The Finding Nemo effect

In economic downturns, organisations delve deeper into their margins. This means they are either finding efficiencies through cost reduction or discovering new revenue streams. Nowadays, it appears that AI is the solution to all our problems. We've heard similar prophecies before with other technologies, and many leaders fall into what we can call the 'finding Nemo' effect: chasing after the latest shiny object without a clear purpose or goal in mind. You can easily imagine the outcome of such actions

There is no question about the pivotal role that technology plays in our current world. Inflation, high interest rates (which make funding new projects more challenging and impact the stock market), supply chain crises, and talent shortages are top concerns for leaders. AI emerges as a potential solution. It involves finding ways to accomplish more with less, automating processes, expediting time-to-market, boosting margins, and accelerating growth. When we examine the top-performing companies, such as those in the S&P 500 index, a common pattern emerges—they are AI-based companies delivering substantial returns on investments. Consequently, investors across various sectors are pursuing a similar trend and seeking the same strategy: the incorporation of AI (or invest on Generative AI in different ways, like Visa)

The roles of technology and AI are evolving, shifting from merely supporting organizations in their operations to becoming essential business enablers. Greater business impact can be achieved when organisations are aligned and coordinated towards a common goal. We have all heard about agile methodologies, which, among other things, aim to address specific needs by developing iterative solutions with cross-functional teams. A clear trend is emerging, with IT teams being positioned as business enablers, actively engaging in corporate initiatives rather than operating in isolation

The past 12-18 months have brought significant developments in IT innovation (Take a look at Generative AI searches on Google). Artificial Intelligence is undoubtedly experiencing a surge in popularity, and it's here to stay. As evidence of its staying power, recent news highlights how companies are continuously generating new use cases, with Spotify serving as a notable example. In recent months, we've witnessed a barrage of impactful announcements ranging from the introduction of new models and performance enhancements to substantial company investments and regulatory initiatives.

The pace of innovation is currently faster than ever, and its initial economic and business impacts are becoming tangible. Some industries, like the audiovisual sector (comprising authors, journalism, photographers, video editors, etc.), are already experiencing disruption, while others are facing greater challenges in realizing their first results. The undeniable truth is that this technology is reshaping the world. Interestingly, the term 'Artificial Intelligence' was coined as far back as 1947 (Dartmouth Conference), and even the first language model, Eliza, made its debut in 1967.

What sets the current landscape apart from the past? Should we embrace the hype? The unequivocal answer is yes, unless you wish to see your business left behind. The playing field is undergoing constant transformation, welcoming new entrants, and witnessing the emergence of advanced models with enhanced capabilities and performance. Meanwhile, some established players and models may phase out due to various factors.

The reality is that Machine Learning has a history dating back quite some time. However, the distinction lies in the fact that the advent of new models has unlocked a plethora of new business opportunities. True innovation is poised to materialize sooner rather than later, bearing proven and tangible business outcomes.

So, the question arises: where should one begin? This is a critical juncture where we must avoid falling into what we can call the 'Nemo effect.' Perhaps you've seen the Disney movie 'Finding Nemo,' where a small fish becomes lost, and the father, along with another fish, embarks on a journey to locate the little one. Along the way, they get distracted in the vast ocean by various distractions like reefs, sharks, and more, losing sight of their primary goal—finding Nemo. Similarly, some organizations embark on new projects with cutting-edge technologies but without a clear business objective in mind.

It's essential to explore and understand the emerging tech landscape, becoming familiar with what's available and acknowledging their limitations. However, when we talk about corporate initiatives, it's imperative to approach them from a business perspective. We should start by addressing critical questions: What challenge are we currently facing? What do we aim to achieve, and how will we measure success? While it's true that in most cases, we may need to iterate and pivot our solutions until we reach the final one, it's crucial always to keep a business goal in mind.

Having a firm grasp of the technology landscape will guide us in determining what's most suitable and what should be tested first. Nevertheless, the initial hurdle often lies in achieving alignment across the entire organisation.

Organisations are structured in various ways, and even in startups, every role and team carries its own set of responsibilities. Typically, as organisations grow in terms of the number of employees, coordination can become less effective, and different, sometimes conflicting goals may emerge. This becomes a significant challenge to address. If we introduce new cutting-edge solutions with the latest AI models without addressing these underlying coordination and alignment issues, they won't effectively resolve the challenges we face, particularly at the enterprise level, where there may not be a consensus on what needs to be achieved. Detractors will likely voice concerns about the resources being invested. But does this mean that technology is inherently ineffective? This doesn't imply that technology exploration, such as conducting small pilots, should be discarded. Establishing playgrounds, testing models, and gaining insights into their strengths, weaknesses, and limitations are all crucial steps. However, where this approach truly becomes impactful is when it's preceded by thoughtful reflection on the specific challenges we aim to address. Starting with a clear understanding of the problem at hand and then piloting solutions to tackle those challenges is the key to success. The opposite can fall into the fallacy of this technology is not mature enough and the investment doesn’t make sense (as it’s not enabling the business to move forward)

Major tech companies are actively promoting their solutions in the market, and there are indeed excellent offerings available. Even within the broader spectrum of Artificial Intelligence and Machine Learning, we've been using some of these technologies for years in various applications such as customer segmentation, spam filters, classification, price predictions, recommendation engines, and more. Generative AI, in particular, has made significant strides in recent months, offering impressive solutions that can drive increased business output, faster processes, and improved margins through the automation of specific tasks. The question we must ask is: do we have a clear understanding of where within our business we should apply these techniques to effectively address the real challenges we want to solve?