Sep
Achieve Breakthroughs By Tapping Into Your Primal Intelligence
jerry9789 0 comments artificial intelligence, Burning Questions
Is Artificial Intelligence going to be the be-all, end-all for decision-making, problem-solving and innovation? If AI is faster, more logical and more efficient than humans, why hasn’t it overtaken us already? Is there a way for human genius to gain the edge over AI?
Yes, AI has changed the way we’ve done things these last few years – optimizing and streamlining processes to improve efficiency and effectiveness while maximizing output and in some cases, reducing costs. AI thrives in a production environment with defined datasets, but take away all that reliable data and introduce new variables, and it crumbles.
AI can generate art and stories, but it does this based on the data it was fed and trained on. It’s truly incapable of producing anything genuinely novel from outside that frame. This means it has the potential to suffer in a self-loop of homogeneous and biased outputs over time, building towards incoherence.
In a world that is increasingly being taken over by AI processes and outputs, recognizing and understanding this limitation is going to help human intelligence find its place and continue to thrive and evolve. In fact, just as AI gained mainstream popularity these past few years, another type of human intelligence has captured the attention and imagination of researchers – and the U.S. Army at the same time.
Image: Lukas
What Is Primal Intelligence?
Ohio State University professor Angus Fletcher, along with other researchers at Ohio State’s Project Narrative, started to investigate in 2021 something called “primordial brainpower” that reportedly drives human intuition. Their research led them to something they call Primal Intelligence, a kind of “natural cleverness” by humans that can be strengthened through training, but which AI can’t replicate.
Professor Fletcher describes Primal Intelligence as part of our lost nature and a key to activating intuition, imagination, emotion, and common sense. The U.S. Army Special Operations then adopted primal training for its most classified units and according to Professor Fletcher, Special Operators saw the future faster, thought more quickly, acted more swiftly, and healed more rapidly from trauma. After the Army authorized trials on civilians such as entrepreneurs, doctors, engineers, managers, coaches, teachers, investors, and NFL players, they found their leadership and innovation significantly improved. These civilians coped better with change and uncertainty, and experienced less anger and anxiety. The Army subsequently provided primal training to college and K–12 classrooms with students as young as eight, and they reported substantial beneficial effects from the training. In 2023, the Army awarded Professor Fletcher the Commendation Medal for his research on retraining the human mind.
Professor Fletcher’s research has been endorsed by renowned psychologists, neuroscientists, and doctors. He has received support from major institutions such as the National Science Foundation. He also wrote the book Primal Intelligence: You Are Smarter Than You Know, from which the following five key insights for sharpening our primal intelligence are derived.
Image: Alana Jordan
1. Exceptional Information
Special Operators “see” or anticipate the future faster than other soldiers on the battlefield, thanks to their unusually high level of intuition. But how does one improve their intuition?
To begin with, you’ll need to dissociate intuition from the decades-old concept that intuition is just mere pattern matching. The aforementioned Operators have trained their brains to find what the Army has dubbed “exceptional information.” Succinctly put, exceptional information is an exception to a previously established rule; it’s the opposite of a pattern because it’s the breaking of a pattern.
Young children score high on intuition but lower at pattern matching because their brains are focused more on unusual details than on familiar patterns. Training your brain to spot exceptions rather than thinking in patterns can help improve your intuition.
Travel or go on trips to immerse your brain in places that break the pattern of your regular life. Reading the works of authors like Shakespeare can also help improve intuition because of characters who are exceptions to traditional narrative tropes, like Hamlet the deep-thinking action hero or the cold and scheming Cleopatra who possesses a loving heart. Known Shakespeare readers who were able to anticipate the future by spotting exceptions range from Nikola Tesla with his AC motor, Marie Curie with radioactivity, and even Vincent van Gogh and his use of aquamarine.
Image: Donald Tong
2. Rethinking Optimism
Optimism pushes us to take on chances which could lead to growth, but why do many people fall back into pessimism? Why do we need to remind ourselves that optimism works better than pessimism instead of instinctively switching to the former frame of mind whenever something new or unusual comes up? Why is optimism this fragile in our minds?
This has something to do with how we think of optimism. We’ve been taught that optimism is “this will succeed,” when “this can succeed” is much more powerful and stronger. Instead of convincing us that this will succeed, we remember that we can succeed.
Remembrance over visualization. Special Operators call this method antifragile because remembering that one time in the past when you’ve been successful is more resilient than magically thinking of achieving success. Building your optimism on the foundation of faith where you’ve succeeded one time no matter how many times you fail can be all you need to keep on going. This is an improvement over being buoyed by the hope that you can succeed, but having your confidence eroded when you don’t.
Image: Kaboompics.com
3. Thinking In Story
In new situations with little to no precedent values (where AI falls apart), how are Special Operators not only able to pull through but also excel?
We have to remind ourselves that the human brain features more than one type of intelligence. While it evolved over a million years with the capacity to think logically and process data from which computers were modeled, the human brain also developed narrative cognition or simply put, the ability to think in story.
Thinking in story means the brain sees the “movement” of story, from beginning to middle to end, with room for imagination to explore possibilities and wisdom to sensibly tie things together. AI has the ability to generate story plots and images from datasets, but it still doesn’t read the story. It’s still our human brains that instill story and meaning to these generated plots and images.
High-data environment and conditions might not be conducive for thinking in story, but it finds its place in volatile and uncertain situations. Special Operators who performed well in volatility were found to have brains proficient at thinking in story.
Image: cottonbro studio
4. Empowerment Through Role-playing
Anger and anxiety are physiological indicators of a threat response. So what’s the best way to mitigate the brain’s threat response? Removing the threat, usually with outside force, might spring to mind but what if we tell you that conjuring a gameplan with your brain to deal with the threat is more effective?
That’s exactly how Special Operators function. Instead of avoiding threats, they advanced toward the threats and they do this without feeling anxious or angry. That’s because these Operators have trained their brains to imagine plans for dealing with threats. And they train their imagination by engaging in role-playing exercises.
Outside of Special Operations, role-playing can be taught to regular people or students by taking part in arts and humanities activities such as theater, literature, and history. The key is to be able to imagine oneself as somebody else in another place or situation, engaging the brain’s ability to imagine plans for handling threats.
Image: geralt
5. Possibility Over Probability
How do you train people so that you produce leaders and not just managers? Focus your training on expanding their ability to think not in probability but in possibility.
What’s the difference? Probability is calculated or based from past events while possibility assumes an event that hasn’t happened could happen. Probability is employed by statistics and computer AI while possibility empowers story and imagination. With a fundamentally different mental process involved, possibility engages original thinking, enterprise, and initiative: key mental qualities of entrepreneurs and leaders in general.
You can expand your sense of possibility by stimulating your brain’s premotor cortex and boosting your practical imagination with realistic tales of make-believe. The Wright brothers read the creative works of Charles Dickens and Mark Twain, while Special Operators prefer novels set in the near future or in a different culture.
Image: Katrin Bolovtsova
Cascade Strategies and Primal Intelligence
Cascade Strategies has not only been advocating for the “appropriate use of AI” but has also been consistently pushing for the appreciation of high-level human thinking, especially when it comes to market research. This research on primal intelligence underscores our stance on the irreplicable and inimitable merits of human intelligence. We believe there are values and experiences humans bring to the table that AI simply cannot match. We’ve always believed that breakthroughs in market research can only be unlocked only with the proper application of human intelligence. To learn more about how our thinking can help you with your brand development and market research needs, please contact us here.
Additional Reading:
Why AI Will Never Defeat Primal Intelligence
‘Primal Intelligence’ Review: Why Brains Are Better
Featured Image: johnhain
Top Image: geralt
Sep
Some years ago, Publix Supermarkets allowed us to conduct qualitative and quantitative research for them. The result was a key brand insight about a kind of consumer called the Reluctant Shopper. Despite the ironic name, this kind of consumer hewed more closely to the shopping experience Publix offered than to competitive shopping experiences. The campaigns built around the Reluctant Shopper helped Publix weather the storm of competition from well-heeled operators like Walmart. Winn-Dixie, a much larger chain with many more stores, perished.
We recently asked Gemini to review the same dataset and report on it. Gemini provided a sparkling and quite accurate report on the data but perceived nothing about the Reluctant Shopper. This is something to bear in mind if you’re a consultant advising a brand on how to thrive: perspicacity counts.
There’s a kind of intelligence AI can’t reach. It has dimension, soul, and human inspiration. We’d do well to remember this as we pour more datasets into the maw of AI. If you’re a consultant and need perspicacity, you might call Cascade Strategies. We can help you see things AI can’t see.
Image: Harrison Keely
Aug
Can Synthetic Respondents Take Over Surveys?
jerry9789 0 comments artificial intelligence, Burning Questions
What Are Synthetic Respondents?
AI has increased operational efficiency by streamlining knowledge bases and shortcutting processes so it’s no surprise people and companies are looking for more ways for its application. For market research, one curious consideration is whether it could take over surveys, essentially by replacing actual respondents with synthetic respondents.
Also known as virtual respondents, digital personas, and Virtual Audiences, synthetic respondents are individual profiles constructed by Large Language Models (LLMs) from real or simulated data. Ideally, the data or descriptions used to generate these profiles come from previously conducted surveys and are combined with individual-level demographics, attitudes and behaviors.
Using these synthetic respondents over real respondents could benefit your research with speed, accuracy and cost savings, at least according to their advocates. Basically, you just need to conduct one survey and from the profile description or data you gathered from the actual respondents, you’re able to generate results from the constructed individuals over and over for succeeding studies and research.
Testing Synthetic Respondents
While synthetic respondents could accurately represent real respondents, relying exclusively on the results from these AI-based individuals may not be entirely beneficial. A webinar hosted by Radius Global took a closer look at the potential of AI-generated synthetic respondents through three case studies of quantitative concept testing, quantitative communications research, and qualitative communications research.
Aggregate results for the concept tests involving game controllers indicate somewhat strong similarities between the results of the real and synthetic respondents. This extends to the results from the quantitative communications research when it comes to the believability of statements on the benefits of milk, although there were some differences. The differences were much more pronounced though when it comes to surprise over the same statements, and there was incongruence when considering how each statement could possibly increase milk consumption.
The qualitative communications research was seeking in-depth insights into women’s needs, perceptions, and preferences for running a race or marathon, with the feedback gathered meant to be used for creative content. Personas were constructed from the profiles of six women aged between 18 and 64 years old who ran at least once in an average week. They had an LLM assume each persona to allow a comparison between findings from real participants to synthetic respondents.
They found that while both real and synthetic respondents have somewhat similar responses when it comes to functional aspects as goals for women in general pursuing fitness, the AI responses lacked emotional expressions. There are also little differences in the synthetic respondents’ responses despite having different profiles, and there was even a lack of subtle differences.
As for concerns among women who are aspirational marathon runners, the synthetic personas were consistent in their responses while the real respondents provided more nuances, variety, and perspectives more prevalent among women.
Synthetic Respondents vs. Real Respondents
Synthetic respondents appear to be useful if you’re evaluating existing ideas and concepts; however, if you’re looking for “breakthroughs” or essentially new insights you would’ve never arrived at had you not performed the case study or research, you would need to engage with real respondents, relying exclusively on their results or combining them with that of synthetic respondents. Yes, there could be cases where synthetic respondents could be used, but the results must be extensively validated. It would also require increasing the efficiency of how data used to construct these individuals are analyzed in addition to enhancing the quality of the data and information gathered for these profiles through thorough screening, intelligent probing, and smart choice models.
There is a place for synthetic respondents in market research, but as another tool in a researcher’s toolbox. They won’t be taking over surveys or replacing actual respondents wholesale anytime soon, it seems, as that elusive “Eureka” moment researchers seek is inherently tied to the nuances and perspectives of human emotion and experience you simply can’t construct.
Photo courtesy of Pavel Danilyuk
Aug
How Excellent Market Research Benefits Manufacturing Companies
jerry9789 0 comments artificial intelligence, Brandview World, Burning Questions
More than just an invaluable asset, market research is an essential tool to any company — or industry, for that matter. From identifying and tailoring your messaging towards your ideal customer with consumer research to understanding the competition and strategically positioning your company with competitor research, great market research grants you and your firm vital and actionable insights that would prove key to the success of your marketing efforts. In addition, excellent market research helps companies manage risks effectively and efficiently, as well as aid in measuring the progress and success of projects or even your company as a whole.
The manufacturing industry not only stands to benefit from high quality market research, it’s crucial to its continued growth, innovation and evolution, especially in an industrial landscape that’s continually transforming with technological advancements along with global, cultural and attitudinal shifts. From the steam and watered-power machines of the First Industrial Revolution to the expansion of network systems and electrification of the Second Industrial Revolution to the information technology focus of the Third Industrial Revolution (the Digital Revolution), the manufacturing industry’s evolution continues on in its latest iteration with Industry 4.0, harnessing modern and emerging technologies to facilitate the merging of the physical and digital realms.
And on that note, we take a look at 10 manufacturing industry trends today that exceptional market research can help manufacturers navigate and adapt to as the Industry 4.0 era unfolds.
Image: Livia Wong
1. Smart Factories
Perhaps the best representative of things to come with the Fourth Industrial Revolution, smart factories utilize Industry 4.0 technologies to streamline and improve operational efficiency, quality and maintenance while reducing errors and waste. Older machines are gradually giving way to newer counterparts built with onboard sensors, monitoring tools, interconnected systems and in some cases, machine learning capabilities.
With more and more manufacturing companies transitioning to automated facilities plus the decreasing costs to acquire sensors, software and equipment, manufacturers big and small are all the more incentivized to join the smart factory revolution — if they haven’t yet — to not only keep up with the competition and the changing times but also take advantage of the irresistible operational benefits.
2. Artificial Intelligence
AI has disrupted multiple industries, and manufacturing isn’t immune to it; in fact, it has openly and quickly embraced and adopted it, seeing all the tremendous advantages it brings with its data-crunching prowess and advanced decision-making insights to the core aspects of smart production, quality control, supply chain management, servicing and maintenance, along with enhancements to processes, products and services.
More and more manufacturing companies are finding success and are able to scale competitively when strategically leveraging AI in automating and streamlining their operations, especially when it’s combined with other contemporary technologies. But perhaps the best combination of them all is when AI is combined with human creativity and experience, opening doors for innovation and further advancements.
3. Digital Twins and Data-driven Predictive Maintenance
If smart factories are revolutionizing manufacturing operations, digital twin technology and data-driven predictive maintenance are transforming equipment maintenance and operational downtimes. By utilizing virtual replicas or “digital twins” of equipment and devices, manufacturers can simulate equipment performance under different scenarios and situations to gain valuable insights. These data-driven insights would help manufacturing companies anticipate or predict when an equipment would need servicing or maintenance, reducing or eliminating unexpected downtimes and equipment breakdowns. At the same time, maintenance costs are reduced, material cost savings are increased, and the usage or life cycle of the asset is optimized.
And digital twins aren’t limited to physical assets only, as they can also replicate systems or processes to test new ideas or optimize existing ones before applying any changes or updates to live production. The digital twins approach not only helps minimize resource consumption and waste, but also improves business decisions by backing them with data-driven insights.
4. Other Notable Industry 4.0 Technologies (AR/VR/Robotics)
Arising from the realms of gaming and entertainment, augmented reality (AR) and virtual reality (VR) have now begun revolutionizing manufacturing. Product design, quality control, maintenance and repairs, remote collaboration and even employee training — all these are being impacted and improved by the application of AR and VR technology.
Robotics may have been around longer than AR and VR but modern robots are far more advanced than their forerunners programmed for repetitive tasks. Thanks to AI and automation software, today’s robots are autonomous, collaborative, and far more capable of performing complex tasks and operations.
These technologies in conjunction with AI make it possible for manufacturing operations to be run remotely or without any operator onsite. And as these technologies grow popular to become widely used and accepted, we might even see more fully automated manufacturing facilities called “dark factories” be developed in the near future.
5. Sustainability and Carbon Neutrality
No other industry is perhaps under greater pressure to pursue sustainable processes and carbon-neutral practices than manufacturing. Contracts with governments and institutions and eventually commercial clients require compliance with sustainability efforts while more and more consumers are supporting reputable, sustainable brands.
The manufacturing industry itself is advancing sustainability efforts by developing and employing green software to aid with carbon neutrality, waste reduction, and energy consumption optimization. Renewable energy integration in physical locations is also being embraced, while cloud infrastructure solutions and carbon capture technology are being viewed for their potential. Working toward sustainable practices and carbon neutrality isn’t without its own rewards for the business, as it’s been found that eco-conscious manufacturing companies are able to significantly reduce costs and improve efficiency with their sustainability efforts over time.
6. Reshoring
Reshoring refers to returning production operations back to the manufacturing company’s home country from overseas locations. This trend was a result of recent global events disrupting supply chains. It benefits the manufacturer with shorter supply chains, better quality control, faster market delivery, domestic economic boost, and improved sustainability efforts.
However, reshoring isn’t a decision a manufacturing company should take lightly, as one would need to factor in labor costs, skill, infrastructure, and more, as smaller-scale firms might find it more costly to operate domestically than overseas.
7. Decentralized Manufacturing
Another approach to improving supply chain resilience from disruptions is decentralized manufacturing, which is the distribution of production activities across multiple locations in the form of microfactories. Additional benefits of decentralized manufacturing include reduced logistics costs and quicker response times to local market demands.
While the coordination of multiple microfactories and achieving standardization across all sites may prove to be challenging, Industry 4.0 technologies can aid in making decentralized manufacturing more accessible and manageable through improved transparency and responsive production models.
8. Tapping into B2C
With the ever-growing popularity of e-commerce, manufacturing companies can now bypass the traditional lines of retailers and distributors and sell directly to the end consumer. Smart factories, 3d printing and additive manufacturing also make it possible to offer customized products based on a customer’s preferences. The advent of new manufacturing technology or the evolution of existing ones would only open up more opportunities for enterprising manufacturers looking to connect further with consumers.
9. Cybersecurity
The manufacturing industry’s increasing digitization has made it an irresistible target for cybercriminals, exploiting vulnerabilities with cyberthreats and attacks ranging from ransomware to industrial espionage or even supply chain and/or operational disruption. It’s no surprise then that cybersecurity has joined the elite group of paramount concerns for any manufacturing company.
Measures include multi-layered security, secure-by-design, zero-trust architecture, AI-driven threat detection, advanced encryption, and regular updates and patches, as well as employee cybersecurity training. Cybersecurity is more than just data protection or an IT concern now for manufacturing companies as it safeguards their production, finances, integrity, and reputation.
10. The Workforce of Industry 4.0
In spite of all the exciting technologies emerging in the Fourth Industrial Revolution, the manufacturing industry is experiencing widening skills gaps and labor shortages. These difficulties could translate to a loss in revenue of $1 trillion if approximately 2.1 million jobs aren’t filled in by 2030.
To address these challenges, manufacturing companies could start with reviewing all of their production processes from the ground up and assessing areas that could be improved by a highly skilled and competent workforce. Yes, the manufacturing industry is moving towards automation and advanced technologies but it can’t truly innovate without human creativity and experience.
Manufacturing companies are planning to offer higher wages by at least 3%. At the same time, they’re investing in training programs to reskill or upskill existing employees for the Industry 4.0 work environment. Incorporating new manufacturing technologies like AI and AR in these training programs can help employees not only learn faster, but also give them familiarity and first-hand experience with these digital trends. The same technologies can also be deployed for improving employee health and safety at the workplace.
Other approaches that manufacturing companies can consider taking range from partnering with local educational institutions in creating curriculums tailored for manufacturing careers, diversifying the recruitment pool, and creating appealing work environments which offer flexible schedules, potential promotions, and career development.
Image: InWay
How Cascade Strategies Can Help Manufacturing Companies with Advanced Market Research
Hewlett-Packard wanted to discover what feature-price combinations in high-frequency oscilloscopes would optimize profit. We conducted an advanced conjoint study followed by AI-based modeling to evaluate sales scenarios. Out of hundreds of attributes, we found the qualities below to be most salient. Using the most salient attributes as predictive vectors, we developed an AI model to determine the unique price-feature combinations that would produce the most profit and presented the top 3 to Hewlett-Packard.
We’ve highlighted 10 manufacturing trends shaping the future of the manufacturing industry in this selection but there are actually more out there that we didn’t touch on. And as new technologies arise, existing ones improve, and other industry changes or shifts happen, more trends are sure to emerge.
Regardless of trends, you can be sure to count on market research to help you determine the best approach to leveraging new technologies or guide business decisions to ensure your manufacturing company stays competitive and relevant. Would it be beneficial or costly for your company to go with a dark factory over a smart factory? Which of your AI-driven production processes would benefit from human supervision and input? Are your sustainability efforts being seen and appreciated by your consumer base or do you need to do more?
Between reshoring and decentralized manufacturing, which one would work best for your company? Are you able to expand into B2C? Are your training programs effective in making your employees understand and uphold cybersecurity commitments?
As with any AI-powered or data-driven Industry 4.0 technology, the high quality market research Cascade Strategies provides grants valuable and actionable insights into the operations, perception, and potential of your manufacturing company. If you would like to find out more about how Cascade Strategies can help your manufacturing company thrive in the Fourth Industrial Revolution, please contact us here.
Featured Image: Hyundai Motor Group
Top Image: Foto-Rabe
Jun
How Great Research Helps Financial Services Companies
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World, Burning Questions
From maintaining loyalty and fostering customer relationships to focusing marketing messages on the most profit-optimal consumer, great research is helping Financial Services companies identify not only their best customers and prospects but also their needs and why they prefer their brand over others. This is done through a high-quality segmentation study and persona development, which break a company’s market into different groups so that different strategies for marketing to these groups can be leveraged. The main reason a company wants to complete market segmentation research is so they can gain actionable insights, like how to sell more of their product or services.
Take for example, Banner Bank for whom Cascade Strategies developed a brand model which identified “Strivers” as the primary segment they should focus on. The bank initiated special promotions and appeals featuring the products of greatest interest to Strivers. A Striver-specific ROI program was developed to measure the degree of program success. Modeled indices were used to set Striver product targets by branch. After 2 years of activity, Banner Bank exceeded all key Striver product targets system-wide.
Read about our approach to high-quality brand development research here.
Another example is the case of Capital One where they needed help trying to identify the “persona” or psychographic type most interested in adopting a certain mobile app for personal investing. We conducted in-person depth interviews with “mobile-minded consumers.” We also conducted an unpacking session with Capital One staff where individual staff members were given assignments to review videos and be prepared to discuss key traits and behaviors of respondents, such as outlooks, investing styles, goals, worries, needs, plans, and so forth.
We identified that the “Empath” is the most interested in adopting a certain mobile app for personal investing. The “Empath” persona is fairly immersed in pragmatic thinking and probably best understands the possibilities for “thinking” or guidance apps, allowing Capital One to focus their brand campaign effort on the needs of this psychographic type. The net result was a highly successful new product introduction.
Read about our approach to segmentation studies and the development of brand personas here.
Image: Cottonbro Studio
“But wait, can’t AI do this?” you may ask. While there is a faddish and hype-driven tendency to turn quickly to AI for short-cuts in market research, AI cannot yet fully replicate the deep interpretive and intuitive skills of the human brain, especially the right brain (the nonrational side). Moreover, effective AI deployment requires human supervision and involvement as final arbiter of how the outcomes will be leveraged.
As you can see, market research results as good as the examples above come from human-centered thinking, the kind that allows brands to break past ordinary bounds and achieve true excellence. It’s this kind of extraordinary thinking that Cascade Strategies has consistently provided a long list of US and international clients for 33 years.
Feel free to reach out to us to discuss your needs at https://cascadestrategies.com/contact-us/
Jun
AI In Retail: Market Research In Play
jerry9789 0 comments artificial intelligence, Brandview World, Burning Questions
Perhaps there is no better example of market research in play than the utilization of artificial intelligence in retail. AI has disrupted industries after gaining traction and mainstream popularity just in the last few years, but its transformative power is arguably most visible in the retail landscape. From personalized shopping experiences to visual merchandising, we’ll take a look at the impact AI has had so far not only on the retail industry, but also on retail market research today.
Personalized Shopping Experience
We’re now at the point where customers expect brands to not only acknowledge them but understand and cater to their preferences. Retailers have traditionally prized such insights as vital to conversion rates, marketing campaigns, and brand loyalty. AI is in the perfect position to deliver these insights at a more incisive and actionable level. By analyzing a customer’s data such as browsing history, purchasing behavior, preferences, wishlists, and shopping cart items, AI is able to create personalized and tailored recommendations that help simplify the shopping process and guide consumers toward better purchasing decisions, thus contributing to a more enjoyable shopping experience. In fact, a McKinsey report found that 35% of Amazon purchases were due to personalized recommendations.
Aggregate customer data of this type helps shape the direction of a brand’s marketing campaign by identifying and homing in on the ideal customer for a particular product or service, reducing traditional marketing costs while optimizing conversion. This also extends to lead generation as the customer data gathered from transaction history and personal preferences can help form a prospect list for future products and campaigns.
The extensive data of browsing and purchase history along with personal preferences also benefits product searching and product description generation. The former means that AI is able to produce highly accurate site search results based on context and intent even if a customer struggles with the appropriate keywords for the product they’re looking for. The latter saves time and increases efficiency by generating comprehensive, unique and engaging product descriptions that are also SEO-optimized, especially when combined with product image analysis and natural language processing.
Loyalty and rewards programs can also be made more effective with incentives and exclusive deals aligned to a customer’s taste and preferences over generic, random, and unenticing offers, encouraging engagement and increased or repeated visits or spending while also improving retention.
Copyright: Pexels
Dynamic Pricing and Promotions
Retailers are now able to unlock another advantage with AI through dynamic pricing strategies and promotions. This ability allows retailers to adjust pricing based on real-time analysis of market conditions, competitor pricing, inventory levels, consumer demand and behavior, just to name a few factors. This can be done not only by branch or region but also on an individual level, and can take advantage of peak hours, promotional activities or clearance sales. Dynamic pricing allows retailers to maximize profits, maintain competitiveness as well as engage or retain customers looking for better deals.
Predictive Analytics for Inventory Management and Demand Forecasting
It’s important for retailers to optimize inventory management to prevent overstocks or stockouts, maximizing sales while minimizing losses. Step away from those time-consuming and fallible spreadsheets, though; AI-powered predictive analytics is now the key tool in any retailers’ arsenal for demand forecasting. By analyzing sales data based on purchasing history, market trends, inventory levels, consumer behavior and preferences, retailers are able to predict future trends to improve operational efficiency by making smart decisions with stock planning and supply chain management. This is further augmented by employing cameras, digital sensors or smart shelves to monitor inventory levels in real-time, allowing store staff to replenish shelf stock from the supply room when needed.
In-store Navigation
AI-powered chatbots and virtual assistants are helping improve customer satisfaction by assisting and guiding with site navigation and other queries, but they’re not limited to the online realm as they can also be used by physical locations. Aside from simple navigation instructions, in-store navigation can be taken to the next level with an AI-generated foot trail map optimized with the best path for navigating across the store based on the items on the customer’s shopping list.
AI can also provide valuable feedback and insights for optimizing store layout and foot traffic from the customer movement patterns it captures and analyzes. These insights can also help with the placement of particular products a store would like to promote or increase visibility of.
Copyright: TyliJura
Visual Merchandising
The days of static print and basic digital signage are slowly going out of style; retail visual merchandising is now evolving to tell dynamic and engaging brand narratives that extend from the purely informational to the experiential. Powered by AI, retail experiences now allow brands to connect and resonate with consumers more effectively than before with visual merchandising content that’s not only relevant to the buyer’s journey but also convey a strong and intelligent creative direction.
Imagine AI-powered signage that reacts or adjusts content in real time to help you with your purchasing decision at a store, especially at times when you’re looking at the shelf or aisle and not sure which brand to pick. Dynamic visual merchandising can highlight products based on current popularity or stock to influence your choice. During peak hours, this could mean you’re able to get your hands on the best-selling item before it runs out or select the product with enough available stock if you need to meet a certain quantity. Unlike manual content, which could outdate if the product presented has stocked out, AI-driven merchandising can present alternatives and introduce new brands which would otherwise have been unexplored or missed by consumers.
Emerging technologies such as virtual try-on and augmented reality (AR) add another layer to how customers interact with products. The former uses AI to simulate how clothing or accessories would look like or fit on a customer, while AR helps visualize products in different settings or styles. These technologies help reduce return rates and drive customer satisfaction and brand loyalty.
Copyright: TyliJura
We’ve touched on only a few of the current and noteworthy applications of AI in retail. There are many others, and more will likely be added in the near future as this technology continues to evolve. Embracing AI can reward a brand or company with competitive advantages and success, but the rewards aren’t necessarily reaped overnight. AI saves time, reduces costs, and optimizes sales and operations; but companies need to be strategic, adaptable, innovative, and ethical when harnessing this technology. Not only would a retailer need to invest time, effort, and resources to build the extensive data and foundational systems of their AI infrastructure, they would also need to gain an understanding of how everything connects and works with one another, as well as how it all aligns with their company’s goals.
If you’re a retail company looking for help in adopting AI in the best way possible, Cascade Strategies can assist you in this endeavor. Not only are we well versed in AI technology, we are advocates of “appropriate use of AI.” We appreciate the advantages and benefits AI brings, but we firmly believe that it’s at its most effective when harnessed and guided by human values and experience. Contact us today to learn how Cascade Strategies can help your company enrich retail operations and the shopping experience you offer to your customers with AI.
Featured Image Copyright: Demian Smit
Featured Image Copyright: ArtisticOperations
Apr
AI’s Impact On Critical Thinking and Learning – What Studies Are Saying So Far
jerry9789 0 comments artificial intelligence, Burning Questions
Generative AI and Critical Thinking
On our last blog, we touched on two studies suggesting that Generative AI is making us dumber. One of those studies, which was published in the journal Societies, aimed to look deeper into GenAI’s impact on our critical thinking by surveying and interviewing over 600 UK participants of varying age groups and academic backgrounds. The study found “a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading.”
Cognitive offloading refers to the utilization of external tools and processes to simplify tasks or optimize productivity. Cognitive offloading has always raised concerns over the perceived decline of certain skills — in this instance, the dulling of one’s critical thinking. In fact, the study found that cognitive offloading was worse with younger participants who demonstrated higher reliance on AI tools and less aptitude when it comes to their own critical thinking skills.
Conversely, participants with higher educational backgrounds showed better command of their critical thinking no matter the degree of AI usage, putting more confidence in their own mental acuity than the AI-based outputs. Aligning with our advocacy for the “appropriate use of AI,” the study emphasizes the importance and appreciation of high-level human thinking over thoughtless and unmitigated adoption of AI technology.
Copyright: jambulboy
Generative AI and Learning
In truth, a number of earlier studies have revealed that the arbitrary adoption of AI tools can be detrimental to one’s ability to learn or develop new skills. A 2024 Wharton study on the impact of OpenAI’s GPT-4 demonstrated that unmitigated deployment of GenAI fostered overreliance on the technology as a “crutch” and led to poor performance when such tools are taken away. The field experiment involved 1,000 high school math students who, following a math lesson, were asked to solve a practice test. They were divided into three groups, with two of these groups having access to ChatGPT while the third had only their class notes. One group of students with ChatGPT performed 48 percent better than those without; however, a follow-up exam without the aid of any laptop or books saw the same students scoring worse by 17 percent than their peers who had only their notes.
What about the second group with the GenAI tutor? They not only performed 127 percent higher than the group without ChatGPT access on the first exam, but they also scored close to the latter during the follow-up exam. The difference? Sometime down the line of their interactions, the first group with ChatGPT access would prompt their AI tutor to divulge the answers, resulting in an increased reliance on GenAI to provide the solutions instead of making use of their own problem-solving abilities. On the other hand, the other group’s AI tutor version was customized to be closer to how real-world and highly effective tutors would interact with students: it would help by giving hints and providing feedback on the learner’s performance, but it would never directly give the answer.
Similar tests with a GenAI tutor in 2023 studied the same issue of AI dependence and the value of careful deployment of AI tools. Khanmigo, a GenAI tutor developed by Khan Academy, was voluntarily tested by Newark elementary school teachers, who belong to the largest public school system in New Jersey. They came back with mixed results, with some complaining that the AI tutor gave away answers, even incorrect ones in some cases, while others appreciated the bot’s usefulness as a “co-teacher.”
Other studies regarding the effectiveness of AI tutors have shown increases in learning and student engagement. These studies have also shown that GenAI can help reduce the time it takes to get through learning materials compared to traditional methods. One study that extolled the benefits of GenAI tutors involved Harvard undergraduates learning physics in 2024, and similar to the third group in the Wharton research, the AI was prevented from directly providing the answer to students. It would guide the student throughout the learning process one step at a time, providing incremental updates of the student’s progress, but never outright telling them the answer. There are merits to the idea of Generative AI as a teaching assistant, but it serves students better when it is positioned to engage one’s attention and abilities rather than induce dependence on it to generate the answers.
Copyright: Only-shot
Can We Use GenAI Without Making Us Dumber?
These studies shed light on how we should approach AI solutions and development, whether the end product is being deployed in learning, productivity or other relevant applications. Beyond thoughtful planning and considerations on how AI tools would be deployed, there should be a focus on engaging the human faculties involved, with safeguards empowering man throughout the entire process instead of letting the machine take over the process wholesale. AI technology is developing rapidly, but we can keep pace and remain reasonable as long as human engagement and empowerment is kept at the core of its utilization and adoption.
Amid contemporary fears that anyone could be replaced anytime by AI, these studies highlight the importance of how vital and interconnected the human factor is to the effective deployment and development of AI tools. One could be content with the constant and consistent output AI tools generate, but progress is only possible when competent human minds are involved in the process and direction. Students can easily find answers with AI tools at their disposal, but why not advance their understanding of how solutions are formed with engaging and relatable AI-powered educational experiences? High-level human thinking grounded by values and experience can’t be replicated by machines, and perhaps there’s no better time than now to incorporate it into the heart of the AI revolution.
While AI development hopes that optimization and automation free the human mind to go after bigger and more creative pursuits, we here at Cascade Strategies simply hope that humanity emerges from all of these advancements more and not less than what it was when we entered the AI revolution.
Additional Reading:
Why AI is no substitute for human teachers – Megan Morrone, Axios
AI Tutors Can Work—With the Right Guardrails – Daniel Leonard, Edutopia
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Mar
Are We Getting Dumber Because of AI?
jerry9789 0 comments artificial intelligence, Burning Questions
Is Generative AI making us dumber? Two recent studies suggest so.
A study published early this year titled “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking” showed that growing dependence on AI could lead to a decline in critical thinking. Submitted by Michael Gerlich of the SBS Swiss Business School, the study was based on surveys and interviews of 666 UK participants from different age groups and academic backgrounds. The problem is more pronounced with younger participants who demonstrated increased reliance on AI to perform routine tasks and scored lower when it comes to critical thinking than their older counterparts.
More recently, a study by Microsoft and Carnegie Mellon University shared similar findings that the more workers depended on AI for their work, the duller their critical thinking becomes. The study surveyed 319 knowledge workers who used generative AI at least once a week and examined how and when they apply AI or their critical skills when performing tasks. The more faith the participant put in genAI to produce acceptable outcome, the less they use their critical thinking skills. On the other hand, participants who have higher confidence in their abilities than that of AI’s are found to exercise their critical thinking more out of concerns over unintended and overlooked machine output.
Copyright: Tara Winstead
What is Cognitive Offloading?
Both studies are linking overreliance on AI with cognitive offloading, which is when someone utilizes external tools or processes for completing tasks, resulting in their reduced engagement with deep, reflective thinking. Yes, AI is improving efficiency and saves time and financial costs, but these studies are suggesting that it could make humans less smart over time.
However, cognitive offloading isn’t new as it existed in a variety of forms throughout time, such as using a calculator instead of performing mental mathematics or simply making a grocery list instead of memorizing all the items you need to buy. It’s no surprise then that there are questions about the merits of the studies, such as self-reporting bias or how critical thinking was measured. Forbes suggests that AI isn’t making us dumb but lazy, while another emphasizes that in order for there to be harm to one’s critical thinking abilities, one must have critical thinking to begin with.
Copyright: Pavel Danilyuk
Rethinking AI Development
Nevertheless, these studies contribute to the conversation regarding the direction of genAI development, now with the nuance of being mindful and respectful of its human users’ intelligence and faculties. Recommendations include rethinking AI designs and processes which incorporates and engages human critical thinking. They’re helping bring back focus to AI serving as a tool augmenting instead of overtaking human capabilities.
For us at Cascade Strategies, we’re glad that these studies have renewed awareness and appreciation of human intelligence and creativity. Our world could’ve easily devolved into settling for more of the same output so it pleases us to learn that more voices are becoming advocates and proponents not only of the “appropriate use of AI” but also of high level human thinking.
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Mar
4 Trends Indicate AI Is Disrupting Labor Market
jerry9789 0 comments artificial intelligence, Burning Questions
Is AI going to disrupt the labor market? Researchers think it has already started to do so.
Harvard economists David Deming and Lawrence H. Summers, along with Kennedy School predoctoral fellow Christopher Ong, have presented a new paper that looks at over 100 years of “occupational churn” for a study of technological disruption. “Occupational churn” refers to each profession’s share in the U.S. labor market, which Deming and Summers have always been interested in gauging. With the help of Ong, they applied the metric to 124 years of U.S. Census data, initially sharing their findings in a volume published last fall by the Aspen Economic Strategy Group.
So are robots going to take over human jobs? This sentiment has always been present, and for good cause, whenever breakthrough technologies such as keyboards, electricity, and computer-based manufacturing emerge. The 1950s, ’60s, and ’70s demonstrated volatility that surprised but eventually made sense to Summers, while Deming characterized the 2000s and 2010s as having “automation anxiety.” The study, however, revealed that the labor market enjoyed stability and low churn between 1990 and 2017, when the pace of disruption slowed.
But for 2019 onwards, it appears that there’s a major change set to happen, with four labor market trends pointing towards AI as the new breakthrough technology.
Copyright: Kindel Media
1. High-paying jobs are on the rise
The first trend sees job polarization being replaced by general skill upgrading. Job polarization refers to increased employment opportunities in the high- and low-skill occupations, while middle-skill jobs go through a relative decline. Extending across multiple decades and various economic states, this phenomenon has been influenced by technological shifts such as manufacturing automation and the widespread adoption of office software.
However, the report noted that between 2016 and 2022 low- and middle-skill jobs have both declined, while high-skilled, high-paying jobs have slightly increased. The report adds that data collected through 2024 show similar results, denoting the end of polarization as of 2016 and the start of a trend toward skill upgrading.
2. Low-paying service work employment is flat or in decline
Job polarization during the 2000s was seen as a result of middle-skill production jobs being replaced by low-paid service work. Low-skill jobs enjoyed robust growth during the 2000s but slowed in the early 2010s and was flat throughout the rest of the decade, falling rapidly in 2020 when the COVID-19 pandemic happened. While low-paying occupations have partly recovered in 2024, most service sector employment is back to the same level it started at before its rapid growth back in the 2000s.
The decline in low-paying service work can’t be pinned solely on the emergence of AI, however, as other factors include the aforementioned pandemic disruption, increasing wages, and a tighter labor market.
3. STEM occupations are on the rise
After a decline in the 2000s, STEM (science, technology, engineering, and math) jobs are now enjoying rapid growth from 6.5 percent in 2010 to nearly 10 percent in 2024. This growth also extends to business and management occupations such as science and engineering managers, management analysts, and other business operations specialists.
Firms have also increased their investments in AI-related technologies to match the rising number of technical talents they’re hiring and developing. Mostly driven by the need for more computing power, software and information processing investments are now above 4 percent once again — the same level they were at prior to the bursting of the dot-com bubble and the 2001 recession — while research and development spending as a share of GDP has now reached a record high of 2.9 percent.
4. Retail sales jobs are in decline
Even before the pandemic, retail sales occupations had been declining. Retail sales jobs dropped by 850,000 between 2013 and 2023, which translates to a decrease from a 7.5 percent share to a 5.7 percent share of employment and represents a 25 percent reduction of share in the job market in just a decade.
This reduction is seen as one effect of e-commerce’s early adoption of predictive AI models around the mid-2010s to generate personal recommendations based on customers’ browsing and buying histories, along with predicting local product needs for stocking warehouses. Online retail has more than doubled its share of all retail sales, increasing to 15.6 percent from 7 percent in 2015.
Meanwhile, retail labor productivity has increased while the number of jobs declined, mimicking what had happened with manufacturing production jobs 50 years prior. Online retail’s demand for light delivery service truck drivers for their last-mile package delivery and “stockers and order fillers” in their large warehouses resulted in employment growth in these occupations.
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Conclusion
Is AI going to replace you at work? Looking at these four trends, the answer is going to depend on what you do for a living.
Summers pointed out that that “highly empowering” forms of AI might be so transformative that “certain types of activities simply won’t be done by people anymore.” Data exists to corroborate the assertion that automation claims jobs, Deming citing early 20th-century telephone operators in a Substack post. The study notes that sales and administrative support occupations may experience future declines in employment as AI innovates and improves on certain tasks — for example, personalized product recommendations, rapid pricing adjustments, inventory management, transcription, and automated scheduling.
As AI is increasingly used to boost productivity, there are some tasks where it still might not be as effective as humans. Based on this observation, some firms may conclude that, rather than replace human knowledge workers with robots, it might be wiser to simply increase their expectations from their human workforce. This is one of the reasons the study recommends higher levels of public and private investment in STEM education, reasoning that such training and reskilling of workers will help them adapt to a world in which the new technologies are here to stay.
Additional Reference Article: Is AI already shaking up labor market? – The Harvard Gazette, Christy DeSmith (February 14, 2025)
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Feb
“Distillation” Is Shaking Up The AI Industry
jerry9789 0 comments artificial intelligence, Brandview World
Paradigm Shift
We’ve recently written about recent AI advancements and popularity, particularly generative AI like that of ChatGPT, driving renewed demand for data centers not seen in decades. This surging demand pushed tech investors to put $39.6 billion into data center development in 2024, which is 12 times the amount invested back in 2016.
A recent development, however, has stirred things up, especially the concept that billions of dollars needed to be spent for AI advancement. Developed by a Chinese AI research lab, an open-source large language model named DeepSeek was released and performed on par with OpenAI, but it reportedly operates for just a fraction of the cost of Western AI models. OpenAI, however, is investigating if DeepSeek utilized distillation of the former’s AI models to develop their systems.
Copyright: cottonbro studio
What Is “Distillation?”
According to Labelbox, model distillation (or knowledge distillation) is a machine learning technique involving the transfer of knowledge from a large model to a smaller one. Distillation bridges the gap between computational demand and the cost for training large models while maintaining performance. Basically, the large model learns from an enormous amount of raw data for a number of months and a huge sum of money typically in a training lab, then passes on that knowledge to its smaller counterpart primed for real-world application and production for less time and money.
Distillation has been around for some time and has been used by AI developers, but not to the same degree of success as DeepSeek. The Chinese AI developer had said that aside from their own models, they also distilled from open-source AIs released by Meta Platforms and Alibaba.
However, the terms of service for OpenAI prohibits the use of its models for developing competing applications. While OpenAI had banned suspected accounts for distillation during its investigation, US President Donald Trump’s AI czar David Sacks had called out DeepSeek for distilling from OpenAI models. Sacks added that US AI companies should take measures to protect their models or make it difficult for their models to be distilled.
Copyright: Darlene Anderson
How Does Distillation Affect AI Investments?
On the back of DeepSeek’s success, distillation might give tech giants cause to reexamine their business models and investors to question the amount of dollars they put into AI advancements. Is it worth it to be a pioneer or industry leader when the same efforts can be replicated by smaller rivals at less cost? Can an advantage still exist for tech companies that ask for huge investments to blaze a trail when others are too quick to follow and build upon the leader’s achievements?
A recent Wall Street Journal article notes that tech executives expect distillation to produce more high-quality models. The same article mentions Anthropic CEO Dario Amodei blogging that DeepSeek’s R1 model “is not a unique breakthrough or something that fundamentally changes the economics” of advanced AI systems. This is an expected development as the costs for AI operations continue to fall and models move towards being more open-source.
Perhaps that’s where the advantage for tech leaders and investors lies: the opportunity to break new ground and the understanding that you’re seeking answers from unexplored spaces while the rest limit themselves and reiterate within the same technological confines. Established tech giants continue to enjoy the prestige of their AI models being more widely used in Silicon Valley — despite DeepSeek’s economical advantage — and the expectation of being the first to bring new advancements and developments to the digital world.
And maybe, just maybe, in that space between the pursuit of new AI breakthroughs and lower-cost AI models lie solutions to help meet the increasing demand for data centers and computing power.
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