Artificial Intelligence, Agents, and Prompting: How AI will light the Future
We are living in an extraordinary time, in the midst of a technological revolution that is transforming the way we work and innovate. If – like me – you've experienced the eras of Web 1.0, Web 2.0, and Web 3.0, you’ll understand the pervasive impact AI will have on our future. Artificial intelligence today is a bit like electricity in the early 20th century: it's changing the world in ways we’re only beginning to see. There will be companies and individuals who fully embrace it – reinventing products, services, and jobs – and others who will struggle to adapt. What will make the difference is the mindset we bring to this revolution: fear or curiosity, resistance or continuous learning. History teaches us that technologies are not fate, but tools – it's up to us to direct them toward the common good.
AI is not just a technology, but a true catalyst, pushing us to rethink how we think, decide, and act. Today, AI increasingly permeates daily life and work – from smartphone suggestions to strategic analyses in business. Faced with this silent but profound revolution, it's not enough to adopt new software – a shift in perspective is required. AI forces us to reexamine traditional decision-making and organizational processes, challenging old habits and encouraging us to imagine new models of work and society. In this introduction, we’ll explore why AI must be understood as both a technological and cultural transformation, and how it is becoming a driver of innovation at the core of our daily actions.
In other words, AI represents a paradigm shift: from passive tool to active partner in our activities. This means AI not only helps us do things faster, but also changes the how and why we do them. For example, a manager today can use an agent to support strategic decisions, shifting their role from mere decision-maker to orchestrator of a human-machine hybrid team.
The Evolution of AI
To understand AI’s current impact, it’s helpful to briefly retrace its historical evolution. The term "Artificial Intelligence" was officially coined in 1956 during a workshop at Dartmouth College. In that pioneering period, optimism was high – it was imagined that machines would soon match human capabilities. Early programs tried to mimic logical and mathematical reasoning, and in the ‘60s and ‘70s, “expert systems” emerged, using rule-based software to make decisions in specific domains. But initial expectations were premature: limited computing power and the complexity of human common sense led to slower-than-expected progress. There were alternating periods of excitement and “AI winters,” when interest and funding declined due to lack of tangible results.
The real breakthrough came in the late 20th and early 21st century. On one hand, exponential increases in computing power and the availability of large digital datasets enabled innovative methods. On the other, breakthroughs in neural networks and machine learning allowed machines to learn from data rather than follow rigid rules. A symbolic moment came in 1997, when IBM's Deep Blue beat world chess champion Garry Kasparov – proving machines could surpass humans in complex, well-defined tasks. Milestones followed: in 2012, a deep learning system outperformed all previous image recognition results, sparking the modern AI boom. In 2016, Google DeepMind’s AlphaGo defeated the world champion of Go, a game long considered too sophisticated for computers.
Generative AI and Agents
Today we are in the era of generative AI and agents. Tools like OpenAI’s GPT, Google’s Gemini, Anthropic’s Claude, and Deepseek have revolutionized the field, enabling machines to create original content: text, images, code, and more. ChatGPT, launched in late 2022, brought conversational AI to the masses in weeks, highlighting both its power and limitations. Unlike early rule-based AIs, these systems learn language patterns from massive datasets and can now answer questions, translate, summarize, and even chat naturally with users.
The shift from rule-based to learning-based, generative AI has opened the door to widespread adoption in businesses and society. This rapid progress is driven by affordable computing (cloud), vast online datasets, and algorithmic innovations. We’ve moved from “artificial” AI to “autonomous” AI – capable of learning and creating. This is the foundation of AI’s expanding role in our lives.
A Mindset Shift
Adopting AI effectively requires not just technical skills, but – more importantly – a change in mindset. Often, the biggest obstacle isn’t the technology but how people and organizations relate to it. We must rethink established practices and transition from direct operation to intelligent coordination, from micromanagement to trust in machines, from instinct to data analysis. In short, the shift involves:
Changing this mindset means investing in training and cultivating an environment where error is part of learning. Organizations that embrace this shift see increased productivity and employee satisfaction: AI handles repetitive tasks, freeing humans for more meaningful work.
Prompting: The New Literacy
If AI is a powerful engine, prompting is the steering wheel – in natural language. With generative AI (e.g., GPT models, advanced chatbots), prompt literacy has become essential. Being able to ask the right question in the right way is now a core digital skill – like using a search engine or writing basic code.
Prompting means communicating with AI clearly and effectively. A good prompt provides context and guidance to elicit a useful response. In businesses, prompting is part of digital transformation: training employees to interact with AI unlocks its full potential. This is why prompt engineering courses are emerging, and companies are including it in internal training. Asking the right question is becoming as important as finding the right answer.
AI and Generational Differences
AI’s rapid rise highlights generational divides. Digital natives (Millennials and Gen Z) are comfortable experimenting with new tools. Older generations (Gen X and Baby Boomers) often find AI more challenging to adopt, having learned technology later in life.
These differences require thoughtful action. Companies should provide training, mentoring (e.g., digital champions), and share success stories to show that AI complements – not replaces – human expertise.
Generational exchange can enrich both sides: seniors offer critical thinking and experience; younger colleagues bring agility and openness to new tools. Inclusion will ensure AI benefits everyone.
Business Impact and Productivity
AI already transforms business processes: automating repetitive tasks, analyzing data, customizing services at scale, and enhancing speed and quality. Robotic Process Automation (RPA) with AI handles routine tasks like data entry or document verification. In customer service, virtual assistants manage simple queries, reducing wait times and costs.
Data quality is crucial. “Garbage in, garbage out” applies: companies must invest in structured, machine-readable data to fully harness AI’s power.
Tangible results include faster cycle times, fewer errors, lower costs, and better outcomes. A Stanford-MIT study found AI support raised call center productivity by 14%, and up to 35% for less experienced operators. McKinsey estimates that up to 50% of call center tasks could be automated, with 30–45% productivity gains.
Looking Ahead: The Future of AI
In the coming years, AI will become even more pervasive – but also face challenges in technology, energy, geopolitics, and governance.
Technologically, we’ll see more generalist, multimodal agents. These will unlock solutions once too complex or expensive, democratizing access to AI.
However, energy demands will rise. AI models require vast power to train and operate. Making AI sustainable means developing efficient algorithms, energy-saving hardware (GPUs, TPUs, quantum computing), and using renewable energy to power data centers.
Geopolitically, AI is already a global battleground. The U.S. and China lead, with Europe investing and regulating. By 2030, AI will likely be ubiquitous – as invisible and essential as electricity or the internet.
It’s up to us to ensure it will "light"
the future—without blinding us.
Great read, Andrea Tessera! AI is undoubtedly reshaping the way we work, think, and create. I particularly love the emphasis on how the shift in mindset, from doing to delegating, control to trust, and intuition to analysis, is just as crucial as the technology itself. This is where true transformation will happen. What do you think will be the biggest challenge in transitioning from AI as a tool to AI as an active partner in business decision-making?