Konstantin Dinev, the Director of Product Development at Infragistics, explains how low-code tools can help companies bring modern skills to their existing teams without adding overhead. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The tech-driven workforce and the technology industry, in general, are undergoing a significant makeover: development processes are changing in response to the sector’s dynamic pace, innovations need to be built in a fraction of the time, and businesses plan faster time to market to stay competitive. New technology trends like AI, Machine Learning, and low-code tools are also paving the way for the future of programming.

While AI and Machine Learning enhance our ability to work with data and improve our productivity with technology that we’re already familiar with, low-code tools allow people with little technical expertise to build projects varying from basic layouts to more complex single-page applications (SPA). As the market becomes more mature with an increasing number of competitive solutions, organizations are trying to tackle the skill gap so they can assemble teams with the right know-how that can utilize modern-day platforms without difficulty. 

According to the Reveal 2024 Top Software Challenges Survey, limited resources, high workloads, development backlogs, and a shortage of skilled developers are some of the biggest roadblocks that software developers, CIOs, enterprises, and teams face.  Here’s something to keep in mind: Low-code tools bring modern skills to existing teams without a steep learning curve. In other words, low-code eliminates the additional time, resources, and difficulty that is typically required with the complex tasks of designing, coding, testing, and deploying apps.

According to the Reveal survey, “nearly all of the 585 respondents (90.4 percent) said that low-code tools boosted developer productivity in their organizations.”

Low-code tools accomplish this in several distinct ways: 

• Low-code platforms typically feature intuitive visual interfaces and drag-and-drop functionality. 
• Existing team members can quickly learn to use these tools with minimal training. 
• Teams can develop and deploy pixel-perfect apps up to 80 percent faster, as low-code tools are flexible enough to deliver production-ready, enterprise-grade code for all major web platforms. 

What Are the Range of Skills That Low-Code Adds?

There are core programming skills like knowing a programming language for front-end and back-end, including JavaScript, Python, PHP, C#, etc., without which you cannot start a career as a developer. Developers should be able to create and style web pages with HTML and CSS, as well as responsive designs, to ensure websites work on various devices and screen sizes. Database management skills, handling APIs and web services, version control, testing, and debugging are also fundamentals for developers. In addition, developers need to master cloud computing and software development methodologies such as Agile and Scrum, as well as DevOps practices. 

Many modern skills are emerging as mandatory for building great UX and high-performing apps faster and more effectively: 

• Rapid application development: Teams should be able to quickly create prototypes, validate ideas, gather feedback, and move forward with quicker iterations. 
• Cross-functional collaboration: Bridges the gap between non-technical stakeholders, developers, and designers, especially when using a single source of truth, such as low-code tools. 
• Diversification of skills and democratization of code: Empowering citizen developers can allow for faster product delivery by reducing the number of resources invested in mundane and repetitive tasks. While citizen developers work on simpler tasks, automated workflows, building dashboards, etc., more experienced software engineers can handle the business logic and more complex tasks. 
• Agility, flexibility, and continuous improvement: As business requirements and market conditions change, people should be able to adapt quickly to ensure scalability. 

Here are three ways low-code tools bring these skills to teams without overhead: through their drag-and-drop design, code generation, automation, and almost no learning curve. 

Factor #1: Drag-And-Drop Interface + Real UI Components 

Low-code platforms like App Builder deliver intuitive visual interfaces and drag-and-drop functionality, making them accessible to team members with varying levels of technical expertise. Users can easily choose from a toolbox of pre-built but reusable components for different frameworks, drag them, and then drop them into the design canvas. From then on, people can benefit from easily connecting live data from Web API endpoints, OpenAPI schemas, or static JSON. Built-in themes and options for additional customizations are also available. 

Factor #2: Generating Production-Ready Code Instantly 

Teams can develop and deploy pixel-perfect apps up to 80 percent faster, as low-code tools are flexible enough to deliver production-ready, enterprise-grade code for all major web platforms (Angular, Blazor, Web Components, and React). This allows them to refocus time and talent on the challenges that matter most. With App Builder, for example, developers benefit from a Common App Model that is the centerpiece of the system.

It allows users to describe applications in a framework-agnostic manner. 

• Abstracts are a way to define application structure, views, and interactions. 
• It allows the same app to be used with different technologies. Switching from React to Angular, for example, may take months or even years, depending on the size of the application and the skillset of the developers, but with App Builder, this happens with the click of a button. 
• Takes a design, makes it understandable, and enables code generation for Angular, Blazor, Web Components, or React. 
• Defines common patterns across technologies. 
• It is generated by design tool parsers on top of the UI kits, the App Builder, or other third-party plugins. 

Factor #3: No Steep Learning Curve 

Existing team members can quickly learn to use these tools with minimal training, enabling them to acquire modern skills without extensive retraining or hiring. Comprehensive documentation, how-to video tutorials, and step-by-step blog posts allow everyone to access useful learning resources that speed up the adoption process. These ensure clear, actionable guidance that helps users swiftly understand and apply new concepts. In other words, teams know how to leverage modern-day low-code tools, and they’ve learned to use them fast. 

In Conclusion 

Introducing modern skills to existing teams without adding significant overhead can be challenging. Sometimes, this may require a structured approach involving assessment of skill gaps, specific areas for modernization, targeting training, planning what tasks to optimize, etc. But low-code platforms offer a streamlined solution to this challenge, enabling companies, developers, and even C-level executives to enhance their capabilities efficiently and effectively.

Automating processes with minimal hand-coding thanks to a user-friendly platform interface, boosting the productivity of teams with reusable components and code democratization, and reducing complexity in terms of system integration and learning are among the benefits of low-code tools. This is how organizations can revolutionize their approach to technological advancement and business efficiency in the digital age. 

The post Bringing Modern Skills to Existing Teams Without Overhead appeared first on Best Talent Management Suites, Software, Vendors.

Alexey Korotich, the Vice President of Product at Wrike, shares his commentary on why companies should stop trying to compete with AI and focus on the human intelligence of their team teams instead. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The introduction of generative artificial intelligence (GenAI) has made a lasting impact on our world. Despite the technology’s potential to significantly improve how we live and work, most of the discussion hinges on competition—the idea that humans must now compete with AI to keep their edge, especially in the workplace.

This has left many organizations with the difficult task of understanding how AI can be applied across the business to create efficiencies while empowering employees with the right tools and education to instill confidence rather than fear. For many, this could feel like an insurmountable task, but I am here to shed light on a key perspective shift that will help organizations more effectively harness the power of AI-leading with human intelligence.

This shift requires both the right mindset and the right technology. Regarding the former, leaders must shift their thinking to understand how AI and human intelligence can work together (instead of pitting them against each other) to optimize the business and uplift the work teams are doing rather than replacing individual job functions.

Within my organization, we do this by building a culture around AI. This means taking the time to understand where it can most positively impact our business, promoting open dialogue amongst employees about usage, best practices, and limitations, encouraging internal knowledge sharing, and creating opportunities for upskilling. This helps to temper a central fear of “humans vs. tech” and allows teams to explore AI within a secure, trusted environment.

When it comes to technology, there is an opportunity for IT leaders to have a sizable impact on how AI drives the future of the business. This starts with sourcing tools that can decrease the number of applications employees are using on a daily basis. This may feel like a familiar task, as teams report actively trying to reduce this number last year. But now, with increased demand for AI tools, it’s important not to lose sight of progress already made to address app sprawl.

When evaluating AI tools, organizations should seek to add those that pair with existing solutions and connect cross-functional workflows rather than taking a piecemeal approach. A single source of truth, like a work management platform with built-in AI, can also help bring everything into one place.

Tying back to human intelligence, IT leaders must also seek out tools that draw on insights from how work is being done across their specific organization rather than relying solely on data from large language models (LLM). For example, we are in the unique position of using our own solution, which allows us to draw from a rich and sophisticated data model.

Our interactions with AI are informed by 500 billion historical data points and support for connected work structures, counting 100K items and more. This means that whether a marketing team is using generative AI to develop copy or a professional services team is relying on automation for staffing their client projects, they can have peace of mind knowing that AI is applying knowledge from their work patterns and behaviors, in addition to what an LLM might recommend.

And I would be remiss not to point out security’s role in this conversation. Entrusting a partner with your data—especially if the technology is learning from how your teams work—must be backed by enterprise-grade security and a rigorously enforced ethics policy. In our case, we’ve invested in leveraging ML, GenAI, and automation to develop intelligent capabilities since 2017, so this level of diligence is part of our DNA.

The proliferation of AI has opened the door for IT leaders to have a measurable impact on the business. Setting the tone for how both leadership and employees interact with the technology is essential to building a culture of AI that fosters partnership rather than competition. It also creates an opportunity to deliver and implement the right tools for the business, which is crucial for building trust among employees and senior leadership.

The post Stop Competing with AI: Lead with Human Intelligence Instead appeared first on Best Talent Management Suites, Software, Vendors.

Cameron van Orman—the Chief Strategy and Marketing Officer and GM of Automotive Solutions at Planview—explains employee engagement’s critical role in successfully executing an AI strategy. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Organizational success can be measured in many ways. It is often defined by linking strategy and planning to business execution. However, for organizations undergoing digital transformations, likely accelerated by AI, bridging that gap can be complex. Unraveling the intricacies of strategy execution must include identifying the root causes of strategy failures. Research reveals that an overwhelming majority of executives surveyed (86 percent) indicate that their organization lacks a critical element of strategy implementation—accountability. As such, employee engagement is essential to bridging the gap between AI strategy formulation and execution.

Leaders who fall into the trap, sometimes unwittingly, of prioritizing employee busywork over tangible outcomes negatively impact engagement and impede organizational success. Disengaged employees hinder productivity and innovation and pose a significant obstacle to achieving business objectives.

Four Actions to Improve Engagement Across an Organization

To tackle this challenge head-on, there are four practical strategies leaders can implement to enhance employee engagement and optimize strategy execution:

• Making strategy social
• Building two-way communication
• Adopting a single source of truth
• Acknowledging failure as a steppingstone

It’s no secret that, historically, business strategies don’t always achieve their intended outcomes. Studies have found that roughly 60 to 90 percent of strategic plans are never fully launched. AI is poised to prove its value, but if enterprise leaders want their AI investments to pay off, strategy implementation needs to be transformed from an organizational liability to a core competency.

According to a global study on why strategies fail from Economist Impact, the research arm of The Economist, leaders face common challenges and pitfalls related to strategy implementation. The research found that it is equally crucial for executives to shape a company’s strategy as it is for them to direct the course of strategy implementation through leadership, guidance, oversight, and support. However, directing the course of a strategy does not entail overemphasizing employee busyness and losing sight of outcomes. If the productivity mindset goes into overdrive, the resulting “output myopia” will undermine employee engagement and the pursuit of business outcomes.

Employee Engagement and Strategy Execution

Strategy and execution work in tandem to accomplish objectives, like the two hemispheres of the brain. Both are necessary for humans and companies to excel, and neither can function optimally unless they are connected. In the brain, the connection is bridged by the corpus callosum; for strategy and execution, the bridge is engagement.

The Economist Impact report also found that more than four out of five executives surveyed acknowledged the need to improve employee engagement, suggesting it’s a common detriment to strategic plans. The performance of disengaged employees who lack productivity and motivation often falls short of their potential in multiple areas.

In contrast, engaged employees demonstrate strong self-motivation and productivity, even in remote work settings, and their efficiency supports a company’s competitiveness. This contributes to innovation, customer service excellence, job satisfaction and retention, and a deeper connection to the organization’s mission and values. Leaders, especially executives, can use the following recommendations to set expectations, direct policy creation, and build a companywide culture that fosters higher engagement to achieve business outcomes.

Make Strategy Social: Communication and Collaboration

Strategies cannot succeed in silos, but many companies haven’t taken steps to avoid this pitfall. When developing a strategy, over 80 percent of executives in The Economist survey acknowledged weaknesses in their companies’ internal communication and cross-functional collaboration efforts. One way to avoid making plans in ivory towers or focusing too much on financial returns is for leaders to take a social approach to strategy.

By crowdsourcing customer needs and operational challenges to inform strategy, leaders remain grounded in their organization’s reality, middle managers have a voice in the process, and team members can visualize that the strategy is created by exchanging recent, relevant knowledge. Early involvement and alignment among key stakeholders drive stronger buy-in and ownership of strategy implementation, which in turn leads to higher employee engagement.

Ensure Knowledge is Shared

Another element that companies tend to overlook when making strategy social is fostering two-way communication. According to Harvard Business Review, employees who get enough information to excel in their jobs are nearly three times more likely to be engaged. When they get daily feedback from their manager, they’re over two times more likely to trust leadership. Bestselling author Rita McGrath, a strategy professor at Columbia Business School, says leaders should be “in constant touch with the edges of the organization, establishing an ongoing system rather than a one-time interaction.”

Given the adage that knowledge is most powerful when shared, organizations must provide regularly scheduled updates during implementation to maintain the standard set when the strategy was created. By reciprocally exchanging knowledge, leaders and their teams can more fully understand risks, barriers, and changes that impact the market and customers.

Connect People and Work Through a Single Source

Within the strategy framework and its execution, the software enables everything from running scenario-based planning around OKRs to interfacing with AI copilots and managing routine implementation tasks. The software connects people to their work, but it also comes with a caveat—to optimize its value, the software must be connected. Among the issues that can arise from disconnected or fragmented systems is the lack of visibility created by siloed data. Data siloes leave leaders without the information they need to make informed decisions and develop uncertainties for teams about what course of action is best or if the information they have is accurate.

In contrast, connected data serves as the foundation of enterprise AI value realization because it enables AI systems to leverage comprehensive, integrated, and contextually rich datasets. This, in turn, enhances the accuracy of AI models, drives deeper insights, improves decision-making, and supports organizational agility and scalability. As such, a single source of truth is critical to connect people, their tools, and their work with accurate, real-time information to drive higher engagement.

Failure Isn’t a Brick Wall; It’s a Stepping Stone

Corporate culture plays a significant and often overlooked role for companies that strive to embrace winning strategies. In this context, a critical element is how a company views failure. In the early 2000s, LEGO acknowledged and rectified its failures to reverse itself from the brink of bankruptcy. As of 2024, it’s the world’s largest toymaker by revenue. For Pixar, before its $7.4 billion purchase by Walt Disney Studios, failure was viewed by its executives as a necessary element in the pursuit of creating something completely original.

Although it’s debatable whether a company’s culture falls upstream or downstream of policies, research shows that frameworks such as performance management, reward programs, and skills development initiatives shape employees’ commitment to an organization. These factors also impact how employees view accountability and their propensity to discuss risks or issues they’ve identified. Cultures that acknowledge failure as a stepping stone tend to have higher levels of engagement than those that don’t.

Successful AI Strategies Require People

Like any other strategy, capitalizing on an AI strategy is just a vision until the work is done. However, when united by the proactive action of leadership, a result that goes beyond nominal output is when an organization’s workforce gets the right work done at the best possible time. This distinguishing characteristic of a business focused on outcomes is impossible without highly engaged employees.

The post Avoiding Common Pitfalls in AI Strategy Execution: The Crucial Role of Employee Engagement appeared first on Best Talent Management Suites, Software, Vendors.

Mitri Dahdaly, the VP of Solution Design at Legion Technologies, recently commented on how companies can transform hourly work with artificial intelligence (AI) and automation technologies. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The hourly work experience needs an upgrade. New research shows that half of all hourly workers plan to leave their jobs within the next year, and more than a quarter seek unionization due to a poor workplace experience. This widespread dissatisfaction illustrates an ongoing employee experience crisis that will further imperil the operations of numerous industries—from retail to grocery, hospitality to medical services, and more—if left unaddressed.

Following a year of rapid advancement and heightened public interest, artificial intelligence has been billed as a panacea for workplace woes, and the excitement is far from unfounded. AI promises to transform the hourly work experience at its full known potential, though not in the way ominous predictions about AI replacing workers might suggest. Instead, companies can leverage AI to improve decision-making and execution of workplace tasks, with humans remaining in control.

That said, properly implementing AI in the hourly workplace requires a thorough understanding of how different AI types work together and where human interaction becomes necessary.

Understanding Different Types of AI 

A system is considered artificial intelligence if it automates a process that would otherwise require human intelligence or judgment. When applied to workforce management (WFM) applications, these processes range from demand prediction to compliance management to time-related functions like scheduling, timesheet approvals, and paid time off management.

Automating these time-consuming tasks can dramatically change the job experience for hourly workers. The intelligent automation of administrative tasks like schedule creation will save managers hours each week, which they can channel into more valuable activities like training new hires and interacting with customers. According to recent research, hourly managers ranked employee scheduling as the top workplace task they want intelligently automated.

AI that enables intelligent automation contributes to a more flexible work experience, giving employees more control over their schedules without placing additional stress on their managers. As a result, managers have more time to focus on training and coaching – the top area where they’d like to reinvest their time, per the Legion survey. With this extra guidance from their managers, hourly workers can perform their jobs more efficiently, competently, and confidently.

AI also has positive effects on a business’s financial performance. The intelligent automation of workforce management can lead to increased sales, drive down labor costs, and ensure proper staffing, the third of which plays a crucial role in business reputation, employee engagement, and customer satisfaction. However, these benefits don’t appear overnight. Organizations must implement automation strategically, meaning they need to know which types of AI perform which functions and to what extent.

As a primer, let’s examine some of the different types of AI and explore how they automate specific hourly workplace tasks.

Machine Learning

Machine learning describes AI algorithms that continuously improve over time based on experience and the use of the data that they process. Self-learning technology is especially valuable because it doesn’t require constant manual training. Instead of an engineer regularly updating the code, the algorithm automatically reconfigures itself in response to new data.

Machine learning models can power more accurate demand forecasting in a WFM platform. Before AI, managers often relied on gut feeling and instinct to predict demand, leading to frequent understaffing and overstaffing, jeopardizing employee well-being and the company’s bottom line. With machine learning, a WFM platform ingests a swath of influential data—from customer behaviors to weather forecasts—to create accurate demand projections for labor planning. These projections serve as a critical condition for employee scheduling, which, when automated against the forecast, can minimize the labor costs associated with each shift.

Ideally, algorithms will update regularly to create a more accurate portrait of how influencing factors evolve. With more frequent data pulls, the margin of error decreases; not only is it working off newer data, but the algorithm automatically improves as it processes more data over time. As a result, staffing is optimized based on highly precise demand forecasts.

However, machine learning should not replace human judgment entirely, especially in acute situations that could sway demand at a moment’s notice, like a traffic accident that blocks access to the store. Instead, the idea is to augment and improve human judgment with data-driven insights.

Expert Systems

Expert systems are the decision-making AI, emulating the cognitive capabilities of human experts in a fraction of the time. This type of AI works under strict constraints to generate viable solutions, such as an employee schedule that accounts for projected demand, employee availability, budgets, staffing rules, and compliance stipulations all at once. As part of a larger AI-powered WFM platform, expert systems can help frontline managers develop an optimal labor plan for every shift.

These systems can generate accurate schedules in as little as a few seconds. However, many companies are still relying on manual processes to create schedules that might align to accommodate peak busyness. According to Legion’s research, a quarter of managers still rely on Excel sheets to create hourly employee schedules, and 13 percent use extremely outdated paper schedules. Switching to an AI-powered scheduling solution reduces the risk of costly planning errors and saves managers valuable time.

Generative AI and Natural Language Processing

AI anxiety is real and could be holding companies back from maximizing automation. According to Legion data, 24 percent of hourly workers fear AI could replace them. In another survey from SafetyCulture, 40 percent of frontline workers ranked AI among their top three job concerns. However, AI is already improving the experience for these workers – not taking their jobs away.

Thanks to groundbreaking advancements, generative AI has dominated the headlines over the past year. Gartner defines generative AI (genAI) as “AI techniques that learn a representation of artifacts from data and use it to generate brand-new, unique artifacts that resemble but don’t repeat the original data.” With genAI, a pre-trained algorithm creates new content based on other content.

The information delivered and processed by a genAI copilot can service the workforce management processes of labor optimization and shift scheduling. Employees can easily express their needs through simple, natural-language conversations with the AI. This information feeds into the scheduling algorithm, creating new conditions for the expert systems AI to accommodate when generating schedules.

GenAI can also power virtual assistants to help with training, answer quick questions, and guide execution. For example, a warehouse employee may consult with the genAI assistant on a mobile device to help them quickly find an item. In another example, a new employee could ask company-specific questions as they’re onboarding, like “When is my meal break?” and “Can I wear open-toed shoes?” They’ll receive answers instantly, allowing their managers to focus on more strategic coaching to help them nurture their skills.

Effective Implementation 

Facilitating the use of AI in WFM requires a full team effort. Having the appropriate resources to execute implementations will ensure that tasks are properly automated and optimized for efficiency.

Human control is critical in embracing AI that enables intelligent automation—but it’s different from human intervention. Intervention implies lapses in the software, which is not true automation. Control, on the other hand, is a security feature: the software can perform its work autonomously, but human operators can take back control should the need arise. My colleague likened this to having a self-driving car.

In short, humans need to trust these machines to enhance their decision-making. Intelligent automation limits human error and bias to drive workers’ innovation and productivity while also freeing up time for them to invest in more meaningful parts of their jobs.

The Future of Frontline Work is AI-Powered

New AI evolutions are helping cement pandemic-era work experiments into formal processes, promising a new way of working for all, including hourly employees. The future of work is flexible—that’s why the gig economy is expanding three times faster than the rate of the total US workforce. However, employers relying on hourly work can offer that same gig-like flexibility when AI powers their labor processes and technologies.

AI-enabled automation takes over rote, manual tasks, streamlines their results, and puts humans back in the human parts of their jobs: talking with customers, training employees, and building connections with the communities they serve. A well-informed AI strategy, bolstered by cutting-edge technology, will unlock unprecedented business productivity and automation and ultimately lead to a better quality of life for all.

---

The post Transforming Hourly Work with AI and Automation appeared first on Best Talent Management Suites, Software, Vendors.

Elaine Pulakos, Ph.D.—the CEO of PDRI by Pearson—evaluates the risks and rewards companies may experience when using artificial intelligence (AI) in the hiring process. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Historians will likely see the advent of ChatGPT in late 2022 as a pivotal moment in technological progress. Generative AI (GenAI) is unlikely to leave any aspect of the business world untouched, including HR. However, despite its potential to aid hiring processes, HR professionals should exercise caution in adopting this nascent technology, as there is still much to learn about its implications and applications.

One intriguing prospect for GenAI is its ability to facilitate a shift from degree-centric hiring to a skills-based approach. This transition is gaining momentum in government and corporate sectors as organizations increasingly recognize that mandating a specific academic degree can be an arbitrary barrier. Many highly competent candidates may lack such credentials, which are not necessarily indicative of job performance. After all, the founders of Microsoft (Bill Gates), Facebook (Mark Zuckerberg), and Apple (Steve Jobs) all became technology titans without completing college.

Moreover, the skill sets that organizations need are evolving rapidly. Research from Gartner shows that one-third of the skills listed in tech and other job postings from just a few years ago were no longer relevant just four years later, and the overall number of required skills continues to grow yearly. This dynamic environment makes it difficult for traditional degree programs to keep pace, and job seekers must continuously develop new skills and competencies to remain competitive. Fortunately, there are numerous online resources available for skill development and certification.

The Importance of Enduring Skills

While technical skills are important, hiring managers should also prioritize more general skills, such as adaptability, analytical thinking, and effective communication. General skills are often enabled by innate personal characteristics. They are more challenging to learn but crucial for effectively working with others and keeping one’s technical skills relevant in a rapidly changing workplace.

The shift towards skills-based hiring presents a significant challenge: How can hiring managers assess a wide range of constantly evolving skills at scale? The answer lies in having access to accurate, effective skill assessments. GenAI can likely play a key role here by assisting subject matter experts in crafting high-quality assessment questions, administering the assessments, and evaluating candidate responses.

As an example, consider the candidate interview, which is a type of assessment that is an essential component of the recruitment and hiring process. While traditional, unstructured interviews are poor predictors of job performance, interviews conducted in a structured, consistent manner are highly effective. GenAI could aid in generating effective questions to assess the most critical skills for the job, suggesting follow-up inquiries to capture complete information about candidates, and potentially even conducting and evaluating structured interviews autonomously. However, candidate acceptance of AI-led interviews remains uncertain.

Challenges and Risks of Using AI to Support Hiring

Despite these potential benefits, there are significant risks and challenges associated with leveraging GenAI to facilitate hiring processes today. The lack of extensive research on its safe and effective use in recruitment and hiring has raised concerns, with numerous reports of AI systems inadvertently perpetuating biases against women, minority groups, and people with disabilities or making arbitrary decisions based, for instance, on a first name it has somehow learned to prefer. Proper safeguards, ongoing monitoring, and specialized training for HR professionals are essential to prevent adverse outcomes.

Public perception is another crucial factor to consider. A recent survey from the Pew Research Center indicates that most job seekers would be hesitant to apply for positions if they knew AI was involved in the decision-making process. Ethical considerations require that organizations be transparent about using AI in recruitment, which may deter some candidates until they gain more confidence and public opinion becomes more favorable.

Given these considerations, the prudent approach for organizations to take regarding the use of GenAI in high-stakes applications, like hiring, is to proceed with caution. While the technology holds immense promise, the absence of established best practices grounded in thorough research makes early adoption risky. Ongoing studies are exploring how to harness this cutting-edge technology to improve the efficiency and effectiveness of recruitment and hiring processes. For now, HR departments would be wise to exercise patience and await further insights into the judicious and safe use of GenAI in the hiring process.

The post Evaluating the Risks and Potential Rewards of AI in the Hiring Process appeared first on Best Talent Management Suites, Software, Vendors.

As part of Solutions Review’s Contributed Content Series—a collection of contributed articles written by our enterprise tech thought leader community—Simon Haighton-Williams, the CEO of The Adaptavist Group, talks about the “digital divides” between intergenerational workplaces and shares insights on how companies can address the challenges they face. 

Before the advent of AI and the concept of four-day workweeks, enterprises struggled with divides among their multigenerational workforces. While much of that past division was fueled by generational stereotypes, recent data from The Adaptavist Group’s fourth annual Digital Etiquette: Mind the Generational Gap study highlights technology’s role in intergenerational conflicts as workplaces balance four working generations for the first time.

One significant finding from our study found that different age groups experience varying levels of familiarity and comfort with technology, with 92 percent of knowledge workers facing conflicts over digital tools. Past research conducted by the Pew Research Center supports this data as well. Their findings illustrate that younger adults outperform older colleagues in tasks requiring digital skills, such as using social media or troubleshooting electronic devices.

On the other hand, older workers typically excel in a wide variety of soft skills, such as communication, empathy, and interpersonal relations. Years of experience in various professional and personal contexts have honed their ability to navigate complex social dynamics, resolve conflicts, and build meaningful relationships.

Don’t just take it from me; according to Tom Strong, Director of Employer Activation for the National Fund for Workforce Solutions, older workers possess various sought-after skills, including communication, creative problem-solving, innovation, collaboration and teamwork, and conflict management. “Mature workers tend to come with experience and soft skills honed over decades of employment,” Strong said. “Some have technical skills already but may need to be introduced to next-gen. technology fields.”

However, considering that more than half of companies employ three or more generations, now is the time to examine how each generation interacts with technology differently. Organizations need to harness the real benefits of different age groups’ unique thinking and use of technology, which can drive businesses and innovation forward.

Technical Approach: Differences in generational working styles and digital savviness

One of the primary challenges in bridging the ‘digital divide,’ or differences in generational work styles regarding technology, is understanding that it doesn’t only apply to new technology. Efficacy with legacy technology is also a vital part of any workplace. For instance, 51 percent of Gen Z respondents admire their older colleagues’ phone confidence, while much of Gen Z is racked with ‘phone phobia.’

However, older generations’ mastery of older technology can also frustrate younger teammates. Over half of Gen Z respondents cited frustration toward older colleagues as a result of the perception that older workers impede progress by relying on outdated techniques.

There does appear to be some semblance of common ground when it comes to email, which remains the number one application for 66 percent of all workers across generations. Perhaps the best attribute a tool can possess is longevity—email has been a tried-and-true form of communication for decades, and you’d be hard-pressed to find an office of workers who are unfamiliar with it. But, despite most generations knowing how to use email, adhering to communication standards when interacting through email can get a little nebulous without firmly established rules.

Communication Breakdown: Getting lost in translation

It wasn’t a huge surprise that our research found that 43 percent of respondents struggle with misinterpretations of tone or context when engaging in digital communication with their teammates today. As teams increasingly rely on digital tools for communication and collaboration, a staggering 92 percent of knowledge workers admitted to experiencing conflicts arising from their use. More alarmingly, 60 percent acknowledge that these disagreements impede productivity and teamwork, highlighting the urgent need for more precise communication standards across generations in the digital realm.

Unfortunately, without adequate training and when teams feel they must adhere to ‘unwritten rules’ at work, enterprises jeopardize their productivity goals and inadvertently establish a breeding ground for toxicity. Although technology undoubtedly offers immense potential for enhancing productivity and collaboration in the modern workplace, its practical implementation requires a nuanced understanding of intergenerational dynamics and a concerted effort to bridge the digital divide.

Considering this information, it’s evident that intergenerational workplaces face a communication paradox—while older knowledge workers are more adept at professionally communicating, younger workers are more comfortable and often better with the modern communication used on collaborative platforms, such as Slack and Microsoft Teams.

Everybody’s Doing It: AI adoption and concerns 

Unlike past tech disruptions, which occurred gradually, today’s workplace is being reshaped by technology at an unprecedented pace. Previous technological shifts, like the introduction of the Internet or email, happened over the years or even decades. But today, technology like AI is going from niche to the most important technology in the enterprise in a matter of months, demanding continuous learning from workers. For example, Gen Z leads AI adoption at 32 percent, while 12 percent of workers over 50 leverage AI platforms like ChatGPT and Claude.

As AI usage skyrockets at an unprecedented pace, so too is a deep concern among 65 percent of knowledge workers who fear AI may exacerbate existing divides. Indeed, companies need to be cautious about allowing new technology like AI to drive an unnecessary wedge between age groups. Therefore, the focus should be on fostering human connections around the tool and mutual understanding of its application and impact across the workforce.

For example, 68 percent of knowledge workers believe AI can accelerate Gen Z’s ascendancy in the workplace, highlighting the need for reverse mentoring around using AI for older generations. That said, there is little doubt that managing the multigenerational workforce is more crucial than ever as AI enters our lives and poses a greater risk of dehumanizing interactions between generations.

Ultimately, the challenge for employers in addressing the digital divide for intergenerational workplaces and their use of technology is threefold: to create a culture that values individual contributions, encourages cohesive teamwork, and respects generational diversity without resorting to stereotypes.

The post Tackling 3 Technology Challenges of Intergenerational Workplaces appeared first on Best Talent Management Suites, Software, Vendors.

As part of Solutions Review’s Contributed Content Series—a collection of contributed articles written by our enterprise tech thought leader community—Danny Abdo, the Chief Operations Officer at Skillable, asks if the industry is looking at the skills gap all wrong and might be overestimating its scale.

Skills gap estimates state that around 85 million jobs will be unfilled globally by 2030, costing the global economy $8.5 trillion in potentially lost revenue. But like all estimates, this figure is based on what we know and assume. What if we’re looking at the skills gap all wrong and overestimating its scale? We believe there’s a smaller skills gap than what actually exists. 

Our current measures are based on traditional hiring methods going out of fashion. In the past, a hiring manager who needed to fill a vacancy would look at a narrow set of candidates who could prove they could succeed in that role. How? By showing they had done the job before. If you needed an operations manager, you’d look for someone who had worked for several years in operations roles.  

Roles Are an Easy Default 

The pool of skills and talent will massively expand if you consider those who have developed skills outside of just performing the job or have transferable skills from other jobs. However, this population is not making it through existing hiring practices. Try as we might, with skills-based hiring and broadening job descriptions, we default back to previous roles and years of experience as the de facto way of assessing someone’s suitability for a vacancy. It’s almost laughable, as the many memes on Reddit show disgruntled job seekers poking fun at employers “needing ten years of experience before the age of 22”.  

Skills Are a Leap of Faith 

There are two main reasons behind this stubbornness. Firstly, shifting solely to a skills-based hiring model takes a tremendous leap of faith in trusting that those skills reflect a candidate’s competency and potential. In fact, “validating skills, competencies, and references” is the top obstacle cited by business leaders who are attempting a skills-based hiring approach. 

This doesn’t just rule out those without the requisite years of experience but also candidates from non-traditional academic backgrounds. Although 80 percent of leaders want to prioritize skills over degrees, 52 percent still default to degree programs because it feels less risky. 

The Cost of a New Hire 

Secondly, onboarding a new employee is costly, so adding a need for more upskilling or the potential for a hire to be unsuitable for the role is a risk many employers don’t want to take, especially in the current economic climate. Indeed, employer confidence in finding the right fit for their open roles continues to decline. Everyone is looking for the perfect person, but like every good relationship coach will tell you, that person doesn’t exist.  

Skills Pressures Are Breaking Traditional Hiring 

The skills gap is widening across all industries in multiple ways. The brain drain is causing experienced talent to retire, taking their decades of knowledge with them. Others are leaving permanent employment to join the contingent workforce. Then you have perception issues with industries like oil and gas and manufacturing, causing younger generations to pass on those job opportunities. The skills mismatch means potential candidates don’t have the necessary skills for in-demand roles.  

Finally, urgent skill needs in digital, cybersecurity, and green industries are accelerating the skills gap for those specific hard skills. CIOs report significant challenges with filling cybersecurity, data science, AI, machine learning, and software engineering roles. Many candidates for these positions don’t have years of experience to prove they can do well in a role.  

Not to mention, roles are being created today that didn’t exist before. Very soon, we’ll be seeking out algorithm-bias auditors, robot-human team managers, smart building designers, and cryptocurrency advisors. Just like how the internet created new roles in social media, email, and blogging, nobody alive today can prove their competency in these positions by experience alone. By necessity, hiring managers will have to look outside of their narrow definition of what makes someone suitable for a role.  

That is a good thing because when we shift away from solely looking for a marketing manager to replace another marketing manager, we level the playing field for those looking for a career change, who want to take advantage of emerging career paths, and who may have a non-traditional route into the industry. So, how can we build the same confidence in hiring managers who are looking beyond role experiences for the best candidates? 

The Closest Thing to Role-Based Experience 

The answer lies in hands-on learning and skill validation. It’s the closest you can get to proving your skill without doing the job. Hence, some employers are turning to this learning to upskill for future role needs. For example, a U.S.-based healthcare provider needed nursing assistants but couldn’t find suitable candidates through job postings. It partnered with a local technical college on an end-to-end clinical training program that upskilled 200 new nursing assistants with the right skills and qualifications.  

Employers are picking up the gauntlet—Deloitte has its leadership shadowing program, Unilever has its Future Leaders program, and Amazon has its Technical Academy. These are steps in the right direction. Yet, these programs aren’t easily scalable; they take a lot of effort and investment to set up and, therefore, tend to be limited in scope to specific groups (like high potentials) or roles (leadership, AI, and data science).    

Virtual labs and challenges offer a more scalable and inclusive method for the roles and skills that cannot be taught via stretch assignments and shadowing or for immediate skill needs that appear unexpectedly (like ChatGPT-related prompt engineering skills). With a virtual skills challenge, candidates can practice and apply their knowledge and skills in a safe environment and be tested to ensure they can complete specific tasks. This is as varied as stopping a DDoS attack, troubleshooting a customer service ticket, or completing a sales call. This kind of hands-on experience gives the validation needed for confident hiring, and it can scale to anyone, anywhere in the world.  

It isn’t just hiring manager confidence that gets a boost. With nearly a third of workers lacking confidence in their ability to move into different roles and sectors, offering validation through challenges and simulations can show them that they are ready for a new position.  

Time to Shift Our Perceptions 

As we face more headlines about skills shortages and unrealized revenue, it’s time to shift perceptions away from simply hiring like-for-like in a role. Instead, broaden your horizons using hands-on learning to build the experience you need in your next hire.

The post What if the Skills Gap is Overstated? appeared first on Best Talent Management Suites, Software, Vendors.

As part of Solutions Review’s Contributed Content Series—a collection of contributed articles written by our enterprise tech thought leader community—Jeremy Rafuse, the Head of Digital Workplace at GoTo, explains how IT democratization can become a significant driver of ongoing business success.

When implemented correctly, technology has proven to be one of the most significant enablers and equalizers in the workplace today. Throughout the pandemic and the shift to remote and hybrid work, employees worldwide became empowered to collaborate from anywhere, and IT went from behind-the-scenes to center stage. IT teams don’t just support the business but drive it. However, due to this shift, IT teams face increased pressure, and companies are left to contend with tech talent shortages amid a tight labor market, rising prices, and economic jitters.

As organizations grapple with these new challenges, solution providers can up-level their own businesses, open up new markets, and dramatically improve operations for their customers. Through the “democratization of IT,” or making powerful technologies and specialized knowledge more broadly available to businesses of all sizes, the businesses behind these technologies can empower their customers to streamline operations, improve customer and employee experiences, and ultimately drive the business forward.

So, how can solution providers facilitate IT democratization, and what does success look like?

If It’s Not Simple and Affordable, No One Will Use It

IT has historically held a reputation for being overly complex and out-of-reach. In the past, just walking to the physical helpdesk or dialing IT and waiting for assistance was a point of frustration for busy employees. Legacy systems and manual processes are a headache for all, and the friction they can create between IT teams and the departments they service compromises productivity, security, and compliance.

However, with the recent emergence of more user-friendly IT and remote support technologies, the relationship between IT and other departments has evolved dramatically—and for the better. Of course, customers drive demand, which means it’s up to solution providers to continue this trajectory of modernization and make comprehensive tools more accessible.

In the current economic climate, businesses need the most bang for their buck regarding IT spending. On the one hand, IT leaders are looking to consolidate tech stacks and streamline processes to reduce the burden on IT teams amid the shift to remote work while maintaining customer satisfaction. On the other hand, business leaders want to invest in strategic technologies that drive the business forward by increasing employee productivity and collaboration while growing the bottom line.

Solutions that reduce overhead costs and provide more functionality—without needing additional dedicated IT staff—are essential for both these goals and, ultimately, simplify IT. However, this software needs to be accessible and affordable in the first place so that businesses of any size and scale can reap the benefits of upgraded and consolidated tools.

It Starts with Solution Providers

With 56 percent of decision-makers viewing IT spending as an investment that will ultimately increase revenue and help transform operations, solution providers face increased pressure to expand their IT solutions. In today’s world, corporations want a one-stop-shop for their IT needs, meaning solution providers must innovate to provide complete, unified services—rather than cumbersome, fragmented products—that meet those demands.

To start, technology providers need to understand an organization’s unique needs and challenges. They should be involved in operational discussions and work with business leaders as their objectives grow and evolve. Furthermore, a vital part of this strategic process is gathering insights and feedback to share with the solution providers to fuel innovations. Most providers have experts across specialties, and communicating feedback can help connect businesses to the right resources to bridge gaps and bolster other tech elements.

At the end of the day, the relationship between customers and solution providers is mutually beneficial—they work in tandem to drive results and create better overall outcomes. Solutions providers consider customer feedback as they develop and test new solutions and drive innovations, which the customer will benefit from in the long run.

With the increased complexity and scale of the IT solutions utilized by many organizations today—from the small business to the larger enterprise—solution providers must recognize the critical role they play in democratizing technology to ensure that even the smallest IT teams have access and can effectively deploy the technologies for employees and customers to use. User-friendly, collaborative, and full-service products can make or break employee and customer satisfaction and loyalty.

A Strategic Mindset is Where IT is At

In today’s overheated economy, businesses aim to be more strategic in their technology purchases, selectively choosing new software and consolidating their tech stacks for productivity and cost savings.

To avoid buyer’s remorse, IT decision-makers must first involve key stakeholders to co-create value and ensure alignment with the organization’s broader goals. They should outline the specific capabilities they need, build a plan for actioning and operationalizing the investments, and measure back against the right KPIs. Determining a framework and expectations will be essential for creating positive outcomes.

To remain resilient in the face of ongoing economic pressures, businesses and IT teams must be equipped with the right technology to streamline daily tasks and mitigate future risks. And given tightening budgets, it’s incumbent on leaders to invest in the tools that will support their IT teams and empower other business functions.

IT democratization and support can unlock new levels of business success, and companies who commit to this strategic process will undoubtedly have a competitive advantage in today’s business environment.

The post How IT Democratization Drives Business Success appeared first on Best Talent Management Suites, Software, Vendors.

As part of Solutions Review’s Contributed Content Series—a collection of contributed articles written by our enterprise tech thought leader community—Nicole Kyle, the Managing Director and Co-Founder of CMP Research, talks about the ways AI and automation technologies are affecting the future of work across customer-facing teams.

Generative AI and automation will transform the workforce. As customers become increasingly digitally dexterous and prioritize interactions that save time, companies are quickly transitioning to automate self-help, increasing efficiency and innovation, especially within customer service call centers. Most customer contact organizations already invest in improved self-service for customer and employee-facing tools, particularly agent knowledge bases. AI, particularly conversational and generative AI, deployments for employee-facing tools are widely considered lower risk for organizations.

It is better to experiment with tools internally and apply lessons learned to customer-facing pilots later. That way, disruption to customers is limited. Ubiquitous AI-enabled tools to improve employee and customer contact agent experience are interactive agent knowledge bases. These are directories that employees can interact with to find answers to their queries, ideally faster than consulting with a supervisor or peer. Customer-centric divisions consistently remind executives that AI is a spectrum, from rules-based to conversational to generative AI. The correct form of AI for a given tool has everything to do with the use case; generative AI is only sometimes better purely because it is the newest and most advanced. 

Customer contact executives cite reconciling internal technology systems as a top barrier to delivering effective self-service. To increase customer adoption of self-service and improve outcomes, customer contact executives evaluate the technology that underpins self-service interactions, particularly artificial intelligence.

Executives’ confidence in the current technology marketplace to deliver on AI use cases and requirements is variable. Rules-based chatbots and conversational interactional voice responses are the technologies that executives are most confident the current marketplace can provide. In contrast, they feel least confident in the market’s ability to support infrastructure for entirely self-service customer portals on websites and mobile apps and plug-ins for messaging platforms like WhatsApp and Meta Messenger. This makes sense since some of these applications are newer. Likewise, it explains why many organizations are building in-house portals instead. 

Risks to artificial intelligence in customer contact organizations are two-fold: risks to employees and risks to customers. With risks to employee experience, generative AI will reduce volume-induced burnout since it automates low-complexity, high-volume tasks. But when all that’s left over for employees is complex work, that can spark complexity-induced burnout. This is why proactive workforce and workflow management has never been more critical.

Similarly, AI tools, especially a generative agent knowledge base, will make training and onboarding easier in virtual contexts. The generative knowledge base is akin to a coach! If this results in employees, especially new ones, reaching out less often to their managers and team members, that can exacerbate silos. As far as risks to customers, it’s evident that poor self-service experiences make it less likely that a customer will seek self-service again. If customer-facing AI tools are rolled out before they are mature, they could harm CX and cause customers to avoid self-service in the future, which is not the outcome executives want. 

Across both categories of risks, artificial intelligence—like algorithms and most technology—have bias built into it. There are many examples of artificial intelligence being biased toward perfect English, toward men, or white people. These tools, especially generative ones, are still in their infancy and need human intervention and company-specific customization to mitigate harmful biases. 

A significant amount of customers do not know the difference between generative AI and conversational AI. Good conversational AI is more than sufficient for many customer contact use cases, and a strong contact center will advise executives not to get caught up in the buzz and noise around generative AI purely for generative AI’s sake. Only a tiny percentage of customers embrace generative AI, like ChatGPT, in their personal lives, so while customers are increasingly digitally dexterous, their expectations and experience with generative AI are still limited. This should give executives confidence that there is time to reevaluate their technology stack and to exert experiments and patience with their technology roadmaps. 

As we navigate the evolving landscape of AI and automation, one thing is clear: the key to success lies in a thoughtful, balanced approach that considers the unique needs of both employees and customers. With continued experimentation, customization, and a commitment to mitigating biases, organizations will truly harness the potential of AI and automation, paving the way for a future of work where processes become more efficient and human-centric. 

Solutions Review brings all of the technology news, opinion, best practices and industry events together in one place. Every day our editors scan the Web looking for the most relevant content about Talent Management platforms and solutions and posts it here.

The post How AI and Automation Impact the Future of Work appeared first on Best Talent Management Suites, Software, Vendors.

As part of Solutions Review’s Contributed Content Series—a collection of contributed articles written by our enterprise tech thought leader community—Silvia Rocha, the VP of Engineering at OutSystems, goes in-depth into how upskilling and low-code can help companies close the developer skills gap.

Digitalization has taken over all aspects of modern business. While there may be more technology available for companies to accomplish their goals, there is a scarce number of developers to lead these projects. Over the past couple of years, developers have seen an overwhelming demand for their skills as businesses recognize that to have a competitive advantage, they need to invest in software and cloud development. Reading past the sensational headlines of tech layoffs, you will find very few developers being let go and a continued increase of developer jobs added to the market each month.

A talent shortage in the tech industry is creating a skills gap, meaning that while developers are in high demand, the industry is struggling to hire and retain them. As a result, businesses are grappling with how to make the most of their present teams so innovation doesn’t slow.

The promising news is there are ways to uncover talent that already exists in your organization. Managers must take every opportunity to empower their existing developers and encourage them to become technical skills leaders. Upskilling existing teams will be the best investment for your business and will ultimately inspire a new generation of developers.

The Fundamentals of Upskilling

To create a rewarding upskilling experience amid this talent gap, businesses must invest in their employees and ensure they remain supported and challenged. The talent shortage has shown that this has the highest ROI if done correctly. According to research by OutSystems and Lucid, IT leaders need more—and more specialized—talent to see their existing cloud-native strategies through. Managers must upskill in areas of high business impact and avoid undifferentiated heavy lifting alongside manual and repetitive tasks that hinder creative thinking. In doing so, managers can maximize developer productivity.

A fully equipped developer team includes talent from 13 roles, from back-end, full stack, and mobile developers to enterprise architects and designers. If IT leaders don’t implement the proper protocols and tools, they won’t be able to track how these roles should collaborate and work in unison for the businesses’ upcoming projects. If this does not take priority, it could result in burnout. To avoid this unwanted outcome and ultimately alleviate the team’s workload, managers should consider a low-code platform.

Uncovering Existing Talent Through Low-Code

The skills gap is driven by the developer and time shortages. With the amount of projects businesses are aiming to take on, traditional development has proven to take way too long. While the industry has been somewhat apprehensive about changing their habits and integrating low-code, there are an increasing number of success stories where businesses have seen that low-code can effectively tackle the time shortage aspect of the skills gap and give developers the tools to get more done in less time.

Low-code has evolved over the years and is more than just the “short-cut” the industry initially labeled it. Low-code entered the conversation as a necessary platform because it empowers developers to focus on what the majority would agree is the most interesting and important part of their jobs—innovation. When developers feel their work is important and transformative, they will be happier in their roles.

Even though the primary motivation for most individuals to become developers is creating and adding value, developers typically allocate less than one-third of their time to coding business logic. As demand for digital projects and application building keeps growing, developers have been tasked with building these projects from scratch, and the groundwork can feel repetitive and exhausting. Managers have to recognize how much is being asked of their developers and give them a platform to eliminate tedious work.

The Benefits of Low-Code

In the long term, whether there continues to be a skills gap or not, investment into high-performance low-code will consistently allow IT leaders to uncover the hidden talents of developers who now have the time and space to innovate in their projects. Low-code platforms provide the opportunity to upskill the existing workforce, increase collaboration to scale, and evolve future digital transformation strategies.

Low-code solutions increase developer productivity by improving corporate agility. Traditional development approaches are not as cost or time-efficient. There’s been significant growth within low-code over the past couple of years, and it’s estimated that 3 out of 4 apps are made with low-code. Both business and career developers have been brought together through low-code, and companies that implement these platforms into their plans have created a larger pool of talent to help facilitate low-risk and high-quality work to ease the demands involved in cloud and digital transformation.

More than 71 percent of low-code developers said they could stick to the typical 40-hour work week, compared to only 44 percent of traditional developers. Additionally, 63 percent of low-code developers indicate they are happy with their salary and benefits compared to 40 percent of traditional developers. While low-code developers have received an average of three and a half job promotions at their current company, traditional developers have been promoted just two times.

In many ways, high-performance low-code was ahead of its time. Some developers viewed it as a threat to their jobs, but in reality, low-code only proves how necessary developers are to the business. Now that low-code platforms have become more mainstream, companies that have taken advantage of the platform and implemented it early are reaping the benefits.

For instance, Park Industries, a manufacturer of stone working machinery, used a low-code platform to save about $350,000 a year. By lessening their reliance on IT and modernizing and consolidating their legacy applications, Park Industries significantly reduced their IT spending and overhead, empowering them to do more with less.

The Path from Traditional Developer to Business Innovator

As organizations adopt low-code platforms, developer productivity will increase, with more time spent on business innovation and less on undifferentiated heavy-lifting activities. IT managers can expect success. Developer job satisfaction and engagement will continue to rise as more projects are completed on time with higher quality using low-code platforms. Developer impact will also increase and, in turn, increase the return on investment in skilling low-code developers, creating a virtuous cycle of innovation through low-code development.

With the right tools and upskilling practices, businesses can overcome the hurdles of the ongoing developer shortage. Empower your current team of developers and give them the space to grow their technical skills and increase innovation opportunities. This will uncover the talent that already exists in your organization and foster a rewarding upskilling experience.

The post How to Close the Developer Skills Gap Through Upskilling appeared first on Best Talent Management Suites, Software, Vendors.