The path ahead for AI: Low-hanging fruits, bad apples, and long-term strategy
Over the last couple of weeks, I have had the pleasure of exploring the use of AI with two very different top leadership teams from two very different industries in two very different European countries. The key questions and strategic challenges around the adoption of AI were strikingly similar; where do we go from here with AI, and where do we find the low-hanging fruits?
Does this sound familiar?
Chasing quick-wins
As an advisor to boards and C-suites, I often encounter this desire to see AI as the silver bullet solving the need for quick returns and immediate effects on productivity. In some cases, the board has already been promised AI-driven cost reductions and layoffs, whilst the organisation is scrambling to figure out how the magical genAI tools may be wielded to provide the promised effects.
Sometimes it may indeed be wise to chase the quick-wins first. However, when it comes to AI, there are several reasons to think twice about chasing the low-hanging fruits too zealously. If you are not careful, you may end up collecting bad apples.
The term AI is an umbrella, covering a wide range of tools and techniques, from expert systems and predictive maintenance to chatbots and multi-agent LLM systems. Generative AI has been all the hype over the last couple of years, fueled by visions of artificial general intelligence (AGI) endorsed by powerful American tech players. It is highly understandable that leaders choose to chase for seemingly quick wins. However, several recent studies, including the “State of AI in business 2025” report by MiT NANDA, indicate that the majority of companies investing in genAI do not achieve the expected RoI. In fact, the study found that only 5% of the companies included did in fact reap the expected fruits. A key success factor for these companies was deep integration at scale with business and operational workflows. Among the 95% which did not reap the expected fruits, a ruling tendency was high visibility - low value use cases with limited workflow integration.
The report also debunks the myth about AI replacing most jobs in the next few years. Although large international corporations like Accenture have announced five-digit layoffs in 2025 due to the need to shift towards high-value roles and automation, this constitutes only about 1.5% of their global workforce. In a study by Orgvue published in April 2025, 55% of businesses who had laid off people as a result of deploying AI regretted the redundancy decisions.
The natural next step of digital transformation
Over the last decade, every company has embraced digital transformation to some extent. Today, most organizations have or can get access to digital data about their assets and processes, rely on digital systems to manage operations, and can tap into the mathematical tools and computing power needed to make sense of that data. This makes mathematical algorithms applied to large amounts of data, aka artificial intelligence, the natural next step of digital transformation.
If you follow my reasoning so far, let's take it one step further. To truly reap the value of mathematical algorithms applied to large amounts of business data, you need to understand your data piping, your business processes, and the digital representations of your assets. You also need to understand the dynamics and the risks of making decisions based on probabilities. Did I lose you now? If so, you have just demonstrated why your low-hanging fruits might actually turn out to be bad apples.
How to create long-term strategic value without collecting bad apples
Successful adoption of AI means ensuring deep and scalable workflow integration, real problem-solving, and adaptation to the context of the concrete operational processes. When developing long-term strategic plans for your business, ask yourself and your peers the following questions:
Do you understand and are you able to articulate the difference between chatbots, large language models (LLMs), and AI agents? Understanding the differences between these three compartments of the AI toolbox is essential in order to handle generative AI initiatives wisely.
Do you have a clear understanding of the difference between generative and non-generative AI technologies, and how these may provide different advantages to different problems?
Which data about your assets and processes are available across your internal IT landscape, and how can that data be securely used to automate workflows?
Instead of chasing those notorious low-hanging fruits, my advice is to build long-term strategic knowledge, competence and technical infrastructure. Artificial intelligence is the natural next step of digital transformation. To embrace this toolbox in a value-creating, responsible, and sustainable way you need to focus on building the relevant mindset, competence, and capabilities. That requires stamina, determination, and discipline. Not a basket of bad apples. If you do this right, you will also collect low-hanging fruits as you move forward with the execution of your long-term strategy.
If this resonates with you, I can help navigate the AI landscape. Whether it’s by supporting your leadership team in building realistic, actionable AI plans or by inspiring new thinking through keynotes and workshops, my mission is to bring clarity and structure to what often feels like an overwhelming fog. Together, we can make sure your low-hanging fruits don’t turn into bad apples and that your strategy stands the test of time.
This is the second article in my series “Where Do We Go From Here? AI as the Natural Next Step in Digital Transformation”. Want the rest delivered straight to your inbox? Subscribe to my newsletter.
About the Author
Elin Hauge is a business and data strategist, pragmatic futurist, and eternal rebel. With a unique background spanning physics, mathematics, business, and law, she brings fresh and thought-provoking insights into artificial intelligence and other data-driven technologies. Her work connects today’s realities with tomorrow’s possibilities, focusing on demystifying AI, harnessing data power, managing algorithmic risk, and guiding leaders through digital transformation.