In a broad market survey conducted recently by AI Trends, respondents outlined which AI solutions they have implemented and which they expect to implement in the coming months. Together, the results reveal which areas are ripe for the most growth in short-term AI implementation.
AI Trends surveyed readers representing 28 different industries, though IT services, computer software, and government made up well over a third of the responses. We asked which AI solutions have been implemented so far, and which are coming in less than 12 months.
Among the solutions already implemented, IT automation leads the pack with 39% of responses, though cybersecurity, customer services, and virtual assistants each accounted for more than 25% of the responses. Sales optimization and workforce management accounted for the fewest votes at only 13% each.
In the coming 12 months, IT automation is still the leader, though forecasting is a close second with 27% of respondents mentioning that as an area of emphasis. Workforce management remains a fairly low priority with only 14% of respondents choosing that option. Sales optimization, though, was included as a priority for 19% of survey-takers.
Nearly one-third of respondents said implementing AI has given the company a slight lead over competitors, while just slightly fewer said the technology has “allowed us to remain competitive.”
For the “why” within the business, the most respondents chose enhancing customer experience and enhancing existing products as reasons to implement AI. Optimizing internal operations including reducing headcount through automation and streamlining employee roles were chosen less frequently by respondents, but all three options earned at least 17% of answers.
With implementations underway and new ones on the horizon, AI skills continue to be a challenge. Data scientists (24%) and AI software developers (23%) were the top skill sets that companies found lacking in house when they began to implement AI solutions. But AI researchers, project managers, and user experience designers are also needed.
The US Patent and Trademark Office (USPTO) is getting more involved in AI. One effort is an AI project that aims to speed patent examinations. The office receives approximately 2,500 patent applications per day.
The project took some nine months to develop and makes a “really compelling case” for the use of AI, stated Tom Beach, Chief Data Strategist and Portfolio Manager at USPTO, in an account in MeriTalk. Beach was speaking at a recent Veritas Public Sector Vision Day event.
The project calls for extracting technical data from patent applications and using that to enhance Cooperative Patent Classification (CPC) data, which is reviewed by USPTO patent examiners to evaluate patent applications. The aim is to speed the overall evaluation process. “That’s the ROI for this project,” Beach stated.
The USPTO is also actively seeking comments on the impact of AI on creative works. The office published a notice in the Federal Register in August 2019 seeking comments. It sought comment on the interplay between patent law and AI. In October, the USPTO expanded the inquiry to include copyright, trademark and other IP rights, according to an account in Patently-O. Comments are now being accepted until Jan. 10, 2020.
(Anyone can respond; interested AI Trends readers are encouraged to respond.)
The questions have no concrete answers in US law, experts suggest. “I think what’s protectable is conscious steps made by a person to be involved in authorship,” stated Zvi S. Rosen, lecturer at the George Washington University School of Law, in an account in The Verge. A person executing a single click might not be so recognized. “My opinion is if it’s really a push button thing, and you get a result, I don’t think there’s any copyright in that,” Rosen stated.
This push-button creativity discussion gets a little more murky when considering the deal Warner Music reached with AI startup Endel in March 2019. Endel used its algorithm to create 600 short tracks on 20 albums that were then put on streaming services, returning a 50 / 50 royalty split to Endel, The Verge reported.
Rosen encouraged people to respond. “If a musician has worked with AI and can attest to a particular experience or grievance, that’s helpful,” he stated.
For those interested, here are the questions:
Should a work produced by an AI algorithm or process, without the involvement of a natural person contributing expression to the resulting work, qualify as a work of authorship protectable under U.S. copyright law? Why or why not?
Assuming involvement by a natural person is or should be required, what kind of involvement would or should be sufficient so that the work qualifies for copyright protection? For example, should it be sufficient if a person (i) designed the AI algorithm or process that created the work; (ii) contributed to the design of the algorithm or process; (iii) chose data used by the algorithm for training or otherwise; (iv) caused the AI algorithm or process to be used to yield the work; or (v) engaged in some specific combination of the foregoing activities? Are there other contributions a person could make in a potentially copyrightable AI-generated work in order to be considered an “author”?
To the extent an AI algorithm or process learns its function(s) by ingesting large volumes of copyrighted material, does the existing statutory language (e.g., the fair use doctrine) and related case law adequately address the legality of making such use? Should authors be recognized for this type of use of their works? If so, how?
Are current laws for assigning liability for copyright infringement adequate to address a situation in which an AI process creates a work that infringes a copyrighted work?
Should an entity or entities other than a natural person, or company to which a natural person assigns a copyrighted work, be able to own the copyright on the AI work? For example: Should a company who trains the artificial intelligence process that creates the work be able to be an owner?
Are there other copyright issues that need to be addressed to promote the goals of copyright law in connection with the use of AI?
Would the use of AI in trademark searching impact the registrability of trademarks? If so, how?
How, if at all, does AI impact trademark law? Is the existing statutory language in the Lanham Act adequate to address the use of AI in the marketplace?
How, if at all, does AI impact the need to protect databases and data sets? Are existing laws adequate to protect such data?
How, if at all, does AI impact trade secret law? Is the Defend Trade Secrets Act (DTSA), 18 U.S.C. 1836 et seq., adequate to address the use of AI in the marketplace?
Do any laws, policies, or practices need to change in order to ensure an appropriate balance between maintaining trade secrets on the one hand and obtaining patents, copyrights, or other forms of intellectual property protection related to AI on the other?
Are there any other AI-related issues pertinent to intellectual property rights (other than those related to patent rights) that the USPTO should examine?
Are there any relevant policies or practices from intellectual property agencies or legal systems in other countries that may help inform USPTO’s policies and practices regarding intellectual property rights (other than those related to patent rights)?
The goal to be a data-driven organization has been a rallying cry for enterprises for the past decade. The aspiration is to leverage content to gain powerful insights into ways businesses can better serve customers, improve operational efficiency and respond to market dynamics faster. However, these efforts have come up short and have not been attainable at the promised level—until now.
Advances in AI technology are enabling organizations to maximize the value of their content.
Getting Content AI Ready
Most future-focused enterprises have already solved the problem of data to a large extent. They know how to digitize it with advanced OCR technology and store it within CRM, BPM, and ERP systems, which is the first step for readying content for AI. They also have successful use cases of applying machine learning to make the processing into systems more efficient, such as in the processing of invoices.
Formtran, for example, is a systems integrator that digitizes and processes more than 50 million pages per year of various types of unstructured documents, such as invoices, real estate documents, and check and wire transfer documents. They do this for global organizations in consumer goods, financial services and ecommerce, and illustrate how businesses are digitizing and transforming unstructured data in order to maximize the value of content and information assets.
After digitization, data is ready to be infused with a set of technologies known as content intelligence (or content IQ) that use AI to carry out tasks such as reading and categorizing a document, routing a document, extracting and validating data from documents, and other tasks related to understanding and processing unstructured content. Content intelligence is flourishing with companies expecting to increase spend on these technology over the next year by 31%.
By incorporating content IQ, organizations are able to make quicker, more accurate decisions and deliver greater business value. These technologies include computer vision, machine learning, natural language processing and content analytics.
Factors Driving Content Intelligence
Within the past few years, the biggest demand for AI was in the automation of repetitive tasks via robotic process automation (RPA) software robots, or digital workers. However, organizations are now demanding greater insights from their complex and semi-structured data-streams other than simplistic automation.
To understand the need and drivers for content IQ, leading research firm IDC conducted a global survey among 500 IT executives. Respondents indicated manual sorting and classification of documents, manual data extraction from documents, inadequate compliance with security/privacy regulations, and poor data, errors, and inaccuracy of information as top pain points that content IQ can address (see chart).
These pain points highlight that poorly managed, unstructured content leads to lack of customer information, which in turn leads to inadequate data to support decision making. Furthermore, lack of control over content can leave sensitive data vulnerable to risk and fraud.
Business Benefits of Content Intelligence
The IDC survey also found there are a number of benefits by deploying content IQ, ranging from increased employee productivity to increased customer satisfaction. Over one-third of respondents saw an improvement in responsiveness to customers, new product or revenue opportunities, increased visibility and/or accountability, or increased customer engagement.
For example, solution integrator Ripcord is leveraging content IQ technology for Coca-Cola Bottlers’ Sales and Services (CCBSS). Ripcord is digitizing millions of proof of delivery documents for CCBSS’s key fulfillment and logistics records using RPA and intelligent capture. Its chief procurement officer says the company will increase operational efficiencies, customer service, and add innovative value and contribute to financial risk mitigation for their bottling partners by using these technologies.
Another notable business benefit is it can be used to augment human labor. Content IQ fosters a new era of human-machine collaboration where machines will increasingly perform cognitive skills such as evaluating information, reasoning, and decision-making and administering (see chart). In fact, IDC estimates digital workers will increase by 50% within the next two years.
The increase in digital workers enables organizations to redirect human workers to higher-value tasks. With invoices, for example, machines can transfer data from invoices into ERP systems and authorize it for payment when all conditions are met. Staff are then able to investigate and expedite any exceptions to ensure delays are minimized. The great thing about content IQ is its machine learning capabilities learn how to handle exceptions over time. There is a clear adoption trajectory as the system evolves and learns. Machine augmentation becomes the source of additional data and further improvement for the AI algorithms that enable the transfer from an augmented process to an automated process.
Need for CEO Support
For content intelligence and any AI initiative to be successful, there must be a strong vision and support from the C-suite. According to IDC, a lack of upper management support along with siloed business units and disparate legacy systems were all barriers to executing content IQ.
The good news is that most organizations are realizing the importance of senior management buy-in and that content intelligence is imperative to make their digital transformation successful. Seventy-eight percent of IDC’s respondents said the CEO was the top decision maker for content IQ, and half also included the CIO, followed by the CTO and chief digital officer as decision makers along with line of business executives. Furthermore, there is a growing trend among organizations to create a center of excellence (COE) which embodies key stakeholders for identifying and deploying AI initiatives.
Content IQ opportunities are omnipresent across all industries, with the most spending taking place among industries heavy laden with content such as financial services, transportation and logistics, insurance, and healthcare. It is also beneficial in horizontal business processes such as invoice processing, customer onboarding, claims processing and wherever you would connect customer-facing applications, such as mobile apps and chat bots, with backend systems. A common scenario is an organization will incorporate content IQ in a finance function and the project lead, amazed by the results, evangelizes the benefits with other business functions. They are able to transfer results and methods from one application to another in a straightforward manner making deployment faster.
Successful organizations will understand the need for AI in content and embrace the opportunities within their business processes. IDC provides three tips to ensure your content IQ investment goes smooth: invest in modular, enterprise wide digital platforms; develop a strategy for ongoing training and development of employees to ensure they have the digital skills set; and think human-machine collaboration as the new normal.
Ivan Yamschchikov is AI Evangelist at ABBYY. He currently works as a Post-Doc Researcher at the Max Planck Institute for Mathematics in Germany. Ivan received a PhD from Technical University Cottbus-Senftberg, Germany and MSc in Mathematical Physics from Saint-Petersburg State University, Russia. He is fluent in English, Russian and German with good knowledge of Bulgarian, French, Japanese and Swedish. He can be reached at firstname.lastname@example.org.