Machine Learning Operations
Machine Learning Operations
Microsoft released new features for the Azure Cognitive Services suite, as well as a form recognizer and responsible AI dashboard aimed at both data scientists and business users.
Even if AI technology is accessible, implementing some of the AI systems from the cloud providers is not so simple, said Yugal Joshi, Partner at Everest Group.
Over the past seven years, almost all large companies made substantial progress in implementing digital transformation across a wide variety of functions. At the core of those enormous investments and efforts was building software-defined operating platforms, which put companies on a trajectory to fundamentally change how they operate their business. However, studies show many companies (70%) failed or underperformed against their digital transformation objectives. In this blog, I’ll discuss three tips for how to avoid that outcome and, instead, reap the significant benefits of software-defined operating platforms.
You may not be able to peek into the future, but predictive network technology can spot and troubleshoot potential problems before they occur.
Using artificial intelligence (AI) and machine language (ML) mathematical models and algorithms, predictive network technology alerts an organization to network issues as early as possible and offers problem-solving solutions. “The technology enables networks to learn from past instances using massive amounts of data through predictive analytics,” explains Titus M, Senior Analyst with Everest Group. “It collects network telemetry data, recognizes trends, and forecasts network difficulties that might negatively impact user experience and offers potential solutions to the issue.”
The pandemic’s effects on the digital landscape are long-lasting. Businesses are evolving to rely on the intelligent process automation market (IPA) to promote growth and keep up with competitors. Read on to learn more about five growing IPA trends.
In a world becoming increasingly reliant on technology, financial services organizations are digitizing and automating more processes to keep up with the competition. The intelligent process automation market, growing by about 20% across all fields, is now becoming ubiquitous.
IPA is defined as automation in business processes that use a combination of next-generation automation technologies — such as robotic process automation (RPA) and cognitive or artificial intelligence (AI)-based automation, including intelligent document processing and conversational AI. Solution providers are offering solutions across RPA, Intelligent Document Processing (IDP), and workflow/orchestration, as well as crafting innovative solutions such as digital Centers of Excellence (CoE) and investing more in as-a-Service offerings.
In our recent Intelligent Process Automation (IPA) – Solution Provider Landscape with PEAK Matrix® Assessment 2022 report, our analysts ranked IPA technology vendors and looked at the market for IPA solutions. Based on the research, the growth of IPA technology and reliance will expand to around 25% over the next three years.
The question of how to become faster, more efficient, and more resilient is the focus for just about any organization undergoing digital transformation. Very often, the answer to this question is at least, in part, intelligent process automation. In the near future, we can see five emerging IPA trends:
A greater proportion of cognitive elements is finding its way into the intelligent process automation market. About 60% of new automation projects involve more advanced cognitive tools such as IDP, conversational AI and anomaly detection. As the maturity of AI-based solutions increases, cognitive automation will be in greater demand. All-round adoption of IPA will be fueled by providers entering new geographies and organizations starting IA initiatives.
Although many organizations are trying to adopt intelligent process automation, the real question is if it can be scaled up or, in other words, if it can be brought across the organization. To help enterprises scale automation, solution providers are investing in expanding their partner ecosystem, strengthening technology capabilities, and enhancing their services portfolio.
Providers are also expected to help enterprises scale up through more effective change management and CoE set-up strategies. Aided by the prevalence of process intelligence solutions to form robust pipelines and orchestration tools to facilitate holistic automation, enterprises are better equipped now to move away from siloed applications of IA to scaled-up automation implementations.
Many organizations are experimenting with what they can do with citizen development, especially with the current talent shortage. Citizen-led development also holds the power to disrupt the current state of building automation and addresses the issue of talent availability. Solution providers are expected to invest in citizen development and low-code/no-code technologies enabling business users to build automation, consequently also addressing the talent shortage in the market.
Solution and technology providers are also expected to invest substantially in developing the low-code/no-code capabilities of their platforms to enable business users with limited technical exposure to build automation solutions on their own. A few solution providers are implementing citizen development programs in their own organizations and are planning to leverage the learnings to develop effective governance programs for enterprises.
Packaged solutions are gaining traction in the IPA market due to their ease of implementation and quick Return on Investment (RoI). Solutions for F&A are the most prevalent in the market. These solutions will need training on particular data sets to make them functional for a particular process, but they will speed up implementation. Providers are expected to take conscious steps toward promoting sustainable AI by developing solutions complying with environmental, social, and governance (ESG) parameters. They are also investing in AI solutions that are transparent about their working and usage of data.
There are a host of technologies, including RPA, conversational AI, process mining, and process orchestration in the IA ecosystem. Very often these IA solutions need to talk to the various other systems. Many IPA service providers are driving innovation and crafting new solutions to keep pace with the fast-moving IPA market and create a more holistic integration process. One such method is offering enabling capabilities like pre-built connectors for a faster and less complex implementation.
If you would like to learn more or discuss the intelligent process automation market and IPA trends, reach out to [email protected].
Learn how the healthcare industry is utilizing intelligent automation, digitalization, and telehealth as fundamental driving forces to transform and evolve in the webinar, How Intelligent Document Processing Is Transforming the Healthcare Industry.
With inflation in the United States at a 40-year high and unemployment near a 50-year low, these are tough times to attract and retain employees in just about every sector. When you add the growing demand for talent in high tech sectors like big data and AI, you get a job market that’s great for these workers, but tough for companies.
David Rickard of Everest Group, a respected provider of insight for the global BPO industry, says that while countries like India have a lot to offer now, there are some other locales that should be on your radar, including Africa.
Nitish Mittal, Partner in the digital transformation practice at Everest Group, commented on this, he said: “I can’t stress this enough: data or the lack of the right data strategy is the number one bottleneck to scaling or doing anything with AI. When clients come to us with what they think is an AI problem, it is almost always a data problem. AI depends on viable data to prosper. That’s why it’s important to think about the data first.”
Artificial Intelligence (AI) solutions that aim to recognize human emotions can provide useful insights for hiring, marketing, and other purposes. But their use also raises serious questions about accuracy, bias, and privacy. To learn about three common barriers that need to be overcome for AI emotion detection to become more mainstream, read on.
By using machine learning to mimic human intelligence, AI can execute everything from minimal and repetitive tasks to those requiring more “human” cognition. Now, AI solutions are popping up that go as far as to interpret human emotion. In solutions where AI and human emotion intersect, does the technology help, or deliver more trouble than value?
While we are starting to see emotion detection using AI in various technologies, several barriers to adoption exist, and serious questions arise as to whether the technology is ready to be widely used. AI that aims to interpret or replace human interactions can be flawed because of underlying assumptions made when the machine was trained. Another concern is the broader question of why anyone would want to have this technology used on them. Is the relationship equal between the organization using the technology and the individual on whom the technology is being used? Concerns like these need to be addressed for this type of AI to take off.
Let’s explore three common barriers to emotion detection using AI:
Newly launched AI-based solutions that track human sentiment for sales, human resources, instruction, and telehealth can help provide useful insights by understanding people’s reactions during virtual conversations.
While talking through the screens, the AI tracks the sentiment of the person, or people, who are taking the information in, including their reactions and feedback. The person being tracked could be a prospective customer, employee, student, patient, etc., where it’s beneficial for the person leading the virtual interaction to better understand how the individual receiving the information is feeling and what they could be thinking.
This kind of AI could be viewed as ethical in human resources, telehealth, or educational use cases because it could benefit both the person delivering the information and those receiving the information to track reactions, such as fear, concern, or boredom. In this situation, the software could help deliver a better outcome for the person being assessed. However, few other use cases are available where it is advantageous for everyone involved to have one person get a “competitive advantage” over another in a virtual conversation by using AI technology.
This brings us to the next barrier – why should anyone agree to have this software turned on during a virtual conversation? If someone knows of an offset in control during a virtual conversation, the AI software comes across as incredibly intrusive. If people need to agree to be judged by the AI software in some form or another, many could decline just because of its invasive nature.
People are becoming more comfortable with technology and what it can do for us; however, people still want to feel like they have control of their decisions and emotions.
We put a lot of trust in the accuracy of technology today, and generally, we don’t always consider how technology develops its abilities. The results for emotion-detecting AI depend heavily on the quality of the inputs that are training the AI. For example, the technology must consider not only how human emotion varies from person to person but the vast differences in body language and non-verbal communication from one culture to another. Users also will want to consider the value and impact of the recommendations that come out of the analysis and if it drives the desired behaviors that were intended.
Getting accurate data from using this kind of AI software could help businesses better meet the needs of customers and employees, and health and education institutions deliver better services. AI can pick up on small nuances that may otherwise be missed entirely and be useful in job hiring and other decision making.
But inaccurate data could alter what would otherwise have been a genuine conversation. Until accuracy improves, users should focus on whether the analytics determine the messages correctly and if overall patterns exist that can be used for future interactions. While potentially promising, AI emotion detection may still have some learning to do before it’s ready for prime time.
Contact us for questions or to discuss this topic further.
Learn more about recent advances in technology in our webinar, Building Successful Digital Product Engineering Businesses. Everest Group experts will discuss the massive digital wave in the engineering world as smart, connected, autonomous, and intelligent physical and hardware products take center stage.