Like many professionals in the industry, I have witnessed firsthand how technology can accelerate or disrupt how businesses operate. As a computer and data scientist by education, I might be biased toward data and its various applications since I've seen the transformative potential of data and advanced analytics. However, the recent surge in Artificial intelligence (AI) enthusiasm, particularly in 2023 and 2024, has led to debates, opposing viewpoints in boardrooms and management meetings, confusion, anxiety, and many misconceptions and expectations about what AI truly is and what it can offer. This is understandable given the impact of various technology waves, from Personal Computers to the Internet, Social Networks to Mobile Computing, the Internet of Things to Cloud Computing, and Big Data to Artificial Intelligence.
Roy Amara (Amara’s Law) aptly said, “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” This article aims to clarify these misconceptions for decision-makers, from board members down, and provide a clear understanding of AI's capabilities, limitations, and strategic value.
Understanding AI: Beyond the Buzzwords
We live in a time where people and machines produce vast amounts of data at almost every moment (As much as 2.5 quintillion bytes of data according to Bernard Marr & Co.). Think about our daily lives: we constantly interact with applications or devices to read information, give instructions, or complete transactions. These data points form information when they come together in specific shapes and formats. When this information is compared or combined with other sets of information, we gain insights. These insights help us make decisions or take action, which in turn generate more data, creating a continuous cycle of data to action. So Data > Information > Insights > Action > Data.
Consider a simple example like the weather forecast. A piece of data in this context includes temperature, wind speed, precipitation, humidity, the time of day, location, and the equipment used to read the data. Together, these create a small story about the current weather. Insights are gained by comparing this information with past data and combining them with other information. For example, if the forecast predicts a warm and sunny day in January, families might decide to spend the day outdoors, which is an insight that might create opportunities for businesses to offer relevant products and services and trigger actions on both ends, the supplier and the consumer; all in a quick and repetitive matter.
This weather example illustrates how AI can simulate human intelligence by transforming data into actionable insights in seconds. AI processes include learning (acquiring data and the rules associated with it), reasoning (using rules to reach conclusions), self-correction, and the generation of a decision, recommendation, or anything that this AI process has previously learned. Depending on the complexity of the process, AI technologies such as machine learning, deep learning, and generative AI are applied.
The AI value chain is extensive, involving many players. Massive amounts of data must be ingested and processed, requiring robust data centers and advanced capabilities. The processes must be superior, scalable, and supported by powerful GPUs and CPUs. Energy is needed to keep data centers operational and cool. These are necessary key elements for any significant AI application.
AI is Not New
AI was introduced as early as the 1950s and has been researched and presented at conferences since then. Remember the first time you played chess against a computer? That was an AI application. The commercial use of AI began with enterprise resource planning software, but computing power and storage limitations hindered AI's potential. Advances in computer chips, data centers, and the widespread adoption of technology have fueled today's AI hype, enabling applications from autonomous customer service agents to cybersecurity threat detection systems. As data storage and processing become cheaper and more accessible, AI will continue to evolve and become smarter.
The Potential Impact (So What?)
Why billions of dollars are being spent on AI companies across the globe and through the entire value chain? According to a study by PWC “AI could contribute up to $15.7 trillion to the global economy in 2030.” $135 billion of which is the contribution of AI to KSA’s economy. Specifically, gains will come from efficiencies to automate some processes and product and service enhancements. For example, improving the operating cost of key equipment sets, and distribution networks can save the Energy & Utilities sector as much as $78 billion in operational costs. Improving customer service and deploying smart marketing and personalization can unlock an additional $23 billion in consumer spending by 2030. AI can unlock similar opportunities for the Healthcare, Public Sector, Financial Services, Transport & Logistics, Technology, Media, and telecommunications industries.
Key Misconceptions about AI:
Many believe AI can solve all problems instantly. However, according to a recent Gartner report, only 53% of AI projects make it from prototype to production. Building an AI solution from data to action takes time, and the entire value chain must be in place for AI projects to generate meaningful value.
While AI will automate certain tasks, it is more likely to augment human capabilities than replace them entirely. Think of AI as a powerful new tool in the toolbox, not a replacement for the craftsman. The World Economic Forum estimates that by 2025, AI will create 97 million new jobs while displacing 85 million.
Boards often expect quick returns on AI investments. However, successful AI initiatives require a long-term perspective, with initial investments in data infrastructure, talent and iterative development cycles. A recent McKinsey report found that 70% of companies saw a positive impact from AI, but the timeline for ROI varied significantly depending on the complexity and scale of the implementation.
Strategic Considerations for Decision Makers
So what can boards and decision-makers do about the AI hype? How can they ensure they capture some of the value generated by this new wave? If you haven't started yet, you should start now. As the saying goes, “The best time to plant a tree was 20 years ago. The second best time is now.” According to Scott Galloway in his 2024 predictions, we are at the peak of AI valuations, “this year the AI bubble won’t burst, but it will deflate.” So M&A activities would be expensive, not to mention the cost of integration. Not ruling out M&A options as a way to augment AI capabilities in your organization, but even with that, the below considerations are still valid:
AI projects should be closely aligned with the organization's strategic objectives and business strategy. This ensures that AI investments drive meaningful business outcomes rather than being pursued for their own sake. For example, to improve customer satisfaction, ensure your AI agents are well-trained to handle requests and complaints efficiently.
High-quality, relevant data is critical for effective AI. Boards should prioritize investments in data governance, management, and security. With the rise of data protection laws, relying on third-party data is no longer a valid strategy. Organizations that capture and use first-party data—data willingly shared by customers—are more likely to succeed. A study by MIT Sloan Management Review highlighted that companies leading in AI are twice as likely to have a strong data foundation.
AI development often involves experimentation and learning from failures. Boards should encourage a culture that embraces innovation, agility and iterative improvement. Celebrating controlled failures and learning from them is crucial for catching the AI wave and maximizing value without destroying it.
AI systems must be designed and deployed ethically, considering bias, fairness, transparency, and accountability. Boards should establish frameworks to govern the ethical use of AI within the organization, including balanced human review. Microsoft's AI principles focus on fairness, reliability, privacy, security, inclusiveness, transparency and accountability.
Building and maintaining AI capabilities requires specialized skills. Boards should support initiatives to attract, retain, and develop talent with expertise in AI, data science, and related fields. The AI talent pool is growing, with LinkedIn reporting a 74% increase in AI-related job postings in 2023, but finding the right people with needed AI-related skills remains challenging.
Artificial Intelligence holds immense potential to drive business transformation and competitive advantage. However, realizing this potential requires a nuanced understanding of AI's capabilities and limitations, a strategic approach to its deployment, and a commitment to ethical and responsible use. As the old proverb goes, "Measure twice, cut once." By demystifying AI and aligning its initiatives with broader business goals, boards can navigate the hype and harness the true power of AI for sustainable success.
By focusing on these key areas, board members and management teams can make informed decisions about AI investments, ensuring their organizations are well-positioned to leverage this transformative technology effectively.