The Rapid Rise and Looming Reality of AI: Hype vs. Substance

Lets start by getting real … Artificial Intelligence (AI) is not new, it has been a topic of conversation for over four decades, with its roots stretching back to the early days of computing. Over this time, AI has evolved from a theoretical concept into a transformative force that continues to reshape industries, markets, and our daily lives. The journey of AI is marked by many notable milestones, from beating world champions in chess and Go to helping develop life-saving vaccines during the recent global pandemic. Yet, despite its long history, AI has never been more accessible or widely discussed than it is today, largely thanks to the emergence of generative AI (starting with OpenAI and its chatGPT) to the everyday consumer.

However, as we marvel at AI’s current capabilities, it is important to remember that the current hype surrounding AI is not new. We have seen waves of AI enthusiasm before, often followed by periods of disillusionment. To really understand AI’s potential, and its limitations, we must separate the technological reality from the marketing-driven narrative that has brought AI into the spotlight today.

A Look Back: The Historic Milestones of AI

AI’s history is filled with notable achievements that have pushed the boundaries of what machines can do. In 1997, IBM’s Deep Blue made headlines when it defeated world chess champion and grandmaster, Garry Kasparov, demonstrating that a machine could outperform a human in this complex, strategic game. This was followed by another major AI milestone in 2011 when IBM’s Watson, won the quiz show Jeopardy! , beating two of the show’s greatest champions. Watson’s ability to understand and respond to complex natural language questions showcased the potential of AI in processing and analysing vast amounts of information quickly and accurately.

Another pivotal moment came in 2016, when Google’s DeepMind AI, known as AlphaGo, defeated Lee Sedol, one of the world’s best Go players. Go, is a game with more possible moves than there are atoms in the universe and had long been considered a final frontier for AI. AlphaGo’s victory was a testament to the advances made in machine learning and neural networks.

A visible application of AI today is in customer hyper-personalisation, particularly in e-commerce. Amazon, for example, uses AI to analyse vast amounts of its customer data, enabling it to offer you and I personalised recommendations, dynamic pricing, and targeted marketing. This level of personalisation has set new standards for customer experience; which has been a key enabler of Amazon’s success.

More recently, AI has played a critical role in the research and development of vaccines during the COVID-19 pandemic. AI algorithms helped researchers analyse data, identify potential vaccine candidates, and accelerate the development process, ultimately saving countless lives. This application of AI highlighted its potential not just as a tool for winning games but also as a powerful enabler of human progress.

These examples illustrate AI’s incredible potential, but they also serve as reminders to readers that AI’s success depends on the context in which it is applied. The technology itself is not a magic bullet; it requires careful planning, implementation, and alignment with specific goals to deliver meaningful results.

The Present: AI’s Consumerisation and the Role of Marketing

Since late 2022 we have seen AI move from the confines of research labs and specialised applications into the hands of everyday consumers. The emergence of generative AI, capable of creating text, images, music, and more, has made AI accessible to non-experts, opening up new possibilities for creativity, productivity, and personalisation.

However, the rapid consumerisation of AI has not been driven solely by technological advances. It has been fuelled by a sophisticated marketing push from the world’s largest technology vendors. The "Magnificent 7" technology companies [Amazon, Apple, Meta (Facebook), Alphabet (Google), Microsoft, Tesla, and NVIDIA] have been instrumental in driving todays AI’s mainstream adoption. All of these global (and US based) companies have invested heavily in AI research and development, while also crafting narratives that position AI as the future of everything from business to entertainment.

This marketing-driven narrative has created a perception that AI is the solution to virtually every problem. But as we’ve seen with past AI hype cycles, this perception often leads to unrealistic expectations and, eventually, disappointment when the technology fails to deliver on its promises.

The Future: Agile Approaches and Strategic Implementation

As we look to our future, it is clear that AI will continue to play a significant role in shaping industries and markets. But to realise AI’s full potential, organisations will have to adopt a more measured and strategic approach to AI implementation. This will mean moving away from the traditional, waterfall-style development processes that attempt to deliver fully-formed solutions in one go, and instead embracing agile methodologies that focus on iterative, incremental progress.

The key to successful AI adoption lies in developing Proofs of Concept (PoCs) and Minimum Viable Products (MVPs) that allow organisations to test ideas, gather feedback, understand business impact and value creation and to then make adjustments before committing themselves to large-scale implementations. This approach will not only reduce the risk of failure but will also ensure that AI initiatives are aligned with business strategy and growth.

Recent advancements in technology have made it easier than ever for organisations to experiment with AI. Many of the large software vendors have accelerated their development and release cycles for the technologies that can assist in the enablement of AI. A good example of these platforms (but there are others) is Microsoft Fabric and Microsoft Purview. These technologies provide robust ecosystems for developing, managing, and governing AI solutions. Microsoft Fabric offers a unified environment for data integration, analytics, and AI, enabling organisations to build and deploy AI models quickly and efficiently. Microsoft Purview provides data governance tools that ensure AI initiatives are compliant, secure, and aligned with organisational standards.

These technology platforms are tools that can empower businesses to innovate and experiment with AI in a controlled, manageable way. They support the development of PoCs and MVPs, allowing organisations to explore AI’s potential without overcommitting resources or exposing themselves to unnecessary risk.

A Business Change, Not Just a Technology Shift

However, as organisations embark on their AI journeys, it’s crucial to recognise that AI represents more than just a technological shift, it is a fundamental business change. AI is underpinned by sophisticated analytics powered by data, and it offers a suite of tools that can generate exceptional insights and drive smarter, faster decisions. But for AI to be effective, it must be integrated into the fabric of the business, influencing strategy, operations, and decision-making at every level.

Given this, the responsibility for driving AI adoption should not rest solely with the Chief Technology Officer (CTO) or Chief Information Officer (CIO). While these roles are critical in implementing the technical infrastructure required for AI, the strategic leadership of AI initiatives should be spearheaded by the Chief Data Officer (CDO) or Head of Data, These leaders are best positioned to understand the value of data and to align AI initiatives with business goals, ensuring that AI is used not just as a technological tool but as a driver of business transformation. Indeed, as I was drafting this blog I read that my former employer, Pfizer, have just hired a Chief AI and Analytics Officer so I am happy to report that forward thinking organisations are now taking this initiative.

The Chief AI and Analytics Officer can help the CEO, CIO and other senior leaders recognise that AI is not an end in itself but a means to achieving broader business objectives. By framing AI as a business change underpinned by data and analytics, these leaders can ensure that AI initiatives are grounded in the organisation’s strategic priorities and deliver tangible value.

The Risk of Inaction: Falling Behind in the AI Race

While it’s important to avoid getting caught up in AI hype, it’s equally important not to ignore the potential of AI altogether. Organisations that fail to engage with AI risk being left behind by their competitors, who are increasingly using AI to drive innovation, efficiency, and customer engagement.

The current wave of AI presents a critical opportunity for businesses to reassess their operations, explore the value hidden in their data, and consider how AI can be leveraged to gain a competitive edge. Companies that take a proactive approach to AI, developing small-scale experiments, iterating on their successes, and scaling up where appropriate, will be better positioned to thrive in an increasingly AI-driven world.

Conversely, organisations that remain on the side lines, hesitant to embrace AI, risk missing out on the benefits it can offer. In a rapidly changing business landscape, those who fail to adapt and innovate may find themselves falling behind their more agile, AI-enabled competitors.

Conclusion: Navigating the AI Landscape

The rise and fall of AI hype is not a new phenomenon. We have seen it before with technologies like Deep Blue, AlphaGo, and Watson, and we’re seeing it again with the current wave of generative AI. But despite the cyclical nature of AI enthusiasm, one thing is clear: AI is here to stay.

The key to navigating the AI landscape is to cut through the hype and focus on practical, strategic implementations that deliver real value. This means adopting agile approaches, leveraging modern technology platforms, and staying attuned to the needs of your business and customers.

AI is a powerful enabler, but it is not a solution in itself. It requires thoughtful planning, experimentation, and iteration to unlock its full potential. Organisations that embrace this mindset will be well-positioned to succeed in the AI era, while those that fail to adapt risk being left behind.

AI’s impact should be guided by those who understand its core; the data that fuels it. By placing AI initiatives under the stewardship of the Chief AI and Analytics Officer, organisations can ensure that AI serves as a strategic asset, driving meaningful business change rather than just another technology trend. The future belongs to those who can harness AI’s capabilities, not just as a buzzword, but as a tool for driving innovation, efficiency, and competitive advantage. Whether through small-scale PoCs, agile development cycles, or innovative applications of AI, the opportunities are vast; but only for those who are willing to seize them.