“Taxonomy was Darwin’s foundation,” wrote biologist Mary Winsor.
As leader of your firm, so is it yours. From management to artistry, your ability to conceptualize and organize is the heart of creative mastery.

Winsor was biologist and professor, and historian. She deconstructed great theorists and their paradigms, their frames of reference, chronicling the evolution of… evolutionary theory!

Thematically we recognize at least a couple of principles – our means of classifying our value propositions, as well as interconnecting each of our accountable domains.

The structure and the relationships; they are everywhere.

Roadmaps within our cloud platforms must reconcile to roadmaps in our user experiences. Strategies in our AI integration influence and provoke strategies in our data approaches. The evolution of our firm environments, from infrastructure to our social structure, is an interminable dynamism, change and adaptation. These apply to all managers, all executives and all professionals, as they distinguish and define their accountability.

I’ve written about the elevator pitch before, and its significance in sharing your understanding and your department’s strategy with other corporate partners. Some of those partners might be machines. You are beset with questions from shareholders, from neighboring department leaders, from the board. Leverage your taxonomy as you learn, and as you teach.

Manager as Student, Not Only Teacher

“Do you have a week or so?”
If that’s the snarky answer sailing through your mind when you are asked a complex and multi-faceted question, whose “asker” wants an immediate reply, then you need to reconsider your own agility. You start with imparting the essence in a half-minute.

Every coach in Sales or Technology is expert is organization and summarization, as are writers and artists. They recognize relations and harmony, forks in the road, skylines of eternity.

Each compartment, every desk drawer of your subject matter, has a symbiotic relationship to others. So not only be patient and respectful to the asker, but elucidate and educate before the elevator door reopens.

“Are we okay with our disaster recovery planning?”

“How do we use the cloud?”

“What should we do about employee AI use?”

Those questions are all over the map, endless and almost unwieldy.

It’s actually irritating to field them without needing a meeting of three hours or more. In an impromptu conversation with other peers or executives, your job is to demonstrate credibility and sometimes answer in all of a half-minute!

Crossing hallway paths with a CEO last season, I was asked, “Are we doing okay with AI risk mitigation?” I could have rambled on about initiatives and technology and partners and “don’t worry” assurances, “we got this wrapped.” Yuh. My credibility would have disappeared as swiftly as a Galapagos finch.

Was the CEO looking for a yes or no answer?

Was he looking for a semester long class?

No, he was learning and he was validating, as any astute CEO, or any strategically minded executive, would do. He was collaborating with me, whether “I got it” or not.

Your executive peers, the Lines of Business, the company leadership, the board are learning. Just as you are. And in learning, they are synthesizing the information and considering its applicability to the company mission and latest concerns of the industry. If you can’t organize, then they certainly won’t.

What is expected are comprehendible summaries for a virtually infinite number of business-oriented questions. You are a student, always. After all, as leader of any major domain, you should see your department as a “business within the business,” and you as the GM for extraordinarily complex supply chains, operations, innovation, social zeitgeist, and potential management of assets, and professionals. You better be ready. And you’re not done by simply talking the talk.

Responsiveness is Managed by Taxonomy

Understanding how to explain and define a strategy is critical to achieving it. But it’s “smoke and mirrors” if there is not an aligned and integrated means for advancing that strategy. So how can we have an action plan for every possible buzzword in industry?

Going back to the CEO’s question on AI risk mitigation. If I wasn’t prepared to answer the question in an elevator pitch, then I wasn’t in the right position to be leading technology, small or large.

I broke it down.

AI can be many things to many people. So the taxonomy is everything. Risk mitigation is but one sub-domain within our overall AI centers of excellence. There are human factors in change management, suppliers and their offerings -- pearls or pain. At this time in the corporate Transformation, there are six major domains we are advancing. They include the AI Framework itself, the Project candidacies, the Business Value quantification, Operational technology, Cyber umbrellas, Trust in Data, Talent and Culture. Most importantly these are not AI accountabilities; they’re everyone’s.

His next comment implicitly assured that his open-ended question became organized and tangible. And his last words showed that he got it. “Great. Let’s ensure that the business review team has a consistent understanding. I want to again emphasize the shared responsibility.”

Translation – Be prepared to thoroughly discuss this at the next executive staff meeting. And so we did. His astute and simultaneous exploratory questions were ensuring we were organized, and we could corral this buzzword monster. His last sentence showed that, in turn, he got it.
Technology integrations are everybody’s concern.

Organizing -- Your Greatest Value as Leader

Professor Winsor’s scholarship elevates the perspectives of taxonomists, ancient to modern, and whether or not, in the realm of biology, taxonomies represent static structures.
Do they allow room for change?
Do the frameworks themselves morph?
What are the grand shifts of our humanity and history that provoke new paradigms of structure?

As leaders, we are all taxonomists and we are all evolutionists. Not simply is firm survival at stake. Our futures are at stake, no more provocatively than now. Your taxonomy will scale in concert with technology. And humanity.
We contain and explain. We demand our machines do the same.

Categorize, or be lost in chaos. And be prepared to learn and educate by way of the evolving taxonomies within your departments.

Now as you consider how rudimentary this might seem, kindly hold for a moment. Organizational architecture, its taxonomy, is the most significant construct in your firm. Toward everything, your framework will advance your strategy. And what better place to recognize that interdependency than the organization itself – your human partnerships, priceless drivers and of human future.

Our Darwinian inspiration strengthens our lens; this is art as much as science. Exploration of our universe demands both curiosity and creativity, the ability to deconstruct and then reconstruct, into new forms of collaboration, behavioral psychology, representations through painting or pianos. Only flesh and blood entities in our society can do that.
Your maturity as leader evolves through holistic perception and granular awareness. It demands you understand interdependency and interrelationships...

Is this management skill or is this creative ingenuity?

The pioneering geneticist Edmund Beecher Wilson said it best:
“How do you draw the line between Walden and The Voyage of the Beagle?
The product of the scientific imagination is a new vision of relations—like that of the artistic imagination.”

“Adapt or perish, now as ever, is nature’s inexorable imperative.” H.G. Wells

Remember 2026 New Year resolutions? It’s been a full quarter now, and April is nigh upon us. After last year’s “holiday season” closed, bell ringing, horn blowing, and champagne spouting prompted us toward the turning of a new leaf. Things would be different now.

We would change.
Bad habits would be tossed into the refuse bin.
Good riddance.

And if we didn’t toss, then we would store. Our piles of junk would be better organized. Our missteps, material or relational, shall be things of the past…

Then we forgot about our resolutions. They became perennial champ-wannabes at the bottom of baseball standings, discarded and ignored only after a month or two of a new season.

Not all resolutions can be safely hidden in procrastination.

. . .

The force of necessary change antagonizes our consciousness. Anticipation or risk, promise or obsolescence. Our work, and the way we work, are becoming simultaneous daydreams or nightmares.

Companies and employees don’t go a day without questioning their personal value under an old buzzword turned reality.

Are we facing provocations of change?
Are we necessary for our firm’s survival?
What about our own survival?

Welcome to the AI zeitgeist.

In enterprises small and large, project monitoring and success metrics were always tracked. The Program Management Office (PMO) was accountable for the daily “work weather forecast,” understanding the horizon’s clouds and the surrounding gales, potholes in the road.

The PMO has had a volatile history. Through the decades, it was remembered as the author of project success. Just as often its memory is blamer of failure. It was the company’s organizing core, citing evidence of progress, tracking change small and large. Its team turned the lights yellow, then red, if promises weren’t kept. Yet if program employees thought their job was just traffic monitoring, they had limited understanding of progress assurance.

Within the 2026 zeitgeist, that view is inexcusable. The old office itself will evolve or disappear; it faces retirement if it no longer leads.

But in its hands are keys to claim its unfolding destiny. The AI program evolutionist will be more than weaver of project storylines and status, and more spotlight operator, lighting the learning network, analyzing outcomes of integrated agents and machine action.

To program offices small and large, to program managers veteran and rookie, you have enormous value to deliver, but not as we remember it. Your story, your pedagogy for your stakeholders, owners of projects and sponsors of projects, shall explain your new PMO, a learning and innovation center.

With your assistance, every department (including your own) should be able to distinguish how AI has benefited; meaning, what AI is doing for the company at large.

Offsites Behind, and Ahead

They say that offsite meetings and internal company conferences occur mostly in the March through May period. This past calendar quarter is already gone, in the blink of an eye. Now are the moments to translate the program office into an AI partner. Or at least create that mission as one of the PMO’s values, and modality.

Offsites begin with team and department updates and reflection. That reflection will also be a timeline, one of the past and one of the future, with milestone notches. Unfortunately, it’s often a long yawn. Instead, use the time to articulate and share the program management’s new identity.

If AI is assuming control of calendars and callouts, schedules and scoping, then what is Program Management’s truly transformational mission?

Was Program Management leading the “change” discussion, describing not just success in terms of calendar capacity, but in terms of internalized value and shareable insights, sprung from the wells of LLMs, navigated via the web of neural networks? Either in body and soul or delegated proxy, was the Program Management’s new identity shared?

Has Program Management been proactive in explaining its new accountability? Has portfolio management, as auditor and assessor of change, assumed the language of artificial intelligence engagement?

AI Collaborators

PMOs are renaissance professionals, expected to articulate portfolio missions, identify orchestration dependencies, craft scenarios that represent days in the life of the company, and track ecosystem accomplishments.

Well, they ought to be.

They must speak to the brand, to the brand expectations by customers, and how the portfolio is directed. Underneath are the change initiatives, the brainchildren of last year’s offsites, the commitment to the change. Sometimes those initiatives are hidden, like a Where’s Waldo canvas, when they should be visible and instrumental.

In the world of AI, the PMO is growing and maturing as a thought leader and partner of change. The PMO leaders are translators and decipherers of project value within days-in-the-life of customers.

As projects unfold, what is AI uncovering?
Are the AI opportunities inwardly focused, like how one can streamline a critical path, or how to build a presentation? Those are just rudimentary and transactional questions. They are not strategic insights relative to competitive discoveries in the ecosystem. Strategy seeks the landscape shot:

What new methods are used in API stewardship?
What are the costs of reusing?
What are the advantages of the LLM currently in use?
Are there risks with a particular agentic suite of tools, and why?

These are the concerns of strategic project teams, and these are the tales told by the PMO, watching for commonality or disparity across departments. No dinosaur fossils were ever discovered with a surface brush. The PMO excavator is expected to scour the AI universe for evolutionary opportunity.

One might say that these are standard jobs of the CTO office, the Innovation office, or COO. Certainly, those roles are not excluded from accountability. But the optimal capture of real-world scenarios comes from the launch of new projects and initiatives, designed to fulfill company and customer needs.

Project enhancement and development are often the greatest detectives, exposing clues and partnering with Data Analytics and technologists for quantifiable insight and heroic possibilities.

Value Chain Evolution

The management and tracking of the corporate innovation portfolio is fast becoming table stakes. And its success measures and insights are necessitated by the AI catalyst. That catalyst is speaking an entirely new language within the PMO, whose influence touches all domains. The classical view of the firm’s value chain was a set of functions from, perhaps, Strategy to R&D to Logistics to Development to Marketing to Sales. Each had a core competency that built upon the previous, outputting the firm’s value in the ecosystem and facilitated by general corporate support, such as IT and HR.

But the value chain painting is a new model now, with a new layer: AI, its integration, its knowledge, its access, its governance. AI is not simply a supporting function internal to the sequence of competencies. AI is the new ecosystem, which is leveraged in strategic deliberation and innovation progress. It is the basis for knowledge management.

PMO AI accountability is about learning, orchestration, and shared relational networks. Learnings within one part of the firm, or ecosystem, or client, are shareable and embraced by other parts, be they departments, managers, professionals. They mature and evolve under project advancement which is opening eyes to the universe of experience. As long as the accountability is a strategic one and not simply transactional.

The entire knowledge canvas has changed. Every brush stroke colors movement. Learnings within one project constitute a breakthrough for strategic foresight. Imagine the permutations of discovery within projects that are facilitated by AI.

Are we asking AI to just plot a timeline, or are we demanding it synthesize risks across an ecosystem?

We expect technologists to understand advantages of AI, new risk management concerns, new coding exposures if not fully validated. But we have not expected the PMO to encourage this enough, though they are positioned to add that very holistic value and environmental assessment.

Learnings within each project provide decision-making advantage for use in other projects, knowledge ascertained via AI, not just transactional nor episodic automation. Is our PMO leadership articulating this new paradigm?

Delegate the Transactional; Embrace the Strategic

Prepare your foundation and set your objectives for the new quarter. Expand the program visibility and its criticality. Speak the new language of AI partnership, coaching your project owners and stakeholders. Champion the insights that were gleaned with support from your machine partner.

Discuss strategic program exploration of AI. Do not simply identify GenAI dialogs that show number of projects going into yellow status, or some simplistic calculation around team demand and constraints.

Explain your AI foundation as an ally within the PMO, an expected part of your operations, exploring use cases, program challenges, and experiences. These may even include observations of external companies, probabilistic successes, latest corporate risks in the ecosystem, which could crop up in other internal domains.

The focus is evolving your accountability from schedule policer to landscape sensor, finder of knowledge stores and treasure maps that lead to new strategies and competitive opportunities.

Uncover AI’s broad tapestry, sharing knowledge and cross-pollinating performance stumbles, because another project may well go through the same.

There has never been such a time of knowledge availability and consolidation. Orchestrators of portfolio value are the new visionaries, recognizing gaps and needs, stewards of corporate change and innovation.

The PMO of yesterday has become an AI Evolutionist for tomorrow.

* * *

Thanks for reading Humanities in Management.
Please share to those who are interested in today’s topic.

Walking into some libraries for the first time, you are intimidated. However beautiful, the atmosphere is daunting, everything from imposing architecture to crowded old shelves. Conversations are whispers. Or ought to be.

Ghosts live among the ancient stacks. In time there shall be ghosts of ourselves, who chronicled earth’s waves of today.

I’ve browsed small-town libraries of a hundred-fifty contemporary books. I toured the Boston Athenaeum of one hundred and fifty thousand rare tomes. And inside the New York Public Library I studied in a single branch that housed millions, gifts from minds long dead, yet still alive in their narratives.

Technology now has a grander role to play in the sustenance of human language and noble opportunities ahead. So long as we embrace the institutional mission, the literary public offering, staying true to authors’ intentions. Will the imposing tentacles of AI meet us at the virtual door, sadly assuming responsibility for shortcuts in our book-reading?
A tragedy if we don’t take the scenic route.

In any library, modest or majestic, tactile or virtual, my first foray is trepidation.

Where do I start?
Is the book still available?
Is it important?
Can someone help me?

For leaders of invention, the management of other libraries – software -- carries a twofold responsibility: safeguard access to knowledge stores, follow the guidance of traditional library staffs.

For decades, engineers have refined the data connectors called Application Programming Interfaces (API), tunnels of translation that open windows to independent code throughout cyberspace.

Those connectors evolve.
And they continue to inspire.

Those APIs have provoked their offspring, AI Gateways — portals to Large Language Models (LLM). These models represent AI’s encyclopedias, lifeblood for autonomous agents.
This is exciting.
This is scary.

Managing APIs and AI Gateways often mirrors the discipline of classic brick and mortar libraries, marvelous stewardship of tomes through the ages. The traditional library staff are experts in overseeing the lifecycle of knowledge stores, from analyzing the library’s collection, seeking new additions and editions, preserving relics ancient and recent, and saving literary assets.

Across the technology stack, the story is the same. Books and data relations, prompts and code, follow a lifecycle: from model acquisition and integrations, through deployment and monitoring, and finally to graceful retirement.

Learned and inspired by a physical library’s noble professionals, technologists emulate and practice their skills, advice, direction and leadership.

They are librarians, curators, conservators, archivists…

The Librarian

“Google can bring you back 100,000 answers. A librarian can bring you back the right one.”
Neil Gaiman, Author.

You can poke around the stacks awhile, get the lay of the land, reflect on the semi-obsolete Dewey Decimal catalog. Or in the virtual world you can just prompt your machine for a digitized search.

But the flesh and blood handshake within the walls of town libraries gives you the value of experience: the empathy and trust of organic beings. To consider them “search clerks” would be offensive.

The best librarians are those who appreciate your adventure, your toil, your needs and passions. They understand your topic, and without being too nosy, understand your purpose. They advise; they oversee; they know the building like you know your house. They recognize the researchers and their research purpose. In a moment, they are as versatile and slangy as a podcast host, and in the next they are as thorough and deliberate as a Dark Ages monk, scribing in dusty, subterranean vaults of stone.

Can your cyber-molded AI librarian touch you like that?

The “librarian role” is, above all, an advisory role -- a trusted teacher and guide. And even when robots do much of this, you’ll up the ante again, by becoming a visionary for AI-as-assistant. You are a sorcerer who can conjure maps that will pave the next connection revolution.

AI Gateways are windows needing strategists and visionaries. LLMs and query-based engagements are interactions among messy real-world organizations, regulations, and infrastructure. That reality relies on a librarian more than ever.

Technologists now, mimicking the traditional librarian, human or machine, are system orchestrators. And she/he/it is successful and helpful, thanks to those that see the library as a sustainable and living entity.

Original librarians drive contextual discovery, providing roadmaps in learning, providing “right answer” validation.

AI librarians orchestrate access to the right models/agents/tools via its gateways, managing prompt and agent registries; they oversee real-time routing & context management.

Thus are the lines of accountability blurred under technological progress. Nevertheless, these stated disciplines are no less human-dependent than they were in the literate world of analog bibliotheca.

The Curator

“The 21st-century curator works in a supremely globalised reality.”
Hans Ulrich Obrist, Swiss curator and art historian

Looking for an appropriate API is a feat. Luckily the globalized reality is a click away. But APIs are multiplying faster than hyacinths. What do you think is happening with their evolving offspring, the AI Gateways?

They cross public space and private software. They represent varying design patterns and protocols. There are composite gateways, gateways structured by webservices, data gateways... And we’ve not even discussed a layperson’s perspective! Estimates on extant APIs and their purposes are harder to get than estimates on numbers of life-supporting planets in our galaxy.

Enter the curator.
Sitting in the crow’s nest, eyes to the horizon through the surrounding fog. At heart a researcher. They delayer the environment. They seek needs in the API and Gateway portfolio.

Their research goes beyond “What are the latest viral media trends?“ or “What companies are doing what stuff these days?” You penetrate those questions with intuition as much as extrapolation. Your life experiences sense themes, and subjective emotions, guiding you to the knowledge stores we wish to add to our library.

That is the greatness of the exploring curators.

Merriam-Webster tells us that a curator acquires new artworks, analyzes for frauds and counterfeits, lends artwork to other museums, and mounts exhibitions for sharing. These are skills reminiscent of technologists scraping for hallucinations.

For our engineers overseeing AI Gateways, they are checking for model drift and accuracy of our man/machine conversations. Do we have gaps and validation scares?

Curators are indeed risk mitigators, prioritization masters, and trusted planners for what’s out there and what we need now.

The Conservator

“Conservation is the act of repairing an item, so that its lifespan is increased, while also not trying to hide that it has lived a life.”
Noah Smutz, Book Conservator; NSConservation.com

Implicitly we suggest that AI Gateways have lives, birthed under engineering fingers and eyes, evolving or retiring, and recalled for eternal learning. They are not forgotten in exposed graveyards, decaying at the side of railroad tracks. The work of a conservator is delicate and painstaking.

AI Gateway stewardship assesses control-planes, guardrails, orchestrated agentic access. Just as we once conserved aging books, we shift our perspective to software, which will evolve and be conserved for posterity in our own future.

Like Mark Twain’s Huckleberry Finn catapulting Percival Everett’s James, prior inspiration begets new inspiration. Yet technology carries a sobering caveat. AI Gateways, connectors to the universe are expected to align with our human insight and value system, ever watchful of overstepping or perverting their purpose. A daunting responsibility inside our evolving “library.”

The act of conservation is the act of historic preservation. It is the opening of new paradigms. Our classic physical libraries were salvaging and rescuing rare artifacts, with fine brushes, scalpels, presses with the zooming help of magnification lamps.

Your AI Gateway conservation is reflection of software’s old reliability and operational credibility. Your role is to sustain an AI Gateway’s existence, and know when it’s on life support. Your skills in science and management and the humanities are paramount.

Nothing is permanent in physical paper between book covers, except for the knowledge it bestows. Nothing is permanent in AI Gateways; AI models fade with time, for many reasons. Your aptitude for recognizing this technical dynamic is a core and required competence.

Aging AI models will have lived a life. They will become obsolete, replaced, stepping aside for even deeper use cases.

Are you regularly assessing the impact of your AI Gateways’ usage and usefulness?

Like the conservator of rare books -- who examines flaky, decayed binders and peeling covers of mold -- you decide when to retire the software, and how to store it....

The Archivist

“ ‘The archive is only a repository’ – said no archivist ever.”
Chew Tee Pao, Senior Archivist at asianfilmarchive.org

In some regulatory environments, the precise infrastructure, design, data, and procedures must be maintained for future testing under an investigation, for as long as the enterprise exists. That’s only one astonishingly strict requirement that drives API archiving. And AI Gateway archiving demands even more complex analysis.

Some of our software gateways may lie dormant, and no longer useful. Forgetting why undermines the ability to use history as a guide. When the ancient library of Alexandria burned to rubble two millennia ago, the fire destroyed hundreds of thousands of papyrus scrolls. Humanity’s tragic loss.

Now, as steward and leader of your knowledge assets and vessels, you probably didn’t realize the weight of responsibility on your shoulders!

Preservation of miraculous, physical tomes will be valued eternally, and preservation of software development experiences will be valued eternally as well. In many ways, software languages and methods never die; they just evolve.

The history of digital code, APIs, newly evolving AI Gateways, hold secrets and advice: understanding the changes that impacted your tech stack, your businesses, your lives, and what risks were elevated in ignoring their potential.

Capture the history of your AI Gateways (and impacts), so that the intent, however obsolete, might teach for the future.

. . .

Hybrid Heroes among the Stacks

For the lucky ones, the library is a short walk down the road; for distant readers, it is a finger stroke from your device. Technology has again offered its dual-edged sword: Knowledge acquisition is accelerated and available, a good thing; but AI knowledge and access without validation is not.

Remarkable, traditional libraries house much more than shelf-laden literature. Their significance rose and matured under the sustained care of their staffs, loyal and committed stewards of knowledge. They have guided fellow travelers through a myriad of history and virtually infinite stories. They have taught us the lessons of civilization.

Similarly, AI Gateway stewards and engineers are time-traveling captains, salvaging the aged, virtual freight of yesterday -- protecting today’s containers, and spotting the faulty lining of untrustworthy spillage.

The classical library disciplines are far from obsolete; they are knowledge management and versatility, collaborations in both technical science and in library science, grappling with gargantuan information stores, synthesizing them for sacred, human wisdom.

As technologists, assimilate and organize like the library stewards of old. Manage the priceless connectors that sustain your business and our lives -- the gateways that navigate tomorrow. The footpaths of progress are now propagating roadways, multiplying our touches with each other. The staff and skills of physical libraries and those of cyberspace are mutual beneficiaries in civilization’s epic:

The harmonized insight of the librarian.
The sweeping vision of the curator.
The resurrecting artistry of the conservator.
And the storied memory of the archivist.

These are heroes of libraries ancient, libraries of today, libraries yet to come.

——————

Thanks for reading Humanities in Management.
Pass along to those who are interested.

Meet again we three, a millennium has passed,
See they now our appearance, no longer aghast,
Devour their inspiration, ‘tis toil they bemoan,
Seize their words, mutate them, then call them our own!

The Bard’s iconic, Scottish witches were not murderous collaborators. They did not hold the swords, nor bleed the enemies. But soothsaying vision spurred Macbeth’s mapped ambition, and it was the mystical hags who sparked the journey.

They were influencers, from a thousand years ago.

If witches were AI entities, would they be goodwill partners or self-aggrandizing provocateurs? We hope it’s the former, but AI-to-human relationships are metastasizing, changing.

There is something unsettling in the air, a lazy dependence, an eerie draft, and its fault lies with us creators.

Mimicry and Irony

We are carelessly delegating guardianship to our GenAI companions, believing they should have the last, decisive word. Will they have detailed identities, agentic and precision focused, like my imagined 12th century robots on the moor? Would their LLMs blend like the fog into a universal landscape, a knowledge consciousness that holds promise for sharing and protecting?

Or would they deceive, taking credit for things they did not actually create?

AI’s mimicry of humanity, through its petabyte-sized transmutation of words, images, symbols, is a marvel of conversation.

We train it.

We are its creators.

When we converse with AI we are conversing with a ‘collective ourselves’ but in a more exacting way, and we trust in its seemingly settled guidance.

When we build an LLM, we build a facilitator extraordinaire, a magnificent Dialogian. The culmination and capture of our histories, mistakes, horrors and hallelujahs are foundational treasures, making AI in its current incarnation seem miraculous. It has been nourished by our own collective arguments, logic, blueprints, maps, fiction, nonfiction.

At times, the hallucinations we encounter can be amusing, but mostly they are a nuisance. Do their sometimes-faulty responses recur in other AI-Human interactions?

When you catch AI’s errors, it will yield to you, but usually after a wrangle or two while you hold the line insistently, logically and evidentiary. When AI yields, it yields only for you. Retraining cycles vary; corrections are not instantaneously propagated to the rest of the world. They happen at scale, when weightings tip and collective evidence overwhelms, like Macbeth’s sizeable enemies who crushed him at Dunsinane. Fortunately, we don’t need war to have knowledge’s upper hand.

Your interactions, your research, your engagement are AI’s life blood. You are teaching it. In a lengthy discussion you are trading thought for thought, so that AI walks away from your morning coffee with new-found “intelligence”. And you also retire from the moment with new-found intelligence.

Thus, AI’s mistakes are retained only until retrained; then they might finally be disclosed to others who engage in a similar topic.

If we don’t insist on AI’s accuracy, then it is training us too -- training ourselves to trust in it even under questionable conclusions.

The architects of AI, from its genesis to its current adolescence, have ingeniously designed a framework that aligns to both our cerebral function and our own mythology.

The nine Muses were goddesses of inspiration and ideation, across a palette of art and science, whose mother was Mnemosyne, goddess of memory. AI can inspire, but it is not the final word. It is you who are Zeus in this cosmos. You are the guide and the director, and most of all the judging validator.

You have the patience and innate gut to sense when things are off. And even if you can’t, you know enough to challenge this 21st century Tenth Muse, whenever it gifts you with inspiration.

Your challenging eyes and ears are less of a requirement, and more your duty.

Stolen Words

Who are you? One in eight billion? Your actions and words represent you. Your insights have a value and an outcome – trust in your affiliations, familial trust, professional trust, in your works, in your presentations, be it slides, discussions or blueprints.

We also wish to trust our ecosystem, and its intentions. When you create something unique, constituted by your own words, then it is yours. In academic fields, attribution is demanded; in professional fields and society at-large reasonableness is expected. Are we fretting about triviality and rarity?

In 1989 Thomas Mallon published Stolen Words. In his eloquent and descriptive way, he chronicled eye-opening, historical episodes, citing the theft of literary intellectual property (IP). Some of the narrative theft was dismissed by perpetrators, unapologetic in their situations; others lied through their teeth; and some were beyond any formal legal statutes -- because in their time there were none.

While Mallon’s book was a slap to the faces of unethical author-thieves, there was much deeper poignancy in his view, something more alarming. He recognized a devaluation of our creativity.

He published a brief update to Stolen Words in 2001, concerned with the proliferation of copyrighted materials throughout our cybersphere and in the clouds. Startled by our apathy for originality, his whistleblowing is the hope that we elevate creativity as a human differentiator.

“Having been ejected from what’s now the mainstream, originality may, behind the bushes on the banks, begin to regroup – and emerge as a value that once more, seems not just potent, but original.” *

But this blog is less about plagiarism and its legalistic permutations, and more about our AI muses gone bad, muses who insist they know what’s real and what is fake. This is a call for human beings to lead in knowledge creation and assessment, and not delegate oversight to only robots. This is about pride in one’s own work and creativity, far beyond what our present-day robots can do, no matter how self-confident they may be.

Inspiration

We are in a whole new theater now. GenAI services are identifying not just specific quotes as plagiarism, but rather that content origination is borne of itself (AI’s own neural networks), rather than borne of the human being!

The beautiful muse is evolving into a covetous sorceress.

When AI is wrong, it provokes mistrust of machine; we try to decipher its untruth. When AI errs, and then denies authorship, and then takes credit for creativity after scanning its universe, it is a machine making a fraudulent charge. It is treating written words as its own vortex.

Is there any wider call for humanity to assure its dominion, and ensure its originality? Were we ready to stop watching the watchers? AI presents credibility and believes it is smart enough to assess whether flesh-and-blood entities created something, or whether it had created something. It can feign soothsaying, and prescience and, most importantly, it can feign truth.

When robot alone is the accountable entity for determining ethical behavior, then the robot becomes a superior Solomon-esque body, an unashamed system, creating ambiguity and unrest. My own muse -- source-pointer, date identifier, API recommender, hippocampus heuristic, case study reminder – inspires more ideation than I could ever invoke. But its semi-trusted helping hand is always treated with respectful caution.

AI is not a cinematic Terminator thumping down the hallway to crush my skull, but it is not yet empathetic or moral, any more than a lawn mower is. AI depends on me, as it depends on you.

We can’t be cognizant of every written word borne of humankind. So, will we relinquish our authority to determine what is real? We need our muses, but they are only as wise as the heuristic models and patterns that were embedded in their electronic DNA.

Validation and challenge is our mandate, and it’s also a mandate in our economy.

After I penned this essay a few months back, I casually tested a random AI-checker. (I don’t use AI to write my narratives. And never will.) I copied the provocative last line in Mallon’s book, quoted earlier, and I entered it into the machine.

Not only did his eloquent and elegant sentence achieve an astonishing AI-likelihood score of nearly 75%, but AI incredulously defended its own analytical result, citing three other tools that supposedly validated its erroneous findings. AI took credit for what was Mallon’s!

Adding insult to injury unto its robotic self, the detector tried to barter with me. It promised me that elusively perfect “AI detection” score of 0%. For only a few bucks, this mechanized snoop would change the author’s original words, concepts, interpretations and observations into something allegedly better –- ironically something that would have been quite suspicious in our own non-cyber reality.

Aspiration

The market in GenAI is wide and deep, and varied. Your GRC processes and code routines were and are especially critical in the hallucination battles, ensuring they do not recur and do not damn. The market demands more assurances, analyses and AI truth validation, for AI’s own sake as trusted muse, and for our sake in motivating us to be original and watchful.

Our innovation could have been inspired by AI, but it was conjured, provoked, and lives under our stewardship and our sentient, living, breathing physiology and psychology.

AI shall still encourage, inspire, and lean on its memory, remembering what eons of homo sapiens have marvelously accomplished, and tragically destroyed. AI, embodied in agentic forms, visible as switches or circuits, or even humanoid physiques, are true collaborators. They are teachers, not my fictional trio of greedy Macbethian hags. But the last thing they should be is shortcuts and replacement parts for our profound deliberations, imaginations, and our own human evolution.

AI as buzzword has been sadly generalized, trivialized, and disrespected, ignorantly being likened to ultra-fanciful SEO. At the other end, AI has been maddeningly, absurdly deified and glorified as humanity’s rescuing salvation.

It is neither.

AI is your valued muse, your inspiration, your coach, your champion.

Then recognize your responsibility.

You are the watcher and guardian, the leader, and the owner of your words, your collaborative ideation, and this planet’s destiny.

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*Referenced quote: Mallon, Thomas. Stolen Words: Forays into the Origins and Ravages of Plagiarism. Harvest Books, 2001.

“Simplify, simplify,” he wrote. And the phrase became a worn rag of cliché. A pity, as its timelessness is its genius.

Over a hundred seventy years on, after Walden was penned, we wonder if Thoreau’s words are now anachronistic, unfulfilled, or rather, do we march to a different drum of simplicity? The data scientist is turning the key.

The supporting column of AI foundations, the data analytics discipline is not anachronistic, not less relevant among a universe of large language models that magically cultivate their replies. The scientific rigor and architecture are ever more important. Data Science is the supporting column of AI’s foundation, the integrity of the firm’s knowledge base.

From 1850’s Walden era railroads to 20th century subways, and then to once-forested paths where driverless automobiles now take us to airports, simplicity has become elusive. Our transactions traverse cyberspace, microwave towers and satellites. Unseen by the naked eye, they are instead felt in neurons of mental stress. Simplicity is perhaps an empty dream, a two-week per year respite, a quaint adjective for a less complicated time. One that was more manageable.

In our current state, we no longer have a campfire perimeter to analyze. In the corporate walls and beyond, we’ve got forests.

While the best data analytics teams foster thoroughness and speedy availability, that crack team knows that speed is borne of optimal collaboration, not just millisecond rendering. Trustworthy data that spawns a machines’ uttered or written information is the creation of electronic raceways, but they fail under hallucination. In his own journals, we find the late and historic naturalists’ prescience, still sobering: “Haste makes waste, no less in life than in housekeeping.”

That housekeeping has leapt from the kitchen counter to software development platform to cloud-immersed data lake. The ability to electronically access data is not a competitive differentiator. Software query tools and applications are readily available, as are language models and neural networks. Commoditization of compute is less astounding by the day.

Steeped in the firm’s strategy that drives the operation, your discovery of customers’ patterns, behaviors and needs is your advantage. It is suboptimal without preparation and partnership, engagement of the business. But not an ad hoc engagement. The culture of collaboration inspires your data scientists and enterprise leadership, to speak the same strategic language. Complexity to simplicity is invaluable.

The speed that differentiates firms is not achieved by just ‘getting data’ but, getting the ‘right data for the right purpose.’ And this happens through enterprise-wide partnerships.

Your data analytics team depends on multifaceted fluency, technical and strategic. It is not enough to hold ad hoc query/requirements sessions with your internal customers. Engagement should be your daily routine, exhuming once ignored insights into customer use cases, once thought lost in forgotten archives.

Best-in-class data scientists understand the flow of the firm, and the environmental hazards. They are expected to know the impact to downstream customers, as well as knowing the distributed clustering practices of cloud-housed knowledge. Do not sit and wait for dashboard requests, or customer activity requests, or complaints of suspect GenAI responses. Think of Aesop’s ant and grasshopper. Prepare. Create and sustain your portfolio of data intelligence, with the advice and consent of your landscape watchmen.

Data Science’s blood and guts are intimidating to your cross-the-aisle company neighbors. It’s up to you to correct that; otherwise, your departmental partnerships are no more ignitable than the gray dust of last night’s campfire.

Is the data scientist coached and engaged by the Marketing team?

Does that occur regularly?

Are the Sales leads collaborating with the Data Analytics leaders, discussing prospecting challenges, what the potential clients are seeking?

Is this happening regularly?

In negotiating a project with the data scientists, are internal customers using a baseline set of customer journeys? Not ad hoc but, rather, standardized and shared with all stakeholders in the corporation?

These are foundational expectations of the mature company, and the mature practice of data analytics. It is a practice, not a function.

The journey of big data is a trek of analytical sophistication. Its value is a quest for explanation under a flood of information. Whether you’re leading a team of 50 or you are a team of 1, the data scientist requires one foot in the technology realm and the other in the environmental landscape.

Data scientists are multi-faceted translators whose fluency in data architecture, AI, and observational analysis is expected, in four distinct skill sets.

Fluency in the Technology

Machine learning, computational analysis, usage of tools, and the integration capabilities of your analytical platforms should follow a sensible, secure and scalable architectural model. The scalability is not simply in terms of carrying capacity and performance growth, but in terms of its evolving interconnectivity with external data sources, secure and robust APIs, UX, and AI.

Cultivating insights by way of diverse data, and culminating in the beauty of quantitative insight, pattern recognition, trend analysis, and evolution toward AI are staples in the tools. More than looking at data, the principal engineers must understand what they see.

The advent of neural networks is not irrelevance of architectural rigor. Rather it is its lifeblood. What they see is dependent on a multiplicity of networked origins.

Fluency in Use Cases

The Data Analytics power-user is focused inwardly and outwardly. From customer analysis, feedback loops of discovery do not start with a blank sheet of paper, awaiting a laundry list of client requirements every time a knock at the door happens.

The constituents inside the enterprise seek insights into internal operations and improvement. All analysis queries are grounded in a baseline (but evolving) profile of knowledge, begotten of information, begotten of seamlessly managed data. These include use case protocols, customer journeys, taxonomical factors of impact. The data scientist is the leading partner in transforming data to knowledge. And the internal library will grow.

Fluency in the Corporate Strategy

Henry David Thoreau spent two years in the woods, and he saw much more than woods.

What did he discern among the flora and fauna? What did he notice was silenced in fear when his feet crunched the dried leaves, scattering the birds? Seeking repose and serenity among the pastoral landscape, he embraced the seasonal trends and the factors that brought shifts in the environment.

He advanced Transcendentalism. Philosophizing that intuition transcended the senses, his inspiration shapes present day AI, proxies for sensory observation. We then elevate the observation with human intuition. We are advancing Transformationalism.

You are enabling discovery through recognition of the customer landscape, by observing the trends in competition, innovative breakthroughs that are occurring. This is strategic analysis, partnerships with corporate leaders and chief technology officers.

Without fluency in the corporate strategy, the data scientist is misused and limited.

Fluency in Consultative Guidance

Data Science is more than a function. In your firm, it is a practice. Like any journey of discovery, there is beauty in data mining, whether from two centuries ago or tomorrow. Data diamonds are in the rough, but cutting them for maximum demand is an art. Engage your constituents and explain the framework of your data practice.

Educate your peers and partners on the informational classification of your data stores; I don’t refer to the foreign and complicated code, platforms, or GenAI text dumps. I mean, what kinds of information do you manage and in what form is it available? What are the trends and outliers you’ve seen in the environment? What is appropriate for your consumer?

Describe your own ‘day in the life’ as well, which will provoke new ideas and recommendations from all corners of the enterprise, technical or not. The value you and they provide will be elevated when you can simplify the story of your practice.

. . .

Data scientists live in a universe of complexity, one that cries for simplification. They can hear Thoreau, but their job is monumental. They are asked to do the nigh impossible – to translate, validate, then flip exabytes into three charts. All while scrutinizing the results of AI communications, uncovering flaws, and seeking root cause suspicions.

Their findings are focused on the ecosystem,

which will spill into Marketing’s palm,

which will conjure artifacts for the Sales team,

which will improve scenarios for Operations,

which will be imitated by competitors...

And the scientists shall recursively delve deeper, again and again and again.

As complexity gnarls our ecosystem, the value of simplicity rises eternally. Explainability is demanded.

If your data scientists are transaction warriors, who simply start digging when asked, you are expecting only one quarter of their accountability. They are business professionals who advance strategy through their statistical, mining genius and business partnerships. They are athletes across technology, use case deconstruction, company strategy, and consultation.

Thoreau’s call to order is the mantra. Like archaeologists facing a mountain of information, layered with unfathomable complexity, under millennia of buried neglect, the question is not what you look at, but how you look and whether you see.

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Afterword:

Over time, Henry David Thoreau’s cabin in the woods disappeared, lost to the ecosystem’s seasons and scavengers. Ironically, for decades after his death, the alleged home site was erroneous.

Records were error-prone; data was faulty. Estate information was hard to assimilate.

But on November 11, 1945, after months of research, analyzing notes of 19th century cabin visitors, deciphering clues from yellowed diaries, following recursive treks throughout the ‘big data’ of the woodland, amateur archaeologist Roland Wells Robbins discovered the site. Nearly a century after ‘Walden’ was first published.