Possibly 60% of Consumer Products Fail: Why Would AI be Different?

As driven as suppliers might be to use artificial intelligence, consumer and user reactions are more complicated. As always, the usefulness of the innovation has to be grasped to be embraced. 

Perhaps one measure of product success is its ability to move from conception, research and development to production and then measurable sales success. Some researchers estimate that up to 95 percent of new products fail. Others dispute the notion, arguing that failure rates are closer to 60 percent. 

Some of the variance is caused by how we define “success” and “failure.” Estimates can vary wildly based on whether we set low or moderate targets for sales volume or market share. 

Study Title	Date	Publisher	Key Estimate
"The Product Development Process: From Idea to Introduction"	1986	Cooper & Kleinschmidt	40% of new products reach the market.
"The Innovator's Dilemma"	1997	Clayton Christensen	High failure rates for disruptive technologies.
"New Products: Management, Development, and Implementation"	2003	Griffin & Hauser	Failure rates vary significantly by industry.
"The Myths About New Product Failure Rates"	2017	McKinsey Global Institute	25-45% failure rate across industries.
Why 95% of New Products Miss the Mark (and How Yours Can Avoid the Same Fate)	2020	MIT Professional Education	Reports that nearly 30,000 new products are introduced each year, with 95% of them failing, according to Clayton Christensen of Harvard Business School. MIT Professional Education
Market View: New Product Success Rate Higher Than Most Believe	2014	Food Processing	Suggests that, contrary to popular belief, 66% of new products are successful, challenging the commonly cited 70-90% failure rate.
20+ Product Launch Statistics You Should Know in 2024	2024	G2 Learning Hub	Indicates that only 40% of developed products reach the market, and among those, only 60% generate revenue.
How to Ensure Your Innovation Doesn't Fail After Launch	2023	Kantar	Finds that 16% of even the most successful first-year new products end up dead or dying by their fourth year.
Success and Failure Rates of New Food and Non-Food Products Introduced on the Market	2022	ResearchGate	Argues that the common claim of 80–90% new product failure is a myth, stating that empirical studies do not confirm this belief.
How Common Is New Product Failure	2020	Ehrenberg-Bass Institute	40% failure rate on average; 41% failure rate for new SKUs two years post-launch
Trends and Drivers of Success in NPD Practices	2009	Journal of Product Innovation Management	41% failure rate
Product Development Institute Study	2010	Stage-Gate International	38% failure rate for top performers, 55% for bottom performers
PDMA Best Practices Study	2004	Product Development & Management Association	41% average failure rate across industries; 35% for healthcare, 45% for consumer goods
Consumer Packaged Goods and Retail Products Study	2024	Business Dasher	75% of products fail to earn $7.5 million in their first year
Harvard Business School Study	Not specified	Harvard Business School	95% of new products fail

We will likely find that such product acceptance rates will affect AI use cases, features and apps as well. Not every AI use case will prove to provide so much value that “everyone” uses it. 

But consumers might be more comfortable with generative AI, for example, than providers expect. Students appear to be widely using it for purposes of writing essays, for example. But most consumers arguably already experience--and “use”--various forms of AI more passively, as when editing photos, using speech-to-text or searching for products to buy.  

Title	Date	Publisher	Key Findings
"2024 Consumer Study: Revolutionize Retail with AI Everywhere"	2024	IBM	Explores how AI can enhance retail experiences, particularly in inventory management and demand forecasting. Highlights the necessity of real-time data integration for building intelligent supply chains that meet specific customer needs.
"Consumer Perception and Use of Generative AI"	2024	Parks Associates	Quantifies consumer familiarity with and usage of generative AI applications. Indicates that these applications are often consumers' first direct interactions with AI, prompting new discussions about AI's capabilities and limitations.
"Consumers Know More About AI Than Business Leaders Think"	2024	Boston Consulting Group	Reveals that consumers possess a higher level of knowledge and excitement about AI than business leaders anticipate. Suggests that businesses should not underestimate consumer awareness and should engage more transparently regarding AI implementations.
"Consumers Open to AI in Marketing, But Data Privacy Matters"	2024	CDP.com	Reports that 81% of consumers are receptive to AI being used in marketing for personalized recommendations, provided that data privacy concerns are adequately addressed. Emphasizes the importance of balancing personalization with privacy.
"What Drives the Acceptance of AI Technology?: The Role of Expectations and Experiences"	2023	arXiv.org	Investigates factors influencing AI acceptance, finding that both direct experiences with AI and prior ICT experiences significantly impact acceptance intentions. Highlights the importance of managing user experiences to foster realistic expectations of AI technology.
"Consumer Acceptance of the Use of Artificial Intelligence in Online Shopping: Evidence from Hungary"	2022	arXiv.org	Examines consumer acceptance of AI in online retail, identifying trust and perceived usefulness as key factors. Suggests that enhancing content quality and automation can improve consumer attitudes toward AI-powered webshops.
"AI Is Ruining Your Laptops Now"	2024	Lifewire	Discusses consumer skepticism towards AI features in laptops, noting that such additions are often seen as unnecessary and resource-consuming, potentially deterring purchases. Highlights a disconnect between tech companies' promotion of AI and actual consumer preferences.
"Artificial Intelligence Marketing"	2024	Wikipedia	Describes how AI enables hyper-personalized advertisements by analyzing consumer data and patterns. Notes that while AI-driven personalization can enhance customer engagement, it also raises concerns about data privacy and the potential for intrusive marketing practices.

AI Doesn't Change Who You Are

Some might argue that the internet makes us lazy, the way people used to worry that student use of calculators would similarly make them mathematically lazy.

It's probably more correct to say that lazy people will use tools in a lazy way. So it will likely be for artificial intelligence: some will argue its use will make users less capable in some ways.

We already see the impact: teachers know that AI can write, but they want to assess whether their students can do so. To be sure, students have, in the past, found other ways to spoof writing. AI only makes it easier. But it will likely be harder to measure writing ability in the future.

But that is true of all tools. Paintbrushes do not make me a better painter. I'm not good at that. My brother is a painter and brushes make a difference for him.

In a perhaps-similar way, thinking habits will not be changed by AI. Thoughtful people still will be thoughtful. Evidence will still matter for those who believe evidence matters.

Some argue that “financial analysis by economists and industry experts has shown that the primary driver behind the sky-high prices of groceries throughout the country is corporate greed,” as an assertion unsupported by references to such analysis. 

Some might point to grocer profit gains of about 2.5 percent as the result of “price gouging,” but others might not there were so many other variables at work during the Covid pandemic that even some amount of market consolidation does not actually explain much of the variance. 

The typical evidence claimed (when provided at all) is that company profits have risen a bit more than the combined input costs since the Covid pandemic began. 

Still, a rational person might argue that many forces have contributed to high grocery prices since the Covid pandemic, and that some portion of higher profits might have occurred because of market concentration (consolidation). 

Among forces causing higher prices:

• Supply chain disruptions that created shortages

• Labor shortages

• Government cash injections at a time when production declined 

• Increased demand (panic buying, stockpiling, and changes in consumer behavior)

• Rising energy and transportation costs

• Global commodity price increases

• Geopolitical instability (Russia-Ukraine war)

AI Can Reduce Product Return Rates and Therefore Boost Profitability

One use case for artificial intelligence that can have significant operating cost advantages for retailers is its use in e-commerce apparel sales to reduce the rate of customer returns (wrong size, don’t like the color or fit, ordered the same item in several sizes expecting only one will fit best). 

Product returns in the retail industry are an ever-present cost of doing business. By some estimates, retail merchandise returns represent as much as 11 percent of all original sales, and might, in some areas as apparel, be as high as 20 percent to 25 percent. 

Aside from the costs of logistics to handle such returns, it often is the case that the returned items cannot be resold. In the case of apparel, such losses are said to reach half to 60 percent of returned goods. In a business with notoriously slim profit margins, that is a major cost of doing business.

The point is that product returns are an important performance indicator among many. That is a key reason why Amazon now charges its retail partners restocking fees when items are returned. 

source: Market Gap 

One study suggests that for an average company, a five percent improvement in the rate of returns has the potential to deliver improvements of about 200 basis points in net margin, or about two percent. 

And though it is hard to quantify the impact of returns on firm profitability, some retailers believe “an individual consumer with a long-term pattern of return rates greater than 20 to 30 percent negatively affects operating profit.”

source: Market Gap 

Study Title	Date	| Publisher	Estimate
The Cost of Product Returns in Online Retail	2023	Journal of Retailing	average return rate: 20.8% of online purchases Processing cost per return: $10-20  Return fraud accounts for 10.1% of return costs Net profit impact: -2.1% average reduction
Reverse Logistics in E-commerce: A Comprehensive Study	2020	Harvard Business Review	Returns reduce gross margins by 4.4-6.8%
Product Returns in Fashion	2019	Journal of Fashion Marketing and Management	Return processing costs: $3-6 per item Apparel return rates: 28-35% Size/fit issues account for 62% of returns
The Economics of E-commerce Returns	2019	MIT Sloan Management Review	Total return costs: 8-10% of revenue Fraudulent returns cost retailers $5.8B annually

So AI use to reduce returns caused by “wrong size; wrong fit; wrong color” should materially affect profitability rates.

Stargate is Essentially "Build it and They Will Come"

In a business advancing as rapidly as artificial intelligence, with as much potential to disrupt economies, industries and life itself, forecasting might not help so much. Sometimes, even without explicit business models certainty, participants simply have to invest. The notion of "build it and they will come" has both worked and failed with regards to internet applications and some amount of connectivity infrastructure. 

Sometimes firms "build it" and users do not come. That is to be expected. In fact, failure rates for initiatives that are very "venture capital" style bets should be expected to reach 70 percent. 

To be sure, many of those "failures" will be soft failures: our assets were acquired. But hard failures (bankruptcy) also will happen. 

For would-be leaders of the artificial general intelligence "race," Stargate is a building block, as high-performance computing will be necessary. 

It is rational to point out the dangers of "build it and they will come" as an investment rationale. During the dot com bubble all sorts of silly firms got funded on that basis. But, eventually, some of the new leaders did find that once built, "they did come." 

Contradictory things can be true at the same time: most of the high-performance computing infra initiatives will fail in some way (soft failures more than hard failures). Most large language Models will face the same fate. Many AI use cases and apps likewise will fail to have appreciable benefits. 

But in all these areas, a few firms will succeed wildly as they emerge as leaders of the HPC/AGI  industry. 

Like many others, I can't really flesh out what "AGI" actually means, in terms of capabilities. What is "general intelligence" and how do we measure it? Less hard to defend is the notion that, as we move towards capabilities that might be properties of AGI, some limited number of market leaders will emerge. 

Stargate backers hope they could be among those eventual leaders. 

Computing, Internet, Now AI Will Disrupt Content Industries

 

The 1957 film "Desk Set" starring Spencer Tracy and Katherine Hepburn partially revolves around the impact of new technology (computing) on the research library at a major newspaper. The themes of disruption, automation, job loss and productivity at a content business are clear enough. 

Technology (front office and production, not just content creation) has continued to affect and shape all content businesses, with the internet and digital media transition arguably having the greatest business models impact. 

But artificial intelligence has potential for transformatiion of business models at least as great as did the internet. 

                                              source: Visual Capitalist 

By some estimates, total 2024 advertising spend was shy of $400 billion. About 78 percent of that advertising is placed on digital media. About 22 percent of total advertising is placed on search engines. 

Social media garners about 19 percent of total ad spend, while spending on non-linear video and e-commerce sites continues to grow. 

The upshot is less advertising going to legacy media of all types (newspapers, magazines, linear TV).