Zdravko Kolev, Manager of Product Development at Infragistics, explains why AI is forcing development teams to rethink their approach to software development. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

A revolution is underway in software development, driven by the emergence of low-code/no-code platforms and the integration of AI. The aim of coding solutions is not to replace developers, but to make them more productive by handling mundane tasks, allowing them to focus on higher-level design and innovation.

All forms of programming are likely to coexist in the near future, rather than one prevailing over the others. Traditional programming will remain for intricate systems, while low-code/no-code and AI will address different needs. As the role of AI continues to expand, we will observe how this technology changes development mindsets and roles, with developers transitioning into “prompt engineers” who guide AI to produce the desired code output.

Exploring Programming Paradigms

In a nutshell, low-code and no-code platforms allow for the creation of software applications with minimal coding knowledge. Low-code no-code software uses visual interfaces, drag-and-drop components, and pre-built templates. These platforms democratize app development, enabling non-programmers, or citizen developers, to build applications faster and more efficiently. At the same time, C-level executives, development team leaders, and enterprise architects can focus on digital innovation, achieving a faster time-to-market. There are tools like Google Forms for simple data collection, as well as platforms like WordPress, Shopify, and App Builder for more complex solutions.

Currently, 7 in 10 developers (71.8 percent) are using low-code/no-code tools, and 90.4 percent of developers report that low-code tools are boosting developer productivity in their organization, according to a recent App Builder survey. Moreover, according to Gartner, “by 2025, 70 percent of new applications developed by organizations will use low-code or no-code technologies, up from less than 25 percent in 2020.” Meaning that low-code/no-code tools are here to stay.

AI’s Role in Programming

AI in programming enables the generation and review of code based on natural language prompts, potentially reducing the time to market for new software. Unlike the deterministic models of the low-code/no-code approach, AI can produce variable outputs, which might require more oversight for consistency. AI programming can also enhance productivity by handling repetitive tasks and providing quick solutions for prototyping or small-scale applications.

However, there is a risk of poor quality when integrating AI capabilities into common and already determined workflows. Additionally, if AI capabilities are not properly managed by skilled developers, the result could be subpar performance or low-quality outcomes.  For example, AI-generated code may not capture the full context of a large, complex system because it lacks a comprehensive understanding of the entire system, leading to integration challenges. That’s why team leaders, CTOs, CIOs, and their development teams must understand what exactly AI does so they can utilize it effectively.

According to the 2024 Stack Overflow Developer Survey, “Developers are increasingly adopting AI tools but remain skeptical about their accuracy and ability to handle complex tasks, viewing them as complementary rather than a replacement for human expertise.”

The survey found that developers learning to code (27 percent) were more uncertain about AI as a threat than seasoned coders (18 percent). Six out of 10 (62 percent) professional developers are using AI tools this year, compared to 44  percent last year. Three-quarters (76 percent) of the developers surveyed are currently using or plan to use AI tools, and 72 percent have a positive attitude towards using AI tools at work.

One of GenAI’s key promises was its ability to deliver quick solutions and speed up time-consuming processes. However, 31 percent of developers are skeptical that these tools offer accurate solutions, and 45 percent believe AI tools are not good at handling complex tasks. AI is not threatening to replace developers because it has not yet replaced humans in the development process.

Traditional Programming

Traditional programming is well-known because it has been the foundational approach to software development for decades. It involves writing code from scratch, offering the highest level of customization, and providing control over every aspect of an application, from scalability to performance.

The following development statistics illustrate the traditional development approach:

• The software development market is predicted to rise to  $858.10 billion by 2028, with a CAGR of 5.27 percent.
• About eight in ten (84.7 percent) of software development projects focus on enterprise applications.
• JavaScript is the most widely used programming language, and it is preferred by 65.82 percent of professional developers.
• About half  (54 percent) of software engineers report being more productive when working from home.
• The Internet of Things (IoT) is expected to include over 75 billion devices in 2025.
• Nearly seven in ten (69 percent) of businesses have adopted cloud computing technology.
• Approximately 15 percent of businesses utilize third-party modern frameworks, such as Ignite UI for Angular, which provide pre-built, high-performance UI components. These components simplify complex development tasks while still allowing developers the flexibility and control they need to customize applications according to their specific requirements.

The events here are likely to occur as low-code tools, such as App Builder, AI agents, and models, grow in popularity and traditional programming becomes relegated to complex, large-scale systems that require fine-tuned performance or meet specific technical requirements.

The Business Advantages and Challenges of Each Paradigm

There are three core pillars to examine when evaluating the business advantages and challenges of each software development paradigm: time to market, customization vs complexity, and skill evolution.

Time to Market

According to the App Builder’s survey, a majority (43.5 percent) of developers save up to 50  percent of their time when they use low-code tools on a project. This enables rapid prototyping and deployment, particularly for simpler applications.

When to use each method for rapid deployment:

• Low/No-Code: Ideal for businesses needing rapid deployment of simple to moderately complex apps where customization isn’t critical.
• Traditional Programming: Best for highly customized, complex systems where performance and scalability are paramount.
• AI: Useful for both prototyping and enhancing productivity in existing workflows, especially for junior developers learning best practices.

Customization vs. Complexity

While low-code/no-code platforms accelerate development, they can become challenging when trying to achieve high levels of customization or when dealing with complex systems. Custom solutions might be more cost-effective for highly specialized applications. Low-code and no-code platforms must provide clear guidance to users within a structured framework to minimize mistakes, and they may offer less flexibility compared to traditional coding.

AI tools can be easily used to generate code, suggest optimizations, or even create entire applications based on natural language prompts. However, they work best when integrated into a broader development ecosystem, not as standalone solutions.

Skill Evolution

Low-code/no-code tools help bridge the gap in skilled labor but can also lead to over-reliance. Developers will need to adapt, focusing not just on coding but also on managing low-code and AI tools and understanding how to best “prompt” them for desired outcomes. For novice developers, exposure to all these technologies is beneficial. However, it’s strongly recommended to gain hands-on coding experience before relying too much on AI or no-code/low-code solutions. Understanding the fundamentals is key to using advanced tools effectively. New developers should engage with code manually to understand its mechanics before moving to automated or visual tools.

How the App Development Sector Will Adjust to Current Trends

The future of software development appears to be a blended approach, where traditional programming, low-code/no-code platforms, and AI each play a role. The key to success in this dynamic landscape is understanding when to use each method, ensuring C-level executives, team leaders, and team members are versatile and leverage technology to enhance, rather than replace, human ingenuity.

Let me share my firsthand experience. When I asked my developers a year ago how they thought using AI tools at work would evolve, many said: “I expect that as the tools improve, I’ll shift from mostly writing code to mostly reviewing AI-generated code.” Fast forward a year, and when we posed the same question, a common theme emerged: “We are spending less time writing the mundane stuff.”

My goal is to emphasize the shift toward more inclusive development environments while also highlighting the need for in-depth technical skills to manage these systems effectively. As AI and low-code/no-code tools evolve, so must developers. Adaptation to new ways of creating software is what drives business growth, workflow efficiency, and innovation.

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With the ongoing rise of vibe coding, the editors at Solution Review compiled a summary of its meaning, history, relevance, and what its potential future in the changing software development marketplace might look like.

Vibe coding represents a fundamental shift in how developers approach software construction. It moves beyond rigid adherence to formal methodologies toward a more intuitive, pattern-driven development process. Rather than following prescriptive frameworks or exhaustive documentation, vibe coding emphasizes the developer’s accumulated experience and subconscious recognition of what “feels right” in a given context.

The Core Principles and Mechanics of Vibe Coding

At its foundation, vibe coding operates on the premise that experienced developers have an internalized sense of code quality, architecture decisions, and implementation approaches that transcend explicit rules. This intuitive understanding manifests through several key mechanisms:

Pattern Recognition at Scale

Developers practicing vibe coding rely heavily on their ability to recognize subtle patterns across codebases, technologies, and problem domains. This goes beyond simple code smells or anti-patterns to encompass broader architectural intuitions about system behavior, performance characteristics, and maintainability trajectories.

Contextual Decision Making

Unlike methodologies that prescribe universal best practices, vibe coding acknowledges that optimal solutions are deeply contextual. The same problem might warrant different approaches depending on team size, timeline constraints, performance requirements, and organizational culture. Vibe coders develop sensitivity to these contextual factors and adjust their strategy accordingly.

Rapid Prototyping and Iteration

The vibe coding methodology encourages quick experimentation with minimal upfront planning. Developers start with rough implementations that capture the essential “shape” of a solution, then iteratively refine based on how the code feels during development and testing. This approach prioritizes learning through direct interaction with the problem space over theoretical analysis.

Emergent Architecture

Rather than designing comprehensive system architectures upfront, vibe coding allows architectural decisions to emerge organically from the development process. For example, developers can make incremental choices that feel appropriate in the moment, trusting that coherent larger patterns will emerge through consistent application of their intuitive judgment.

Historical Context and Evolution

Vibe coding can be traced to several converging trends in software development culture, though it crystallized as a recognized approach relatively recently. The methodology emerged partly as a reaction against the perceived overhead of formal development processes, particularly in environments where rapid iteration and experimentation were more valuable than comprehensive documentation or rigorous upfront design.

For example, the rise of startup culture in the 2010s created environments where traditional software engineering practices felt cumbersome and slow. Teams needed to validate ideas quickly, pivot rapidly, and maintain velocity in the face of constantly changing requirements. This context fostered appreciation for developers who could navigate ambiguity effectively and produce working solutions without extensive process overhead.

Simultaneously, the maturation of specific technology stacks created stable platforms that reduced the need for low-level architectural decisions. When frameworks, cloud services, and development tools handle most of the complexity, developers can focus more on higher-level problem-solving and user experience considerations, creating space for more intuitive approaches to flourish.

The methodology also reflects broader cultural changes in how technical knowledge is transmitted and validated. Online communities, code-sharing platforms, and informal learning channels have created new ways for developers to absorb patterns and best practices outside formal educational structures. Vibe coding leverages this distributed, experiential knowledge base.

Psychological and Cognitive Foundations

Vibe coding taps into several well-established cognitive phenomena that govern expert decision-making across domains. The methodology essentially formalizes the role of tacit knowledge in software development, acknowledging that much of what experienced developers “know” exists below the level of conscious articulation.

Intuitive Expertise

Research in cognitive psychology demonstrates that experts in complex domains can recognize optimal solutions rapidly, often before they can explicitly explain their reasoning. Chess masters see winning moves, experienced doctors diagnose conditions, and seasoned developers identify elegant implementations through pattern matching that occurs largely outside conscious awareness.

Satisficing Behavior

Herbert Simon’s concept of satisficing describes decision-making that seeks solutions that are “good enough” rather than optimal. Vibe coding embraces this approach, recognizing that perfect solutions are often less valuable than working solutions that can be delivered quickly. The methodology optimizes for adequacy and iteration speed rather than theoretical optimality.

Flow State Optimization

The approach explicitly tries to maintain developers in flow states where they can work intuitively without constant interruption for planning, documentation, or process compliance. This psychological consideration acknowledges that cognitive state significantly impacts productivity and code quality.

Technical Implementation Strategies for Vibe Coding

Vibe coding manifests through specific technical practices that support intuitive development while maintaining code quality and system reliability.

Gradual Type Introduction

Rather than designing comprehensive type systems upfront, vibe coders often start with dynamic or loosely typed implementations and introduce type constraints gradually as the system’s behavior becomes clearer. This allows the type system to evolve naturally with the codebase rather than constraining early exploration.

Test-Driven Feel

While not strictly following test-driven development, vibe coding incorporates rapid feedback loops through lightweight testing strategies. Developers write tests when they feel uncertain about behavior or when they want to capture important invariants, but they don’t require comprehensive test coverage before implementing features.

Refactoring as Discovery

The methodology treats refactoring as a primary mechanism for understanding problem domains and discovering optimal abstractions. Rather than trying to identify the right abstractions upfront, developers let abstractions emerge through iterative code improvement driven by intuitive dissatisfaction with current implementations.

Micro-Architecture Evolution

System architecture evolves through many small decisions rather than significant design phases. Developers make local improvements that feel right, trusting that these micro-improvements will aggregate into coherent larger structures over time.

How Does Vibe Coding Affect Scaling and Team Dynamics?

One of the most interesting aspects of vibe coding is how it scales across different team sizes and organizational structures. The approach requires significant adaptation as teams grow beyond individual contributors.

Shared Intuition Development

Successful teams practicing vibe coding invest heavily in developing shared intuitions about code quality, architectural preferences, and problem-solving approaches. This might happen through pair programming, code reviews focused on “feel” rather than compliance, and regular discussions about what makes code satisfying to work with.

Cultural Transmission

The methodology depends heavily on the cultural transmission of tacit knowledge. Senior developers must actively mentor junior team members in technical skills and help them develop the intuitive judgment that makes vibe coding effective. This process is inherently slower and more personal than formal training programs.

Conflict Resolution

When team members have different intuitions about the right approach, vibe coding teams need mechanisms for resolving these differences quickly without falling back into rigid process-driven decision-making. This often involves prototype competitions, time-boxed experiments, or deferring to the developer who will be primarily responsible for maintaining the code.

What’s Next for Vibe Coding?

The methodology has gained particular relevance in the current technological landscape for several reasons that extend beyond mere trend following:

• AI-Assisted Development: As AI coding assistants become more sophisticated, the human contribution to software development increasingly shifts toward higher-level judgment calls that are difficult to automate. Vibe coding’s emphasis on contextual decision-making and pattern recognition aligns well with this division of labor between human intuition and machine capabilities.
• Complexity Management: Modern software systems operate in environments of enormous complexity, where formal analysis and comprehensive planning become increasingly impractical. Vibe coding offers strategies for navigating this complexity through iterative exploration rather than upfront comprehension.
• Remote Work Adaptation: Vibe coding’s emphasis on individual judgment and rapid iteration can reduce the coordination requirements of distributed teams while maintaining productivity.

Potential integration with machine learning systems presents exciting possibilities for vibe coding. For example, future development environments might learn to recognize individual developers’ coding patterns and preferences, providing contextual suggestions that feel natural rather than prescriptive. This could amplify the effectiveness of vibe coding while addressing some of its current scalability limitations.

The primary risks around the continued adoption of vibe coding involve potential inconsistency across large codebases and difficulty onboarding new team members who haven’t developed the requisite intuitions. Teams practicing vibe coding need explicit strategies for knowledge transfer and quality maintenance that don’t undermine the methodology’s core benefits, which could be challenging for smaller businesses to keep up with.

However, vibe coding remains a unique and promising response to the inherent complexity and contextuality of modern software development. Rather than pushing against these characteristics through increasingly elaborate processes, vibe coding embraces them as fundamental aspects of the domain and develops practices that work effectively within these constraints.

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Rodrigo Coutinho, the co-founder and AI product manager at OutSystems, explains how developers can create high-quality applications using low-code and AI technologies. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

AI-driven software development has introduced a new paradigm for legacy modernization and application delivery. With accelerated development timelines and an optimized use of resources, integrating AI into the software development lifecycle (SDLC) is a no-brainer for enterprises. However, as with any new technology, it must be used and governed wisely, and the developer’s role cannot be underestimated. This article will identify the risks associated with AI-generated code, outline the transformative potential of introducing AI to the SDLC, and share attainable best practices for developers integrating AI into their workflows.

Identifying Risks

While artificial intelligence has incredible potential to transform software development and code creation, careful implementation and governance throughout the adoption process must ensure success.

Recent data shows that 81 percent of IT professionals are using generative AI to assist with traditional coding, but 62 percent of those same professionals found that generative AI introduces security and governance concerns that must be identified, addressed, and mitigated. Only 40 percent “mostly” trust generative AI to write code without human assistance, showcasing that while gen AI can provide a helpful framework and starting point, it’s not yet fully capable of working independently.

Because generative AI has mastered quantity—and not yet quality—companies using AI to generate traditional code end up with poor code quality, governance issues, and orphaned code. Developers are faced with the challenge of making sense of AI-generated code. They may even need another tool to make sense of the code the first one generated, making workloads more complex and, in many cases, increasing costs.

This highlights the need for careful monitoring and governance when implementing gen AI and the critical role developers play. While it may be tempting to “set it and forget it,”  a skilled developer and project manager are essential for quality checks, fine-tuning, troubleshooting, and ensuring alignment with overall business goals. However, other solutions exist, such as low-code platforms powered by gen AI, that provide the best of both worlds: accelerated development with the necessary visibility, control, and governance at scale.

Identifying Solutions: Combining Low-Code and AI 

Low-code platforms provide solutions that empower developers and IT teams to build applications with very little traditional code. When coupled with the power of gen AI, they allow companies to reap the benefits of generative AI while mitigating the potential security and control risks, even when used by junior or less skilled developers. With AI-driven development and low-code platforms, developers now have the power to deliver high-quality applications in minutes, not months, providing significant benefits throughout the entire development process.

For example, developers can use AI to create a complete first iteration of the application, leveraging as much contextual data from their software environment as possible, kickstarting the process seamlessly and providing a template to begin. From there, AI can help developers identify patterns for their use case, providing user interface (UI) suggestions that fit their organization’s development guidelines and suggesting unique ways to leverage different data types that bolster application efficacy.

Once a product is final and has gone through iterations and fine-tuning, developers can use AI to validate the code, test against bugs, and ensure security standards are met. Compared to traditional code and AI, using a proper low-code platform ensures the created app has governability, architecture, security, performance, and maintenance built into it. This is possible because low-code platforms work at a higher abstraction level, allowing code validation and optimization before showing it to the developer.

In addition, even after the code is accepted and modified by the developer, low-code tools can use generative AI to track code quality in real-time in a single dashboard, all while proactively detecting issues regularly, freeing up tedious tasks that previously required developer manpower.

Best Practices to Follow When Adopting AI

For companies looking to integrate AI into their tech stack, be sure to keep the following best practices in mind:

• Investigate Multiple Tools: I always recommend investigating multiple AI tools—don’t settle for the first one you use or the shiny new thing. While using AI for code generation is getting much of the attention, it is not always the best option. Low-code tools powered by AI may provide the needed speed and ease of use, but it’s important to ensure you can enact the correct guardrails and maintain visibility as needed.

• Identify Developer Needs and Roles: Be sure to consider the skills required to support AI implementation, particularly if both junior and senior developers will be able to find it helpful. If there are additional skills needed for beneficial use or additional roles required to maintain the platform, those factors should be considered before embarking on the AI journey.

• Create Guardrails: Use AI-powered reviews to enforce best coding practices, ultimately ensuring an app’s architecture, security, performance, and maintenance are running as scheduled without adding additional developer work.

• Think Long-Term: Consider the long-term impact of your choices. Investing in a code-generation tool will generate more code, meaning someone will need to maintain the code, potentially adding new roles and responsibilities to the mix. Identifying the specific business needs and what a tool needs to provide will allow you to view the big picture and make the most informed decision.

• Consider the Full Software Development Lifecycle: Developing is just part of the process; there are a dozen stages in the development life cycle that can benefit from AI, so be sure to shop around for these different stages, and be sure to check that all the bits and pieces work together seamlessly.

While pairing traditional coding tools with AI requires developer expertise, combining AI with low-code allows even junior developers to reap the benefits and accelerate development cycles. Ultimately, you still need a human in the loop. AI is going to continue to accelerate development in the coming months and years, triggering innovation at a broader scale, but the companies that will be truly successful in the long term are those that rely on their people to make their applications unique and tailored to their customers’ use cases.

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Rajan Nagina, Head of AI Practice at Newgen Software, explains why AI in banking is actively reshaping the finance industry. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

There was once a time when Artificial intelligence (AI) was regarded as a vague, futuristic concept. And yet, we have reached a point where it is completely embedded into multiple industries and is revolutionizing how they operate. The banking industry, which had initially been slow to join the bandwagon, has now started deploying new-age technologies like AI in several of its operations, which has opened a new chapter in its future. AI supports the industry in boosting efficiency, reducing risks, and delivering hyper-personalized experiences, from customer service to fraud detection. Banks that fail to adopt these technologies risk falling behind in an increasingly competitive landscape.

By allowing smarter decision-making, automation of repetitive tasks, enhanced security with coded guardrails, and unlocking new revenue streams, AI is inevitably revolutionizing how financial services operate. AI-powered “digital cognitive workers” are reshaping lending processes by reducing approval times from weeks to minutes. The next five years will see AI become the backbone of banking, drastically changing how financial institutions handle risk and interact with consumers.

This article explores how AI is paving the way for a faster, smarter, and more efficient future for banking and the ethical challenges that come with it.

The Rise of Smart Customer Experiences  

 As customer expectations rise, banks can no longer rely on the one-size-fits-all approach. Today, AI enables banks to analyze enormous volumes of customer data, from spending habits and income fluctuations to life events, to offer tailored financial advice.

1. Chatbots & Virtual Assistants  

AI-powered chatbots manage routine inquiries, minimizing wait times and enhancing customer experience. Banks like JPMorgan Chase and HSBC are now utilizing virtual assistants to address account queries, process transactions, and offer investment advice without human intervention.

2. Predictive Banking  

Machine learning can anticipate customer needs and make suggestions before a big purchase or issue an alert regarding potential overdrafts.  For instance, several banks have started analyzing transaction histories to predict when a customer may require a mortgage or credit line adjustment. Boston Consulting Group even reports that when finance companies incorporate AI-driven planning and forecasting, they can increase overall productivity by 20-30 percent.

3. Voice & Facial Recognition  

Biometric authentication expedites the speed of logins and also improves security. Some excellent examples of how AI makes banking seamless and secure are HSBC’s voice recognition system and Citibank’s facial ID verification.

Fraud Detection & Risk Management – AI as the Ultimate Guardian 

Financial fraud costs the world economy billions of dollars annually, but artificial intelligence is here to change that. AI can identify anomalies in real-time, while traditional rule-based systems find it challenging to keep up with shifting threats.

 1. Analytics of Behavior  

AI monitors transaction patterns and flags anomalous activity, such as abrupt, large-sized withdrawals or international transactions. For instance, Mastercard’s AI-powered system can instantly detect fraud by analyzing spending patterns across millions of transactions.

 2. Evaluation of Credit Risk  

To forecast loan defaults more precisely, machine learning models examine non-traditional data, such as social media and utility payments. Fintech companies like Upstart and ZestFinance implement AI to evaluate creditworthiness in ways other than traditional FICO scores, thereby enhancing financial inclusion.

 3. Anti-Money Laundering (AML)

AI can save up to 30 percent on compliance expenses by lowering false positives in AML alerts. For instance, Deutsche Bank uses AI to sort through millions of transactions and detect suspicious activity more accurately than manual reviews.

Operational Efficiency – Doing More with Less  

AI is steadily changing the face of the banking industry by minimizing human error, cutting expenses, and simplifying banking operations.

1. Automated Document Processing

AI reduces processing times from days to minutes by extracting important data from contracts, invoices, and loan applications. In certain cases, AI agents are also automating loan underwriting, which reduces the human workload by more than 70 percent.

2. Adherence to Regulations  

AI monitors changing regulations, ensuring that banks stay compliant without human supervision. For instance, AI can assist organizations in avoiding expensive penalties by scanning through legal documents and identifying inconsistencies.

 3. Employee Productivity

By automating routine tasks like data entry and customer verification, AI helps employees concentrate on more complex and high-value work. According to a McKinsey report, AI could save banks up to $1 trillion by 2030 through operational efficiencies.

Challenges & Ethical Considerations 

Despite its many advantages, banking leaders must consider AI’s drawbacks to utilize it to the best of their capacity.

1. Privacy Issues with Data  

Banks must balance using personalization to appeal to customers and safeguarding their data. They must ensure that AI models don’t misuse sensitive data to comply with stricter laws and regulations, such as the CCPA and GDPR.

 2. Bias in Algorithms  

AI may reinforce discrimination in lending if it is trained on biased data. For instance, an AI model that favors particular groups might unjustly refuse loans to eligible candidates.

 3. Excessive Reliance on Automation  

Human oversight continues to be essential in critical areas to ensure banks avoid any possible errors. The dangers of unrestrained automation are demonstrated by the 2020 ZestFinance case, in which it was discovered that an AI lending model discriminated against minority borrowers.

Regulators are taking action, and US guidelines and the EU’s AI Act influence how banks use these technologies responsibly and ethically.

 AI as the Foundation of Banking in the Future  

The banking industry is at a crucial turning point. AI is steadily becoming the foundation of financial services, rather than just being an add-on.

1. Hyper-personalized Banking  

AI helps banks provide context-aware, real-time financial advice, such as modifying savings plans in response to market fluctuations or life events.

2. Independent Financial Consultants  

Robo-advisors will develop into completely self-sufficient systems that require very little human intervention to manage portfolios.

 3. Integration of Blockchain and AI  

AI-powered fraud detection and smart contracts will speed up transactions and make them more secure. Banks that adopt AI will lead in innovation, efficiency, and customer satisfaction, while those that don’t run the risk of becoming obsolete.

Conclusion  

The banking industry is already experiencing an AI revolution. AI is redefining finance in the blink of an eye, from enhancing fraud detection to automated lending and regulatory compliance. At the same time, to realize AI’s full potential, banks must overcome moral and legal obstacles.

The AI revolution is underway, and only the financial institutions that strike when the iron is hot and successfully incorporate AI into their operations stand the best chance to win in this race against time.

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To help companies remain competitive amidst changing markets, the Solutions Review editors are exploring how an Empathetic AI (EAI) mindset can improve AI adoption, optimize automation initiatives, and future-proof their operations without displacing employees.

Artificial intelligence (AI) has been a fundamental part of enterprise technology for years; it’s helped power manufacturing plants, analyze complex data sets, track customer sentiments, and much more. What’s changed in the last couple of years is the widespread awareness of AI-powered technologies and how closely integrated they are into modern business processes. For example, when it comes to business automation, companies worldwide and across industries are looking to save money and time by providing workers with systems that lessen workloads and, ultimately, enable them to use their professional skills in more valuable ways.

However, it’s not uncommon for traditional automation approaches to prioritize efficiency metrics while ignoring human-centered outcomes, leading to failed implementations, employee resistance, and customer alienation. The issue can be exacerbated by the rapid adoption of AI technology, especially when an organization is not adopting it with an empathy-first mindset. Without that mindset, companies risk creating a systematic blind spot that prevents their “AI transformation” initiatives from achieving the necessary success.

Those failures aren’t technical, though; they’re empathy failures. That’s where the principle of “Empathetic AI,” or EAI, as we’re calling it, comes into play. Empathetic AI doesn’t mean making robots more human-like. Instead, it’s a strategic framework that designs automated systems with explicit consideration for their emotional, psychological, and social impacts on the human workforce working with them. This approach transforms automation from a replacement paradigm into a human augmentation strategy, creating sustainable competitive advantages through stronger stakeholder relationships and higher implementation success rates.

With that perspective in mind, the Solutions Review editors are exploring how an EAI-forward approach to business automation can transform company processes, improve employee productivity, boost morale, and maximize the value AI technologies can provide.

The Three Pillars of EAI Implementation

Implementing EAI into your company’s AI adoption efforts can seem abstract, but it doesn’t have to be. Think of it as another layer in your change management strategy, and initiate a program that creates comprehensive “empathy maps” that document emotional touchpoints, anxiety triggers, and relationship dependencies within existing processes. That info will be crucial for the actual EAI implementation effort, which can be categorized into the three pillars outlined below.

1) Assessing Stakeholder Impact

The first step in implementing empathetic AI is to evaluate how automation can and will affect various stakeholder groups, including employees, customers, and business partners. This means documenting not only what those people do, but also how they feel about doing it. Have users built any informal relationships around current workflows? Are there any sources of professional identity or customer connection that could be disrupted with the introduction of AI-powered automations? Answering those questions before rolling out an AI strategy can transform how easily workers adopt and adapt to the new processes and tools.

For example, imagine a healthcare organization implementing an AI patient scheduling system to reduce call volume and optimize the scheduling process for users and patients. While the ROI on such an initiative would seem obvious, an empathetic assessment might reveal that scheduling staff positively impacts the quality of care regular patients report receiving. With that information, the organization can redesign its operations to free staff from routine scheduling without disrupting the relationship-based care that patients have come to expect.

Employees want this kind of thinking, with a 2025 McKinsey report showing that nearly half of surveyed workers “want more formal training,” “would like access to AI tools in the form of betas or pilots,” and “indicate that incentives such as financial rewards and recognition can improve uptake.” Workers are already using AI—maybe more than executives even realize—and the best way to equip them for success is to provide the resources and scaffolding they need to augment, not replace, their existing workflows.

2) Adopting Gradual Integration Protocols

It takes time for a workforce to adjust to new tools, even if they are relatively easy to use (like generative AI). The next pillar of implementing an EAI strategy is to allow and encourage employees to adapt to the new systems gradually. Failing to do so can trigger defensive responses from employees, making eventual adoption more difficult. According to Vitaliy Tymoshenko, founder and CEO of SmartExpert.ai, “employees and managers often resist the implementation of AI because they perceive automation as complex or unreliable.”

Gradual integration requires a sophisticated, agile technical architecture capable of supporting multiple operational modes simultaneously. This includes confidence thresholds that automatically trigger human involvement, real-time adjustment capabilities based on user feedback, and cultural adaptation algorithms that modify system behavior based on organizational preferences. While this approach can extend the duration of an implementation, the benefits will be longer-lasting. Like Eddy Azad, CEO at Parsec Automation, explained in Forbes, “Small, consistent steps forward enable organizations to integrate AI into their operations seamlessly, mitigating risks, enhancing long-term resilience, and getting planned-for outcomes.”

3) Deploying a Feedback Loop Architecture

The next step in implementing EAI is establishing built-in mechanisms for continuous human input and system adjustment. Unlike traditional feedback collection, an empathetic feedback loop supports a co-creation relationship where affected stakeholders actively participate in the ongoing automation refinement process, instead of only the initial design or post-implementation evaluation.

One of the best ways to include stakeholders is by integrating sentiment analysis and emotional state recognition to help teams adjust system behavior in real-time. For example, companies can involve teams most affected by AI in ongoing “automation labs” where the end-users propose or test system modifications and participate in customer advisory plans to ensure the technology rollout is best situated for success. This collaborative approach treats automation as an evolving capability rather than a fixed implementation and plays a foundational role in promoting transparency throughout the development of an AI policy or system.

However, you still need to measure the results of this feedback. Instead of relying on traditional KPIs, decision-makers should incorporate additional metrics—or even identify new ones—that capture empathetic outcomes alongside operational efficiency. These metrics should include stakeholder comfort indices, adoption velocity measurements, and relationship preservation scores that track whether AI enhances or degrades human connections within business processes.

Making Empathy a Priority

The question isn’t whether your business should adopt AI—it’s whether you’ll implement it in a way that strengthens or weakens your human relationships. By adopting an empathetic AI policy, companies will create sustainable competitive advantages through higher implementation success rates, stronger customer relationships, and more engaged workforces.

Want more insights like this? Register for Insight Jam, Solutions Review’s enterprise tech community, which enables human conversation on AI. You can gain access for free here!

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Brian DeWyer, the CTO and Co-Founder of Reveille Software, outlines the most significant benefits of prioritizing real-time visibility in intelligent automation systems. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

They all have them—cars, planes, and boats. What do they all have? Dashboards! You don’t drive a car without knowing your speed and fuel amount; airplane pilots need to know their altitude, and boat captains want to see the depth of the water. The result can be catastrophic without dashboards that render real-time views of vehicle operation status. Excessive operating temperatures, threshold breaches, and critical maintenance lapses can all cause significant consequences. 

The need for real-time visibility into your content systems is similar; you need visibility into the operational status before applications break down. With the introduction of Intelligent Automation—composed of Intelligent Document Processing (IDP), Enterprise Content Management (ECM), and Robotic Process Automation (RPA)—and collaboration systems into business operations, real-time visibility into your enterprise’s systems has become crucial for smooth and efficient operation. In this article, we’ll look at real-time visibility for Intelligent Automation, how it works, and how it benefits your organization. 

Real-Time Visibility 

Real-time visibility allows you to see and comprehend events as they are happening within your systems and processes. This ability to gain instant insights will enable managers and IT teams to react when a change or challenge arises. With Intelligent Automation solutions, real-time visibility gives you complete knowledge of content access, user behavior, and system performance, which are measured and displayed in dashboard form. For example, Intelligent Automation will alarm before end-users are impacted if a server’s storage is nearing capacity. 

Without real-time visibility, numerous challenges can arise. Knowing what these challenges are is critical for keeping operational efficiency and ensuring prompt response to potentially crippling issues. Additional challenges are: 

• Delays in System Failure Detection: Slow or nonexistent detection of system failures can exacerbate problems and often lead to prolonged system downtime.
• Unauthorized Access: Without immediate alerts, unauthorized access can go unnoticed, posing a risk to data security and integrity.
• Bottlenecks in Workflow Processes: Lack of real-time data can prevent the timely identification and resolution of workflow bottlenecks.
• Compliance Risks: Delayed detection of non-compliance activities can lead to serious legal and financial consequences. 

Without real-time visibility, a business’s ability to manage, repair, and optimize its content is severely limited. Without it, operational inefficiencies, increased costs, and the risk of data exposure could all be “real-time” problems. Intelligent Automation systems manage large volumes of content across various departments and functions. Real-time visibility into these systems is vital, ensuring that any glitch in operations can be caught and immediately addressed before the damage is too costly or destructive to functioning. 

Benefits

Real-time visibility has two significant benefits: It minimizes downtime and reduces operating costs. It also bolsters security, improves records management, and helps ensure compliance by allowing swift responses to threats or anomalies. 

Intelligent Automation systems’ observability provides more than a broad overview of what’s happening; it also targets additional critical areas that need visibility to ensure metrics are being gathered, as well as: 

• System Performance Metrics: Your ability to monitor transaction performance and user response times can significantly improve response efforts. Real-time alerts on spikes in error rates can prompt immediate investigation, helping to maintain system health and reliability.
• Access and Security Controls: Tracking who accessed what and when gives insight into unusual access patterns that could indicate internal threats or breaches. Immediate notifications about changes in document permissions ensure that sensitive information is guarded against unauthorized access.
• Workflow Efficiency: Real-time monitoring helps identify and address bottlenecks quickly, reducing downtime and improving efficiency. Monitoring task completion also helps identify inefficiencies and reveal training needs.
• Compliance and Audit Preparedness: A real-time display of detailed audit trails helps businesses prepare for compliance audits and internal reviews without it being the laborious process that it’s known to be. Continuous monitoring also ensures that all content management activities comply with policies and regulatory requirements.
• Integration and Interoperability: Real-time visibility is essential for monitoring the performance of integrated systems and maintaining the ecosystem’s integrity. Tracking the frequency and response times of the application programming interface (API) provides insights into the system’s scalability and external dependencies. 

Intelligent Automation Observability 

The constant tracking of Intelligent Automation system performance and user activity with real-time observability and monitoring software allows for actionable insights that optimize processes. Here’s how real-time visibility improves enterprise content management: 

Customized Dashboard

The real-time dashboards that provide valuable visibility are customizable. A clear view of your performance metrics gives you a real-time understanding of Intelligent Automation systems. Customizable dashboards allow managers instant access to critical data, allowing them to monitor trends and gauge their system’s health. The immediate awareness and swift actions that address potential issues as they arise are indispensable business resources. These dashboards also improve productivity while ensuring optimal system performance and user satisfaction. 

Alert Systems

Real-time monitoring software instantly and automatically alerts to potential issues. Because the monitoring and tracking are based on defined or ML forecasted thresholds, issues are flagged before they harm the system. These alerts assist IT teams in responding swiftly to anomalies, minimizing downtime, and preventing minor problems from becoming major obstacles. Operational efficiency is improved while maintaining system integrity, security, and reliability. 

Performance Metrics

Monitoring system health and user activity can identify performance trends and anomalies. This visibility in real-time operations results in proactive management of the entire Intelligent Automation software environment, optimizing performance, addressing inefficiencies, reducing risk, and improving customer service. 

Conclusion  

As Intelligent Automation platforms, solutions, and systems evolve, the need for real-time visibility will become increasingly evident for maintaining operations. Observability with customizable dashboards will be integral to managing these newly sophisticated systems efficiently and securely. By integrating Intelligent Automation monitoring and management solutions, organizations will be ready to face any possible risks, eliminate potential harm, and enhance operational agility, which will benefit all users. 

A real-time dashboard that monitors all Intelligent Automation functions provides instant visibility into system behavior as it unfolds. Its proactive insights empower administrators with the data needed to ensure business integrity, just as a dashboard helps keep a car running, a plane flying, and a boat floating. The net result of full observability is greater efficiency, productivity, and profitability.

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Brian DeWyer, the CTO and Co-Founder of Reveille Software, summarizes how intelligent document processing (IDP) is changing business workflows across departments. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The digital era has significantly evolved our ability to create and store content. Today, businesses are choking on terabits of information, which is the new gold. But just like traditional gold, it must be mined and carefully monitored to ensure adequate storage, instant access, and layered security. The need for intense monitoring has morphed into a physical process of Intelligent Data Processing (IDP).  

Emails, social media posts, Word documents, images, videos, audio files, and surveillance footage are several examples of content flooding storage devices. Although its value is immeasurable, most data remains unstructured. Although the volume of unstructured data is overwhelming, IDP helps organizations with data extraction, classification, and analysis.  

IDP turns structured and unstructured content chaos into accurate processes by using artificial intelligence (AI) and machine learning to automatically extract, categorize, and organize all that data without straining the limits of human capabilities.   

IDP Origins 

The roots of IDP go back to the early 1900s when Optical Character Recognition (OCR) was first developed. OCR began by translating text into sound for people with visual impairments. It then evolved to translate written characters into telegraph code, which evolved into digital credit card and barcode systems. The first volume capable of OCR systems appeared in the 1960s and 1970s, designed for specific applications like mail sorting based on zip codes or reading handwritten numbers. Then, OCR was used to digitize historical archives such as newspapers; when it entered the cloud, it became a tool accessible from desktops and mobile devices.  

Now, artificial intelligence, machine learning, natural language processing, and sophisticated computer vision have transformed OCR into IDP and enabled it to take over the tasks associated with document classification and data capture functions entirely. Modern IDP systems can transform unstructured data into usable formats with speed and accuracy while learning from each interaction to improve processing accuracy. In retail banking, for example, IDP streamlines the handling of customer-related documents like account opening forms and identity verifications. With IDP, banks can automatically extract data from these documents, regardless of format or quality; this speeds up decision-making and reduces errors associated with manual data entry. 

The IDP Process 

But how does IDP do it? Here’s how the process unfolds: 

1. IDP recognizes and categorizes documents, such as invoices, emails, or forms. It then facilitates using tailored rules and extraction methods for more precise and efficient data processing.  
2. Once documents are classified, the IDP extracts the needed information such as dates, names, and amounts. For example, it can extract the due date, vendor name, and total amount due from an invoice. 
3. After extraction, IDP validates data, enhancing accuracy. This process reduces manual checks, ensuring data reliability and seamless integration into existing workflows. 
4. IDP systems facilitate continuous learning through user interactions and feedback. User analytics is crucial for analyzing usage patterns and benchmarks. It helps organizations refine data processing strategies and align the system with changing business requirements. 
5. IDP systems track processing time, error rates, and throughput volumes. The insights gained help identify bottlenecks, improve workflows, and enable organizations to make data-driven decisions.  
6. Deploy focused observability and monitoring of IDP applications, processes, and systems for comprehensive IDP management. 

Behind IDP’s Technology 

There are several core technologies behind IDP. There is no ‘one size fits all’ as multiple technologies exist to support different unstructured data processing requirements. 

1. Optical Character Recognition (OCR) converts images of text from scanned documents and photos into machine-encoded text. 
2. Machine Learning (ML) enhances IDP systems by enabling them to learn from data inputs and corrections. ML algorithms can identify patterns and anomalies.  
3. Natural Language Processing (NLP) allows IDP systems to interpret the data and the semantics of information.  
4. Deep learning, a specialized ML technique, uses layers of algorithms called artificial neural networks to further enhance document processing capabilities. 
5. Generative AI can generate summaries and derive actionable insights from unstructured data, further enhancing IDP’s capabilities. 
6. Computer Vision helps recognize and interpret visual elements within documents, such as logos, stamps, and handwritten notes. 
7. Robotic Process Automation (RPA) automates repetitive tasks, such as data entry, sending notifications, and updating records, without human intervention.  

The IDP Benefits 

The following are the benefits associated with IDP: 

• IDP streamlines operations and enhances data management across sectors. Automating the extraction and processing of data streamlines workflows and cuts down on manual labor. IDP improves productivity and optimizes resource allocation across the organization.
• IDP enables rapid data processing and quick accessibility, providing faster access to data and helping organizations make swift decisions. Companies can maintain a competitive edge by staying responsive to market changes and customer needs.
• Reducing the reliance on manual document handling serves two benefits: it decreases labor costs and minimizes the occurrence of errors. Both help prevent financial losses associated with data mishandling and reduce the workload of correcting such mistakes.
• IDP can seamlessly integrate with existing systems, which allows for the automation of complex processes at an enterprise scale. This automation ensures data consistency and enhances quality across various operations, boosting reliability and compliance within the organization. 
• IDP’s efficient data processing allows quick responses to customer queries and ensures accurate request handling. This process improves customer interactions and satisfaction, which fosters loyalty and increases a business’s reputation.  
• IDP systems can adapt to increased workloads without requiring additional resources. Such scalability supports business growth, enabling companies to expand their operations while managing the larger data sets that come with scaling. 

IDP Penetration: From Banking to HR 

The insurance industry is revolutionizing claims processing by automating the extraction and analysis of data from claims forms and related documents. This has reduced processing times, boosted accuracy, expedited claim resolutions, and elevated customer satisfaction. IDP’s ability to detect patterns and anomalies has also helped insurers strengthen their fraud detection efforts. 

In banking and financial institutions, IDP accelerates customer onboarding processes. By automatically extracting data from ID proofs, application forms, and other documents, IDP speeds up the verification process, reduces errors, and improves compliance with regulatory mandates. This results in a more efficient onboarding experience that enhances customer satisfaction. 

IDP streamlines the management of shipping documents, invoices, and freight bills within logistics. It automates data capture from various forms and documents, enabling quicker invoice processing and reducing discrepancies. This efficiency improves supply chain visibility, enhances tracking accuracy, and reduces bottlenecks, resulting in more reliable and faster deliveries. 

HR departments can automate the processing of employee documents, such as legal documents, resumes, onboarding paperwork, and all the forms that come with benefit packages. This process speeds up hiring, improves records management, and ensures compliance with regulations. It also allows HR professionals more time to focus on tasks like talent management and employee engagement rather than toiling away at data entry work. 

Conclusion 

The future of IDP is auspicious and expected to become more sophisticated. Fortune Business Insights states, “The global Intelligent Document Processing market size was valued at USD 5.89 billion in 2023. The market is projected to grow from USD 7.89 billion in 2024 to USD 66.68 billion by 2032, exhibiting a CAGR of 30.6 percent during the forecast period.” 

Artificial intelligence will continue to evolve, impacting IDP’s ability to process complex documents and provide deeper insights into extraction, classification, ingestion, and validation. Because digital transformation is a process, not a goal, IDP will always be essential for automating and streamlining document processes. Five-year calculators, such as the Intelligent Data Processing Community’s calculator, can determine the return on investment (ROI) from using IDP.  

All industries, from banking to HR, need the right tools to process information accurately with as little human intervention as possible. Intelligent Document Processing (IDP) virtually eliminates the paralysis by analysis syndrome, which has hampered the operational efficiencies of document-driven processes with human errors for decades. IDP’s future as a valued business tool shines as brightly as a gold nugget in a clear-water stream. Organizations that embrace these nuggets will prosper faster than those that retain outdated data procedures and workflows. 

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Mike Jennett, Global Director and Head of CloudBlue Platform Strategy, explains why B2C companies are adopting self-service platforms and eliminating the middleman in their subscription models. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Today’s tech-savvy consumers expect frictionless, on-demand shopping and appreciate self-service portals. These platforms empower users with the tools to help themselves throughout the buying experience, putting control in the hands of the customer. Research has found that people prefer dealing with tech over personal interactions.

According to a survey by PlayUSA, 84 percent of Americans like self-service, and 60 percent use kiosks or mobile apps to avoid talking to people. As the subscription model economy evolves, more B2C businesses will shift toward self-service account management. Why? Because it cuts down on costs, reduces friction, and gives customers the flexibility they want. When done right, self-service is a win-win for everyone.

AI-Powered Convenience

Artificial intelligence (AI) is key in enhancing self-service platforms by providing personalized recommendations and predictive customer support. AI-powered chatbots assist users in navigating options, answering common queries, and troubleshooting problems without human intervention. Companies like Netflix and Amazon are leading examples of how self-service can enhance customer satisfaction while reducing operational burdens.

For instance, Amazon leverages self-service for everything from account management to automated returns. Netflix uses AI to recommend content based on viewing habits, helping users discover shows and movies without manual searching. But while AI brings a ton of benefits, many businesses struggle to use it effectively. Simply adding AI tools isn’t enough–you have to be strategic. Here are three things to keep in mind:

• Maintaining Personalization: Striking the right balance between automation and human interaction is crucial. Businesses should ensure that AI-driven solutions complement human expertise rather than replace it, preserving authenticity and meaningful customer engagement.
• Ensuring Customization: A one-size-fits-all approach often falls short, as different industries and organizations have distinct requirements. Tailored solutions are needed.
• Improving AI Usability: Intuitive and user-friendly interfaces and accessibility are needed; if your self-service platform isn’t easy to use, people won’t use it.

AI works best when it enhances customer interactions instead of replacing them.

Why a Unified Platform is Essential

The shift from traditional rule-based processes to AI-driven, dynamic workflows makes automation significantly more effective. When given the right data and guidelines, AI agents can take smarter actions and provide better recommendations. But here’s the catch: AI can’t do its job properly if your systems aren’t connected. A unified platform helps systems work in sync, enhancing the self-service experience:

• Beyond Chatbots: AI agents do more than provide conversational interfaces; they actively complete tasks, automate resolutions, and drive efficiency.
• Personalized Bundling: Machine learning tailors recommendations based on user behavior, enhancing customer experience and increasing conversions.
• Data-Driven Insights: Advanced analytics empower businesses to refine sales strategies, optimize performance, and make informed decisions.

A unified platform helps businesses utilize AI tools in a practical yet impactful way.

Capitalizing on Unified Commerce

Self-service solutions broaden access to software procurement, giving small businesses more options. Independent Software Vendors (ISVs) are increasingly embracing direct-to-consumer approaches, using automation to move from traditional distribution models.

With a unified platform, ISVs have more control over their sales channels and can easily publish on hyperscaler marketplaces for broader distribution. Automating processes eliminates manual tasks such as paperwork, phone calls, and reliance on intermediaries, leading to greater efficiency and cost savings. Businesses can negotiate directly with ISVs to secure custom pricing and private offers, ensuring more flexible and competitive pricing options.

Automated provisioning further streamlines operations by enabling instant access to software, reducing delays, and administrative overhead. A unified platform simplifies sales management across multiple channels, allowing businesses to efficiently oversee and optimize their sales strategies. Centralized reporting and real-time analytics offer valuable insights to help companies track performance and identify trends.

Focus on Customer Satisfaction 

Self-service account management portals are a game-changer for B2C companies in the subscription economy. With these self-service tools, customers can tweak their subscription models to match their changing needs. Whether it’s upgrading, downgrading, or canceling a plan, they have the flexibility to adjust their services anytime.

Updating payment methods is a breeze, too, reducing potential hiccups due to outdated billing info. Users can also customize their preferences, like opting in or out of notifications and promotional emails, for a more personalized experience. And it’s not just customers who benefit. ISVs can use these same tools to manage customer interactions more efficiently. The more seamless the experience, the more engaged customers will be. Businesses that invest in self-service solutions are better prepared to meet the expectations of today’s consumers.

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The editors at Solutions Review have summarized some of the most significant ways AI has impacted software development, from hiring developers to sought-after skillsets, best practices, and more.

One of the least surprising things someone can say in 2025 is that artificial intelligence (AI) has impacted the software development space. What is less clear is the specific impact AI has had on that market and whether developers have cause for concern. As AI is integrated into software development processes at unprecedented levels, the form and function of a company’s dev team will inevitably continue changing and evolving.

To keep track of those changes, the Solutions Review editors have outlined some of the primary ways AI has changed software development, what professionals can do to remain agile during those changes, and what the future may hold for them and the technologies they use.

Note: These insights were informed through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to extract targeted knowledge and optimize content for relevance and utility.

How Has AI Changed Software Development?

In just a few years, AI’s role in software development has dramatically restructured the kinds of roles, responsibilities, and required skill sets companies look for. This transformation has been freeing for many, as AI has streamlined their workloads and empowered them to focus on more specialized, high-value tasks and projects. However, it’s not uncommon for developers to feel uneasy about the rapid adoption of these technologies, as they have already proven capable of rendering some tasks and roles nearly obsolete. Here are some of the development processes that have been impacted the most by AI:

Code Generation and Assistance

Arguably, AI has had the most visible impact on DevOps in code generation. Thanks to large language models (LLMs) like GitHub Copilot, Amazon CodeWhisperer, and more general models like Claude, developers can now generate functional code from natural language descriptions in a fraction of the time. A 2024 report confirms this trend by showing that 80 percent of global developers use AI when writing code. This capability has created a force multiplier effect that enables developers to delegate routine coding tasks to AI assistants, freeing them to focus their skills on higher-level architecture and business logic.

This has reduced implementation time for common patterns but is not without trade-offs. Developers have started reporting a weakened understanding of underlying implementations, leading to what some call “implementation amnesia”—where they become dependent on AI suggestions rather than building mental models of the systems they create. That’s why traditional developers will remain essential, as their experience and thinking, when paired with AI, can improve productivity and efficiency.

Testing and Quality Assurance

Testing has also been revolutionized by AI-powered tools and bots, which can automatically generate test cases, detect edge cases, and even self-heal tests that break due to UI changes. Tools like Mabl, Testim, and Applitools use machine learning to maintain test suites with minimal human intervention.

Perhaps most significantly, AI-powered visual testing tools can detect subtle UI regressions across thousands of screen permutations in minutes rather than days, making previously impossible testing scenarios routine. The most sophisticated implementations combine reinforcement learning techniques to continuously explore application states, identifying critical bugs in production-like environments before deployment.

The benefits can be substantial, as these tools can increase test coverage and decrease maintenance costs. However, this can result in a trend where developers are overly reliant on automated testing that doesn’t use the proper verification frameworks, leading to false confidence in system stability. The most effective teams use AI to augment their human testing expertise rather than replace the contextual understanding and intuition that experienced QA professionals bring to complex systems.

Architecture and Design

Despite what you might think, AI is increasing the premium on good architecture and design rather than diminishing it. As implementation becomes easier, the relative importance of system design, interface definitions, and architectural boundaries has grown. Leading organizations now spend more time on design activities and less on implementation than a few years ago. This represents a healthy evolution, though there’s concern that AI tools aren’t yet sophisticated enough to validate architectural decisions, potentially leading to technically functional but poorly structured systems.

Inbal Shani, the chief product officer (CPO) and head of R&D at Twilio, says in an article by McKinsey, “AI can help analyze data sets and be an unbiased element in the conversation. After making strategic decisions, AI can help continuously monitor metrics and evaluate the progress.” As McKinsey’s article explains, this can accelerate development cycles by increasing the odds that resources are allocated to the most promising ideas, ultimately reducing costs and lowering the chance of failure.

The Emergence of AI-Centric Development Roles

The impact of AI on software development is significant, but the most dramatic effect on the industry is the influx of new, AI-specific roles that these technologies have necessitated. These specialized roles range from generative AI prompt engineers to workflow architects, AI code auditors, and technical debt analysts. LinkedIn’s Skills on the Rise report says as much, reporting that AI literacy is the fastest-growing skill that “professionals are prioritizing and companies are increasingly hiring for.”

One way these technologies can change DevOps teams is by converging several roles into one. For example, if AI can autonomously perform tasks that once required a human, the responsibilities of each team member are destined to change. Varun Parmar, general manager at Adobe and former CPO and COO of Miro, said to McKinsey, “The PMM and PM role will most likely converge under the same product organization.” He explains that, as AI automates more PMM-centric tasks, the “PMM function will need to go really deep into positioning and become fully integrated within the product team.”

Upskilling for the Future

Sulabh Soral, the Chief AI Officer at Deloitte, puts it this way: “Hybrid collaboration between human expertise and AI efficiency will pave the way for not only creating functional and reliable software but also groundbreaking, transformative solutions that push the boundaries of what is possible in the digital world.”

The AI impact on software development is an evolving, fluid thing. As clear as its repercussions have already proven, the continued growth and integration of new AI technologies will result in the impact being an ongoing, ever-changing reality for professionals to reckon with. However, as dramatic as the effects might be, the expertise DevOps professionals bring to the industry will never go out of style. They might need to pivot their skillsets to adapt to new or emerging needs, but the creativity of a human mind will remain essential.

Want more insights like this? Register for Insight Jam, Solutions Review’s enterprise tech community, which enables human conversation on AI. You can gain access for free here!

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