The AI Bottlenecks: A Modern Take on the Theory of Constraints

In the 1980s, Eliyahu (Eli) Goldratt's seminal book "The Goal" introduced the Theory of Constraints, a revolutionary approach that highlights how the throughput of any system is limited by its most critical bottleneck.

https://www.tocinstitute.org/the-goal-summary.html

Fast forward to today, and businesses are racing to adopt artificial intelligence (AI) to transform their business models and gain a competitive edge. However, as with any system, the AI revolution is not without its constraints. Just as Goldratt identified key bottlenecks that limit a system's productivity, the current wave of AI adoption faces four primary constraints that could throttle its progress: the availability of data to train large language models (LLMs), the necessary computational power to process this data, the availability of electrical power to support these demanding operations, and the workforce talent, knowledge, and skills needed to develop and deploy these AI capabilities effectively.

Understanding and addressing these constraints is crucial for businesses aiming to leverage AI to achieve and enhance their strategic goals.

In Eli Goldratt's manufacturing context, the Theory of Constraints taught us that a system's output is only as strong as its weakest link. Today, as we navigate the transformative power of AI, a similar principle emerges: the success of the AI revolution hinges on overcoming a set of interconnected constraints as described in this article.

Axios: Behind the Curtain: AI's ominous scarcity crisis https://www.axios.com/2024/05/28/ai-power-energy-data-chips-talent

Data Availability: Large language models (LLMs) are the engines of modern AI, but they are fuelled by massive amounts of data. The availability, quality, and diversity of this data directly impact the capabilities of these models. Just as a factory can't produce goods without raw materials, LLMs cannot learn and generalize without a rich and varied dataset. Many industry analysts are describing a date in the future where current data sources for training LLMs are exhausted and the models begin to rely on synthetic data.

Forbes: The Pros And Cons Of Using Synthetic Data For Training AI https://www.forbes.com/sites/forbestechcouncil/2023/11/20/the-pros-and-cons-of-using-synthetic-data-for-training-ai

Computational Power: Training and deploying LLMs demand immense computational power. High-performance computing infrastructure, including specialized chips like (GPUs) and my new favourite trending acronym neural processing units (NPUs), is essential. This infrastructure is often expensive and in high demand, creating a potential bottleneck for organizations seeking to harness the full potential of AI.

Energy Consumption: The computational demands of AI translate into significant energy consumption. Data centers housing the infrastructure needed to train and run LLMs can consume as much electricity as small cities. This raises concerns about sustainability and the availability of sufficient power resources to support the growing AI ecosystem.

Talent and Expertise: AI development and deployment require a specialized workforce with skills in machine learning, data science, software engineering, and more. The scarcity of this talent can impede the progress of AI initiatives, particularly for smaller organizations that may struggle to compete for top talent.

While this is true and much has been said and written about the need to re-skill our talent and workforce. It is also critical that we can effectively use and validate the output of AI solutions. In a recent e-book we explore the critical soft skills and process knowledge that becomes even more critical for IT professionals using AI to augment their IT management processes.

Pink Elephant e-Book “AI Augmented ITSM https://blog.pinkelephant.com/blog/ai-augmented-itsm

In this e-book my colleague Robin Hysick and I make the very clear point that AI technical skills training is simply not enough. For IT professionals to stay relevant and use AI to enhance or augment their respective roles, they need higher level skills development related to general IT management and IT service management topics.

To use AI in the most effective manner we need critical thinkers that can test and validate the output of what the AI solution provides based on their own subject matter knowledge, and or access to a peer-based validation community. Enhanced process, leadership, organizational change, relationship management knowledge and skills remains critical for this to occur.

As is indicated in the Axios article linked above, perhaps our talent and workforce is actually the primary bottleneck we need to be concerned with!

Behind the Curtain: AI's ominous scarcity crisis. Every boom has these bottlenecks, notes Axios managing editor for tech Scott Rosenberg, who has been writing about the web for 30 years. What to watch: Of all these cascading shortages, the talent part might be the hardest to solve, because of the years of training it requires. The bottom line: If you don't have sufficient talent, sufficient data and compute power, and sufficient energy you don't have a real company — or sufficient hope.

Implications and Solutions

Just as Goldratt's theory advocated for identifying and addressing bottlenecks to improve system performance, organizations embracing AI must proactively tackle these constraints:

? Data Strategies: Invest in data collection, curation, and labeling initiatives. Explore partnerships to access diverse datasets.

? Infrastructure Investment: Build or acquire the necessary computational infrastructure, consider cloud-based solutions for flexibility.

? Energy Efficiency: Prioritize energy-efficient hardware and software solutions. Investigate renewable energy sources for data centers.

? Workforce and Talent Development: Invest in training and upskilling programs to build in-house AI expertise yes! But also, do not forget the leadership, process organizational change and critical problem-solving skills necessary to take advantage of AI created content and to validate it against possible hallucinations and mis-information risks. Consider the need to return to proactive learning and development strategies for your workforce. Many organizations have continued to let this critical focus and investment lapse as they navigate reactively from crisis to crisis.

The AI Advantage

By acknowledging and addressing these constraints, businesses can position themselves to leverage the full potential of AI. Just as optimizing a manufacturing process can lead to increased throughput and profitability, overcoming AI bottlenecks can unlock new levels of innovation, efficiency, and competitive advantage.

Key Takeaway:

The AI revolution is not immune to the fundamental principles of constraints. By recognizing and strategically addressing the limitations of data, computation, energy, and talent, businesses can chart a path towards a future where AI-powered solutions drive their success.