application of existing technologies. Meanwhile, other organizations are limiting themselves to basic AI tools when their competitive advantage actually requires custom AI development that addresses unique industry challenges or proprietary data advantages.
What makes this distinction particularly critical is the dramatically different investment profiles, risk levels, and success metrics associated with each approach. ‘Doing AI’ requires substantial upfront capital, specialized talent that commands premium salaries, extended development timelines measured in years rather than months, and acceptance of high failure rates inherent in fundamental research. Success in ‘Doing AI’ is measured by breakthrough capabilities, patent portfolios, and technological leadership that may not translate into immediate business value.
‘Using AI’, conversely, focuses on rapid implementation, measurable business impact, and return on investment that can be quantified within quarters rather than years. Success in ‘Using AI’ is measured by operational improvements, cost reductions, revenue enhancements, and competitive advantages that directly contribute to bottom-line performance. The risk profile is lower, the timeline is shorter, and the business case is typically more straightforward.
Now, you might be wondering why this distinction matters so much right now, and why organizations across every industry need to understand these differences with unprecedented urgency. The answer lies in the maturation of AI technologies and the emergence of a sophisticated ecosystem that makes high-quality AI capabilities accessible to organizations that previously would have needed to build everything from scratch.
The AI-as-a-Service market has exploded, offering pre-trained models, automated machine learning platforms, and plug-and-play AI solutions that deliver sophisticated capabilities without requiring deep technical expertise. Companies like OpenAI, Google Cloud, Amazon Web Services, and Microsoft Azure provide access to cutting-edge AI technologies through APIs and platforms that can be integrated into existing business processes within weeks rather than years.
This technological maturation creates both enormous opportunities and significant strategic risks. Organizations that understand when to ‘Use AI’ can achieve remarkable competitive advantages quickly and cost-effectively. Those that insist on ‘Doing AI’ when they should be ‘Using AI’ often find themselves developing inferior solutions at exponentially higher costs while their competitors race ahead using proven technologies.
But the reverse is equally problematic. Companies that limit themselves to ‘Using AI’ when their competitive advantage requires proprietary AI development may find themselves constrained by generic solutions that fail to address their unique value proposition. The key is understanding which approach aligns with organizational strengths, industry dynamics, and strategic objectives.
Let us examine how this distinction plays out across different industries, because the implications vary dramatically depending on sector-specific requirements, competitive dynamics, and technological maturity levels.
In the healthcare industry, pharmaceutical companies developing new drug discovery algorithms represent clear examples of organizations that should be ‘Doing AI’. The proprietary nature of drug development, the unique datasets involved, and the potential for breakthrough discoveries justify the substantial investments required for custom AI development. These companies are creating AI systems that cannot be purchased or replicated, and their competitive advantage depends on technological capabilities that do not exist elsewhere.
Meanwhile, hospitals implementing AI-powered diagnostic tools, scheduling optimization, or patient management systems are typically better served by ‘Using AI’. These organizations benefit from proven solutions that have been tested across multiple healthcare environments, rather than attempting to develop custom medical AI systems that would require regulatory approval, extensive testing, and specialized expertise that falls outside their core competencies.
The financial services industry demonstrates this distinction particularly clearly. Investment banks developing proprietary trading algorithms or risk management systems often need to ‘Do AI’ because their competitive advantage depends on unique analytical capabilities that cannot be commoditized. The alpha generated by superior algorithms justifies the substantial investments in quantitative research, data science teams, and computational infrastructure.
However, community banks implementing fraud detection, customer service chatbots, or loan approval automation are almost always better served by ‘Using AI’. These organizations can access sophisticated financial AI solutions through established vendors, achieving comparable capabilities at a fraction of the cost while focusing their resources on customer relationships and community banking excellence rather than technology development.
The manufacturing sector provides another compelling illustration of this strategic distinction. Companies developing autonomous robotics, predictive maintenance systems for unique industrial processes, or AI-driven quality control for specialized products often require custom AI development. Their manufacturing processes, equipment configurations, and quality requirements are sufficiently unique that generic AI solutions cannot address their specific needs effectively.
Conversely, manufacturers implementing supply chain optimization, demand forecasting, or workforce scheduling typically benefit from established AI platforms that have been refined across thousands of implementations. These business challenges are common across manufacturing organizations, and proven solutions often deliver superior results compared to custom development efforts.
The retail industry showcases both approaches simultaneously. E-commerce giants like Amazon clearly ‘Do AI’ through their recommendation engines, logistics optimization, and market prediction systems. Their scale, unique datasets, and competitive requirements justify massive investments in proprietary AI research and development.
Meanwhile, smaller retailers implementing inventory management, customer segmentation, or pricing optimization are usually better served by ‘Using AI’ through established retail technology vendors. These organizations can access sophisticated retail AI capabilities that would be prohibitively expensive to develop internally while focusing their resources on merchandising, customer experience, and market positioning.
The technology sector itself demonstrates the most nuanced applications of this distinction. Software companies whose core value proposition involves AI capabilities obviously need to ‘Do AI’ to maintain competitive differentiation. Companies like Tesla, developing autonomous driving systems, or Spotify, creating music recommendation algorithms, are essentially AI companies whose success depends on proprietary AI development.
However, technology companies using AI for internal operations — customer support, sales optimization, or software testing — typically benefit from ‘Using AI’ solutions that allow them to focus their technical resources on their core product development rather than building internal AI capabilities that do not directly contribute to customer value.
The implications extend beyond individual companies to entire economic ecosystems. Regions and countries that understand this distinction can develop more effective AI strategies that leverage their strengths rather than attempting to compete in areas where they lack competitive advantages. Silicon Valley excels at ‘Doing AI’ because it concentrates technical talent, venture capital, and research institutions. Meanwhile, other regions might be better served by focusing on ‘Using AI’ to enhance traditional industries, improve government services, or solve local challenges using proven AI technologies.
The venture capital and investment community has begun recognizing this distinction, with different funding strategies for companies that ‘Do AI’ versus those that ‘Use AI’. AI research companies require patient capital, longer development timelines, and higher risk tolerance. Companies applying AI to traditional business problems often present more predictable investment profiles with clearer paths to profitability and faster returns.
This investment pattern recognition creates important implications for entrepreneurs and business leaders considering AI strategies. Understanding whether your business model aligns with ‘Doing AI’ or ‘Using AI’ affects funding requirements, talent acquisition, competitive positioning, and go-to-market strategies. Misalignment between business model and AI approach often leads to funding difficulties, talent mismatches, and strategic confusion that can derail otherwise promising ventures.
The talent implications of this distinction cannot be overstated. ‘Doing AI’ requires rare and expensive talent — PhD-level researchers, experienced machine learning engineers, and data scientists with deep theoretical knowledge. This talent commands premium salaries, requires specialized management approaches, and often prioritizes research challenges over business applications.
‘Using AI’ typically requires different skill sets — integration specialists, business analysts who understand AI applications, and project managers who can implement AI solutions effectively. This talent is more accessible, commands lower salaries, and often focuses on practical problem-solving rather than theoretical advancement.
Organizations that misunderstand their talent requirements often find themselves either unable to attract the researchers they think they need or wasting research talent on implementation challenges that could be solved more efficiently by application specialists. The talent market is increasingly sophisticated about these distinctions, and compensation expectations vary dramatically depending on whether roles involve AI research or AI application.
The competitive dynamics surrounding this distinction are intensifying as AI capabilities mature and become more accessible. First-mover advantages in ‘Doing AI’ can create sustainable competitive moats, but these advantages require continuous investment and innovation to maintain relevance. First-mover advantages in ‘Using AI’ are typically shorter-lived but can provide crucial market positioning during critical business transitions.
Companies that attempt to ‘Do AI’ without sufficient resources, expertise, or strategic justification often find themselves developing inferior solutions while competitors using proven AI technologies capture market opportunities. Conversely, companies that limit themselves to generic AI solutions when proprietary capabilities could provide sustainable advantages may find themselves commoditized by competitors with superior AI integration.
The regulatory and compliance implications also vary significantly between these approaches. Companies ‘Doing AI’ often face additional scrutiny regarding algorithm development, bias testing, and explainability requirements. They may need to engage with regulatory bodies, participate in industry standards development, and invest in compliance capabilities that extend beyond their core business operations.
Companies ‘Using AI’ typically benefit from compliance efforts made by their AI vendors, though they remain responsible for appropriate implementation and monitoring. This distinction affects legal strategies, compliance budgets, and regulatory risk management approaches across different industries and jurisdictions.
Looking toward the immediate future, the next 24-36 months will be crucial for organizations to align their AI strategies with realistic assessments of their capabilities, competitive requirements, and market opportunities. The companies that make this distinction correctly will capture disproportionate value from AI adoption, while those that misalign their approaches will struggle with inefficient resource allocation and suboptimal outcomes.
The democratization of AI capabilities through cloud platforms, pre-trained models, and automated machine learning tools will continue making ‘Using AI’ more accessible and powerful. Simultaneously, the AI research community will continue pushing the boundaries of what is possible, creating new opportunities for organizations with legitimate reasons to ‘Do AI’.
The strategic imperative is not choosing between these approaches based on technological enthusiasm or competitive fear, but rather conducting honest assessments of organizational strengths, market requirements, and resource constraints. The most successful organizations will be those that make this choice deliberately, execute their chosen approach effectively, and remain flexible enough to adjust as circumstances evolve.
As we wrap up today’s exploration of the critical distinction between ‘Doing AI’ and ‘Using AI’, remember that this is not merely an academic exercise or technical discussion. This distinction represents a fundamental strategic choice that will determine which organizations thrive in our AI-driven future and which struggle with misaligned investments and unrealized potential.
The companies that understand their appropriate relationship with AI technology — whether as creators or consumers — will be positioned to capture enormous value from artificial intelligence capabilities. Those who pursue AI strategies based on misconceptions or competitive anxiety rather than clear strategic thinking will likely find themselves disadvantaged regardless of their investment levels.
And with that crucial strategic insight, we come to the end of this week’s episode of the Cognixia podcast. We hope you have gained valuable clarity on this essential distinction that will shape technology strategy for years to come! Remember, in our rapidly evolving technological landscape, the intersection of strategic thinking and artificial intelligence creates opportunities and challenges that will affect every organization.
Stay curious, stay informed, and keep an eye on how this fundamental distinction continues influencing AI adoption across industries and sectors. We will be back again next week with another fascinating and thought-provoking episode of the Cognixia podcast. Until then, happy learning — and maybe take a moment to evaluate whether your organization’s AI strategy aligns with the distinction between ‘Doing AI’ and ‘Using AI’ that could determine your competitive future!
