This Innovation Index is an end-to-end strategic impact analysis combining market attractiveness, business model maturity, infrastructure and support impact for Artificial Intelligence. This last part analyses the Technical Readiness to Market, Structural Readiness to Market and Legal, Economic and Environment impact of markets and services related to AI based on innovation analyses shared by experts and analysts on WAI social networks.
Although businesses and leading companies both invest and align in data oriented strategic transition, technical, regulatory and organizational barriers still drastically lower AI infrastructure and support impact. That being said, experts and analysts still remind the existing added value intelligent use of information generates across sectors, and highlight the learning and business model innovation potential laying beyond structural shortcomings to overcome. Overall, the underlined risks and threats from lack of security and transparency to fully aligned services and strategies seem to remain superior to the overall business benefits identified across sectors in the eyes of AI experts and analysts.
Technical readiness to market: A growing influence
Major Internet actors are seeing AI as the next innovation power lift over the coming years.
Google sees AI as an emerging technology frontier that will power new products and services to give it an edge over rivals in the next decade and beyond. Artificial intelligence has already begun to influence how Google’s search engine answers questions, and the company has amassed some of the world’s leading experts in the field.
Read more: “Google’s head of artificial intelligence takes charge of search”, AR Guess, Dataversity
| Impact to TRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | Medium | +2 |
Yet data still proves difficult to exploit through integrated solutions. This difficulty reveals the early level of data analytics and interoperability of analysis throughout systems and sectors.
“The amount of data crossing the wires and airwaves is mind-boggling,” says Seth Robinson, senior director, technology analysis, with the nonprofit Computing Technology Industry Association (CompTIA) trade association. “So while individual pieces of a holistic data solution may be improving, these individual pieces are not yet integrated in a way that drives ideal results.”
| Impact to TRM | Rate of Impact | WAI est. value |
|---|---|---|
| Negative | Medium | -2 |
Read more: “Big Data Projects on the rise but data use could be better”, Thor Olavsrud, CIO
This identified limit doesn’t restrain monitored results to be showcased in valuable areas. Healthcare in particular promises to offer AI interesting space of improvements.
Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways. Machine intelligence already helps us get the important business information we need more quickly, monitors critical systems, feeds our population more efficiently, reduces the cost of health care, detects disease earlier, and so on.
| Impact to TRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | High | +3 |
Read more: “Comprehending Machine Learning @ThingsExpo” Bob Gourley, Wireless
As a result, “putting more intelligence” is driving most software related developments.
Putting more intelligence (sometimes called AI or deep learning) into rudimentary big data applications (currently lacking any true statistical science) such as recommendation engines, crowdsourcing or collaborative filtering. Purpose: detecting and eliminating spam, fake profiles, fake traffic, propaganda, attacks, scams, bad recommendations and other abuses, as early as possible.
Read more: “13 new trends in big data and data science” Vincent Granville, Data Science Central
| Impact to TRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | Medium | +2 |
The fact that intelligent systems can also learn from their own experiences and failures enhances the interest from companies into artificial intelligence.
Masergy’s data prediction gradient can use data from all six subsystems. Then it processes the data using multiple learning models, comparing the learned data with the original raw data and using regression analysis to grade each data stream against its own learning models. In this way, the system determines the predictability of any data model.
Read more: “What every CISO should know about machine learning” Mike Stute, Infoworld
| Impact to TRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | High | +3 |
The organisation of data intelligence systems implies a market specification which directs targeted trends and knowledge towards identified and known issues. This strengthen the intelligence built into systems.
The earliest approaches to AI were computer programs designed to solve problems that human brains performed easily, such as understanding text or recognizing objects in an image. Results of this work were disappointing and progress was slow. For many problems, researchers concluded that a computer had to have access to large amounts of knowledge in order to be ‘smart’. Thus they introduced ‘expert systems’, computer programs combined with rules provided by domain experts to solve problems, such as medical diagnoses, by asking a series of questions.
Read more: “Machine learning vs machine intelligence vs AI”, AR Guess, Dataversity
| Impact to TRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | Low | +1 |
The amount of data to be processed is continuously increasing and the variety of sources makes it difficult for experts to produce the necessary infrastructure to properly analyse such traffic.
As the market for IoT devices grows and sensors are added to more and more things and places, faster and heavier data transmission will be required. Our current infrastructure simply cannot handle the quantity of data that will need to be transmitted if the IoT continues to grow at predicted rates.
Read more: “Will LIFI take Big Data and the Internet of Things to a new level?”, Bernard Marr, Forbes
| Impact to TRM | Rate of Impact | WAI est. value |
|---|---|---|
| Negative | Strong | -3 |
Instead of investing in human ressources and time consuming processes to be rolled our trough a production chain or an organization, deploying artificial intelligence based systems only requires investment into software.
The ability to extract dependable and actionable information from the vibration of machines will allow businesses to keep their assets running for longer while spending far less in maintenance. Also, the investment to get there will be just software,” says Dr. Teixeira, who is the technical lead for the Health and Usage Monitoring Systems (HUMS) analytics project at UAH’s Reliability and Failure Analysis Laboratory (RFAL).
Read More: “Scientist creates AI algorithm to monitor machinery health”, Jim Steele,The University of Alabama in Huntsville
| Impact to TRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | Medium | +2 |
| WAI Comments and Analysis |
|---|
| Experts and analysts repeatedly outline business and end-user benefits AI promises to deliver, showcasing tangible cases where intelligent systems have not only reduced costs and increased revenues, but also generated higher added value. As a result, major Internet companies are developing concrete plans and investments to position themselves on AI market. On the other hand, most experts also agree there remain severe technical shortages affecting data processing tools and infrastructure impeding an efficient roll-out of AI services. The initial readiness to market rate of AI therefore reaches 2.2/5. |
Structural readiness to market: Agility and intelligent systems in organizations
End-user equipment trends are favoring the rise of technological hopes for more accurate data to be re-used for sharpened market intelligence services and ensuing operational tools.
The global market for internet-connected wearable devices has grown 223%in 2015, with Fitbit shipping 4.4 million devices and Apple selling 3.6 million Apple Watches in the second quarter of 2015.
Read more:“17 mind blowing Internet of Things facts everyone should read”, Bernard Marr,Linkedin
| Impact to SRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | High | +3 |
From an organizational and human resources viewpoint, priorities are being set to align with changes implied by the use of Artificial Intelligence.
The role of the CIO is, of course, to structure that flow of information,” Brynjolfsson said. “If the CIO does it well, the organization will be more intelligent than the sum of its parts.”
Read more: “Minsky, early explorer of AI future, dies at 88”, Jason Spararani, TechTarget
| Impact to SRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | Medium | +2 |
As a result, the identified need for organizational transformation is now spreading into strategies and consulting services are shaped to answer these needs. This also highlights a slower organizational transition, implying some inefficiencies that might also slow Artificial Intelligence developments.
Every enterprise now has to go through a transition to remain competitive. And it’s a transition to data, analytics, and application driven differentiation. The results will show huge shifts in methodology and technology, driven by Platform as a Service (PaaS), microservices, big data and analytics, and agile programming for continuous delivery. As enterprises go through this shift and are faced with competing in it, they need to leverage these new technology stacks aggressively, but do it in as low a risk way as possible.
Read more: “Why 2016 is the year for IoT, Big Data and Analysis”, Melissa Dipento, Sevone
| Impact to SRM | Rate of Impact | WAI est. value |
|---|---|---|
| Negative | Medium | -2 |
At the same time, technical developments clearly indicate thorough advances, allowing experts to foresee drastic changes in user experiences, and ensuing increasing growth potentials. The strategic positioning of major Internet players around those technologies help understand the market value they represent from a business perspective.
Apple has expressed interest in the field. In a 2014 patent application, it described a software system that would analyze and identify people’s moods based on a variety of clues, including facial expression.
Read more: “Apple buys Artificial Intelligence startup Emotient”, Rolf Winkler, Daisuke Wakabayashi, Elizabeth Dwoskin, The Wall Street Journal
| Impact to SRM | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | Medium | +2 |
| WAI Comments and Analysis |
|---|
| It makes very few doubt AI will or already is influencing business analytics and data analytics for product and services development. Organizations have initiated strategic alignments with platforms, technologies, skills and services needed to transition towards a data intelligent business orientation. Nevertheless, the current status and required changes tend to come across as anticipated barriers to market which only agile businesses are in a position to overcome. The intermediate readiness to market rate of AI therefore reaches 2.6/5. |
Legal, Social and economic impact: Transparency and security as critical requirements
Experts outline major gaps in the legal and regulatory environment surrounding any data related market. One of the missing element they put forward is transparency.
But debating revenue run rates and nuances between the private and public cloud variations misses the point. What’s missing from the cloud equation today is better transparency.
Read more: “A call for more cloud computing transparency”, Larry Dignan, ZD Net
| Impact to LSEI | Rate of Impact | WAI est. value |
|---|---|---|
| Negative | Medium | -2 |
The economic benefits of Artificial Intelligence are more clearly identified, with existing business cases outlining cost and labour efficiency improvements.
The added capabilities, including some powered by artificial intelligence, are designed to help food-processing companies and consumer product makers keep production lines running smoothly despite what’s sometimes a breakneck pace.
Read more: “AI is coming to factories as IBM works with IoT startup Wi-next”, Stephen Lawson,Computer World
| Impact to LSEI | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | High | +3 |
Beyond business applications, the economic impact of Artificial Intelligence is such that experts also foresee changes in our global approach to information. This new learning approach will have repercussions across sectors.
A key application of machine learning algorithms is analysis of data. Advances in analysis of visual data, in addition to increasing speed of analysis, will have impacts across various fields. “We’ll see artificial intelligence capabilities strengthen in the area of understanding imagery, including the context and meaning in specific elements such as objects, people, and places,” writes Banavar in an email.
Read more: “5 predictions for AI in 2016”, Tess Townsend, INC
| Impact to LSEI | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | Medium | +2 |
Among other legal and regulatory framework shortcomings, experts outline the importance of security and remind that companies specializing in this area still struggle to prove efficient enough to protect such a critical amount of data.
L’Internet des objets, c’est bien. Mais l’Internet sécurisé des objets, c’est mieux. Or en la matière, les sociétés de ce secteur peinent à se montrer vraiment convaincantes sur leurs capacités à protéger correctement les données collectées et exploitées par ces appareils. C’est un problème, car le nombre de gadgets électroniques capables de se connecter au réseau des réseaux est en train d’exploser.
Read more: “La sécurité de l’Internet des Objets continue de préoccuper”, Julien Lausson, Numérama
| Impact to LSEI | Rate of Impact | WAI est. value |
|---|---|---|
| Negative | High | -3 |
In addition, experts and analysts remind how difficult it is for transformations to generate higher efficiences with newly developed data analytics needs and intelligence systems required. As a result, many companies claim they struggle with developing proper transparency with their use of customer data.
This makes it clear that businesses are finding more and more ways to turn data into value, but at the same time, the report found, many are hitting stumbling blocks which are frustrating those efforts. Just 34% of respondents said that they feel their organizations are “very effective” at being transparent with customers about how data is used. And 9% say they feel that they are “totally ineffective” in this area, which can be very detrimental to building the all-important customer trust.
Read more: “Big Data Facts: How Many Companies Are Really Making Money From Their Data?”, Bernard Marr, Forbes
| Impact to LSEI | Rate of Impact | WAI est. value |
|---|---|---|
| Negative | Medium | -2 |
To further highlight the upcoming changes that will occur in organizations, experts outline the business models impact Artificial Intelligence is already generating, with the rise of startups specializing in technical fields that companies require.
Such is the demand for machine-learning talent, in fact, that startups see an opportunity to offer deep technical expertise to companies, from financial and insurance firms to Web startups and carmakers, that are hoping to harness AI. A few startups now offer to accelerate the performance of the machine-learning algorithms so that they work well on arrays of computer chips. At least one is designing its own computer chips to squeeze the best performance out of the latest algorithms.
Read more: “Startups aime to exploit a deep learning skills gap”, Will Knight, Technology Review
| Impact to LSEI | Rate of Impact | WAI est. value |
|---|---|---|
| Positive | High | +3 |
| WAI Comments and Analysis |
|---|
| As far as the legal, economic and environmental frameworks of AI are concerned, experts seem concerned by the limits companies face with regards to security and transparency. These limits aren’t stopping businesses to invest and adapt to this new markets, though. Startups in particular have identified new business models and services which exploit organizations’ skills shortages. Nevertheless, the apparent unpreparedness of businesses’ direct and indirect conjectural frameworks remind the fragility of current growth expectations. The final readiness to market rate of AI therefore reaches 1.3/5. |
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