70% of IT and AI Projects Fail for Simple Reasons

Information technology investments are often treated as purely technical endeavors rather than organizational transformations that require changes in processes, culture, and human behavior, which possibly explains the gap between IT investments and observed results. 

source: McKinsey 

Some of the exceptions might be retail; communications and media, where productivity seems higher. For communications and media, technology often creates the platform for services, delivering a higher degree of observable value. 

source: McKinsey 

Still, many studies suggest that IT projects have a high failure rate overall. 

Study/Source	Year	Key Finding	Sample Size/Scope	Failure Rate/Metric
Standish Group CHAOS Report	2020	Only 31% of IT projects are successful (on time, on budget, with required features)	50,000+ projects across multiple industries	69% challenged or failed
McKinsey Global Institute	2012	Large IT projects run 45% over budget and 7% over time, while delivering 56% less value than predicted	Analysis of 5,400 IT projects	17% of projects are "black swans" with cost overruns >200%
Harvard Business Review - Flyvbjerg & Budzier	2011	Average cost overrun for large IT projects is 27%, with one in six projects having cost overruns of 200%	Study of IT project performance patterns	16.7% massive overruns
PwC Global CEO Survey	2019	73% of CEOs believe their digital investments are not delivering expected returns	1,378 CEOs globally	73% not meeting ROI expectations
Deloitte Tech Trends	2021	70% of digital transformation initiatives fail to meet their goals	Survey of 1,000+ executives	70% failure to meet objectives
MIT Sloan - Brynjolfsson & Hitt	2003	IT productivity paradox: firms with higher IT spending don't always show proportional productivity gains	Longitudinal study of 527 large firms	Mixed correlation between IT spending and productivity
Gartner IT Spending Analysis	2019	85% of big data projects fail to deliver business value	Analysis of enterprise big data initiatives	85% failure rate
Accenture Technology Vision	2020	Only 37% of organizations successfully scale their digital pilots to enterprise-wide implementations	Survey of 4,000+ business and IT executives	63% fail to scale successfully
Boston Consulting Group	2018	70% of digital transformation efforts fall short of their goals	Analysis of transformation initiatives across industries	70% shortfall rate
KPMG Global CEO Outlook	2018	65% of CEOs question whether their technology investments create competitive advantage	Survey of 1,300 CEOs	65% uncertain about competitive value
IBM Institute for Business Value	2019	Organizations realize only 20% of anticipated benefits from AI investments	Study of AI implementation across enterprises	80% benefit shortfall
Forrester Research	2020	60% of customer experience technology investments fail to improve customer satisfaction scores	Analysis of CX technology implementations	60% fail to improve target metrics
EY Digital Transformation Study	2018	55% of digital transformation programs are abandoned before completion	Survey of 500+ executives across industries	55% abandonment rate
Capgemini Digital Transformation Institute	2017	Only 36% of organizations are digital transformation leaders achieving significant benefits	Study of 1,000+ organizations globally	64% are laggards or followers
McKinsey Technology Trends	2021	Cloud migration projects deliver only 65% of expected cost savings on average	Analysis of cloud transformation initiatives	35% savings shortfall

"Five Nines" is Gone and Almost Nobody Actually Cares

Our user experience of applications, devices and networks is far from the “five nines” standards (99.999 percent availability) telcos used to tout. And it probably doesn't matter as much as some would suggest, as we all now have protocols for dropped calls, lost emails, momentary glitches in streaming content.

If the value of an experience or app is high enough, we simply work around availability or "reliability" issues. "Best effort" means some amount of failure for any given instance of an app session.

So, as a practical matter, today’s heterogenous, edge-powered, internet transport fabric, IP-based application environment absolutely means user experience cannot approach 99.999-percent availability for any applications. And nobody cares much, most of the time.

To be sure, some might claim "core systems" in banking, financial trading or some security-critical use cases are "five nines" in terms of availability, but that only applies to the core systems, not the end user experience of those systems. 

The problem is that no matter what any single participant in the value chain might claim for its own availability, and even if that availability is between 99 percent and 99.99 percent, the entire end-to-end value chain depends on the sum total of availability across the whole value chain, and that math is challenging. 

Consider an example where contributor availabilities are:

• Device: 99%

• Home broadband access: 99.5%

• Internet backbone: 99.99%

• App server: 99.9%

• Local power: 99.5%

The end-to-end availability requires multiplying all those discrete availabilities. So the formula is 

0.99 × 0.995 × 0.9999 × 0.999 × 0.995 ≈ 97.4 percent. That means 229 hours of downtime per year, not the 5.26 minutes per year allowed by "five nines” standard.

The only reason end users seem unaware of the change is that much of the downtime happens when they are not actively using their connections (devices not present; devices in “do not disturb” mode; user is sleeping; apps not in immediate use). 

Value Chain Component	Typical Availability (%)	Major Downtime Factors
User Devices – Mobile	95%–99%	Battery loss, OS/software issues, dropped connections
User Devices – Fixed	96%–99.5%	Power outages, device crashes, local network (Wi-Fi) issues
Access Network – Mobile	97%–99.9%	Tower outages, congestion, interference, maintenance
Access Network – Fixed	98%–99.9%	Fiber/cable cuts, power issues, last-mile failures
Global Internet Backbone	99.99%+	Rare fiber cuts, DDoS attacks, routing errors
Application Servers (Cloud)	99.5%–99.99%	Cloud region outages, software bugs, maintenance, cyberattacks
Local Power Supply	99.0%–99.9% (urban)	Grid instability, storms, infrastructure failures
End-to-End Availability	Often < 95%–98%	Cumulative failures across components

The point is that people have learned to adapt to services, networks, applications and experiences that are never "five nines" reliable. We have work-around solutions that funciton so well nobody really cares about the lack of "five nines" reliability. 

Maybe AI Doesn't Kill "Search"

One reason Alphabet’s equity valuation has been muted recently, compared to some other “Magnificent 7” firms, is the overhang from potential antitrust action, which has been partially addressed in the United States v. Google decision by Judge Amit Mehta, where the ruling does not require structural separation of the Chrome browser from the rest of Alphabet. 

The other problem is the concern that the search business model could be disrupted by language model chatbots. 

We might not know much about some other antitrust cases involving Google for some time. 

But we might already be seeing signs that Alphabet’s innovations around integrating chatbot functionality with search are paying off. 

“We know how popular AI Overviews are because they are now driving over 10 percent more queries globally for the types of queries that show them, and this growth continues to increase over time,” said Sundar Pichai, Alphabet CEO. 

The point is that we still do not know the longer-term changes in either search or chatbot business models, especially since agent capabilities are coming. That will blur the functions of “learning, research or finding” with “taking action.

Feature	Classic Google Search	Chatbots	Google with Agentic AI
Research (Info Retrieval)	Yes	Yes	Yes
Personalized Recommendations	Yes	Yes	Yes
Multi-step Task Execution	Limited	Limited	Yes
Real-world Action Capability	No	Rarely	Yes (book, buy, schedule, etc.)
Trust & Security	High	Varies	High (Google brand)
Ecosystem Integration	Extensive	Siloed/fragmented	Extensive

Both chatbots and search engines will be able to retrieve information and act on it. The perhaps classic example is trip planning, which blends task-oriented activities and research.

 

But add agent capabilities and the “search” platform becomes the “take action” platform. For a firm such as Alphabet, that means the value of Google search as an advertising revenue generator is augmented by e-commerce revenue, something Google already does to an extent with product searches.

 

The point is that Google search has a plausible path to surviving and perhaps even outshining chabots also outfitted with agent capabilities. 

Somes "Fiber" is Not the Answer

If you have spent any time at all thinking about the cost of building next-generation access networks, you know that customer density really matters. Where rural fiber deployment costs range from $3,000 to $6,000 per household passed, for the most part, urban costs are $700 to $1,500 per household passed, primarily because of customer density. 

Very-low population density (less than 10-20 households per square mile) is one challenge. But challenging terrain also matters, which is why fixed networks are so scarce in mountainous regions, for example. 

To be sure, government subsidy programs can help, but there are always going to be some locations in the United States, for example, that simply won’t be logical candidates, even with subsidies. One reality is that about six percent of the U.S. land mass is “developed” and relatively highly populated. 

Conversely, about 94 percent is unsettled or lightly populated, including mountains, rangeland, cropland and forests.

source: USDA

So it is possible that AT&T might find that up to 15 percent of its copper access lines cannot be upgraded for fiber, and will have to use some form of wireless access.

Verizon, with a denser customer footprint, might ultimately find that up to 10 percent of its access lines, likewise will have to use a wireless platform. 

Lumen, with the least-dense territory of all the former Regional Bell Operating Companies, could easily find that 20 percent to 30 percent of remaining copper access lines cannot be upgraded for fiber. 

The point is that there are physical reasons, beyond customer preferences, for using fixed wireless, mobile wireless or satellite for access to customers in remote and thinly-populated areas.

How Big a Market for "Direct to Phone" Services Such as SpaceX Wants to Provide?

The purchase by SpaceX of EchoStar  AWS-4 and H-block spectrum licenses marks a beginning and an end. The end is EchoStar’s hopes of becoming a fourth facilities-based mobile service provider in the U.S. market.

The beginning is SpaceX’s ambition to become a facilities-based provider of satellite “direct to mobile phone” (D2M) service in the U.S. market. It remains unclear how big that market could become, and some will doubt it is a full-on substitute for conventional mobile service. 

Low estimates by Juniper Research might suggest global revenues of  $30 million in 2025 scaling to $1.7 billion  by 2029, assuming the U.S. market represents 60 percent of the global market. 

Grand View Research estimates are higher, forecasting global revenues of as much as $435 million in 2025, with U.S. share at about 31 percent.

MarketsandMarkets suggests a 2025 market of $560 million in 2025, with U.S. at 42 percent share). 

Those estimates include direct-to-phone services that are either exclusive (as SpaceX will provide) or as supplementary service to a terrestrial mobile service for which an extra charge is required. 

Year	Low Estimate (Juniper-based, $M)	Mid Estimate (Grand View, $M)	High Estimate (MarketsandMarkets, $M)	Average Estimate ($M)
2025	18	135	235	129
2026	45	181	324	183
2027	113	242	447	267
2028	283	324	617	408
2029	708	434	851	664
2030	N/A	581	1,174	918

Compare that to estimated total U.S. mobile operator revenue in 2025 ranging from $340 billion to $344 billion. Satellite direct-to-phone is going to be a niche, though it will be an important niche for users who cannot get cell service otherwise, in hard-to-reach areas (mountains, very-rural areas not alongside major highways, on the oceans, in disaster areas where the mobile network is inoperable). 

The transaction provides SpaceX with valuable mid-band spectrum assets—AWS-4 covering 2000-2020 MHz downlink and 2180 MHz uplink, and H-block spanning 1915-1920 MHz uplink and 1995-2000 MHz downlink. The key is that those frequencies allow satellite signals to be used by standard smartphones. 

Such D2M services could enable seamless texting, calling, and low-bandwidth browsing during natural disasters or for off-grid users, without requiring specialized hardware like external antennas. 

Speeds might start modest (2-4 Mbps), but the value lies in ubiquitous global coverage.

D2M could serve as a complementary "always-on" layer, filling coverage gaps that affect about 20 percent to 30 percent of the U.S. land area where people normally are found.