Artificial intelligence
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.
Featured Image Copyright: GrumpyBeere
Top Image Copyright: Darlene Anderson
Oct
Concerns Over AI in The Workplace
Much has been said and concerns have been raised regarding the impact and influence of Artificial Intelligence in the way we do work and conduct business, despite AI’s popularity and appeal reaching mainstream and cultural heights in just the last few years. Most of these reservations stem from the thought of machines replacing people, as AI is continually applied to automate tasks, optimize efficiency and cut down costs wherever and whenever possible. But is the workplace of the future looking to be that dire and devoid of human ministration with the ever-increasing utilization of AI?
The Wall Street Journal did a good job of trying to answer that question by asking several top thinkers on this subject for their point of view. This article touches on those viewpoints and provides some of our own.
Many people fear that our growing dependency on AI could lead to indiscriminate and thoughtless applications, which may result in huge and unintended errors, perhaps even to the point of irreparable consequences. Even well-intentioned initiatives might bear inadvertent effects with one example being the use of digital assistants. Jonathan Gratch, professor of computer science at the University of Southern California, shares some insights from their research that indicates the weakening of emotional bonds and social checks present in a traditional team setup, or even in typical face-to-face negotiations. These ethical checks are reduced when AI-based virtual or digital assistants are involved, with interactions becoming more transactional and self-interested. Some people have also been observed instructing AI to channel deception and manipulation in order to gain unfair advantage.
Deliberate unethical usage is cause enough for alarm and worry as Stuart Madnick, professor of information technologies at the MIT Sloan School of Management, pointed out that AI makes scamming involving the use of personal details much easier and more dangerous. Whereas hackers and scammers were traditionally selective of who they victimize based on the ratio of the money they would be swindling to the effort and time involved to get the scheme going, an AI-driven con could widen the net they cast to include targets of lesser finances and resources they would normally not go after. In an office environment, this might mean AI-generated emails or phone or video calls with deep fakes that are so convincing an unsuspecting employee might transfer funds upon the instructions of their “boss.”
Sherry Turkle, author and Abby Rockefeller Mauzé Professor of the Social Studies of Science and Technology at the Massachusetts Institute of Technology, also points out the effects of pretend-empathy machines that are now commonly being employed for job interviews and performance assessments. When the opportunity to be hired or retain one’s job depends on AI judging and scoring us based on qualities it values the most, we might find ourselves training or adjusting “to please the machines.”
Copyright: cottonbro studio
Opportunities at The Workplace with AI
But it’s not all sad and gloom for the workplace of tomorrow with AI. One can also find opportunities if they’re able to look past all that uncertainty and apprehension. AI technology is bridging us to the future, but human intelligence can ultimately shape how that bridge is formed.
Circling back to task automation, this could mean that organizations would eventually have fewer employees and fewer layers of management. Depending on perspective and needs, this could either be the bane sceptics have been heralding or the boon that would serve as the catalyst for more change. With routine tasks having been automated, this would open up avenues to explore on what new work you could assign to your employees that a machine simply could not perform. You might also find some middle-tier work that could be trickled down to lower or greener employees, challenging them and helping them grow within your organization. This could also reduce the need to hire for midcareer posts outside your company which is a likely recourse if an organization is unable to grow their own talents in-house.
Alberto Rossi, finance professor and director of the AI, Analytics and Future of Work Initiative, at Georgetown University, shared that asset managers are now hiring more human advisers instead of laying them off, a result of offering hybrid advisory services that involve both human input and algorithms. The advisory service algorithms are assigned certain simple tasks while advisory personnels provide that essential human connection that’s key to client satisfaction and retention.
With the right data, AI tools and data analytics can transform not only how companies find and recruit talents but also build for the future with team recommendations and identifying organizational gaps. Guided by human judgment, AI can boost better collaboration and productivity at the workplace. Generative AI such as ChatGPT has been found likely to improve the productivity of all workers, especially low-skilled workers who appreciate the availability of such tools, helping narrow the productivity gap between this group and their higher-skilled peers.
An interesting development is the possible rise in value of workers older in age. The experience and knowledge they gain through the years can be leveraged to identify which issues need the most attention and immediate or timely resolution along with validating AI-based solutions and recommendations. Investing in further training and upskilling can also lead the way to improved AI utilization and collaboration; just as AI makes automated customer interaction feel much more natural, AI services can decrease complexity with coaching and continued education at the workplace with conversational interfaces and natural-language processing. This reduction in complexity can also extend to AI-powered “concierge” systems when assisting employees seeking benefits and services from their employer.
Ultimately, these exercises in the utilization and collaboration with AI can pave the road for organizational development from task-oriented AI to goal-oriented AI.
Copyright: Pavel Danilyuk
Transforming The Workplace with AI
As you might have noticed, we’ve painted two different futures for the workplace with AI: one conveying caution and warning, the other promoting potential and exploration. One of the key differences that separate both realities is the thoughtful and ethical application of AI. At Cascade Strategies, we’ve always advocated for the “Appropriate Use of AI” as we firmly believe that the effective and productive application of AI in shaping the future can only be achieved when tempered with human values and experience.
The AI boom has driven organizations and businesses to rethink the workplace. And in the midst of all the automation and optimization that would involve, we are hopeful that there’s always room for high level and inspired human thinking, the kind that unlocks genuine breakthroughs and achieves true excellence.
Copyright: Kindel Media
Featured Image Copyright: beasternchen
Top Image Copyright: Anna Shvets
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.
May
AI Webinars Are Everywhere – What Are They Really Saying?
jerry9789 0 comments artificial intelligence, Burning Questions
With AI becoming more ubiquitous each passing year, it’s no surprise that webinars dedicated to the subject have been springing up everywhere. Amid the hype, people are either curious, interested, or to some degree invested in what AI’s increasing popularity means for them as well as the industries they’re part of. These webinars serve as the perfect platform for industry experts to share their experiences, thoughts, and opinions on AI’s current and future implications.
What are these AI webinars really saying? We sent one of our staff members in search of the answers. Each webinar talks about the impact of AI on the economy, society, and culture, but they must share some common themes or overarching ideas. What are these common ideas? To get at the answers, we asked our staff member (Emil Deverala) to focus on the impacts on an industry we truly understand: market research.
During April and May 2024, Emil attended three AI webinars: “Market Research in an AI World,” “AI in Marketing Research: Expert Panel Discussion,” and “Building New Business: Five Ways Firms Are Driving New Revenue with Automation And AI.” After each webinar, Emil was asked to not only summarize the items that were discussed, but also share his larger thoughts about what the webinar was really trying to say to the world about what we can expect from AI.
Emil eventually boiled things down to four main ideas or themes. In this series of blogs, we’ll be exploring each of those themes. Here’s the first in our series. It focuses on why people in the market research industry need to pay attention to AI in the first place.
Sep
What It Means to Choose or Decide In The Age of AI
jerry9789 0 comments artificial intelligence, Burning Questions
Longstanding Concerns Over AI
From an open letter endorsed by tech leaders like Elon Musk and Steve Wozniak which proposed a six-month pause on AI development to Henry Kissinger co-writing a book on the pitfalls of unchecked, self-learning machines, it may come as no surprise that AI’s mainstream rise comes with its own share of caution and warnings. But these worries didn’t pop up with the sudden popularity of AI apps like ChatGPT; rather, concerns over AI’s influence have existed decades long before, expressed even by one of its early researchers, Joseph Weizenbaum.
ELIZA
In his book Computer Power and Human Reason: From Judgment to Calculation (1976), Weizenbaum recounted how he gradually transitioned from exalting the advancement of computer technology to a cautionary, philosophical outlook on machines imitating human behavior. As encapsulated in a 1996 review of his book by Amy Stout, Weizenbaum created a natural-language processing system he called ELIZA which is capable of conversing in a human-like fashion. When ELIZA began to be considered by psychiatrists for human therapy and his own secretary interacted with it too personally for Weizenbaum’s comfort, it led him to start pondering philosophically on what would be lost when aspects of humanity are compromised for production and efficiency.
Copyright chenspec (Pixabay)
The Importance of Human Intelligence
Weizenbaum posits that human intelligence can’t be simply measured nor can it be restricted by rationality. Human intelligence isn’t just scientific as it is also artistic and creative. He remarked with the following on what a monopoly of scientific approach would stand for, “We can count, but we are rapidly forgetting how to say what is worth counting and why.”
Weizenbaum’s ambivalence towards computer technology is further supported by the distinction he made between deciding and choosing; a computer can make decisions based on its calculation and programming but it can not ultimately choose since that requires judgment which is capable of factoring in emotions, values, and experience. Choice fundamentally is a human quality. Thus, we shouldn’t leave the most important decisions to be made for us by machines but rather, resolve matters from a perspective of choice and human understanding.
AI and Human Intelligence in Market Research
In the field of market research, AI is being utilized to analyze a multitude of data to produce accurate and actionable results or insights. One such example is deep learning models which, as Health IT Analytics explains, filter data through a cascade of multiple layers. Each successive layer improves its result by using or “learning” from the output of the previous one. This means the more data deep learning models process, the more accurate the results they provide thanks to the continuing refinement of their ability to correlate and connect information.
While you can depend on the accuracy of AI-generated results, Cascade Strategies takes it one step further by applying a high level of human thinking. This allows Cascade Strategies to interpret and unravel insights a machine would’ve otherwise missed because it can only decide, not choose.
Take a look at the market research project we performed for HP to help create a new marketing campaign. As part of our efforts, we chose to employ very perceptive researchers to spend time with worldwide HP engineers as well as engineers from other companies.
This resulted in our researchers discovering that HP engineers showed greater qualities of “mentorship” than other engineers. Yes, conducting their own technical work was important but just as significant for them was the opportunity to impart to others, especially younger people, what they were doing and why what they were doing was important. This deeper level of understanding led the way for a different approach to expressing the meaning of the HP brand for people and ultimately resulted in the award-winning and profitable “Mentor” campaign.
If you’re tired of the hype about AI-generated market research results and would like more thoughtful and original solutions for your brand, choose the high level of intuitive, interpretive, and synthesis-building thinking Cascade Strategies brings to the table. Please visit https://cascadestrategies.com/ to learn more about Cascade Strategies and more examples of our better thinking for clients.
info@cascadestrategies.com
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