Can AI Enable Some Business Models that Were Unworkable Before?

It’s probably fair to say that most observers expect artificial intelligence to be embedded into most legacy products and processes over time, with an expectation that successful implementations will produce outcomes such as lower costs; higher profit margins; faster delivery times or higher customer satisfaction. 

But it might also happen that AI is applied in ways that create new opportunities--such as business models that were not possible or sustainable before--as well. For some of us, that is the more-interesting opportunity. 

Consider the way the shift to digital product creation and delivery possible with the internet created the opportunity for big, industry-leading technology firms supported by advertising revenue models. That was pretty much unthinkable prior to the era of digital media and internet distribution. 

So many AI-enabled possibilities might exist in the software business, for example, where previously expensive solutions are cost-reduced or capability-enhanced by AI, making them available to new customers. 

Where only enterprises might have been able to afford such tools, AI might enable small business and medium-sized business access to tools including:

• AI-powered software that analyzes customer data and automates personalized marketing campaigns with targeted messaging and budget optimization.

• AI-powered financial planning software that analyzes user spending habits and recommends personalized budgeting and investment strategies.

• AI-powered tutoring software that personalizes learning plans based on student strengths and weaknesses, offering adaptive instruction at a mid-range price point between basic learning apps and expensive human tutoring.

• AI-powered software that analyzes medical records and patient data to assist healthcare professionals in diagnosis and treatment planning, potentially making such tools more accessible to smaller clinics and individual practitioners.

Though it might be harder to do so with physical products, the same principle should apply: AI might reduce some barrier to creating products that are more specialized, targeted or positioned someplace between the high-volume, low-cost segment and the low-volume, high-cost market segments in any market. 

Prior to the internet era, at least some entrepreneurs had explored creating ad-supported telecom or content services, for example. 

That was not a huge leap of imagination, given the ad-supported revenue models of broadcast radio and television; smaller newspapers and magazines. So some explored ad-supported phone calls, for example, none successfully. 

Only with the shift to internet-delivered messaging and voice features is it feasible to offer free or ad-supported communications, sometimes with an incremental usage-based fee (for international calls to public switched telephone network devices, for example). 

So for some, the interesting possibility is that some previously-unworkable business models might be possible if AI solves some key business problem: value, price, cost, distribution, support or something else crucial to the business model. 

And that might happen most often for a broad range of products in any industry that are neither the volume nor the value leader; occupying neither the lower-cost, volume position nor the high-cost, lower-volume, premium position. 

It might be conventional wisdom that high-volume, low-price products as well as low-volume, high-price products are “easiest” to create business models around, whereas many products somewhere in the “middle” are more difficult, as selling prices do not provide enough margin for robust customer and product support.

Generally speaking, think of consumer or business subscription products costing $20 to $30 a month, and sold directly using the internet, or high-price enterprise products that are expensive, but also provide enough gross revenue and profit to be sold using a direct sales force.

Then think of all sorts of products that a potential customer has to think about--it is not an impulse buy--and do not represent high sales volumes, and also are priced at levels that require some form of indirect sales channels (channel partners, for example). 

The price-value relationship might be part of the problem. Low-cost products can leverage economies of scale to achieve high sales volume and profitability even with low margins. Think of discount retailers or subscription services with minimal features.

That, in turn, can create profit margin issues. Mid-range products can get squeezed on profit margins, having neither the high markups of luxury goods nor the economies of scale of low-cost products.

Luxury or premium products often can command higher prices due to brand recognition, unique features, or exclusive materials and often can be sold  to a niche market willing to pay more.

Mid-priced products often lack the compelling features of high-end options or the affordability of low-end ones. They can struggle to attract enough customers at a price point that allows for substantial profit margins.

Customers seeking the best value might be drawn to lower-priced options, while those prioritizing premium features might be willing to pay more.

Study Title/Source	Key Findings	Focus on the Value Gap
"Blue Ocean Strategy" by W. Chan Kim & Renée Mauborgne	Emphasizes the importance of creating a new value proposition that avoids direct competition. Stuck-in-the-middle products often compete head-on with established players.	Argues that successful businesses either create low-cost, high-volume products or high-value, premium offerings.
"Winning in the Middle: Avoiding the Commodity Trap" by Michael Porter (Article explores strategies for success in mid-tier markets)	Acknowledges the challenges of mid-tier markets but suggests strategies like differentiation, operational excellence, or niche targeting to succeed.	Offers a more nuanced view, suggesting that success in the middle is possible with careful strategic planning.
"Pricing Strategy: Setting Price to Maximize Profits" by Nirmalya Kumar (Book explores various pricing strategies)	Discusses the importance of understanding customer value perceptions when setting prices. Mid-priced products risk failing to deliver a value proposition strong enough to justify their price.	Emphasizes that price should be aligned with the perceived value customers receive, which can be challenging for mid-tier products.
"Value Proposition Design" by Alexander Osterwalder, Yves Pigneur, & Greg Bernarda	Highlights the importance of a clear value proposition that resonates with the target customer segment.	Implies that mid-priced products might struggle to offer a compelling value proposition that stands out from both high-end and low-end options.

Channel strategy also comes into play. Generally speaking, high-volume, low-price items can be sold directly using the internet or mass market retail. The low-volume, high-price goods are sold using direct sales. The medium-price, medium-volume products use distributors, resellers or agents. 

The issue is whether artificial intelligence can change key costs of manufacturing, distribution, support, sales and marketing enough that formerly-difficult models become more feasible. That will matter for the fortunes of all sorts of legacy or startup firms, allowing marginal business models to become more robust. 

Perhaps some “one-time sale” products can be changed into subscriptions, increasing lifetime customer value. Perhaps channel sales can be converted into direct online sales, boosting profit margins.

Maybe high after-sale support costs can be sliced. The need for channel partners might be reduced or eliminated by AI that simplifies product manufacturing cost, improves “zero touch” performance so support costs are reduced or allows online direct sales. 

Previously Difficult Functions	How AI Could  Lower Costs	Use Case
Personalized Mass Customization	AI can analyze customer data and preferences to design and manufacture products tailored to individual needs, without sacrificing economies of scale.	A clothing company uses AI to personalize fabric choices, cuts, and styles based on customer body scans and preferences.
Predictive Maintenance in Manufacturing	AI can analyze sensor data from machines to predict potential failures, allowing for preventive maintenance and avoiding costly downtime.	A factory uses AI to monitor equipment vibrations and predict bearing wear, enabling proactive maintenance before breakdowns occur.
Automated Customer Support	AI-powered chatbots can handle routine customer inquiries, freeing up human agents for complex issues and reducing support costs.	A bank uses AI chatbots to answer basic banking questions 24/7, reducing the need for human customer service representatives for simple inquiries.
Intelligent Content Delivery Networks (CDNs)	AI can optimize content delivery based on user location, network conditions, and content type, minimizing bandwidth usage and delivery costs.	A streaming service uses AI to personalize content delivery based on viewer location and device capabilities, reducing bandwidth consumption.
AI-powered Training for Employees	AI can tailor training programs to individual employee needs and learning styles, reducing training time and costs.	A software company uses AI to create personalized training modules for new employees based on their skill gaps and learning pace.

Happy Talk about "No Job Losses Because of AI" is Wrong

if automation destroys jobs, then why has the total number of jobs not decreased as we have automated more? The obvious answer is that new jobs in new areas get created as demand for older jobs decreases. 

So some might argue AI will not decrease jobs in many industries. But that might strike you as unrealistic happy talk. Automation and other new technology such as the internet always seems to reduce existing jobs, even as new possibilities are created elsewhere. 

Since 1900, the percentage of people employed in U.S. agriculture has shrunk considerably, from nearly 40 percent to less than two percent today. And though there are other key issues besides the application of new technology, U.S. manufacturing jobs also have declined over time. 

Since the 1950s, the United States, for example, has seen a dramatic decline in manufacturing jobs, dropping from a peak of over 25 percent of total employment to less than 10 percent today (though issues other than applied technology were involved as well). 

Since 1990, the United States has lost over half of its newspaper journalism jobs, with a similar decline in other traditional media sectors, as well. 

Industry	Pre-internet (1990)	Post-internet (2020)	Change
Television	2.0 million (estimated)	1.6 million (Bureau of Labor Statistics)	-20%
Print Journalism	625,000 (American Journalism Review)	338,000 (Pew Research Center)	-46%
Radio	140,000 (Bureau of Labor Statistics)	103,000 (Bureau of Labor Statistics)	-27%

That noted, new jobs are created in new industries as well. Today, more than 80 percent of jobs in the U.S. are in the service sectors. In 1900, service industries only accounted for around 30 percent of total employment. 

As the introduction of other general-purpose technologies such as the steam engine, electricity and computers also led to job displacement, so will AI have a similar impact, reducing demand for existing jobs. 

Industry	Impact of Computers	Estimated Job Change (Pre- vs. Post-Computer Era)
Finance	Automated back-office tasks like recordkeeping and calculations. Enabled online banking and trading, reducing need for tellers and some brokers.	Bank tellers: -25% to -50% but financial analysts +20%
Manufacturing	Automated production lines and quality control processes. Reduced need for assembly line workers and some inspectors.	Production workers: -30% to -50%
Education	Created online learning platforms and educational software. May impact roles like teacher's aides for repetitive tasks.	Teacher's aides: Potential decrease, but overall teacher demand may remain steady.
Hospitality	Self-service kiosks for check-in and reservations. Online travel booking platforms reduce reliance on travel agents.	Hotel front desk clerks: -10% to -20%. Travel agents: -40% to -60%
Banking	ATMs replaced tellers for basic transactions. Online banking reduces foot traffic in branches.	Bank tellers: -25% to -50%

Happy talk about how AI will not lead to job losses in content or media industries, for example, seems completely out of line with past precedents when major new technology is introduced. 

Sure, there are all sorts of reasons why executives in those industries would say such things. But history suggests the predictions are incorrect. AI will lead to job losses in existing areas. 

Just as surely, new jobs will be created elsewhere. But it always is reasonable to assume that those losing existing jobs and those taking newly-created jobs will not typically be the same individuals. 

How Might AI Demand Affect Edge Computing?

Industry analysts and data center operators--also including enterprise customers--seemingly continue to evaluate the costs of adding edge computing capabilities to their existing data center architectures, resulting in arguably-slower market growth than some had predicted and expected. 

And now there is the additional consideration of how support for artificial intelligence operations “as a service” could affect edge computing as well. And it might be fair to say that, at the moment, AI requirements might not boost prospects for edge computing all that much. 

In fact, one might make an argument for a barbell type growth pattern, with AI increasing demand for remote processing on one hand and “on-the-device” on the other hand. Remote processing might still be the best option for heavy processing and storage requirements, while many latency-dependent operations will be increasingly handled right on the device. 

The arguments around edge computing “as a service” have always focused on use cases requiring very-low latency, with additional value from less capacity network demand and possibly some privacy and security advantages. For enterprise computing in general, processing power has been a concern when evaluating private and cloud computing alternatives. 

But cost has been a growing issue as “cloud computing as a service” volume and scale have grown. 

But on-the-device and on-the-premises private processing alternatives always are a possibility. And remote processing always is an alternative for non-real-time use cases and needs. 

So the additional new requirements for AI “as a service” still hinge on latency and processing power requirements. 

Some might attribute the present state of thinking around edge computing as more focused on cost-benefit and payback than hype, with less of the “either/or” rhetoric around edge computing as a “replacement” for remote cloud computing some espoused early on. 

Forecasts of revenue growth have varied significantly, looking at service revenue, capital investment, endpoints supported or processing capacity. 

Firm	Year	Metric	Projected Growth
BCC Research	2022	Global Edge Computing Market (USD Billion)	$60.0 to $110.6 (2024-2029)
Grand View Research	2023	Global Edge Computing Market Size (USD Billion)	$16.45 to $36.9 (2024-2030)
MarketsandMarkets	2023	Edge Computing Market Size (USD Billion)	$60.0 to $110.6 (2024-2029)
Gartner	2022	Edge Computing Endpoints Worldwide (Billions)	1.25 to 25 (2021-2027)
IDC	2022	Global Edge Computing Infrastructure Spending (USD Billion)	$17.9 to $176.1 (2020-2025)
McKinsey & Company	2018	Edge Computing Adoption Rate (%)	30% of enterprises to adopt edge by 2025
STL Partners	2019	Edge Computing Market Trends	Global market growth estimated at a CAGR of 34% during the period 2019-2025.

There's a strong possibility that AI processing will follow a barbell pattern, with the majority of processing happening on devices or at remote data centers, leaving edge processing with a smaller role. Here's a breakdown of the reasoning:

On-device processing will be preferred or necessary for real-time use cases, including facial recognition; augmented reality; image processing and speech interfaces. Privacy issues also are addressed when processing happens on the device and not at a remote data center of some type. 

Remote computing will still make sense when processing power and storage are key requirements, but not real-time response. 

Also, centralized management and control will have advantages for processing-intensive or data-intensive operations, or in instances where collaboration with many other entities is required. 

So one might postulate that edge computing will have value in between the on-device and remote cloud processing ends of the barbell. 

Edge devices (sensors, network gateways) often have limited processing power and battery life, which restricts their ability to handle complex AI tasks, for example, but might also have latency requirements. 

Also, in some instances, connectivity impediments or cost might make local processing attractive. 

So AI processing might follow a barbell pattern, prioritizing on-device processing for applications requiring low latency and prioritizing user privacy. Remote data center processing will be essential for training complex AI models, managing large datasets, and facilitating collaboration.

Edge computing has to provide value someplace in-between. And that is part of the business value issue. As often is the case, products have clear value at the high-end and low-end parts of any market. In the middle is where balances have to be struck.

Video is Becoming a Hybrid

One hears a lot of talk about how different broadcast TV content strategies are from video streaming content strategies. The argument is that broadcast TV and video streaming services catered to different audiences, with distinct revenue models. Some of us cannot see that continuing. 

At a high level, the consistent trend in “video” markets has been in the direction of personalization or customization, on-demand consumption and a shift to viewing of titles rather than “channels” that mirrors and is part of the same trends in nearly all forms of media. 

The key implication is a shift of most of the business towards personalized, niche, custom or more-specialized audiences rather than the broad-appeal audiences the broadcast TV networks have required. 

Where broadcast TV has relied on advertising revenue generated by content pitched to large audiences with content of wide appeal, including sitcoms, dramas, and reality shows, streaming has been characterized as a subscription model, with content often aimed at niche audiences often featuring genre-specific shows, documentaries and foreign language content.

But those generalizations are breaking down. Already, video streaming services are moving to ad support plus live (scheduled) programming, especially sports. And that blurs the lines between “broadcast” and “streaming” content and revenue models. 

The example one always hears is that “eventually, the Super Bowl will be on streaming,” the point being that the National Football League consistently generated the top “live” programming events, with advertising being the revenue driver. 

Were NFL events to shift to streaming distribution, so would audiences and revenue models follow the shift. The point is simply that streaming substantially substitutes for broadcasting in terms of viewership and revenues.

And most predict that the role of broadcast programming will also shift over time as a result, becoming a smaller part of the total content revenue stream and also therefore shifting its role. Many expect a shift to substantially more reality TV as the broadcast staple.

Others also expect the broadcast role to shift more towards the role theatrical exhibition has had for video markets, namely building interest and word of mouth for eventual on-demand or pre-recorded media release. 

In that sense, the content distinctions between broadcast and streaming services might be less important. Perhaps it is the role of broadcast TV that changes, in the direction of the role theatrical release now has for movies: building word of mouth for eventual on-demand exhibition. 

Beyond those changes, one might note a generalized switch in business models in many industries from one-time purchase to subscriptions, enabled by online and internet platforms that enable them. 

The other obvious change is from one-time purchase to on-demand purchase and consumption. 

AI might affect on-demand and one-time-purchase models using personalization. 

But the impact on subscription business models is less clear, except that subscription payment models often are a way to support on-demand consumption. Streaming video services and e-commerce subscriptions such as Amazon Prime provide examples. 

“U.S. consumers may be willing to endure more ads and be corralled into bundles, but they may not be willing to give up the degree of customization they gained from unbundling pay TV, or the personalization they enjoy from social media, say consultants at Deloitte. 

In other words, streaming “subscriptions” basically are favored because they provide on-demand viewing, much as earlier generations of technology (VCR tape rentals, DVD rentals) enabled on-demand, personalized consumption of content.

Linear broadcast bundles have tried to provide choice by providing many genres and content formats, even if broadcast schedules are part of legacy media formats requiring consumers to adapt to provider schedules. 

Streaming subscriptions also are a platform for custom and personalized choice, allowing consumers to watch what they want, when they want it, and not on a fixed schedule. Digital video recorders, in essence, are an attempt by linear video providers to create some amount of on-demand consumption. 

To a large extent, streaming also is favored because it is a “multi-screen” format, allowing native consumption on phones, tablets, gaming devices or televisions. Linear TV providers replicate that feature using their own proprietary streaming services. 

So the argument that content strategies for linear broadcast and streaming are “different,” as much sense as that has made, arguably is changing. “Appointment television” now consists largely of sports or other events, but a slow movement of such content to streaming is happening. 

As linear audiences shrink, so will the necessity of assuming large audiences are the target (“large” is a relative term, of course, looking at streaming content audiences by title instead of TV networks). In fact, we already can see that many linear TV series that did not do so well on linear services can become “hits” on streaming services. 

And the same can hold for movies that did not perform so well theatrically. 

The point is that the same “big audience” formats thought to be essential for linear broadcast also often make sense for supposedly-niche streaming services. So a blurring of lines and strategies is taking place. 

Such a hybrid model is found in other industries as well. Private computing coexists with cloud computing. The rise of cloud computing hasn't eliminated the need for on-premise data centers, nor have entities stopped buying their own computing gear in preference to renting computing cycles and storage. 

Streaming services such as Spotify and Apple Music have become popular. However, vinyl records and physical CDs continue to be produced and collected by enthusiasts.

The rise of e-readers and audiobooks hasn't eradicated traditional printed books. Movie theaters still have a role in the content value chain. 

People use online shopping but also visit stores. They travel but also use videoconferencing. You get the point: a hybrid video model is coming.