Previous Blogs

September 26, 2017
Microsoft Takes Computing to the Extremes

September 19, 2017
What is the Future of Upgrades?

September 12, 2017
It’s Time for Modern Digital Identities

September 5, 2017
The Autonomous Car Charade

August 29, 2017
The Golden Era of Notebooks

August 22, 2017
The Evolution of Smart Speakers

August 15, 2017
The Myth of General Purpose Wearables

August 8, 2017
IoT Connections Made Easy

August 1, 2017
Smarter Computing

July 25, 2017
The Value of Limits

July 18, 2017
Tech in the Heartland

June 27, 2017
Business Realities vs. Tech Dreams

June 20, 2017
The Power of Hidden Tech

June 13, 2017
Computing Evolves from Outside In to Inside Out

June 6, 2017
The Overlooked Surprises of Apple’s WWDC Keynote

May 30, 2017
Are AR and VR Only for Special Occasions?

May 23, 2017
The Digital Car

May 16, 2017
Digital Assistants Drive New Meta-Platform Battle

May 9, 2017
Getting Smart on Smart Speakers

May 5, 2017
Intel Opens High-Tech "Garage"

May 2, 2017
The Hidden Value of Analog

April 28, 2017
Google’s Waymo Starts Driving Passengers

April 25, 2017
The Robotic Future

April 21, 2017
Sony Debuts New Pro Camera

April 18, 2017
Should Apple Build a Car?

April 14, 2017
PC Market Outlook Improving

April 11, 2017
Little Data Analytics

April 7, 2017
Facebook Debuts Free Version of Workplace Collaboration Tool

April 4, 2017
Samsung Building a Platform Without an OS

March 31, 2017
Microsoft Announces Windows 10 Creators Update Release Date

March 28, 2017
Augmented Reality Finally Delivers on 3D Promise

March 24, 2017
Intel Creates AI Organization

March 21, 2017
Chip Magic

March 17, 2017
Microsoft Unveils Teams Chat App

March 14, 2017
Computing on the Edge

March 7, 2017
Cars Need Digital Safety Standards Too

February 28, 2017
The Messy Path to 5G

February 24, 2017
AMD Launches Ryzen CPU

February 21, 2017
Rethinking Wearable Computing

February 17, 2017
Samsung Heir Arrest Unlikely to Impact Sales

February 14, 2017
Modern Workplaces Still More Vision Than Reality

February 10, 2017
Lenovo Develops Energy-Efficient Soldering Technology

February 7, 2017
The Missing Map from Silicon Valley to Main Street

January 31, 2017
The Network vs. The Computer

January 27, 2017
Facebook Adds Support For FIDO Security Keys

January 24, 2017
Voice Drives New Software Paradigm

January 20, 2017
Tesla Cleared of Fault in NHTSA Crash Probe

January 17, 2017
Inside the Mind of a Hacker

January 13, 2017
PC Shipments Stumble but Turnaround is Closer

January 10, 2017
Takeaways from CES 2017

January 3, 2017
Top 10 Tech Predictions for 2017

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TECHnalysis Research Blog

October 3, 2017
The Business Challenges of Artificial Intelligence

By Bob O'Donnell

From a pure technology perspective, it’s hard to imagine something much hotter than artificial intelligence, or AI. Everywhere you look, companies of all types and sizes are talking about their AI initiatives and all the amazing things those projects will enable. Similarly, it’s nearly impossible to read recent stories in both the tech and general press that don’t mention AI.

While I have no doubt that, technologically, AI is a fascinating new area of development that’s bound to drive some incredible new innovations, I am starting to have some doubts about the business opportunity for AI.

In many ways, the business challenges for AI are similar to those that arguably still face “big data.” First, it’s hard to do, and the number of people with the skills to really create AI algorithms and other software is very limited, making the cost of creating any products and services with the right people very expensive.

Second, the end results of the effort can be hard to quantify from a return-on-investment (ROI) perspective. In some cases, it’s easy to point to clear monetary savings or revenue increases from the results of either big data analysis or AI-driven outputs, but in a very large percentage of cases, it’s not. Sometimes it’s a process improvement that comes from the work—often a positive development, but not necessarily one that’s easy to associate with a dollar figure.

The third and most fundamental challenge for many AI applications is with the inherent nature of what they’re designed to do. In essence, if an AI project is done properly, it should basically put itself out of a job.

Let me explain. In many instances, AI is applied to a set of data in order to determine hidden patterns, more efficient ways of achieving/doing something, or just making a process easier or more natural. If the technology is applied in an intelligent way, then the results will be an improved process that’s cheaper, faster, easier or generally better than the manner with which it was done previously.

That’s great, but often that discovery process only has to be done once. So, once you’ve figured out a better way to do something, the project is done. It’s essentially a one and done. Yes, there can often be iterative improvements made after the fact, but it’s often a case of diminishing returns. You might make 95% of the possible improvements thanks to that first round of AI-driven analysis, but then only make very minor improvements after that.

From a business perspective, that’s clearly a challenge, because most tech-related business models are built around a continuous, ongoing stream of revenue and not just a one-time sale. You can certainly build successful businesses based on a single sale/project, but it’s definitely more challenging, especially because many of the efforts necessary to build a strong AI algorithm for a particular application are very unique to that project. As a result, it may be difficult to leverage that work across different projects/applications, which would be critical for building an ongoing, viable business.

To be clear, there are certainly applications for which a constant flow of AI-driven analysis is essential—keeping an autonomous car driving for example—and those types of applications won’t necessarily face the business model challenges of other AI approaches. Even in these cases, however, there will likely be challenges in monetizing ongoing services, because the AI-driven features are going to be sold as part of a given device or service.

Process-driven services are likely the best opportunity for AI from a business perspective, but it’s not exactly clear what those will be, how they will work, how they will be marketed, and if people will be able to understand the value in them.

The bottom line is that, while there are clearly great opportunities to build some amazing AI technologies, the manner with which money can be made from those technologies isn’t quite as clear. Providing new levels of capabilities, improving how goods are designed, manufactured, sold and used, and generally increasing the overall “expertise” built into other products is clearly a noble goal, but AI increasingly looks like an “ingredient” technology that could be challenging to monetize on its own.

Here's a link to the column:

Bob O’Donnell is the president and chief analyst of TECHnalysis Research, LLC a market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on Twitter @bobodtech.

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