InsideTheShift #1 - How Generative AI is Redefining Innovation Strategy: from Prediction to Creation.
"The future of innovation isn’t found, it’s generated."
The Shift in Focus: from prediction to creation.
In the not-so-distant past, the power of artificial intelligence in business was largely predictive – machines crunching data to forecast trends, optimize supply chains, or anticipate customer behavior. Today, we stand at a transformative inflection point: AI has moved beyond prediction into creation. Thanks to generative AI, algorithms are now capable of producing original content – from drafting marketing copy and designing products to composing music and discovering new drugs – fundamentally redefining how we approach innovation.
This shift from using AI merely as an oracle of probabilities to a partner in creativity marks a profound change in mindset for strategists and innovators.
Early AI applications helped us predict what might happen; now generative AI helps us create what we want to happen. In their influential book Prediction Machines, economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb framed AI as a powerful engine for making fast, cheap predictions. When given the goal of, say, estimating market demand or risk, AI excelled at forecasting outcomes better than any manual analysis.
But what about problems that aren’t just about predicting a number – such as designing a groundbreaking product or crafting a compelling story?
Generative AI tackles these challenges by reframing creation as a form of prediction.
For example, an AI like OpenAI’s DALL·E doesn’t guess what design a human will draw next; it generates a brand new design based on patterns it learned, essentially “predicting” a plausible image that has never existed before. In doing so, AI steps into the role of a creative collaborator, turning our prompts and questions into novel solutions.
This new paradigm carries an inspiring message: we are no longer limited to forecasting the future – we can begin to invent the future hand-in-hand with our AI co-creators. Organizations that recognize this shift are reimagining their innovation strategies. Instead of relying solely on human brainstorming or historical data trends, forward-thinking teams are leveraging generative AI to jump-start ideation, iterate prototypes at lightning speed, and explore spaces that were previously too costly or time-consuming to consider.
The narrative of innovation is expanding from one of human genius occasionally aided by computers to one of human–AI synergy where creative leaps are achieved together. The following sections delve into this shift, charting the data and trends that signal its rise, examining the technical and strategic core of generative AI, connecting it to broader cultural currents, and peering into the new scenarios this revolution is opening up.
“The best way to predict the future is to invent it.” — Alan Kay
Understanding the Shift: data reveals rapid adoption.
The emergence of generative AI as a driving force in innovation is backed by remarkable data and real-world signals. Perhaps the most visible indicator has been the unprecedented adoption rate of generative AI tools.
OpenAI’s ChatGPT, released publicly in late 2022, reached 100 million monthly users in just two months – a feat which a UBS study hailed as the fastest-growing consumer application in history. This astonishing uptake outpaced even the likes of Instagram and TikTok, signaling that AI-created content had captured the world’s imagination. Within months, millions were using generative AI for everything from drafting emails to writing business plans, illustrating a massive appetite for AI’s creative assistance.
On the enterprise side, the trend is even more pronounced. According to Stanford University’s 2025 AI Index report, the number of organizations using generative AI in at least one business area more than doubled in one year, leaping from 33% in 2023 to 71% in 2024. In other words, what was a novelty experiment in many boardrooms quickly became a strategic priority.
Over three-quarters of companies surveyed by McKinsey now report using AI (including generative models) in some capacity, and the usage is rapidly expanding across functions. This explosive growth is not just in deployment but also in investment: venture capital and corporate funding for generative AI startups have skyrocketed. Stanford’s 2024 AI Index highlighted a nearly eightfold increase in private investment for generative AI, soaring to $25.2 billion in 2023. That accounted for over a quarter of all AI-related funding that year. By 2024, global VC funding for generative AI reached roughly $45 billion – almost double the year before – as investors raced to back the next wave of AI-driven innovation. Tech giants have poured resources into this space as well, exemplified by Microsoft’s multibillion-dollar partnership with OpenAI and numerous acquisitions of generative AI ventures.
What’s driving this gold rush is the belief among leaders that generative AI isn’t just another tech trend, but a game-changer for innovation and growth.
In a recent survey of 2,300 global executives, an overwhelming 97% said that generative AI will be transformative for their company and industry. It’s rare to see near-unanimous agreement on anything in the business world, which underscores just how significant this shift appears. These leaders aren’t just passively interested – the same study found virtually all of them are actively exploring investments in generative AI within the current year. Such confidence is bolstered by early signals of concrete impact: for example, initial deployments of generative AI in marketing and sales have already led to measurable revenue gains, with about 71% of companies using AI in marketing reporting positive ROI. And as organizations integrate AI into product development, some report drastically shortened development cycles and faster time-to-market, hinting at a brewing competitive advantage.
Another signal comes from the cost side of the equation. The cost of generative AI capabilities has plummeted, removing barriers to entry. By late 2024, the price of running advanced AI models had fallen by orders of magnitude – for instance, querying a language model with the power of GPT-3.5 dropped from about $20 per million characters in 2022 to just $0.07 by October 2024. This 280-fold cost reduction in roughly 18 months means that what was once a pricey experiment for tech giants is now affordable to startups, students, and innovation teams worldwide. Cheaper AI “fuel” translates into more experimentation and broader adoption, further feeding the generative boom.
In summary, the data paints a clear picture of a shift in focus across industries. Generative AI has moved from the fringes to the mainstream in record time, backed by surging user engagement, corporate adoption, investment dollars, and enabling cost trends. The world is not only ready for AI-created ideas – it’s actively demanding them. These trends set the stage for a deeper exploration of how generative AI actually works and how it can be harnessed strategically.
📶 Signal | AI in Drug Discovery: One striking example of generative AI’s innovative power comes from biotechnology. In 2022, startup Insilico Medicine announced that an AI-discovered and AI-designed drug for pulmonary fibrosis had entered Phase I human trials – from initial idea to clinical stage in under 30 months. The AI system autonomously hypothesized a novel biological target and generated a molecule to act on it, something that traditionally takes years of human-led research. This was one of the world’s first drug candidates invented by generative AI, highlighting how “creation” isn’t limited to text and images. It can extend to life-saving inventions, potentially revolutionizing R&D strategy in pharmaceuticals by drastically speeding up discovery while cutting costs.
The Core : how generative AI works.
To understand how generative AI is redefining innovation strategy, we need to look under the hood at how these systems operate and what they enable. Generative AI models, at their core, are trained on vast amounts of data to recognize patterns and then produce new data with similar characteristics. Unlike traditional discriminative AI models that simply categorize existing data (e.g. predicting which customers will churn), generative models create data – be it natural language, images, designs, or even code – that feels new and authentic.
Technologies like Transformers (the architecture behind large language models such as GPT-4) and diffusion models (used in image generators like Stable Diffusion) have been key breakthroughs. They allow AI to learn the deep structure of language and visuals, enabling the algorithm to generate remarkably coherent and creative outputs from simple prompts. For instance, feed a traditional algorithm a prompt about a “futuristic city skyline” and it might retrieve similar images; feed a generative model the same prompt and it will paint a unique skyline that no one has ever seen, constructed pixel by pixel from its learned knowledge.
From a strategic perspective, this technical leap means that AI can now participate in early-stage innovation tasks that were once exclusively human territory. Idea generation, concept development, and prototyping – those nebulous, creative processes at the start of any innovation pipeline – can be accelerated and augmented by AI. Brainstorming with a generative AI is like having a tireless colleague with encyclopedic knowledge and boundless imagination. It can propose hundreds of variations on a theme (say, product design ideas or marketing slogans) in the time it takes a human team to sketch out one or two. This doesn’t render human creativity obsolete; rather, it amplifies it. Teams can use AI to cast a wide net of possibilities, then use their expertise to filter and refine the results. The effect is a more expansive exploration of options at a speed and scale previously unattainable. Companies like Adobe, for example, have integrated generative tools into their design software, allowing creators to instantly generate variations of graphics and artwork, dramatically shortening design iteration cycles. In software development, tools such as GitHub Copilot (powered by OpenAI’s Codex model) can generate code snippets or even entire functions based on a description, changing the strategy of software innovation – developers now focus more on architectural and high-level problem-solving, letting the AI handle routine coding patterns.
Embracing generative AI strategically also means rethinking the processes and skills within an organization. Many leading innovators are creating AI innovation task forces or dedicated teams to integrate these tools into their workflows. New roles are emerging, like the prompt engineer who specializes in crafting effective inputs to get the best outputs from AI, or the AI ethicist who ensures generated content meets ethical and quality standards. Senior leadership is getting involved too – some companies have appointed chief AI officers or established AI governance boards, recognizing that harnessing generative AI is now a top-level strategic priority. McKinsey analysts observe that organizations doing this “organizational rewiring” – redesigning workflows and governance to incorporate AI – are seeing the most bottom-line impact from generative AI.
For instance, a company might redesign its product development process to include an AI-generated prototype phase: rather than starting from a blank slate, human designers begin by reviewing AI-produced mockups, which can inspire directions they hadn’t considered. This requires openness to co-creation with machines and a culture that encourages experimentation.
From the technical to the strategic, generative AI also challenges us to maintain rigor amid the excitement. AI can produce a torrent of outputs, but quantity is not the same as quality. Strategic use of generative AI involves setting up robust evaluation and feedback loops. Consider a generative model that proposes 50 new product concepts – human experts must vet these for feasibility, market fit, and alignment with company strategy.
Some ideas will be gems, others impractical. The winners might even be hybrids, where human and AI ideas are combined. Thus, innovation strategy in the age of generative AI becomes as much about curation and validation as creation. Companies are developing frameworks for this: using data to test AI-generated options (market simulations, A/B testing campaign concepts generated by AI, etc.), and honing the AI by feeding back the results (reinforcement learning on what worked and what didn’t). Over time, this human-in-the-loop approach makes the AI an ever more effective innovation partner.
Another strategic consideration is the alignment of AI outputs with business goals and values. Generative models do not have inherent judgment – they will produce whatever they are asked, for better or worse. This means that without guidance, AI could suggest a product design that is stylish but too expensive to manufacture, or write marketing copy that is catchy but off-brand. Leading companies address this by carefully curating the training data (to imbue the model with domain-specific knowledge and style) and by constraining the AI’s output through guidelines. Some have created custom generative models with their proprietary data, ensuring the AI “speaks” in the company’s voice and understands industry-specific nuances.
For example, a car manufacturer might train a generative model on decades of automotive design, so that when it proposes a new concept car, the design aligns with engineering realities and brand identity. Strategically, this raises the bar for data governance and AI training as part of innovation strategy: organizations that can leverage unique data to train their generative AI will have more distinctive, competitive outputs than those using only public, generic models.
It’s also important to note that generative AI is pushing strategy into unexplored territory ethically and economically. Questions about intellectual property (IP) and ownership of AI-generated inventions are now on the strategy agenda. If an AI designs a new widget, who owns the patent – the company, the AI provider, or the creators of the AI’s training data? These are new considerations for innovation strategists, who must collaborate with legal teams to navigate the evolving landscape of AI and IP law.
Additionally, the cost dynamics of innovation are shifting. Traditional R&D often required significant upfront investment with uncertain outcomes, but an AI that can simulate thousands of experiments or designs virtually allows a kind of lean innovation approach – rapid prototyping with low marginal cost. This can tilt the classic calculation of “explore vs exploit” in innovation strategy toward more exploration, since the cost of exploring ideas is lower with AI assistance. In sum, mastering generative AI technically and strategically means not only understanding what the models can do, but also shaping how they integrate with people, processes, and objectives. Those who get this right are poised to unlock the full creative potential of AI in service of their innovation goals.
The Broader Shift : redefining creativity and culture.
The shift from prediction to creation in AI isn’t happening in a vacuum; it’s deeply intertwined with cultural and historical currents in how we view creativity, work, and technology. To put today’s breakthrough in perspective, consider the insights of Ada Lovelace in the 19th century. Often regarded as the world’s first computer programmer, Lovelace mused on whether machines could ever truly create. In 1843, she famously wrote that the Analytical Engine (an early mechanical computer)
“Has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform”.
This assertion – that a machine could only do what it’s told, lacking the spark of originality – has echoed through debates on artificial intelligence for decades. For a long time, it appeared fundamentally true. Early AI could beat grandmasters at chess and compute complex equations, but it couldn’t compose a symphony or design a product from scratch without human direction at every step.
Generative AI is challenging Lovelace’s objection. While it’s still true that today’s AI operates within the bounds of its programming and training data, the appearance of originality has arrived. When a machine learning model creates a painting in the style of Van Gogh or writes a poem about life on Mars, it’s following mathematical instructions – yet the output can surprise even its creators, seeming to come from a place of creativity.
This blurring of the line between human and machine creativity is causing society to grapple with new questions: What do we consider authentic creativity? Is an AI-generated artwork less legitimate or valuable than one by a human hand? In 2018, an AI-generated portrait titled “Portrait of Edmond de Belamy” was sold at Christie’s auction for an astounding $432,500, grabbing headlines worldwide. The painting was created by a generative algorithm trained on thousands of human-painted portraits. Its sale – at 45 times the expected price – was a cultural watershed moment, signaling that the art world was willing to ascribe value and originality to AI-generated works.
Around the same time, mainstream music witnessed AI’s creative incursion. In April 2023, a catchy song called “Heart on My Sleeve” went viral, with listeners marveling at a collaboration between famous artists Drake and The Weeknd – except neither artist had actually recorded it. The track was produced by an anonymous creator using generative AI to mimic the musicians’ voices and style. It amassed millions of streams across TikTok and Spotify before legal challenges from the record label forced its removal. This episode sparked intense debate in pop culture: some fans were thrilled by the novelty and even preferred the AI-made track, while others raised concerns about artistry, consent, and copyright. The genie was out of the bottle; AI could convincingly produce culturally relevant artifacts, from fine art to chart-topping songs, and society had to catch up.
Culturally, generative AI taps into our collective fascination with creation and our age-old narrative of man vs. machine. We’ve long been intrigued by the idea of machines that can create – think of the myth of Pygmalion or the golem of medieval folklore, where human creations come to life. In modern times, films and literature have imagined AI composers, writers, and artists (from HAL 9000 composing a song, to the holographic AI companion in the movie Her writing a love poem). Now that fiction is becoming reality, there’s both excitement and anxiety. Creatives are pondering their future: graphic designers wonder if AI will diminish the demand for their work or, conversely, free them from drudge tasks so they can focus on higher-level creativity.
Writers and advertising creatives are experimenting with co-writing content with AI, but also rallying to ensure human creativity retains its prized place (witness how Hollywood scriptwriters in recent labor negotiations insisted on regulations around the use of AI in writing).
In education and academia, the notion of originality is being reexamined – if a student uses AI to generate an essay or a researcher uses AI to draft a paper, where do we draw the line between tool and author? These cultural conversations force us to refine our understanding of creativity as a process.
If creativity is seen not as divine inspiration but as the recombination of existing ideas in novel ways (a view many cognitive scientists hold), then AI is just an extension of human creative processes, turbocharged with more data than any person could hold. As one Harvard Business Review analysis noted, generative AI can excel at “fostering divergent thinking” by combining ideas in ways humans might overlook, thus acting as a creativity catalyst rather than a replacement.
History also shows that technology-driven shifts in creativity are not new. The camera’s invention in the 19th century sparked fears among painters that their craft would become obsolete – instead, painting evolved in response (Impressionism and abstract art blossomed, focusing on what photography couldn’t do). In a similar vein, generative AI might push human creators to focus on aspects of innovation that AI struggles with: defining the right problems, injecting emotional depth, understanding context and meaning, and crossing domains in truly interdisciplinary ways.
Culturally, we may come to celebrate human-AI collaboration in creative endeavors. Already, new artistic genres are emerging: “AI art” exhibitions showcase works where humans curated or fine-tuned AI outputs, and the creators proudly wear the label of being symbiotic artists. There is even a growing movement of open-source creativity, where artists and innovators share AI models trained on their style, inviting others to remix and build upon them – a fusion of human artistry and algorithmic generation as a community effort.
In this broader context, generative AI can be seen as part of a larger shift in our relationship with technology. We are moving from an era where tools were inert and required explicit instruction, into an era where tools appear to have a life of their own – they surprise us, they challenge us, and they often require us to adapt culturally and ethically. Just as society adapted to the printing press, the camera, and the internet, we are now adapting to creative machines. And just as those past technologies ultimately empowered more creators and democratized knowledge, there is a hopeful view that generative AI will democratize innovation – enabling anyone with an internet connection and an idea to bring something novel into the world, whether or not they have formal training in that domain.
The cultural shift is as much about inclusivity as it is about disruption.
What’s Next : imagining AI-powered futures.
If the present is any indication, the future shaped by generative AI promises to be dynamic, uncharted, and full of opportunity. Looking ahead, we can imagine several scenarios – not as definitive predictions, but as visions of what might be possible as generative AI matures and we learn to wield it wisely in innovation strategy.
In one optimistic scenario, augmented creativity becomes the norm across organizations. Every product team, marketing department, and strategy unit will have AI-powered creative assistants integrated into their workflows. Ideation sessions might routinely involve humans and AIs bouncing ideas off each other in real-time, with AI generating instant mock-ups or business model variations. This could lead to a renaissance of innovation, where companies launch a flurry of new products and services tailored to niche markets, because AI lowers the cost and time of exploration. Imagine a future where a solo entrepreneur can design an entire smart device – schematics, code, packaging, ad campaign – by collaborating with various generative AIs, effectively having a one-person R&D lab.
Innovation becomes more accessible and accelerated, leading to an explosion of micro-innovations that collectively push industries forward. The competitive landscape in this scenario rewards those who can iterate fastest and learn from an abundance of prototypes, many of which are generated by AI. We might see a shift in the role of the innovator: from being the one who comes up with each idea to being a curator, editor, and strategic guide for ideas generated in concert with AI.
Another scenario revolves around transforming the strategic function of organizations. As generative AI gets better at handling complex analyses and proposing solutions, it could become a standard tool in strategy formulation and decision-making. Future AI systems might simulate entire market scenarios or generate strategic options that human planners hadn’t considered.
McKinsey experts suggest we are at a new inflection point in strategy development, potentially as significant as the advent of strategic planning frameworks in the 20th century. In the coming years, an AI might analyze global trends, a company’s internal data, and consumer sentiments to suggest, for example, a bold entry into an adjacent market or a radical pivot in product strategy – complete with projected outcomes for multiple scenarios. Human executives will still provide vision and judgment (the irreplaceable “strategic courage” to choose a path amid uncertainty), but much of the grunt work of strategy – data crunching, modeling, scenario testing – could be largely AI-driven.
The vision here is one of AI-augmented strategists: leaders who partner with AI to navigate complexity. This might also democratize strategy development; mid-level managers armed with powerful AI insights could drive innovative proposals upwards, reducing the reliance on top-down strategy in favor of a more agile, data-informed approach from all levels of the organization.
We can also foresee a scenario where generative AI catalyzes a new era of personalized innovation for customers. With AI able to generate on-the-fly solutions, companies might offer bespoke products or experiences as a standard offering. For instance, instead of choosing from a catalog, a customer could collaborate with an AI to design a product exactly to their needs (be it a custom piece of furniture, a tailored travel itinerary generated by AI, or even personalized media content like a short story or game crafted to their preferences). This mass personalization, powered by generative AI, would push companies to shift their innovation strategy from producing a few universally appealing products to orchestrating platforms that co-create with each user.
It’s a vision of markets-of-one at scale. The scenario raises new strategic questions: How do companies capture value when every product is unique? How do supply chains adapt when designs change constantly via AI? It also paints an exciting picture for consumers and client experience – a future where each person becomes a co-innovator in the products and services they consume, blurring the line between producer and consumer.
Of course, any forward-looking vision must also consider challenges. One foreseeable challenge is the risk of creative overload and noise. If every organization is generating a flood of AI-driven ideas, the battle for quality and attention intensifies. Innovation strategy will need robust filters – not just more ideas. This could lead to the rise of new kinds of businesses or roles: for example, firms that specialize in validating and filtering AI-generated concepts to find the true gold nuggets, or curators who manage an “innovation pipeline” ensuring that only the most promising AI-assisted ideas move forward.
Another concern is maintaining human touch and originality in a world awash with AI-generated content. There could be a counter-movement emphasizing human-made, artisanal products as premium or authentic, much as handmade goods regained appeal in the industrial age. Strategists might need to balance efficiency with authenticity, deciding where to use AI for creation and where to intentionally rely on human craft to stand out.
Ethics and regulation will play a big role in shaping scenarios. It’s plausible that within the next decade, we will have clearer rules on AI-generated content – perhaps watermarking requirements, licensing frameworks for training data, and laws addressing AI-generated patents or creative works. In a future scenario where these guardrails are established, innovation strategies will have to align with them by design. Companies might adopt internal policies ensuring their generative AI is used responsibly: for instance, always keeping a human in the loop for final approval, or avoiding certain sensitive tasks (like generating risky medical advice or financial recommendations) without expert oversight.
The vision of “what’s next” is therefore not just about unbridled creation, but responsible creation. The most successful innovators of tomorrow could be those who build trust with consumers and society by being transparent about their use of AI and by championing a vision of technology that augments humanity, not automates it away.
In the grandest sense, the generative AI revolution hints at a future where imagination becomes the only limit to innovation. When we can summon prototypes at will, test ideas in simulation by the thousands, and enlist non-human creativity at any moment, the bottleneck shifts. It shifts from “what can we make?” to “what should we make?” – a fundamentally human question about purpose, value, and impact. Thus, the next era of innovation strategy may be defined less by technical constraints and more by our intentions and creativity in guiding these powerful tools. In that future, perhaps we will drop the adjective “generative” and simply call it creative AI, as it becomes an accepted collaborator in all creative endeavors. And perhaps, as we embrace this, we’ll find ourselves in a world where breakthroughs that once took years happen in weeks, where the canvas of innovation is infinitely large, and where predicting the future gives way to continuously creating it.
📌 Takeaways
From Prediction to Creation: We have entered a new phase of AI where algorithms don’t just predict outcomes but actively generate new ideas, designs, and content. This marks a fundamental shift in innovation strategy, with AI becoming a creative collaborator rather than just an analytical tool.
Explosion in Adoption and Investment: Generative AI’s rise is backed by surging adoption (organizational use jumped from 33% to 71% in one year) and massive investment flows (over $25 billion in 2023 alone for generative AI startups). Such trends reflect a broad consensus that this technology is transformative, with 97% of global leaders calling it a game-changer for their business.
Augmenting Human Creativity, Not Replacing It: Generative AI can dramatically speed up and expand the early creative stages of innovation – from brainstorming to prototyping – but human judgment remains critical. The best results come from an interactive process: AI generates options and humans guide, evaluate, and refine them. Organizations should see AI as amplifying their creative capacity, while putting in place the curation mechanisms to focus on quality over quantity.
New Skills and Processes for AI-Driven Innovation: To leverage generative AI effectively, companies are redesigning workflows and cultivating new skills. This includes integrating AI into team processes, training staff in AI tools (e.g. prompt engineering), and establishing governance (ethical guidelines, oversight roles) to ensure AI use aligns with business values and goals. Those that reorganize around AI (e.g. creating dedicated AI innovation teams, reimagining R&D pipelines) are gaining early competitive advantages.
Cultural and Ethical Implications are Significant: The rise of creative AI is reshaping cultural notions of originality and authorship. AI-generated art selling for hundreds of thousands of dollars and AI-written music going viral exemplify this shift. Companies and creators must navigate public sentiment, legal frameworks, and ethical considerations – addressing questions of IP, authenticity, and societal impact. Embracing generative AI responsibly will be key to public acceptance and long-term success.
Vision of the Future – Continuous Co-Creation: Looking ahead, generative AI is poised to become a standard part of the innovator’s toolkit. We can expect a future where ideation is continuous and on-demand, strategy formulation is augmented by AI-driven simulations, and products or experiences can be co-designed with each individual customer through AI. The organizations that thrive will be those that combine human insight with AI’s capabilities to invent (and reinvent) their offerings faster and more purposefully than ever, essentially inventing the future rather than just predicting it.
📚 Recommended Resources
Agrawal, Ajay; Gans, Joshua; & Goldfarb, Avi (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. – A foundational book that introduced the idea of AI lowering the cost of prediction, providing insight into how the role of AI in business is evolving from prediction toward generation.
Stanford University – AI Index Report 2024 & 2025. – Comprehensive annual reports from the Stanford Institute for Human-Centered AI, covering trends in AI adoption, investment, and technical progress. The 2024 edition highlights the surge in generative AI funding and its growing share of overall AI investments, while the 2025 edition details the rapid rise in generative AI deployment across industries and its economic impact.
Accenture (Hintermann, Francis, 2023). “A New Era of Generative AI for Everyone.” – A report and analysis by Accenture Research on how generative AI is driving business reinvention. Based on a survey of global leaders, it discusses why 97% of executives see generative AI as transformational and outlines strategies to harness it across various sectors.
McKinsey & Company (2025). “How AI is Transforming Strategy Development.” – An article exploring the implications of AI (including generative AI) on corporate strategy. It provides examples of how AI can enhance strategy formulation by accelerating analysis and scenario planning, and posits that AI’s impact on strategy could be as profound as the advent of modern strategic frameworks decades ago.
Harvard Business Review (July–August 2023). “How Generative AI Can Augment Human Creativity” by Tojin Eapen et al. – A practitioner-focused piece examining ways generative AI can boost creative processes in organizations. It offers case studies on using AI for idea generation (promoting divergent thinking) and addresses how teams can integrate AI while overcoming cognitive biases.
Fabio Lalli (2024) on Medium. Beyond Efficiency: How Generative AI is Transforming Work, Skills and Organizational Models - An analysis of how generative AI is reshaping the skills we need, the way organizations operate, and the very definition of productivity. From efficiency to cultural transformation of work.
🧰 Toolbox - Real tools and patterns I actually use
This section isn’t a showcase of trending apps — it’s a distilled selection of tools and strategic patterns I personally use to think, build, and explore innovation with generative AI. Practical, tested, and applied in real workflows.
🔹 Pattern: Human Outlining, AI Filling
A method I rely on for strategic writing and concept development: I define the structure, intent, and logic, then delegate initial content generation to AI. It accelerates execution without losing clarity of vision. Every article, canvas, or pitch I design starts here.
🔹 Tools: Whimsical + Heptabase
I use Whimsical to visually map out concepts, systems, and AI-human interaction flows. For deeper knowledge organization, I turn to Heptabase, a visual thinking environment where I connect insights, papers, and reflections over time.
🔹 Pattern: Creative Divergence → Human Convergence
A go-to creative pattern: I let the AI generate a wide range of ideas — titles, prompts, product names — then apply critical thinking to converge on direction. This helps break cognitive bias and expose unexpected angles.
🔹 Pattern: Narrative Simulation
When designing strategies or testing messages, I use AI agents to simulate roles — users, stakeholders, competitors — and get diverse perspectives on assumptions, communication, and decision points. It’s become one of my favorite techniques to validate product direction.
The Shift Continues
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Next up on InsideTheShift #2:
"When AI Becomes the New Interface: the rise of Cognitive Platforms.".
This journey has just begun. Stay inside the shift.
Fabio Lalli | 05/05/25