If Time is Money, and IT Saves Time, is that ROI?

A survey by IDC commissioned by Microsoft focusing on ways Copilot saves time and therefore increases productivity. It’s a good example of the familiar information technology “return on investment” exercise where we assume “time is money” and that new “technology saves time.”

By definition, such methods cannot capture benefits such as 

• Improved accuracy and quality of work

• Increased employee morale and satisfaction

• Enhanced customer service

• Better decision-making.

But those metrics are likewise hard to quantify for knowledge or office work. But advocates keep trying. 

Lumen Technologies estimates Copilot saves sellers an average of four hours a week, equating to $50 million annually. In healthcare, Chi Mei Medical Center doctors now spend 15 minutes instead of an hour writing medical reports, and nurses can document patient information in under five minutes. 

Pharmacists are now able to double the number of patients they see per day. In retail, AI models help Coles predict the flow of 20,000 stock-keeping units to 850 stores with remarkable accuracy, generating 1.6 billion predictions daily. Microsoft provides 200 such examples.  

source: IDC, Microsoft 

If you have been around such productivity estimates before, you know that the estimates are produced fairly simply: estimate time saved by workers, then multiply by the salaries of those workers. 

If you have worked in sales of information technology products to business customers, and have made such arguments yourself, you also know that buyers discount such claims. 

IDC says generative AI usage jumped from 55 percent of entities using it in 2023 to 75 percent in 2024.

For every $1 a company invests in generative AI, the ROI is $3.7 times, while some leaders using generative AI claim a returns as high as 10 times. 

On average, AI deployments are taking less than eight months and organizations are realizing value within 13 months, IDC reports. 

The ROI of generative AI is highest in financial services, followed by media and telecommunications (including mobility), retail and consumer packaged goods, energy, manufacturing, healthcare and education.

The primary way that organizations are monetizing AI today is through productivity use cases.

Will Non-Fossil-Fuel-Produced Electricity Rates Fall?

Many studies argue that the cost of electricity for consumers will fall as the transition to non-fossil fuels gains traction, but since 2000, consumer electricity rates in the U.S. have generally increased. In fact,electricity rates in the United States have increased significantly since 1950, despite some periods of relatively low or stable rates. 

Despite that track record, advocates continue to argue that a switch to non-fossil fuels will lead to lower prices. 

Wholesale electricity prices are projected to decrease by 20 percent to 80 percent in the medium term (by 2040) in the United States, depending on the region, according to Brookings researchers. 

Other advocates argue that U.S. households could save an average of $500 a year on energy costs from non-fossil-fuel sources. And some advocates say cheaper energy is possible in the G7 countries by 2025. I doubt that can be claimed to be realistic at this point. 

In fact, there is no clear evidence that G7 country energy costs have declined since 2000, or even since 1950. In fact, the information suggests that energy costs have generally increased:

• Electricity investments within the G7 are projected to triple in the coming decade, indicating rising costs rather than declining ones 1.

• Household spending on electricity is expected to increase, although this increase is projected to be offset by declines in spending on coal, natural gas, and oil products 1.

• The share of GDP spent on energy in G7 countries is expected to decline from around 7% today to just over 4% in 2050, but this is due to economic growth rather than falling energy costs 1.

• While total household energy spending in the G7 has not declined since 2000 1.

• The data shows that coal power capacity in G7 countries peaked in 2010 and has since fallen, but this doesn't necessarily translate to lower energy costs for consumers 2.

I find that energy prices for consumers will fall as the transition to non-fossil fuels is made to be questionable. 

Even granting some short-term price increases to create new infrastructure, the theory that long-term prices will drop seems questionable. Serious people used to argue that nuclear power would create such plentiful supplies that it would be “too cheap to meter,” and that never happened. 

One might note that many of the claims about future benefits come from studies conducted or sponsored by the IEA, hardly a disinterested industry source.

Meta Pulls Back AI User Move

Controversy over Facebook’s use of artificial-intelligence-created “user” accounts is not unusual in a business that often has to try innovations, some of which are embraced, some of which are rejected by people. Meta and Instagram had proposed allowing users to create AI user accounts that many say are just bots.

Even under the best of circumstances, up to 70 percent of innovations will fail, whether that is digital transformation projects, information technology projects or change programs in general. 

The same general rule holds for venture capital investments as well. 

Two points to note here are that Meta did react quickly to a policy that was highly unpopular, and also that failures on the way to maximizing the use of AI are inevitable. 

Feature/Innovation	Description	User Opposition	Outcome
Beacon Advertising System (2007)	Tracked users' online purchases and shared them as ads.	Privacy concerns; users felt uninformed and exposed.	Apologized; shut down in 2009 after lawsuits and backlash.
Real Names Policy (2014–2015)	Required users to use legal names on the platform.	Criticized by activists and marginalized groups for safety concerns.	Policy softened, allowing alternative verification methods.
Automatic Facial Recognition (2017–2021)	Auto-tagged people in photos using facial recognition technology.	Privacy concerns and fears of biometric data misuse.	Disabled feature in 2021 and deleted facial recognition templates.
Instagram for Kids (2021)	Aimed to create a version of Instagram for children under 13.	Concerns about mental health, safety, and exploitation.	Paused development following criticism from parents and lawmakers.
News Feed Redesigns	Periodic changes to Facebook’s feed algorithm and layout.	Complaints about irrelevant content and lack of chronological order.	Adjustments made to balance user satisfaction and business goals.
Libra/Meta Diem Cryptocurrency (2019–2022)	Proposed cryptocurrency for global payments.	Regulatory opposition over financial stability and privacy concerns.	Project abandoned in 2022; assets sold.
WhatsApp Privacy Policy Update (2021)	Suggested increased data sharing with Meta.	Perceived compromise of encryption and independence; user migration to competitors.	Delayed implementation; clarified policy and encryption commitments.
Facebook Home and Phone (2013)	Custom Android skin integrating Facebook at the center of the smartphone.	Users found the interface intrusive and not broadly useful.	Discontinued after poor adoption.

We might note that Alphabet and Google have had similar issues when innovating. The process is messy, often unsuccessful and requires agility, including willingness to back away when an innovation generates opposition from users. 

Feature/Innovation	Description	User Opposition	Outcome
Google Buzz (2010–2011)	A social networking tool integrated into Gmail, automatically connecting users.	Privacy concerns over automatic contact sharing without consent.	Discontinued in 2011 after legal settlements and backlash.
Google Glass (2013–2015)	Augmented reality smart glasses targeting early adopters and developers.	Privacy concerns, social stigma ("Glassholes"), and high price point.	Halted consumer version in 2015; pivoted to enterprise applications.
Google Wave (2009–2010)	A real-time collaboration and communication platform.	Confusing interface and unclear use case for mainstream users.	Shut down in 2010 after poor adoption.
Project Ara (2013–2016)	Modular smartphone allowing users to swap out components like a camera or battery.	Cost concerns, technical challenges, and lukewarm market interest.	Canceled in 2016 despite initial excitement.
Google+ (2011–2019)	Social network launched to compete with Facebook.	Low user engagement; criticized for forced integration with other Google services like YouTube.	Shut down in 2019 due to data breaches and low adoption.
YouTube Real Name Policy (2013)	Encouraged users to use their Google+ profile (real name) on YouTube comments.	Resistance from YouTube creators and users valuing anonymity.	Policy abandoned; reverted to original comment system.
Google Nexus Q (2012)	Media streaming device with social sharing features.	Criticized for high price, limited functionality, and reliance on Android devices.	Withdrawn shortly after launch; never returned to market.
Google Allo (2016–2019)	Messaging app with smart assistant integration.	Privacy concerns over lack of end-to-end encryption by default and confusion over app purpose.	Shut down in 2019 in favor of Google Messages (RCS-based).
Stadia (2019–2023)	Cloud gaming platform enabling play without a console or PC.	Criticized for lack of exclusive titles, connectivity issues, and unclear business model.	Discontinued in 2023 due to limited market traction.
Sidewalk Labs Toronto Project (2017–2020)	Smart city initiative to develop a tech-driven urban space in Toronto.	Privacy concerns, data governance issues, and opposition from residents and activists.	Abandoned in 2020 amid public resistance and regulatory challenges.
FLoC (Federated Learning of Cohorts) (2021–2022)	Ad tracking system designed to replace third-party cookies.	Privacy concerns from users, advocacy groups, and some web browser developers.	Replaced by the Topics API after significant criticism

Where AI Could Save Consumers Money

Optimizing energy use is among the more-important use cases for artificial intelligence in the home. The biggest savings might come from optimizing washing machines, where AI could reduce energy consumption by as much as 70 percent. 

In other cases, passive monitoring could reduce water loss from leaks. 

Appliance	AI Use Case	Estimated Savings/Efficiency
Washing Machine	AI energy mode optimizes water and detergent usage	Up to 70% reduction in energy consumption
Clothes Dryer	AI detects fabric type and load size to optimize drying cycles	10-20% energy savings
Refrigerator	AI-powered meal planning and inventory management	Reduces food waste by 25-30%
Water Heater	AI learns usage patterns to heat water only when needed	10-15% reduction in energy costs
Indoor Plumbing	AI-powered leak detection and water flow optimization	Up to 15% reduction in water usage
Lawn Irrigation	AI analyzes weather data to adjust watering schedules	20-30% reduction in water consumption
Kitchen Appliances	AI-enabled smart ovens preheat based on meal selection	10-15% energy savings