Feb
4 Trends Indicate AI Is Shaking Up 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 looked 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 ’70 demonstrated volatility that surprised but eventually made sense to Summers while Deming characterized the 2000s and 2010s with having “automation anxiety.” The study however revealed that the labor market has enjoyed stability and low churn between 1990 and 2017 when the pace of disruption slowed.
Copyright: Kindel Media
But for 2019 onwards, it appears that there’s a major change set to happen with four labor market trends they believe pointing towards AI as the new breakthrough technology.
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 observed to be influenced by technological shifts such manufacturing automation and the widespread adoption of office software.
However between 2016 and 2022, the report noted that 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 share similar results, denoting the end of polarization as of 2016 and the start of a trend towards 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 down 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 they started before their rapid growth back in the 2000s.
The decline in low-paying service work can’t just be pinned on the emergence of AI alone, however, as other factors to consider include the aforementioned pandemic disruption, increasing wages, and a tighter labor market.
3. STEM occupations are on the rise
After having a decline in the 2000s, STEM (science, technology, engineering, and math) jobs are now enjoying rapid employment 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 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 prior to the dot-com bubble burst and the 2001 recession, while research and development spending as a share of GDP have now reached a record high of 2.9 percent.
4. Retail sales jobs are in decline
Even before the pandemic, retail sales occupation has been on the decline. Retail sales dropped by 850,000 jobs between 2013 and 2023, which translates to a decrease from 7.5 to 5.7 percent share of employment and converts into a 25 percent reduction of share in the job market in just a decade.
This is being seen as an effect of online retail or 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 at 15.6 percent from 7 percent in 2015.
Labor productivity growth in retail trade went up in the same period that retail sales occupation declined, mimicking what 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.
Copyright: u_fg0tkeqgiy
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 acquiesced how “highly empowering” AI can be that it might lead to “certain types of activities won’t be done by people anymore.” Data exists to corroborate that automation claims jobs with Deming citing early 20th-century telephone operators in a Substack post. The study notes sales and administrative-support occupations possibly experiencing future declines in employment as AI innovates and improves on tasks relating to these jobs- personalized pricing algorithms and product recommendations, inventory management, oral and written transcription, and automated scheduling, just to name a few.
As AI is being utilized more and more to boost productivity, there are some jobs and tasks it might not be as effective as human knowledge workers. The technology would exist but human output would be much more valuable that rather than replace knowledge workers, companies start increasing expectations from their human workforce. The study closes by recommending investing too in STEM education if this is the case, along with training and reskilling workers to help with their adaptability and effectivity with these new technologies.
Additional Reference Article: Is AI already shaking up labor market? – The Harvard Gazette, Christy DeSmith (February 14, 2025)
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Top Image Copyright: ThisIsEngineering
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.
Copyright: panumas nikhomkhai
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Top Image Copyright: Airam Dato-on
Jan
The Demand For Data Centers
Do you know how much energy a ChatGPT query consumes? If you use a traditional Google search to find the answer, that particular Google search would use about 0.0003 kWh of energy, which is enough power to light up a 60-watt light bulb for 17 seconds. A ChatGPT query (or even Google’s own AI-powered search) consumes an estimated 2.9 Wh of energy, which is ten times the energy used by a traditional Google search. Multiply the energy consumed by 200 million ChatGPT daily queries by a year and you have enough power for approximately 21,602 U.S. homes annually or to run an entire country like Finland or Belgium for a day.
No surprise then that AI’s growing mainstream popularity and usage have led to an increase in data center demand. For years, data centers were able to maintain a stable amount of power consumption despite their workloads being tripled, thanks to efficient use of the power they consume. However, that efficiency is now challenged by the AI revolution, with Goldman Sachs Research estimating data center power demand growing 160% by 2030.
McKinsey & Company noted in a September 2024 report that data centers consume 3% to 4% of total US power demand today, while Goldman Sachs puts worldwide use at 1% to 2% of overall power. By the end of the decade, data centers could be seen accounting for 12% of total US power and 3% to 4% of overall global energy. This level of demand spurred investors to push $39.6 billion into data center development and related assets in 2024, which is 12 times the amount spent in 2016.
Copyright: panumas nikhomkhai
How All That Power Is Used by AI
What were once warehouse facilities hosting servers mostly found in industrial parks or remote areas, data centers have now progressed into vital institutions in the digital world we’re living in today. The “old” problem used to be that the traditional data centers had some space that was underutilized, while the “new” problem is that space is scarce and critically needed. This has created a surging demand for more of these structures in order to address AI’s accelerating demand for more computing power.
As illustrated earlier, AI workloads consume more power than traditional counterparts like cloud service providers. Out of the two primary AI operations, “training” requires more computing power to build and expand models over time. However, the operation that derives responses from existing models called “inferencing” is growing quickly in volume as AI-powered applications become more popular and widely adopted according to a Moody’s Rating report. Their report further predicts inferencing growth over the next five years to make up a majority of AI workloads.
Amazon, Google, Microsoft, and Meta have stepped up to satisfy the demand by building, leasing and developing plans for hyperscale data centers. Moreover, investors have demonstrated a pronounced prioritization of AI projects over traditional IT investments; in fact, an Axios article noted that “tech leaders are actually worrying about spending too little.”
Copyright: Brett Sayles
Chokepoints in the AI Boom
It’s not only power that tech companies and developers have to consider when addressing the demand for new data centers; their location and access to that power are challenging their progress and development. Data centers may consume up to 4% of US power today, but because they are clustered in certain major markets they pose a substantial stress to local resources. McKinsey & Company’s September 2024 report also noted that there is at least a three-year wait time for new data centers to tap into the power grid of a major market like Northern Virginia. The search for available space, power and tax incentives have led developers to look into other markets like Dallas or Atlanta, according to a Pitchbook article.
Unlike investors, some local governments are not as keen on the development of data centers and may pause contracts or prioritize granting access to their power grid to other projects. An October report by the Washington Post revealed that a number of small towns have strongly pushed back against the construction of data centers in their areas.
In addition to concerns from residents over the strain data centers would put on local resources, carbon dioxide emissions can’t be overlooked, as Goldman Sachs estimates that such emissions will more than double by 2030. Areas with rising energy consumption from data centers might therefore find it challenging to meet climate targets.
Accelerated data center equipment demands are also straining the supply chain, as orders are taking years to fulfill. Planned tariff increases could also stress the delivery of offshore-produced parts and components used by data centers.
Copyright: panumas nikhomkhai
Navigating Towards The Future
While it is exciting to witness AI’s mainstream adoption and technological advancements, the challenges brought forth by its explosive demand for data centers could be just as intimidating. Perhaps Forbes expressed best how we should navigate this period of growth and uncertainty: we need to plan with purpose, not panic. We need to build with responsibility, not exuberance.
While the AI boom granted us the opportunity to correct the oversupply of demand centers resulting from the dot-com boom, we need to tread more carefully when addressing this new need for more of these infrastructures. We need to understand what each data center is being planned and built for, its primary purpose, and how it connects to local resources. Yes, we were able to breathe new life into data centers that were underutilized after the Internet boom, but that took years and a new technological revolution to correct. Who knows if we’re also able to enjoy the same chance to recover if we fumble today’s attempts to address the demand for data centers? For all we know, the next technological wave might such be a swerve that it advances beyond the need for data centers.
We should also improve, innovate or discover new renewable and responsible energy solutions as well as increase the efficiency of our developing technologies’ use of power. There’s also an opportunity for the tech industry to enter into dialogues with local audiences about what is happening in the digital world and what they can expect from AI, thus demonstrating transparency and social responsibility.
As investors and developers eagerly meet AI’s surging demand for data centers, perhaps we can pause and appreciate this opportunity to make decisions that not only fortify the connection between the digital space and the world we live in today, but also forge a more responsible and sustainable path towards the future.
Copyright: TheDigitalArtist
The Impact of DeepSeek
While the tech industry searches for solutions to the data center demand, a new player has emerged to shake things up: Chinese AI research lab DeepSeek has released their open-source large language model. Quickly shooting to the top of Apple Store’s downloads, DeepSeek has challenged contemporary views on AI development with a platform that performs just as powerfully and efficiently as OpenAI while reportedly operating at a fraction of its Western counterparts’ cost. While it feels like we’re on the brink of a paradigm shift, we believe its true impact is yet to be seen. We expect to know and learn more in the coming weeks, and we’ll share our thoughts in a future blog.
Copyright: Yanu_jay
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Dec
What’s Going On With Consumer Startups In The Age of AI?
jerry9789 0 comments artificial intelligence, Brandview World, Burning Questions
Enterprise Over Consumer
The dawn of the Internet era witnessed the emergence of huge consumer companies like Amazon while the advent of mobile technology had Uber and the like on the forefront. However, it appears that the tide has changed in this new age of AI with startup founders and investors appearing to favor enterprise over consumer efforts.
This observation is the school of thought on which the PitchBook article “Where are all the consumer AI startups—and why aren’t VCs funding them?” was based and written. It came from the author’s takeaway from her two-day experience attending the recent startup conference Slush in Helsinki where venture capitalists expressed high interests in AI startups as expected, but notably for B2B over B2C.
She further adds that PitchBook data has venture funding for B2B AI startups is at $16.4 billion this year while B2C is only at $7.8 billion. But with the consumer AI market estimated to be doubly larger than its enterprise counterpart by 2032, she posts the question if there is a lack of B2C startups, or if VC are simply just not funding consumer AI companies?
Copyright: fauxels
The Challenges of B2C AI
To start with, it simply seems that investors generally are not keen on consumer startups especially with the VC downturn starting in 2022. A combination of factors such as rising inflation, higher interest rates and valuation markdowns have created a harsh macroeconomic climate for B2C AI to thrive. And when stable profitability is the bottom line, investors would understandably be more attractive to the steady and predictable revenues generated by B2B AI companies over the unsustainable and erratic B2C AI business models.
Jordan Steiner, CEO and developer capital/chief strategy officer at Monadical, shared some unfavorable characteristics he noticed from B2C AI companies he noticed on a LinkedIn post. Most B2C AI ideas these days he found are easily replicable. When competitors can not only easily clone but also improve on an existing idea, this can hamstring any company’s chances from dominating the space or becoming an incumbent. And when these factors create a cycle where users chase the newest cool product and churn when the novelty wears off, it illustrates just how unsustainable B2C AI business models are, especially in this period of time when user acquisition costs are higher.
And when a business model banks more on desirability instead of addressing pain points, there is a continuous struggle to iterate and produce new features or content. This then requires a consistent and ongoing understanding of consumer trends, necessitating access to consumer data and insights that a startup might not have at the beginning and need to build over time, primarily with user acquisition. Incumbent B2C companies would most likely have heavily invested on acquiring consumer data and insights to maintain and defend their longstanding piece of the market.
So why do B2B AI investments seem the more attractive prospects then at this time? By prioritizing pain points over desirability, then selling to and maintaining long-term relationships with key industry players, B2B AI companies are able to eventually build desirability to attract more clients. B2B clients are also more likely to sign up and keep multi-year contracts and subscriptions which not only provide steady and stable revenue but also client data vital for product improvement and customization, helping not only build brand loyalty but also incumbency and low churn.
Copyright: Christina Morillo
Can A B2C AI Company Succeed?
Despite the aforementioned obstacles, there is room for a consumer AI startup to thrive. The PitchBook article suggests focusing “other spaces where big tech has less credibility, such as mental health solutions.” In the same article, Point72 Ventures managing partner Sri Chandrasekar highlights differentiation as being a key characteristic for a B2C AI company to help close investments, this uniqueness holding off attempts to be replicated while tapping into that factor of desirability that excites and engages consumers while attracting investors.
If anything else, a consumer AI startup might need to bootstrap it more than just having an idea to attract investments. Demonstrating and executing on your unique position not only proves your idea as sound and feasible but you are able to get your B2C AI company past the first step towards progressing to the potentially higher rewards offered in this space.
Featured Image Copyright: Pavel Danilyuk
Top Image Copyright: Photo By Kaboompics.com/Karolina Grabowska
Nov
“Humanizing” Market Research with AI
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Burning Questions
The Boon and Bane of AI
The increasing and widespread utilization and demand for Artificial Intelligence have been met with both excitement and reservation. Excitement for the possibilities AI’s implementation unlocks, oftentimes steps ahead of the curve or beyond expectations; reservations not only stemming from the risks over its unethical and unchecked use, but also the ramifications for human involvement now that intelligent machines represent an optimized and economical choice for completing tasks and processes. But can there be a middle ground somewhere where AI and human engagement coexist and collaborate?
The “Humanization” of Market Research
“Capturing the Human Element in an Artificial World” by Eric Tayce (Quirk’s Marketing Research Review, Sep-Oct 2024) posits that such a midground is possible, especially in market research. An industry that’s all aware of its excessive dependence on technology to necessitate a push to “humanize” research data, it saw a dramatic shift from “data-intense tomes and clinical-sounding slide titles” to “streamlined, narrative-style reporting” focused on “the unique motivations and experiences that drive customer behaviors.” The latter “humanized” approach is able to communicate business goals while connecting and engaging on an emotional level. However, generative large language models (LLMs) cast a shadow on this “humanized” approach by offering synthetic outputs and progressive algorithms.
But combining both AI and efforts to “humanize” research can result in the whole being greater than the sum of its parts. The article shared that AI can help collect more unstructured data from survey research by employing conversational chatbots to create a natural, richer experience for the respondent. That unstructured data in turn can potentially provide more organic, more human insights with AI-powered algorithms, an undertaking that was once considered too complex or time-consuming. AI can also build multifaceted perspectives through context by linking survey records with a broad range of data sources. And in lieu of traditional static deliverables, data and insights can be presented in a vibrant and interactive narrative by an AI-powered persona.
The “Human” Element
All these interesting prospects can only be achieved when AI is tempered by high-quality human input and thoughtful implementation considerate of ethical and moral implications. Aside from AI mistakes and hallucinations existing, AI has been observed to be too helpful and excitable. Human oversight and input remain key in ensuring AI models are trained, fine-tuned and grounded with quality and relevant datasets while having enough flexibility to engage appropriately in open-ended interactions.
There’s no denying just how transformative AI is in reshaping industries today, including market research. Despite concerns of machines taking over jobs, one can look at it with the perspective of roles changing and adapting. AI with its generative and synthetic capabilities can elevate the “humanization” of market research, but to get to that point we simply can’t forget that humans are indispensable to the whole process.
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Sep
So Why Use AI For Your Small Business?
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Burning Questions
Artificial Intelligence has actually been around for decades already but it grew past being a buzzword and into the mainstream in 2022 with the surprise popularity of OpenAI’s ChatGPT. Nowadays, it might be challenging to find someone who doesn’t have an iota of an idea of what AI is and what it does. In fact, its widespread cultural adoption belies its real impact behind the scenes where it steadily transforms and shapes businesses and industries towards a more automated and optimized direction.
Now as a small business owner, you might think that last statement doesn’t apply to you and is targeted mostly towards larger scale companies, but that is far from the truth. That last statement is just as relevant to your smaller, local-based trade as it is to any regional or global firm. In fact, 75% of small businesses have taken advantage of AI, according to the Small Business & Entrepreneurship Council. Additionally, 93% of small businesses agree that they save money and improve profitability utilizing AI solutions. We learned about these two interesting points when we attended a webinar hosted by CallRail, “Q&A: Demystifying AI for Small Businesses.”
You might have heard too that AI actually places everybody on the same playing field, and this was underscored at the webinar when they shared that small businesses have access to the same AI technology that big companies employ. At the same time, small businesses are granted a chance to achieve the same impact as their larger counterparts. Small businesses however enjoy being able to adapt or incorporate new technology and processes easier than their larger counterparts.
So how do you join the small businesses using AI to make money and grow? What are examples of AI being utilized by small businesses? Where do you start in understanding and applying AI solutions for your small business?
Copyright: Shafin_Protic
Artificial Intelligence and Its Subsets
Perhaps it’s best to follow suit with the webinar and include a quick look but fundamental understanding of AI and its subset. As you might know, AI technology enables machines like computer systems to simulate or emulate human intelligence and behavior by learning from training data, pattern recognition, decision-making, and problem-solving.
When that pattern recognition is taken one step further by involving huge data sets and advanced algorithms, a subset of AI called Machine Learning is developed. Aside from simulating or emulating human intelligence, Machine Learning allows computer systems to learn and adapt. However, a misstep in ML is the oversight of certain variables affecting the accuracy of the intended output.
A subset of ML called Deep Learning builds upon this limitation of overlooking variables by actually learning from these variables with historical data to generate accurate and high level outputs. DL achieves this by leveraging multiple layers of artificial neural networks for in-depth data processing and analytical tasks.
And when that high level data set is transformed into generated yet fine-tuned content like text, images, or code, we now arrive in the territory of Generative AI. This subset of DL models include the popular ChatGPT.
Copyright: geralt
How Are Small Businesses Using AI?
Like any other company or industry, small businesses have started to use AI to save time by streamlining, automating and optimizing whichever aspect of their processes that they could. One example is speech-to-text where instead of listening to every call, you convert a recorded phone call into summarized text with relevant and possibly actionable information or insight. By filtering calls in this manner, you’re also able to identify which types require the utmost attention and immediate follow-up, an especially valuable feature for qualifying leads.
As they say time is gold and so in the same vein where you free time by outsourcing time-consuming and repeatable tasks to another person or agency, automating processes through AI allow you to devote the time you free up to other more advanced functions or find more opportunities that can help further improve productivity and profitability, growing your business along the way.
Copyright: sohag_hawlader
Will AI Replace Small Businesses?
Now adapting and utilizing AI in your small business isn’t the end-all and be-all; it won’t even be replacing you wholesale anytime since it is, after all, just another tool at one’s disposal. Embracing the hot new tech keeps you at pace with the rest of the pack, but how you stand out will still fall on your business savvy and the intrinsic, unique value you bring to the table. Whether it be for your marketing or improving processes or customer relations, AI will help you glean as many insights as possible from your business transactions, interactions, and communications, but how effective that knowledge becomes will still depend on how well you leverage it.
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’s 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 disposal. They won’t be taking over surveys nor replacing actual respondents wholesale anytime soon it seems, as that elusive “Eureka” moment researchers seek are inherently tied with the nuances and perspectives of human emotion and experience you just simply couldn’t construct.
Photo courtesy of Pavel Danilyuk
Jul
AI In Market Research: The Story So Far – Chapter 3: A Glimpse Into A Future with AI
jerry9789 0 comments artificial intelligence, Burning Questions
This is the third installment in our series on AI webinars. The inspiration for this series is a simple question about what these AI seminars are saying. There are hundreds of these seminars floating about, all based on the premise that AI technology is here to stay, people are curious about it, and they want to know how it will affect their lives.
We asked one of our staff members to attend several of these AI webinars in pursuit of the answer to this question: what are the AI webinars really saying? What are the common themes, if any? While the first and second chapters focused on AI’s ubiquity and limitations respectively, this third installment focuses on the replacement of humans and human work.
Will Jobs Be Lost Because of AI?
AI is a threat to most jobs including those in the market research industry but this is most especially true with any repetitive or routine work grounded by established knowledge or processes. AI provides the advantages of streamlining your knowledge base and shortcutting processes. If you’re on the process side and you fail to embrace AI, clients might find you costlier and less optimized.
The market research industry had already learned this lesson in the early 2000s when the big companies didn’t take online research seriously. They subsequently found themselves trying to catch up some years later after the widespread acceptance and adoption of online research. Whoever waits too long or neglects to embrace the newest tool would most likely fall behind as the industry shifts towards AI-driven processes.
What are the AI webinars saying about Human Replacement?
This doesn’t necessarily mean that humans would be fully replaced and displaced by AI in market research. AI is, after all, a tool. All tools revolutionize optimization but optimization by definition doesn’t make things better. AI will revolutionize things, but it is not the big revolution that will make everything different.
To illustrate that last point, a question was raised in one of the webinars about the possibility of a data collection tool that can replace surveys. There wasn’t a definitive answer given by the panelists since it’s more of a question of what surveys would actually look like. It would depend on what is wanted to be accomplished, the type of information sought, and how they would engage and elicit reactions.
The subject of AI-powered market research alone attracts investors. Embracing AI would not only optimize your market research processes but it would also add value to your insights. Having said that, AI places everybody on the same playing field, except those who recognize and seize the opportunity to experiment with AI are able to gain an edge. The key would be to build on experience rather than purely on the thirst for innovation; try to be at the front of things, but don’t try to be the first one. Try to find a good balance by going with the flow while making smart moves and decisions.
It’s been noted that the profile of the researcher of the future is a little bit more techy and into IT integration. New business intelligence leaders today have IT backgrounds, and this is different from two decades ago. Even in a world with AI-based market research, there would be room for the human factor that adds value from experience — something AI won’t be able to replace.
Jun
AI In Market Research: The Story So Far – Chapter 2: Limitations of AI
jerry9789 0 comments artificial intelligence, Burning Questions
Despite AI’s expanding popularity in market research, experts are fully aware that there is still a lot of ground to cover regarding their effectiveness and optimization for use cases, along with understanding and mitigating their risks and limitations.
These limitations reveal themselves most especially in efforts to replicate human behavior. One research paper on a survey employing Large Language Models observed how effective these LLMs are in understanding consumer preferences with their behaviors consistent with four economic theories, but noted that there were demonstrations of extra sensitivity to the prompts they were given. In addition, there were indications of positional bias wherein the first concept was selected more often than the others that were also presented.
AI has also been found to be too optimistic, tech-forward, and self-interested. For example, ChatGPT is inherently focusing on maximizing expected payoffs, whereas a person would often act in a risk-averse way for gains and risk-seeking for losses. AI also exhibits a generally higher level of brand association than humans, resulting in higher brand scores. However, it struggles with lesser-known topics, notably in scenarios where new commercial products are tested and targeted toward a specific audience.
While it can be addressed by cautious prompt engineering, AI hallucinations are an unintended effect of the helpful aspect of these models where they generate unnecessary output stemming from patterns or elements they perceived but are nonexistent or imperceptible to human observers.
And while more on the side of risks than limitations, there is an understandably and famously increasing concern from artists over how text-to-image generators threaten to replace them and their work, just as there are certain roles in the market research sector that are in danger of being taken over by AI.
Perhaps the ideal recommendation for utilizing AI while keeping in mind its limitations is to use it in cases where it’s most effective and productive with the understanding that it might excel in one scenario, but it doesn’t mean it will be just as effective in another situation.
May
AI In Market Research: The Story So Far – Chapter 1: Adapt or Get Left Behind
jerry9789 0 comments artificial intelligence, Burning Questions
Whether you like it or not, AI is here to stay. Yes, AI is a threat to most jobs, including those in the market research industry, since it shortcuts processes while optimizing operational efficiency. While market research technology didn’t develop as fast as other industries in the early to mid-2000s, the advent and subsequent mainstream appeal of AI has forced market research to get with the times. You’re in trouble if you fail to embrace it but if you do, you get to be on the winning side.
Experts expressed that we’re still in the early exploratory stages of AI but there is already depth in its application in market research. Take, for example, the humanization of surveys. An interactive and dynamically probing AI improved overall data quality in more than one experiment due to an increased engagement from respondents resulting from a sense of appreciation over the perceived but simulated attention paid to them and their responses during the survey. In the same vein, employing a conversational AI voice has been shown to dramatically drive engagement for better data.
That latter effort to humanize surveys has created an influx of voice responses and content, leading to the new question of what we should now do with all those resources, which would be a byproduct of AI-based solutions. Of course, LLMs and other existing AI models would be employed to help find the answer to this question.
Aside from solving dark data, AI has also displayed impressive capabilities to answer choice tasks, especially performing well with well-known topics and products, even outdoing humans in some surveys where humans get confused or find it hard to render a judgment. It’s also been considered for AI to adapt existing survey data for a new topic to save time. AI’s role in market research might still be experimental at this point, but it has grown to the point where it’s being utilized and adapted to take on one challenge after another.
Our second entry in this four-part blog series highlights some of AI’s risks and limitations, and how understanding and mitigating these factors can lead to their effective and optimal utilization.
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