Disappointed by Apple Intelligence? Don't Be

Some might profess disappointment with Apple’s “Apple Intelligence” features including AI-assisted Siri, Writing Tools and Image Playground, as none arguably introduce new “killer app” features and mostly make existing use cases “smarter.” But that is likely to be the case for most AI implementations for a while. 

But there's a strong argument to be made that artificial intelligence will be deployed and experienced by most people primarily through their phones and internet experiences; primarily as enhancements or extensions of existing features. 

Most people already have experienced AI when issuing voice commands to their devices; shopping; using social media; smartphone cameras or any form of content recommendation. AI will be applied more often and more extensively for such use cases.  

As most people experience the internet using their phones, so most people are likely to frequently use AI-assisted phone experiences. And it is perhaps pointless to compare the value or importance of work and consumer AI experiences.   

It is hard to compare the “impact” of AI in consumer smartphone and internet interactions, compared to AI use in business or work applications, in part because the “value proposition" is different. “Fun” or “enjoyment” or “convenience” is often the expected outcome of a consumer AI use case, where “productivity” is typically the desired work outcome. 

The sheer ubiquity of the smartphone means it will be the most-common framework for AI encounters by consumers. Compared to other potential platforms for AI, like smart cars or refrigerators, smartphones are nearly universally carried and used. The constant accessibility makes them a prime real estate for AI.

Also, AI on phones is already pervasive. Many features we take for granted, like facial recognition device unlocking, spam filtering, and voice assistants, are powered by AI, and it seems reasonable to suggest that AI acting as a personal assistant will eventually be a major use case. 

With the caveat that individuals will vary in their usage of various apps, potential use of AI-assisted experiences is significant. 

Activity	Daily Usage	Monthly Usage
Virtual Assistants (Siri, Alexa)	15-30 minutes	7.5-15 hours
Smartphone AI Features (camera, autocorrect)	30-60 minutes	15-30 hours
Web Interactions (search engines, recommendations)	1-2 hours	30-60 hours
Productivity Apps (email, calendar, document editing)	2-4 hours	60-120 hours
Entertainment (music, video streaming)	1-3 hours	30-90 hours
Social Media (content curation, facial recognition)	1-2 hours	30-60 hours
Navigation and Maps	15-45 minutes	7.5-22.5 hours
Smart Home Devices (thermostats, security systems)	15-30 minutes	7.5-15 hours

Also, software interactions that are potentially AI-assisted are frequent for office or knowledge workers; less often the case for workers in some industries such as construction, agriculture or manufacturing. 

Office Worker Activity	Daily Usage	Monthly Usage
Productivity Apps (email, documents)	2 hours	60 hours
Virtual Assistants/Chatbots	30-60 minutes	15-30 hours
AI-powered Software Tools	1-3 hours	30-90 hours
Automated Tasks/Workflows	1-2 hours	30-60 hours
AI Skills Development/Training	15-30 minutes	7.5-15 hours

Also, interactions with software in many industries likely includes significant interaction with machines that might incorporate software and AI (cash registers, scanners, other machinery) rather than productivity apps, content or documents. 

Industry	Daily Usage	Monthly Usage
Construction	1-3 hours	30-90 hours
Agriculture	1-2 hours	30-60 hours
Manufacturing	2-4 hours	60-120 hours
Education	4-6 hours	120-180 hours
Retail	3-5 hours	90-150 hours
Finance	5-7 hours	150-210 hours

In the construction industry, some workers may spend one to three hours daily using project management software, computer-aided design tools, and productivity suites for documentation and communication.

At least some agriculture workers typically interact with farm management software, GPS/GIS mapping tools, and data analytics platforms for one to two hours per day.

Manufacturing employees often monitor or interact with computer-aided manufacturing software, enterprise resource planning systems, and productivity tools, accounting for two to four hours of daily usage.

Educators, including teachers and administrators, might use software, productivity suites and so forth when planning lessons, grading papers or tests or doing other support work, with an estimated four to six hours of daily usage, assuming roughly half the day is spent in actual instruction. 

In the retail industry, workers interact with point-of-sale (POS) systems, inventory management software, and productivity tools for three to five hours per day.

Finance professionals, such as bankers, accountants, and analysts, spend a significant portion of their day using financial software, data analysis tools, and productivity suites. 

But the point is that perceptions of Apple Intelligence as “underwhelming,” compared to the potential of artificial general intelligence, for example, miss the point. Most AI implementations will make existing experiences more useful, more fun, more entertaining or more productive, but perhaps not so noticeably at first. 

Over time, more-developed capabilities will emerge, often in the form of fully-autonomous  apps, actions or machines. But that is a way off. Right now, most AI implementations will “make things better” by making them more predictive, more accurate or faster.

The big leaps will come later. 

When Will it Make Sense to Build a Custom Generative AI App?

Yes, it is becoming easier for larger enterprises to build custom generative AI applications. As always, that will not necessarily mean it is a wise use of time and resources to build "all" GenAI apps and features "custom." In many cases it will make more sense simply to use the features existing or new suppliers can support. 

A new survey of 1,300 CEOs by TCS suggests 72 percent already are retooling their firms to support use of artificial intelligence. 

Some 51 percent of surveyed firms are planning to build their own generative AI implementations. Or at least that is what the CEOs believe. 

Whether those beliefs are ultimately borne out in reality or not is not so clear, but it would be reasonable to suggest that where it does make sense, it will most often be in cases where a particular enterprise has some highly-specific tasks of identifiable importance and a particular set of existing software systems that must be supported.

In principle, executives might push in this direction if unusual security or privacy needs exist, or when such custom development offers the chance of gaining a competitive advantage over competitors. 

In most cases, those “custom” implementations are likely to use small language models more often than not, some might argue, as the cost and complexity are more manageable. Also, there are lots of plausible reasons why a respondent might agree with a question “are you planning to create your own enterprise-specific LLMs for use in Generative AI implementations?” 

Not all “yes” answers might mean full intention to create a custom language model. Many respondents might interpret the question more in the sense of “are you planning to customize a LLM for your own company’s use?” Most respondents could respond affirmatively in that sense, at least in the sense of using proprietary company data. 

Remote Work is an Issue for Younger Worker Engagement, Gallup Study Finds

Every technology or trend has externalities--unanticipated consequences--and remote work seems no exception. According to a new Gallup poll, remote work also leads to loneliness.

The issue is whether, and to what extent, that translates into lower productivity. It might already be clear remote work contributes to less employee engagement. 

In 2023, global employee engagement stagnated and overall employee well being declined, Gallup says in a new report.  While both measures are at or near record highs, the lack of improvement is notable, as they follow multiple years of steady gains, the report says. 

“The result is that the majority of the world’s employees continue to struggle at work and in life, with direct consequences for organizational productivity,” the Gallup study suggests. 

Gallup estimates that low employee engagement costs the global economy US$8.9 trillion, or nine percent of global gross domestic product.

source: Gallup 

And the problem seems squarely among younger workers. 

source: Gallup 

Some might argue that older workers have higher self-assessed well being than younger workers, but that seems not to be the case. “A decade ago, younger workers had consistently higher life evaluations than older workers; therefore, the difference in perspective is unlikely to be a product only of life stage," the Gallup report says.

And Gallup finds that 70 percent of the variance in team engagement can be attributed to the manager. 

And for whatever reason, the “New World” countries (North America and South America) have higher rates of “engaged” workers than the “Old World.” 

source: Gallup 

The point is that if there is a causal relationship between remote work; managerial effectiveness; engagement and productivity, then remote work plausibly has some causal relationship with work productivity and a direct causal relationship with managerial skill.

Generative AI Productivity is an Issue, But So is Most IT

Quantifying or documenting generative artificial intelligence value is a top issue, respondents said in a Gartner survey of information technology executives. That really should not come as a surprise, as documenting the value of most technologies in knowledge or office work is challenging. 

And since generative AI is used for customer service interactions, producing summaries, developing code, drafting documents or messages, the issue is how well we can document the productivity lift from virtually any IT tool, in those instances. 

source: Gartner 

Quantifying the productivity gains from new IT solutions in customer service can be surprisingly challenging, experts often say. As applied to customer service agent operations, IT tools are said to improve customer satisfaction, handle volume fluctuations, and reduce training times. But isolating the impact on individual agent output can be difficult.

Generative AI and other IT might increase the number of customer contacts per hour, for example. Chatbots are a substitute for human agents as well, so might contain customer service costs. But that all hinges on the quality of the chatbot to answer the questions customers actually have.  

In addition, customer service involves interactions with various channels (phone, email, chat), making it hard to isolate the impact of IT on a single metric. Improved customer satisfaction might not directly translate to a quantifiable reduction in call times.

But that might not always correlate with improved ability to actually solve a customer problem. In other words, quantity is not the same as quality. 

Also, changes in productivity may not be immediate.  Learning curves, process adjustments, and cultural shifts within the team can take time to settle before the full impact is realized.

Accurately measuring before-and-after states requires clean data and proper attribution. Factors like seasonal variations, changes in customer behavior, or external promotions can skew the results. 

Demonstrating a clear return on investment (ROI) for new IT implemented in customer service can be challenging. Here's a breakdown of the difficulties:

Generative AI also might not eliminate tasks, but rather shift them. Increased efficiency in handling routine inquiries might free up agents for more complex issues, making it difficult to show a direct reduction in overall workload or quality of outcomes. 

Improved agent morale, reduced stress, and better customer experiences are all positive outcomes, but they're not easily captured in traditional productivity metrics like call resolution times.

Source	Author(s)	Publication	Key Findings
The Impact of Information Technology on Customer Service Productivity	Brynjolfsson, Erik, et al.	Management Science (1993)	Found that the impact of IT on productivity depends on the specific technology and how it's implemented. Identifying productivity gains requires careful analysis.
Does IT Really Pay Off? Measuring the Effects of Information Technology Investment on Customer Service	Lee, Sang-Pil, and Byung-Il Park	Journal of Service Research (2001)	Highlights the difficulty of isolating the impact of IT on productivity due to the presence of confounding variables. Emphasizes the need for a multi-faceted approach to evaluation.
The Challenges of Measuring the Business Value of Customer Relationship Management (CRM) Initiatives	Rust, Roland T., et al.	Journal of Marketing (2004)	Argues that traditional ROI metrics might not capture the full value of CRM systems, which often include customer satisfaction and loyalty benefits alongside productivity improvements.

We might observe similar issues with other tasks GenAI might help with, such as creating documents and text. Past applications of word processing arguably provide speed and quality advantages that are hard to quantify. 

Study Name	Venue	Date	Key Conclusions
"The Effects of Word Processing Software on Writing Performance" by Keith S. McNeil	Educational Technology Research and Development	1988	Found that word processing had a minimal impact on writing speed but improved editing efficiency.
"The Impact of Technology on Writing: A Review of the Literature" by Charles A. MacArthur, et al.	Review of Educational Research	2001	Concluded that the impact of technology on writing quality is mixed and depends on factors like task and user skill level.
"The Myth of Increased Productivity: How New Technologies Slow Us Down" by Sherry Turkle	Basic Books	2015	Argues that constant connectivity and information overload can hinder focused work and deep thinking, potentially impacting writing productivity.
"Beyond the Efficiency Paradigm: Rethinking the Role of IT in Knowledge Work" by Wanda J. Orlikowski	Organization Science	2007	Shifts the focus from measuring just efficiency gains to considering how IT can enhance creativity, innovation, and collaboration in knowledge work like writing.
"The Myth of Increased Productivity: How New Technologies Can Slow Us Down" by Daniel H. Pink	Harvard Business Review	2016	Argues that constant connectivity and information overload can actually decrease focus and productivity, even with advanced tools.
"The Paradox of E-Mail: A Sociotechnical Perspective on Communication Overload" by Stefan Klein, Christian Bartsch, and Jan Marco Leimeister	Journal of Communication	2004	Highlights how email, a seemingly efficient communication tool, can lead to information overload and ultimately hinder productivity.
"The Impact of New Information Technologies on Task Performance: A Meta-Analysis" by Steven E. Fiore, James R. Salas, Michelle H. Cuevas, and Cheryl A. Bowers	Human Factors	2003	Reviews multiple studies on the impact of technology on task performance and concludes that the effect can be positive, negative, or neutral depending on the specific task and user characteristics.