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How to choose GenAI for business: efficiency over complexity

With GenAI predicted to boost global economy, seek best-fit AI models focusing on efficiency and cost-effectiveness, involving MIN strategy.

Generative AI (GenAI) has been hailed as a game-changer. Proponents say it will add $7 trillion dollars to the global economy1 while improving speed — up to 35%2 — and the quality of skilled workers’ output — up to a 40%3 increase. Unsurprisingly, business leaders are pursuing the smartest, most advanced AI solutions.

In their pursuit, business leaders might be wondering: What is the best-in-class model? What is a typical result? How is it measured? It is becoming increasingly evident there is no one-size-fits-all answer. Business context and knowledge are incredibly important for differentiated performance and outcomes. In response, startups with highly specific capabilities are emerging with voice-based customer support for service-based businesses, blog post copy generation and biological molecule design. And many businesses are investing in their own custom-built applications, which can lead to better performance and tailored security standards (on premises, in cloud), but can also lead to increased cost.

With no shortage of options, yet no obvious path to value, how can business leaders determine which AI capabilities are right for their organization? Maybe it requires a change in perspective. Instead of seeking the most advanced AI technology for every business issue, focus on efficiency, asking: What is the minimum intelligence necessary (MIN) for a particular task?

A definition of MIN

Minimum intelligence necessary (MIN) is a concept EY professionals created for the field of artificial intelligence (AI), robotics and intelligence-based software design. It refers to the idea of equipping a system with the right amount of intelligence to perform a specific task or set of tasks efficiently, without involving unnecessary complexities or compute resources. The concept of MIN promotes the idea of streamlined, targeted intelligence. By doing so, it increases efficiency, reduces costs and limits potential risks related to overcomplexity and unpredictability.

MIN is a multifaceted approach that allows us to make architectural decisions efficiently and consistently. It consists of three integral dimensions:

  1. The aptitude assessment, which involves evaluating various AI models against AI and non-AI alternatives, such as human intervention or robotic process automation, verifies that the best solution is utilized for each task
  2. Hardware harmonization, which assigns the right hardware to specific tasks for optimal performance and resource utilization
  3. Data decentralization, which entails bringing AI algorithms closer to the data source, significantly cutting down data movement, speeding up processing and reducing costs

These three dimensions work together to provide efficiency, effectiveness and cost-effectiveness in deploying AI solutions. MIN understands the computational demands of diverse models, aligns resources with task complexity and provides robust cost-effectiveness.

When considering AI approaches, instead of chasing the purported best solution, find the best fit for your organization — the result might surprise you.


Generative AI is poised to revolutionize industries, potentially adding $7 trillion to the global economy and enhancing professional productivity significantly. Leaders seek top AI solutions, yet the quest for a universal best model is futile; success hinges on contextual application and specialized capabilities, as demonstrated by emerging startups. Custom in-house AI can offer improved performance and bespoke security, at a cost. The concept of MIN suggests prioritizing efficiency over advanced AI for tasks, considering an AI’s aptitude, hardware optimization and data proximity for cost-effective, efficient AI deployment. Fitting AI to organizational needs may yield unexpectedly optimal outcomes.

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