Within the Writer Highlight sequence, TDS Editors chat with members of our neighborhood about their profession path in information science and AI, their writing, and their sources of inspiration. At the moment, we’re thrilled to share our dialog with Dr. Janna Lipenkova.
Dr. Janna Lipenkova is an AI strategist, entrepreneur, and writer of the e book The Artwork of AI Product Growth. With a PhD in Computational Linguistics, she combines deep technical perception with enterprise technique to assist organizations flip AI into tangible outcomes. Janna has based and led a number of ventures on the intersection of language, information, and intelligence — together with Anacode, which focuses on enterprise AI transformation, and Equintel, an AI platform that helps company sustainability. By way of her thought management and consulting actions, Janna is repeatedly shaping and refining her complete methodology for AI growth and integration.
You name your “AI Technique Playbook” a set of psychological fashions that assist groups align on what to construct and why. Which fashions most frequently unlock readability in government rooms, and why do they resonate?
One of many greatest challenges in government rooms is communication. Individuals imply various things after they speak about AI, which blocks execution. I exploit three psychological fashions to create a structured frequent floor which permits us to maneuver ahead with out excuses and misunderstandings.
I normally begin with the AI Alternative Tree, which helps us map the panorama of attainable AI use instances. Executives typically are available with a mixture of curiosity and hype — “we have to do one thing with AI” — however not a transparent view of the place worth actually lies. The default path most groups take from there’s constructing a chatbot, however these tasks not often take off (cf. this text). The Alternative Tree breaks this sample by systematically uncovering potential AI use instances and offering a structured, goal foundation for prioritization.
As soon as now we have readability on what and why to construct, we transfer to the how and fill out the AI System Blueprint. This mannequin helps map the info, fashions, consumer expertise, and governance constraints of the envisioned AI system. It’s particularly highly effective in multi-stakeholder environments, the place enterprise, information science, and compliance groups want a shared language. The blueprint turns the complexity of AI into one thing tangible and iterative — we will draw it, focus on it, and refine it collectively.
Lastly, I introduce the AI Resolution House Map. It expands the dialog past at this time’s dominant applied sciences — primarily giant language fashions and brokers — and helps groups think about the complete house of resolution varieties: from classical ML to hybrid architectures, retrieval methods, and rule-based or simulation-driven approaches. This broader view retains us grounded in delivering the fitting resolution, not simply the trendy one.
Collectively, these fashions create a journey that mirrors how profitable AI merchandise evolve: from alternative discovery, to system design, to steady exploration. They resonate with executives as a result of they bridge technique and execution.
In your writing, area experience is essential in constructing AI merchandise. The place have you ever seen area information change all the form of an AI resolution, relatively than simply enhance accuracy on the margins?
One vivid instance the place area experience utterly reshaped the answer was a logistics mission initially began to foretell cargo delays. As soon as the area specialists joined, they reframed the issue: delays weren’t random occasions however signs of deeper enterprise dangers corresponding to provider dependencies, regulatory bottlenecks, or community fragility. We “AI specialists” weren’t in a position to spot these patterns.
To include this area information, we expanded the info layer past transit occasions to incorporate supplier-risk alerts and dependency graphs. The AI structure developed from a single predictive mannequin to a hybrid system combining prediction, information graphs, and rule-based reasoning. The consumer expertise was expanded from reactive delay forecasts to threat eventualities with instructed mitigations, which had been extra actionable for specialists.
Ultimately, area information didn’t simply enhance accuracy, however redefined the issue, the system design, and the worth the enterprise obtained. It turned an AI mannequin into a real decision-support instrument. After that have, I all the time insist on area specialists becoming a member of in in the course of the early phases of an AI initiative.
Along with your posts on TDS, you additionally wrote a e book: The Artwork of AI Product Growth: Delivering enterprise worth. What are a very powerful takeaways that modified your individual method to constructing AI merchandise (particularly something that stunned you or overturned a previous perception)?
Writing the e book motivated me to mirror on all of the bits and items of theoretical information, sensible expertise, and my very own conviction and construction them into reusable frameworks. Since a e book wants to remain related for years, it additionally pressured me to tell apart between fundamentals on the one hand, and hype then again. Listed below are a few my very own learnings:
- First, I discovered methods to discover enterprise worth in expertise. Typically, we oscillate between two extremes — both chasing AI for the sake of AI, or relying solely on user-driven discovery. Within the first case, you aren’t creating actual worth. Within the second case, who is aware of how lengthy you’ll have to attend for the “good” AI downside to return to you. In apply, the candy spot lies in between: utilizing expertise’s distinctive strengths to unlock worth that customers can really feel, however wouldn’t essentially articulate.We all know it from nice innovators like Steve Jobs and Henry Ford, who created radically new experiences earlier than clients requested for them. However to do that efficiently, you want that magic mixture of technical experience, braveness, and instinct about what the market wants.
- Second, I spotted the worth of consumer expertise for AI success. Many AI tasks fail not as a result of the fashions are weak, however as a result of the intelligence isn’t clearly communicated, defined, or made usable. The success of an AI product depends upon how intuitively customers can work together with its capabilities and the way a lot they belief its outcomes. Whereas writing the e book, I used to be rereading the design classics, like Don Norman’s The Design of On a regular basis Issues, and all the time asking myself — how does this apply to AI? I feel we’re nonetheless within the early phases of a brand new UX period. Chat is a vital part, however it’s positively solely part of the complete equation. I’m very excited to see the event of latest consumer interface ideas like generative UX.
- Third, AI methods have to evolve by cycles of suggestions and enchancment, and that course of by no means actually ends. That’s why I exploit the metaphor of a dervish within the e book: spinning, refining, studying repeatedly. Groups that grasp early launch and fixed iteration are inclined to ship much more worth than those that look forward to a “good” mannequin. Sadly, I nonetheless see many groups taking too lengthy earlier than delivering a primary baseline and spending not sufficient time on iterative optimization. These methods may make it into manufacturing, however adoption will possible not occur, and they are going to be shelved as one other AI experiment.
For groups transport an AI characteristic subsequent quarter, what habits would you suggest, and what key pitfalls ought to they keep away from, to remain targeted on delivering actual enterprise worth relatively than chasing hype?
First, as above, grasp the artwork of iteration. Ship early, however do it responsibly — launch one thing that’s helpful sufficient to earn consumer belief, then enhance it relentlessly. Each interplay brings you new information, and every bit of suggestions is a brand new coaching sign.
Second, preserve a wider outlook. It’s straightforward to get tunnel imaginative and prescient across the newest LLM or mannequin launch, however the true innovation typically comes from the way you mix applied sciences — retrieval, reasoning, analytics, UX, and area logic. Design your system in a modular manner so you possibly can prolong it, and repeatedly monitor AI options and developments that would enhance it (see additionally our upcoming AI Radar).
Third, take a look at with actual individuals early and sometimes. AI merchandise stay or die by how people understand and use them. Inside demos and artificial assessments can’t substitute the messy, shocking inputs and suggestions you get from precise customers.
Your long-form writing (e book, deep dives) avoids hype and facilities on delivering worth to organisations. What’s your method for selecting matters and does writing about these matters allow you to higher perceive them?
Writing has all the time been my mind-set out loud. I exploit it to study, course of advanced concepts, and generate new ones. I normally go along with my intestine and write about approaches that I really imagine in and that I’ve seen work in actual organizations.
On the identical time, at my firm, now we have a little bit of our personal “secret sauce.” Through the years, we’ve developed an AI-driven system for monitoring new developments and improvements. We offer it to a few choose clients in industries like aerospace and finance, however after all, we additionally use it for our personal functions. That mix of knowledge and instinct helps me spot matters which might be each related now and more likely to matter not solely in some months, but additionally two or three years down the road.
For instance, in the beginning of 2025, we revealed a report about enterprise AI developments, and virtually each theme from it has turned out to be extremely related all year long. So, whereas my writing is intuitive and private, it’s additionally grounded in proof.
To study extra about Janna‘s work and keep up-to-date together with her newest articles, you possibly can observe her on TDS, Substack, or LinkedIn.
