Technocracy will never win the day

It's the year 2000, and 耐克 , the sportswear titan, is poised to revolutionize its supply chain. The weapon of choice? A cutting-edge system called i2, promising to bring order to chaos through the sheer might of data analytics.

The result? A $100 million inventory write-off and a stock price that plummeted 20%.

Fast forward to Q4 2024.

Nike's data-driven gamble continues to raise eyebrows on Wall Street. Revenues are sinking by 8%, with significant declines in both Nike Direct and Converse . The once-mighty North American business is faltering, a stark contrast to growth in other regions.

This isn't just a story about Nike. It's a parable for our times.

Take Large Language Models (LLMs). These digital behemoths consume vast oceans of data, regurgitating patterns with uncanny accuracy. They can write poetry, code software, and even engage in philosophical debates.

Yet, when faced with tasks requiring logical inference and causal understanding — the kind of thinking that separates humans from machines — these digital giants stumble like newborn foals. They can mimic human language with startling fidelity, but the deeper currents of meaning often elude their grasp.

It's easy to forget the extraordinary machine between our ears. The human brain, honed by millions of years of evolution, possesses capabilities that still elude our most advanced AI systems.

Human experts consistently outperform algorithms in scenarios rife with uncertainty and nuance. Why? Because the human mind is more than a pattern-matching machine. It's a crucible of intuition, creativity, empathy, and social acumen — qualities that remain stubbornly resistant to digital replication.

Netflix provides a masterclass in human-AI collaboration. At its core lies a recommendation engine that processes vast amounts of viewing data. But alongside this algorithmic wizard works a team of human curators, storytellers who understand that what keeps us glued to our screens isn't just data, but narrative.

These human experts create thematic collections, write compelling descriptions, and provide the contextual understanding that algorithms still struggle with. The result? A system that saves Netflix an estimated $1 billion per year by reducing churn and keeping subscribers engaged.

It always comes down to data.

So they say. Why are we so obsessed with data when we know data hygiene is a persistent problem? It's as if we've invented the car but forgotten to build roads. Issues like data quality, talent shortages, and ethical concerns continue to plague AI implementations.

We're hoarders, digital dragons sitting atop mountains of information. Yet most of this data is junk. We're digging through digital detritus, hoping to strike gold. But at what cost?

The future belongs not to AI and being driven by data, nor to human expertise in isolation, but to those who can master the delicate dance between all of the above. It's a future where data informs but doesn't dictate, where algorithms support but don't supplant human judgment. Practitioners like Tiankai Feng are saying this all along - and his book is titled "Humanizing Data Strategy " for good reason! (Get it now if for a great summer vacation read!)

Our challenge is not to become more like machines, but to double down on what makes us uniquely human.

Our creativity, our empathy, our ability to navigate ambiguity and contradiction — these are the qualities that will allow us to harness the power of AI while retaining our humanity.

A PwC study found that 79% of consumers believe companies should understand and anticipate their needs, a task that often requires human intuition and emotional intelligence. Even in customer service, where chatbots have made significant inroads, human agents remain crucial for handling complex issues.

Companies like 亚马逊 have recognized this, implementing a hybrid model where AI handles routine queries and human agents tackle more nuanced problems. It’s a recognition that while AI can process information at superhuman speeds, it still can’t replicate the nuanced understanding that comes from shared human experience.

How do we make sure we do not go the way of mere technocrats but remain committed to use technology as a means to an end?

Here are some guideposts for the journey:

1. Embrace the Hybrid Model: Don’t view AI as a replacement for human expertise, but as a tool to augment it.
2. Invest in Digital Literacy: Ensure your workforce understands both the capabilities and limitations of AI. Preparing them for this future is not just good business — it’s an ethical imperative.
3. Prioritize Ethics: As we give AI systems more power, we must ensure they’re guided by human values. The European Union’s proposed AI Act, expected to be implemented by 2025, provides a framework for ethical AI development and deployment. It’s a recognition that as our tools become more powerful, our responsibility to use them wisely increases.
4. Cultivate Human Skills: In a world of increasing automation, uniquely human skills become more valuable. The World Economic Forum’s Future of Jobs Report 2023 lists critical thinking, creativity, and emotional intelligence among the top skills for the future workforce. These are the areas where humans still hold a decisive edge over machines.
5. Create Feedback Loops: Establish mechanisms for human experts to provide input on algorithmic outputs, creating a virtuous cycle of improvement. Google’s PAIR (People + AI Research) initiative is an example of this approach, focusing on the human side of AI development.

If you want to learn more about the Hybrid Model, make sure to read "Augmented Analytics " from Tobias Zwingmann ! It will show you various ways of not only getting started with Data Analytics & AI - but do it with lasting impact.

One of the ways to use technology to its fullest potential is to understand its specific strengths and weaknesses. Thanks to Gartner we've got this nice illustration for getting a grip on Generative AI:

And that is essentially what an IT architect does: distill the available options, boil down the solution space, provide clarity and transparency in a space full of hard-to-understand dependencies and variations. And ultimately provide the basis for taking decisions.

The skillful blend of techniques and technologies, guided by a clear objective and fueled by our pragmatism, creativity, and ingenuity, yields the solution.

Technocracy on the other hand is detrimental to everything humans value — and really good at diminishing asset values as well, as Nike has proven.

Learn more about the way of the IT Architect:

IT’s not magic, it’s architecture: Leading, Aligning, and Innovating IT & Business with Principles…

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