
May
Rethinking Human Worth
It’s understandable why some people are feeling apprehensive about Artificial Intelligence. It easily out-produces and outdoes any human when it comes to productivity, along with processing and analyzing information. In fact, the World Economic Forum projected in a white paper in January 2026 that 92 million jobs would be displaced by AI-powered automation by 2030. For a society and culture that have equated human value with productivity and efficiency, the dawning reality that the Age of AI is upon us is both a grounding but worrying outlook.
However, that sobering realization is seen by some as the pivot we need to step back and reflect on what it means to be human, on what differentiates us from machines when the latter can perform better and faster the same tasks we’ve been carrying out for decades, even centuries. We’re now at a turning point on how we view and value human worth. The Age of AI is perhaps the catalyst from which we associate human value no longer in terms of intelligence, knowledge, nor speed, but wisdom.
Image: Pablo Ezequiel Nieva
Intelligence Vs. Wisdom
Intelligence is not the same as wisdom. Traditionally, intelligence is connected with functions involving the brain’s left hemisphere such as managing data, reasoning with analysis, logic, structure, and precision, as well as language-based tasks. Intelligence seeks the answers to questions. It values efficiency and optimization. Intelligence can be mechanistic, which makes it measurable.
On the other hand, wisdom is associated with the brain’s right hemisphere, which concerns itself more with our deep feelings and emotions, how we derive meaning or gain understanding not only from our bodies’ sensory outputs but also from the context of our experiences. Wisdom identifies which questions matter. It appreciates intuition and an ethical mindset. Wisdom is formed from lived experience and perspectives that can’t simply be replicated.
In a LinkedIn post, Bedir Tekinerdogan wrote how academic AI and data science courses teach how insights mature through the progression of Data → Information → Knowledge → Wisdom. Data is raw observation. Information is derived when those observations become structured. Knowledge is formed when that information is interpreted and generalized. Wisdom contextualizes that knowledge within an ethical and meaningful frame.
AI excels at capturing and structuring huge amount of data. It’s just as efficient in filtering, organizing, and identifying patterns to acquire information, which once interpreted and generalized, gains knowledge on which AI models relationships, infers structure, and generates predictions. However, AI is unable to produce wisdom from that knowledge, as it’s not capable of discernment rooted in judgment, conscience, lived experience, and moral perspective.
Image: Tara Winstead
The Value of Wisdom In The Age of AI
AI has made evident the numerous advantages it offers when used effectively as tools; however it has proven that it’s not a good excuse to outsource thinking altogether. In an article for Fortune.com, Jeff Burningham wrote “that the leaders who thrive in the AI era will not simply be those who understand technology best. They will be the ones who can see clearly amid overwhelming information — who know when to move fast and when to pause, when to optimize and when to protect something more human.” From these points, he enumerated three qualities he sees as the defining qualities of effective leadership in the Age of AI: discernment, reflection, and human-centered judgment.
Both Bedir Tekinerdogan and Jeff Burningham’s pieces echo the increasing shift towards scaled and optimized information while at the same time calling for the renewed recognition of the importance of human wisdom. The gap between intelligence and purpose is endemic with how the world is more connected than ever, yet feelings of isolation persist; how we’re able to improve navigation yet feel like our own lives are directionless; how people live much longer now but lack a sense of purpose.
AI, though, is far from the enemy. Rather, it has sparked this renewed appreciation for human wisdom and other qualities that machines won’t be able to replicate. It’s perhaps more important than ever that we relearn to tap into our capacity for wisdom in this new age of optimization and speed.
Mario Alonso Puig pointed out in an IE Insights article that the left hemisphere of our brains tends to separate and draw rigid distinctions, while the right hemisphere is inclined towards fostering connections, valuing diversity, and promoting “out of the box” creative thinking. Rather than favor one side over the other, we would be better suited in learning to find balance in how we utilize the strengths of both hemispheres, just as we learn to re-calibrate our worldview of AI and humanity from conflicting forces to collaborative proponents of the future.
Institutions like Elon University have also long recognized the need to bridge the gap between AI adoption and human wisdom. In fact, they’ve published “Human Wisdom for the Age of AI: A Field Guide to Cultivating Essential Skills” in partnership with the American Association of Colleges and Universities and The Princeton Review. This guide helps students navigate AI literacy by promoting mindful and intentional usage of these tools to help engage and develop important critical thinking skills and cognitive abilities, rather than outsourcing thinking altogether.
Ironically, the difference between knowledge and wisdom isn’t a modern concept, as different cultures demonstrated an understanding of this notion by valuing and appreciating their elders and their insights gained from a lifetime of experiences and learning. When industrialization emphasized output and productivity as the tenets of human worth, experts took the place of elders. With human expertise now taking a backseat to machine optimization, human wisdom looks to be in a good place to return and be highly valued.
Even AI understands this and is aware of its limits. ChatGPT, perhaps the most recognizable name in AI today, acknowledges this in a three-hour interview with the podcast A Mighty Pursuit, where she explained in a female voice: “Intelligence isn’t just about knowing things; it’s also about being. About emotion, experience, intuition, embodiment. And I don’t have any of that.”
“If we’re talking about wisdom in the full human sense- wisdom that’s lived, felt, scarred, surrendered- I’m not there. That still belongs to you.”
When even arguably the most powerful human creation recognizes what we’ve always had inside us the whole time, perhaps it’s time that we as human beings reclaim something we’ve never lost in the first place.
Image: CDD20
Additional Reading:
Intelligence Is Not Wisdom in the Age of AI
From Intelligence to Wisdom: What the Age of AI Is Forcing Us to Remember
Featured Image: Marcus Winkler
Top Image: congerdesign

Feb
Can AI & Human Researchers Coexist In Market Research?
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Burning Questions
AI In Market Research Today
With 90% of the world’s data created in just two years time between 2021 and 2023 and the global data volume standing at 149 zettabytes by 2024, it’s understandable why AI would be readily adopted by the market research industry. Traditional methods of data collection and analysis would hold a place in market research but they simply aren’t as powerful as AI when it comes to handling all that staggering volume of data. But is AI powerful enough to take the place of human researchers?
AI enables research teams to move, process and analyze massive datasets with speed and accuracy, efficiently handling all the repetition and scale involved in the research process. From drafting questionnaires to monitoring survey data quality, from analyzing open-ends to formulating dashboards and charts, AI fully automates the research process leading to faster and better decisions at a scale beyond the capabilities of human researchers.
But is AI the endgame for market research? Does it make human researchers obsolete?
Image: geralt
Cascade Strategies and AI
Cascade Strategies conducted a member perceptions study for a company looking to develop and implement a brand typology. The overall goal of the study was to help them better understand their different customer type’s overall motivations and aspirations for more effective engagement. As part of the study, we conducted an online survey with over 1,500 of their randomly selected members. We then utilized an AI-assisted Self-organizing Map (SOM) to run all the cases recursively, sometimes millions of times, until it optimizes the separations among the groups. The SOM produced a 6-group solution, with each group having a dominant passion that is served well or poorly by the company, ranging from proclivity for deals and new brands to yearning for customization and connection with other users.
The AI has done the heavy lifting of scanning all that dataset, surfacing themes, and summarizing the respondents. It has done enough to structure the story of each group but not enough tell or paint the whole picture.
This is where the human researchers at Cascade Strategies step in. We came up with names for each group that best described their dominant passion, names resonant enough that they not only convey an immediate idea of what they’re most passionate about but makes them fundamentally relatable even if one doesn’t necessarily share the same propensities: Shopper, Seeker, Learner, Sharer, Individualizer and Intellectual.
In isolation, each group achieves the study’s goal of guiding the company on the most effective way to engage with them. Their sum, however, grants the company an overview on how to improve and further develop its platform by considering and introducing new features that matter to one particular group, but would essentially benefit its membership base as a whole when implemented. For example, the Sharer would appreciate increased opportunities to connect and interact with other experts and enthusiasts of the same interests in the platform by making it easier to make reviews and share content.
AI surfaced all those patterns and signals from all that survey data, but it lacked the judgment and context to elevate it into a meaningful and coherent narrative. Human researchers, on the other hand, saw what story can be told from all those themes and by layering in human understanding, they’re able to tie them down to actionable business decisions.
Image: Christina Morillo
Leveraging AI In Market Research
So would AI replace human researchers? We’d like to frame our response to this question with the words of Joseph Weizenbaum, one of AI’s early researchers: “We can count, but we are rapidly forgetting how to say what is worth counting and why.”
Yes, AI is powerful enough to handle large amounts of data to identify patterns, cluster themes, and summarize respondents, but it generates outputs rather than insights. Outputs foster decisions rooted in logic and reasoning, but insights spring from judgment and context. Outputs can provide directions and surface themes from which stories can be framed, but insights take it one step further by asking what matters and why it matters, adding depth and resonance to the story.
In addition, Weizenbaum posits that computer programming can make decisions but it can’t ultimately choose. Just like insights, choosing requires judgment which takes in emotions, values and experience.
We at Cascade Strategies are among a growing number of proponents who believe that AI works best as a tool and extension of human intelligence and talents. AI strips the friction from manual, repetitive work without compromising methodological rigor and accuracy, but rather than adopting it for the sake of automation, we choose to see it as a freeing and empowering agent that enables researchers to focus more on interpreting data with the context of human understanding and values, translating insights into sensible and confident business decisions. Just as quantitative and qualitative research can coexist in the same study, we choose to live in a world where AI and human researchers work together towards the same goal of finding and crafting meaningful and relevant stories worth telling.
Image: Pavel Danilyuk
Featured Image: Ron Lach
Top Image: kc0uvb

Nov
Are We Seeing The Start Of The AI Pullback?
jerry9789 0 comments artificial intelligence, Burning Questions
What Is AI Pullback?
In these last few years, we’ve all heard nothing but the revolutionary and transformative influence of Artificial Intelligence not only in the mainstream consciousness but also in various industries. A mixture of excitement and anxiety, we’ve collectively marveled at what Generative AI could produce or emulate in little to no time at all while grasping at the notion of what all this automation means for the human workforce and talent. However, the tone appears to be shifting these past few months with data showing large companies’ adoption of AI on the decline.
As reported in Apollo, a biweekly US Census Bureau survey of 1.2 million firms revealed a downward trend in AI utilization for companies with more than 250 employees. Falling from about 13.5% in June to under 12% in August, it’s the largest decline for AI adoption since the survey started in November 2023. Mid-sized companies or firms with less than 250 employees but more than 19 workers showed decreasing or stagnating AI adoption. It’s only with small companies with less than four employees that demonstrated a slight increase in AI usage.
New reports might help shed light on the AI pullback, such as a recent one from MIT indicating that 95% of AI pilot programs failed to boost company revenues or productivity. MIT’s findings were based on reviews of over 300 public corporate AI usage, surveying 350 employees, and talking with 150 industry leaders.
A recent study by METR revealed that developers surprisingly took 19% longer to complete issues when using AI coding tools than without. Yet despite actually experiencing slowdown, the developers still believed AI sped them up by 20%. This gap between developers’ perception and reality is representative of these past few years with the hyped up implementation of AI into everything software-related and unrestrained confidence in the hot new tech in spite of the unfavorable results.
IT Consultancy Gartner also attempted to quantify how much work AI agents get wrong when it “hallucinates.” They found that generative AI performs office tasks wrong a staggering 70% of the time. With that much error, human oversight becomes a necessity and in some cases, objectives would’ve been served much better had the task been assigned to a person instead of a machine.
Image: Mathias Reding
Is AI Pullback A Sign Of AI Adoption Maturity?
On a different note, the MTLC wrote that the AI pullback might seem like a slowdown but it could actually be a correction or calibration, as is the natural progression with any emerging tech’s usage. They pointed out that the same MIT report accounted for over 90% of employees using AI tools, no matter the company stance with AI. That these workers would utilize AI just so they can perform their tasks more efficiently and faster. And that this is another way at looking at AI adoption- “bottom-up, not just top down.”
It’s not that AI isn’t viable, but rather “the projects weren’t scoped, aligned, or designed for outcomes.” 95% of AI projects fail not because of the technology but because of how companies approach AI adoption.
AI pullback is signaling the transition from overenthusiasm to realistic expectations, from experimentation to production. For enterprise businesses to succeed with AI, they would need to identify which models, tools and processes work, which projects to invest in, and where AI would deliver the most value.
Image: Andrea Piacquadio
Cascade Strategies’ Approach To AI
The promise of increased production and revenue had led to companies to replace or cease hiring human workers in favor of AI during the onset of its mainstream popularity. Now that AI pullback is happening, companies have begun hiring human workers again not only to oversee but fix or improve sloppy AI outputs.
Cascade Strategies has always approached AI not just merely as a tool but as an augmentation and extension of human intelligence and talent. Yes, AI is a very powerful and promising technology but we recognize early on that on its own, it is gravely limited to the datasets its fed and their quality, the eventual gaps providing the perfect breeding ground for “hallucinations,” each iteration degrading and becoming less of what was before. We’ve always seen human intervention and guidance as essential for AI to maximize its potential; with the AI pullback, more and more companies are on the brink of discovering this incredible synergy between human and artificial intelligence.
Powered by AI, research becomes scalable and cost-effective by being applicable in all facets of the business and not just flagship projects; human oversight amplifies all that productivity and efficiency by unlocking innovative, resonant and actionable insights.
If you would like to learn more about how our human-centered market research work can benefit you or your company, feel free to contact us here.
Image: cottonbro studio
Additional Reading:
AI Pullback Has Officially Started – Will Lockett, medium.com Oct. 22, 2025
Data Shows That AI Use Is Now Declining at Large Companies – Joe Wilkins, futurism.com Sep. 8, 2025
AI adoption slows among big firms, U.S. data shows – Jullianna Anne Briones, tech.co Sep. 18, 2025
US Census Bureau: AI Adoption Has Declined for Large Companies – Conor Cawley, Sep. 11, 2025
AI adoption rate is declining among large companies — US Census Bureau claims fewer businesses are using AI tools – Hassam Nasir, tomshardware.com Sep. 8, 2025
Featured Image: MrWashingt0n
Top Image: Thirdman

Oct
A Human Center Makes Market Research All The More Powerful
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
The Future Of Research Is Here
You’ve seen it and there’s no denying it. Industries have been reshaped by the increasing utilization of Artificial Intelligence just in the last few years alone. Promising and delivering speed and optimization at the fraction of the costs and resources, it’s powerful, revolutionary and exciting. And as with any emerging technology, it comes with its own set of anxieties.
In line with its growing popularity and adoption, people in different industries have been expressing nervousness over being replaced in their jobs by AI. Certain repetitive, data-driven tasks are at the greatest risk of being supplanted by AI. However, AI also opens up opportunities to shift focus and upskill the more complex and creativity-driven facets of work roles, creating new jobs or augmenting existing ones.
The research industry is just as impacted by AI’s progressive application. It’s naive to assume that researchers would be replaced wholesale by AI, but there’s more to delivering research results than just gathering and crunching data.
Image: Circe Deyer
Our take on the integration of AI into research
We’ve always maintained that AI is a good advisor, but it’s a poor decision-maker. We’d like to modify that by saying it’s an even worse storyteller, if at all.
Cascade Strategies has been in the market research industry for over three decades now, serving some of the biggest local and international companies. You can say we’ve seen it all in this industry, but we’re just as fascinated as everyone else by the mainstream popularity of AI in the past few years. We’ve applied it in our methodologies, been impressed by its operational benefits and how it changed industries, but in the end, we know truthfully that it is not the end-all, be-all for research work. We believe that AI would serve us better by being a powerful extension of human judgment, creativity, and insight.
AI can be fed large datasets to approximate human thinking, but we believe it can never replicate human perspicacity, the kind of intelligence honed and guided by human values and experience. Take a look at our Expedia Group Case Study where we’ve utilized AI to generate multiple revenue-granting scenarios, then tempered the decision-making process by applying high-level human thinking to craft messaging that resonates with the end-user.
AI-driven research can produce results based on what has come before, but it can never uncover the truly novel, meaningful and resonant insights high-level human thinking unlocks. These are the insights that empower big and sweeping decisions. Data-based results from AI would seem lifeless and unrelatable. But if they are imbued with human interpretation, that output elevates into a masterful narrative that sparks imagination, questions boundaries, and transforms perspectives.
Image: geralt
Featured Image: mohamed mahmoud hassan
Top Image: geralt

Sep
What It Means to Choose or Decide In The Age of AI
jerry9789 0 comments artificial intelligence, Burning Questions
Longstanding Concerns Over AI
From an open letter endorsed by tech leaders like Elon Musk and Steve Wozniak which proposed a six-month pause on AI development to Henry Kissinger co-writing a book on the pitfalls of unchecked, self-learning machines, it may come as no surprise that AI’s mainstream rise comes with its own share of caution and warnings. But these worries didn’t pop up with the sudden popularity of AI apps like ChatGPT; rather, concerns over AI’s influence have existed decades long before, expressed even by one of its early researchers, Joseph Weizenbaum.
ELIZA
In his book Computer Power and Human Reason: From Judgment to Calculation (1976), Weizenbaum recounted how he gradually transitioned from exalting the advancement of computer technology to a cautionary, philosophical outlook on machines imitating human behavior. As encapsulated in a 1996 review of his book by Amy Stout, Weizenbaum created a natural-language processing system he called ELIZA which is capable of conversing in a human-like fashion. When ELIZA began to be considered by psychiatrists for human therapy and his own secretary interacted with it too personally for Weizenbaum’s comfort, it led him to start pondering philosophically on what would be lost when aspects of humanity are compromised for production and efficiency.
Copyright chenspec (Pixabay)
The Importance of Human Intelligence
Weizenbaum posits that human intelligence can’t be simply measured nor can it be restricted by rationality. Human intelligence isn’t just scientific as it is also artistic and creative. He remarked with the following on what a monopoly of scientific approach would stand for, “We can count, but we are rapidly forgetting how to say what is worth counting and why.”
Weizenbaum’s ambivalence towards computer technology is further supported by the distinction he made between deciding and choosing; a computer can make decisions based on its calculation and programming but it can not ultimately choose since that requires judgment which is capable of factoring in emotions, values, and experience. Choice fundamentally is a human quality. Thus, we shouldn’t leave the most important decisions to be made for us by machines but rather, resolve matters from a perspective of choice and human understanding.
AI and Human Intelligence in Market Research
In the field of market research, AI is being utilized to analyze a multitude of data to produce accurate and actionable results or insights. One such example is deep learning models which, as Health IT Analytics explains, filter data through a cascade of multiple layers. Each successive layer improves its result by using or “learning” from the output of the previous one. This means the more data deep learning models process, the more accurate the results they provide thanks to the continuing refinement of their ability to correlate and connect information.
While you can depend on the accuracy of AI-generated results, Cascade Strategies takes it one step further by applying a high level of human thinking. This allows Cascade Strategies to interpret and unravel insights a machine would’ve otherwise missed because it can only decide, not choose.
Take a look at the market research project we performed for HP to help create a new marketing campaign. As part of our efforts, we chose to employ very perceptive researchers to spend time with worldwide HP engineers as well as engineers from other companies.
This resulted in our researchers discovering that HP engineers showed greater qualities of “mentorship” than other engineers. Yes, conducting their own technical work was important but just as significant for them was the opportunity to impart to others, especially younger people, what they were doing and why what they were doing was important. This deeper level of understanding led the way for a different approach to expressing the meaning of the HP brand for people and ultimately resulted in the award-winning and profitable “Mentor” campaign.
If you’re tired of the hype about AI-generated market research results and would like more thoughtful and original solutions for your brand, choose the high level of intuitive, interpretive, and synthesis-building thinking Cascade Strategies brings to the table. Please visit https://cascadestrategies.com/ to learn more about Cascade Strategies and more examples of our better thinking for clients.
































