Tag: automation

Low-code Market Realities: Understanding Common Myths to Avoid Costly Mistakes

Despite their growth, low-code platforms are still surrounded by much confusion. Many enterprises incorrectly believe that real developers don’t need low code, anyone can do it, and it’s only for simple problems. To debunk three common myths in the low-code market, read on.  

With its increasing importance, low-code platforms are also subject to several myths and misunderstandings. As with every evolving technology, enterprises have many questions about optimally using these platforms.

Based on our conversations with multiple enterprises confirming the lack of understanding about the low-code market, we tackle the common misperceptions below:

Myth #1: Low-code platforms are meant for use by citizen developers

The term low code generally evokes the impression of an HR manager who, tired of following up with the IT team multiple times, decides to create a leave approval workflow application. While this impression is not incorrect, professional developers and enterprise IT teams are key stakeholders in the low-code ecosystem as well.

Professional developers increasingly use low-code platforms to improve their efficiency. Some of these platforms can provide code quality alerts and Artificial Intelligence (AI)-powered recommendations, not to mention custom solutions that require minimal tuning.

The built-in DevOps capabilities in these platforms also encourage a culture shift from the commonly used waterfall model among users. For example, supply chain management software provider Nimbi significantly reduced developers in their team from 40 to 24 when they switched to OutSystems from traditional platforms.

We strongly believe central IT teams have a meaningful role in the ecosystem to provide effective oversight and governance, in addition to strategizing the use of the best low-code platforms at the enterprise level. In the absence of centralized governance, low-code platforms may proliferate across the organization leading to aggravation of the shadow IT issues and higher spend.

Myth #2: Low-code development does not require technical skills

As much as we may want to believe, low-code platforms are not a panacea to the ongoing talent crisis. Misleading promises by certain technology vendors have created a common impression that any user can develop any application using low-code platforms. However, low-code development does not imply zero technical skill requirement.

Most low-code platforms enable the extension of their capabilities through traditional programming languages like Java and C#. Off-the-shelf solutions have their limitations, and most applications need custom logic at some point. Typical job descriptions for low-code developer profiles outline technical qualifications like JavaScript, HTML5, and CSS3, alongside Continuous Integration (CI) and Continuous Delivery (CD) pipeline tools like Jenkins.

Thus, it is unrealistic to expect an army of business users to step in and take over all application development-related needs from the IT organization. Low-code development remains a role with a highly demanding skillset across various technologies.

Myth #3: Low code cannot be used for enterprise-grade development

Many enterprise leaders and service providers believe that low-code platforms are only suitable for small-scale department-level needs. However, our conversations indicate that low-code platforms are being rapidly adopted for critical applications used by millions of users. Here are some examples of how low code is solving complex IT problems around the world:

  • A large US commercial insurer has built its entire end-to-end multi-country comprehensive, business-critical application that manages claims, billing, and collection on Appian
  • One of the largest consumer goods companies in the world built a huge global application for financial management on Microsoft Power Platform

As we witness the adoption of low-code platforms garnering pace, a lot of myths and misunderstandings need to be cleared up about low code versus traditional development. Technology providers and service partners play a key role in helping their clients navigate the abundant options to orchestrate a carefully crafted low-code strategy and select the best low-code platforms.

At Everest Group, we are closely tracking the low-code market. For more insights, see our compendium report on various platform providers, the state of the low-code market report shedding light on the enterprise adoption journey, and a PEAK Matrix assessment comparing 14 leading players in the low-code market.

To share your thoughts and discuss our low-code market research, please reach out to [email protected], [email protected] or [email protected].

You can also attend our webinar, Building Successful Digital Product Engineering Businesses, to explore how enterprises are investing in next-generation technologies and talent and the most relevant skillsets for digital product engineering initiatives.

5 Intelligent Process Automation Trends to Watch | In the News

Financial services organizations are digitizing and automating more processes to keep up with competition, and intelligent process automation (IPA) — which has been growing in use about 20% across all fields — is now becoming ubiquitous. Market research firm Everest Group in a recent report ranked IPA technology vendors and looked at the market for process automation.

Read more in Bank Automation News

Three Ways Companies Can Cope with the AI and Analytics Talent Crunch | In the News

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.

Read more in Datanami

Is AI Emotion Detection Ready for Prime Time?

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:

Barrier #1: Is AI emotion detection ethical for all involved?

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.

Barrier #2:  Can discomfort and feelings of intrusion with AI emotion detection be overcome?  

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.

Barrier #3: How do we know if the results of emotion detection using AI are accurate?

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.

Deconstructing the Future of Work | In the News

Four-day weeks, on-demand pay, “rural” talent, digital workers… in recent times, we’ve heard these ideas accompanied by seemingly teleological questions about work as a construct.

The timing is understandable given the confluence of factors at play – the rise of digital, labor pyramid issues, and the after-effects of a global pandemic, including a desire for more meaning in work and convenience through remote work. After years of navel-gazing, society is finally waking up to the fact that our jobs, the way we do them, the time we spend, and the very fundamentals of the nature of work itself are perhaps incongruent with the world we now live in.

This realization opens up the very promising possibility of re-examining and perhaps reconstructing work for the new era. But, beyond the clarion call, what exactly does it entail, how do we understand the future of work, and how do we design for it? Fundamentally, we can break it down into three distinct components: the how, the where, and the who.

Read the full article on Business Reporter

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