GenAI Value Varies by Job Function and Industry

Lots of people will choose different words if asked “what is the one word that best describes the value of generative artificial intelligence.” Some might cite “speed” or “scale” or “augmentation” or “automation.” All are applicable.  

At least part of the reason for differing “key value” descriptions is that GenAI arguably has different key values for different job roles and industries. What creates value for illustrators will be distinct from what creates value for software engineers. 

For example, consider GenAI as providing value in a number of different ways. As with all forms of AI, GenAI amplifies some human capability (sight, sound, taste, speech, muscle power, pattern recognition) and therefore amplifies human capabilities within business processes.

That includes:

• Productivity: By automating routine tasks like data entry, report generation, or email drafting, AI frees up human employees to focus on higher-value, strategic work.

• Creativity: It can generate new ideas, design options, or marketing content, acting as a creative catalyst for human teams.

• Decision Support: AI can process vast amounts of data to identify patterns and trends, providing valuable insights that inform better decision-making.

• Customer Experience: By personalizing interactions, generating tailored content, and providing faster responses, AI enhances customer satisfaction.

• Process Optimization: AI can analyze processes to identify inefficiencies and suggest improvements, leading to streamlined operations and cost savings.

So consider a few examples of value created by applying GenAI to creation and innovation; efficiency and productivity; collaboration or the quality of output. 

For marketers, GenAI produces ad copy. For manufacturers, it automates designs or optimizes production processes. Software developers can use it to write code. Customer service agents can offload some queries. 

Pharmaceutical companies can use GenAI in drug discovery operations. 

Industry	Focus of Generative AI Value
Marketing & Advertising	Creation/Innovation (e.g., ad copy, content generation)
Product Design	Quality of Output (e.g., optimizing designs, generating variations)
Manufacturing	Efficiency/Productivity (e.g., automating design tasks, optimizing production lines)
Software Development	Efficiency/Productivity (e.g., code generation, automating testing)
Healthcare	Quality of Output (e.g., drug discovery, personalized medicine)
Customer Service	Efficiency/Productivity & Collaboration (e.g., chatbots, automating repetitive tasks)
Finance	Quality of Output & Prediction (e.g., fraud detection, risk assessment)
Education	Creation/Innovation & Quality of Output (e.g., personalized learning materials, automated grading)
Legal	Efficiency/Productivity & Quality of Output (e.g., legal document review, contract generation)
Media & Entertainment	Creation/Innovation (e.g., scriptwriting, music composition)
Scientific Research	Creation/Innovation & Prediction (e.g., hypothesis generation, data analysis)
Construction	Efficiency/Productivity & Quality of Output (e.g., optimizing construction plans, identifying safety hazards)

The point is that GenAI value depends on the type of industry; its key tasks; as well as job roles within each industry. 

For coders, GenAI allows fast checking of code. In other instances, it is the time saved conducting research that matters. In the pharma industry, it might well be new drug discovery. For content producers the value is mostly in automating content production (producing images, video or text content).

Generative AI is Mostly Enhancement, Not Disruption, in Most Instances

It is a reasonable enough assumption that an AI-enhanced Alexa will represent an incremental enhancement rather than a disruptive change, at least at first.  And perhaps the same might be said for AI-enhanced search, social media, advertising, e-commerce customer service or virtually any other process that underlines a business model. 

In most cases, generative artificial intelligence will be used to upgrade or enhance existing apps and use cases. 

Perhaps of more interest are ways GenAI might be used to support entirely new use cases, such as AI-driven or automated art generators, music composers, and story writers. Of course, some might argue that AI-generated art is an extension of existing computer-generated graphics functions.

Generative AI might also be used to accelerate and automate drug discovery by generating new molecular structures and predicting their properties.

GenAI also can be used to generate realistic gaming environments, characters, and storylines.

So far, there is not widespread agreement on whether GenAI can be a platform for entirely-new use cases with new business models. 

A related question is whether third-party models or in-house models will drive such developments. 

Many use cases already are being built on third-party models. Siri using a platform provided by Anthropic’s Claude provides an example. The use of third-party platforms rather than in-house models is a choice most firms likely will be making, and for reasons similar to their use of any important technology. 

Core firm competence almost never lies in the area of operating system, computing appliance or platform development. Also, time-to-market concerns plus performance make “build your own” approaches either too time-consuming or too expensive or both. 

Most firms have no interest in building their own chips, operating systems, core apps, computing hardware, networks or AI models. But lots of firms might have interest in customizing existing third party models for a particular industry business process. 

Firm	Model Licensed	Use Case or Application
Apple	OpenAI's GPT-3	Siri Enhancements, Text Analysis
Amazon	Anthropic's Claude	Customer Service, Product Recommendations
Microsoft	OpenAI's GPT-3	Bing Chat, Office Suite Enhancements
Meta	Llama 2 (open-source)	Research, Product Development
Salesforce	OpenAI's GPT-3	Customer Relationship Management, Salesforce Einstein
Snapchat	OpenAI's GPT-3	AI-powered Lenses, Chatbot Features
Spotify	OpenAI's GPT-3	Music Recommendations, Podcast Summaries

Mobile Generative AI Will Be a Huge Driver of Usage

Eventually, the leading generative artificial intelligence apps used by consumers will winnow down from hundreds to a few, though many platforms will likely find niche uses in some industries, market segments, job functions and use cases.

One important difference could arise in the mobile domain, compared to larger-screen use cases, and the reason is simply the huge amount of interactive app usage that now happens on mobile devices, compared to all other screens. 

User Type	Mobile Devices	PCs
Consumers	5-6 hours/day	2-3 hours/day
Business Professionals	3-4 hours/day	5-6 hours/day

Perhaps more important is the amount of data consumed on mobile platforms. While it might be difficult to directly correlate “value” with “data volume,” data consumption is connected with usage volume. Generally speaking, the volume of consumer data used on mobile devices is twice as much as on PCs, for example. 

User Type	Mobile Devices	PCs
Consumers	10-20 GB/month	5-10 GB/month
Business Professionals	20-30 GB/month	10-20 GB/month

That usage is then correlated with the volume of advertising spending on mobile and PC platforms. 

Among the big shifts in U.S. advertising spending in the internet era is not simply the growth of share taken by digital media, but also the share taken by mobile venues, which already are as much as 65 percent of all digital media advertising. 

Venue	Spending  (Billions USD)	Market Share (%)
Digital	233.4	56.7
Television	73.7	17.9
Radio	21.9	5.3
Out-of-Home	19.5	4.7
Print	15.6	3.8
Other	12.7	3.1

source: Statista, Seeking Alpha 

That suggests a huge opportunity for generative AI  use cases on mobiles, as well, assuming that GenAI winds up being a core functionality of most highly-used consumer-facing apps. 

source: Andreessen Horowitz 

source: Andreessen Horowitz 

And to the extent that the costs of GenAI usage for app and experience providers matters, open source models should, over time, increase their share of the market. Already, in mid-2024, enterprise user /.,mnbvcz+net promoter scores for open source GenAI models have rapidly approached those of proprietary models. 

source: Andreessen Horowitz 

As always for new markets, early market share often is not predictive of developed or mature market leadership. Eventual leaders often are not among the early leaders. Also, definitions of “active” users might vary. Some might include users who have accessed a model only once; others will have varying levels of persistent usage that are minimums for the purpose of defining active users. 

Model	Estimated Active Users
OpenAI (GPT-4, GPT-3.5)	100+ million
Google (LaMDA, PaLM, Gemini))	50+ million
Meta (LLaMA)	30+ million
Microsoft (Azure OpenAI Service)	20+ million
Stability AI (Stable Diffusion)	10+ million
Midjourney	10+ million

The term "active user" can include:

• Frequency of Use: How often a user interacts with the AI model. This could be measured by the number of prompts or requests submitted within a specific timeframe (e.g., daily, weekly, monthly).

• Duration of Use: The amount of time a user spends interacting with the AI model during a session or over a period.

• Type of Interaction: The nature of the user's interactions, such as text prompts, image generation requests, or code completion.

• Conversion Rate: The percentage of users who take a desired action, such as creating an account, subscribing to a premium service, or sharing content.

• Engagement Metrics: Measures of user engagement, such as click-through rates, time spent on the platform, and social sharing.

For OpenAI, “active users” are typically defined as those who have interacted with the model within a specific timeframe, such as the past month or year.

Google's definition of "active user" considers frequency of use and engagement metrics. Meta's definition of "active user" might be similar to its definitions for other products, focusing on user engagement and interaction.