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Tag: citizen developers

  • The Rise of Low-Code/No-Code in DevOps

    The Rise of Low-Code/No-Code in DevOps

    The ever-evolving landscape of technology has ushered in a paradigm shift in software development and deployment. One of the prominent trends gaining momentum is the integration of low-code/no-code platforms within the DevOps ecosystem. This article explores how these platforms are revolutionizing the way applications are developed, deployed and managed, fostering collaboration between developers and operations teams.

    1. Democratizing Development:

    Low-code/no-code platforms empower individuals with limited coding experience to actively participate in the application development process. This democratization of development accelerates the delivery of software by allowing business analysts, designers and other non-developers—sometimes referred to as citizen developers—to contribute directly to the creation of applications.

    2. Accelerating Time-to-Market:

    The traditional software development life cycle often involves lengthy coding processes, leading to delayed releases. Low-code/no-code platforms enable rapid prototyping and development through visual interfaces, significantly reducing the time required to bring applications from concept to production. This acceleration aligns with the principles of DevOps, promoting continuous delivery and faster time-to-market.

    3. Enhancing Collaboration:

    DevOps is all about breaking down silos and fostering collaboration between development and operations teams. Low-code/no-code platforms bridge the gap between these traditionally distinct roles by providing a common ground where both technical and non-technical stakeholders can collaborate seamlessly. This collaborative environment promotes better communication, understanding, and alignment of objectives.

    4. Flexibility in Deployment:

    Low-code/no-code solutions are designed to be platform-agnostic, allowing for the deployment of applications across various environments, including on-premises and cloud. This flexibility aligns with the DevOps principle of infrastructure-as-code (IaC), enabling teams to deploy and manage applications consistently across diverse infrastructures.

    5. Reducing Dependency on Specialized Skills:

    In a DevOps environment, reducing bottlenecks and dependencies on specific individuals or roles is crucial. Low-code/no-code platforms enable teams to be more self-sufficient, reducing the reliance on specialized coding skills or DevOps Training. This, in turn, contributes to a more resilient and adaptable DevOps culture.

    6. Challenges and Considerations:

    While the adoption of low-code/no-code in DevOps brings numerous benefits, it also introduces challenges. This section discusses considerations such as security, scalability and the potential need for customization beyond the platform’s capabilities.

    7. Real-World Use Cases:

    Explore real-world examples of organizations successfully incorporating low-code/no-code in their DevOps workflows. Highlight the positive outcomes, lessons learned, and the impact on their overall software development life cycle.

    Conclusion:

    The rise of low-code/no-code platforms in DevOps is reshaping the way software is developed and deployed. By fostering collaboration, accelerating development cycles, and reducing dependencies on specialized skills, these platforms align seamlessly with the principles of DevOps. As organizations continue to embrace this trend, the synergy between low-code/no-code and DevOps is set to redefine the future of application delivery.

  • Low-Code and AI: Friends or Foes?

    Low-Code and AI: Friends or Foes?

    The lines are becoming blurred between low-code and new artificial intelligence (AI). Historically, low-code/no-code platforms have introduced software development automation through graphical user interfaces, enabling professional and citizen developers alike to quickly build workflows and generate applications. But the rise of large language models (LLMs) represents an existential threat to the status quo of low-code development.

    So, are generative AI and low-code friends or foes? Since new generative AI can produce complex code in milliseconds given natural language prompts, the barrier to software development has never been lower. On the other hand, enterprises already have ingrained low-code practices and operations, which may stick around for a while. With that in mind, there are plenty of ways AI and low-code can coexist.

    Below, we’ll consider if AI and low-code are diametrically opposed tools or complementary forces. We’ll see how AI and low-code are merging and consider the benefits of using them in tandem.

    Unleashing the Power of AI

    LLMs, like OpenAI’s GPT-3.5 and GPT-4, can deliver impressive software generation capabilities, such as creating code snippets and even complete programs given natural language prompts. Because of these remarkable capabilities, it makes sense that low-code platforms should be a bit wary of AI.

    Artificial intelligence can actually deliver on some of the original promises of low-code, like software democratization, enhanced agility and filling the widening skills gap. “ChatGPT could allow users to make more significant changes to applications, potentially enabling organizations to achieve far more than with low-code and in half the time,” wrote Romy Hughes on devopsy.kinsta.cloud.

    There are also plenty of ways LLMs could accelerate the DevOps side of the cloud-native equation. In addition to code generation, AI could help generate configuration manifests, run code tests and perform cybersecurity analysis. While AI can’t fully replace the nuance of human oversight, it can indeed automate more and more elements of the software development life cycle.

    Congruence Between Low-Code and AI

    Although it appears likely that AI will replace low-code, there are actually many opportunities for symbiosis between the two concepts. Rather than eradicate low-code platforms entirely, LLMs will likely become more embedded within them. We’ve already seen this occur as low-code providers like Mendix and OutSystems integrated ChatGPT connectors. Microsoft has also embedded ChatGPT into its Power Platform as well as integrated GPT-driven Copilots into various developer environments.

    “Low-code and AI on their own are powerful tools to increase enterprise efficiency and productivity,” said Dinesh Varadharajan, the chief product officer at Kissflow. “But there is potential for the combination of both to unlock game-changing automation for almost every industry. The power comes from the congruence between low-code/no-code and AI.”

    There is also the opportunity to train bespoke LLMs on the inner workings of specific software development platforms, which could generate fully-built templates upon natural language prompts. This would make the LLM more custom-built and relevant to the platform at hand and ease development fluidity. In addition to integrating existing LLMs, another potential synergy is that low-code solutions could help automate the training of new custom AI models.

    Tips to Keep In Mind

    As with any technology solution, the choices should be driven by concrete business outcomes. Start by defining the business objectives and let that determine the technological solutions to arrive at the outcomes you desire. In the context of low-code and AI, this might equate to deciding between which models to support.

    Next, enterprises must avoid risk when using low-code platforms. And on the AI side of the coin, platforms will have to navigate the data privacy and legal complications of using public LLMs. Like any new technology, AI and low-code will require governance, said Varadharajan. “Organizations should step in to standardize the tools, processes and DevOps cycles. Only certain AI-powered tools should be approved for development where there are agreeable security, privacy and IP protection agreements in place.”

    He also explained that a focus on change management would be paramount in reaping the benefits of both strategies. “I suspect that the greatest barrier to adoption and implementation will be change management,” he said. “Enterprise users are accustomed to certain ways of working and can be resistant to change, making them unlikely to embrace AI-powered low-code. Leaders should anticipate this and incorporate change management into their AI/low-code adoption strategy.”

    Closing The Semantic Gap

    Low-code came on the scene to help citizen developers create applications without requiring advanced technical experience. Yet, low-code still requires knowledge of the development platform as well as specific programming terminologies and formulas. But now, since generative AI behaves in response to natural language, it has the potential to instantly bypass this barrier.

    “Generative AI closes this semantic gap,” said Varadharajan. “Generative AI serves as a translator, and users no longer have to understand exactly how the system works.”

    As more AI pair programmers come onto the market, expectations are rising within all areas of software development, including low-code platforms, to integrate these advanced models. If leaders can navigate the technical change effectively, the outcomes will likely significantly increase agility and output.

    “Ultimately, all developers will end up saving time delegating repetitive tasks to these AI-powered pair programmers,” said Varadharajan.

  • Majority of Organizations Report Benefits From Low-Code Platforms

    Majority of Organizations Report Benefits From Low-Code Platforms

    Some IT departments are feeling the strain amid recent economic downturns. 2023 has already seen a disproportionately high degree of layoffs in the tech sector. As a result, there aren’t always enough software developers available to meet the demand. This has led some organizations to turn to new ways to upkeep morale and reduce burnout, retain productivity and do more with less.

    Low-code development is one solution that could help tackle rising application backlogs. Low-code/no-code platforms use reusable components to help engineers (and citizen developers) construct functional applications and internal workflows. Many teams report they are using low-code to rapidly deliver new features. A recent study from Microsoft found that 89% of CIOs and IT pros said low-code is effective in increasing efficiency.

    Microsoft Power Platform recently sponsored a study that surveyed 2,000 IT CIOs and IT professionals on their use of low-code platforms. According to the report, users benefitted from these platforms by modernizing legacy IT, improving efficiency and reducing costs, among other areas. Below, I’ll review some key takeaways from the Microsoft study and compare them with other perspectives.

    The Benefits of Low-Code Development

    The report showcased a slew of benefits from the use of low-code platforms. One is modernizing legacy applications—87% of CIOs and IT pros said low-code is very useful in helping their organizations modernize legacy applications. Solving the technical debt issue is top-of-mind, since 69% of IT leaders identified technical debt as a threat to innovation. Another key benefit is efficiency. The study found that developer efficiency with these platforms significantly increases over time, averaging 62% over three years.

    Low-code platforms are also a prime area to insert artificial intelligence and more automation into the software development process. Microsoft, which recently announced it was integrating ChatGPT into its Power Platform, reported that 87% of CIOs and IT pros said increased AI and automation embedded into low-code platforms would help them better use the full set of capabilities.

    Finally, a significant benefit is cost reduction. A full 87% of those surveyed said low-code is effective in helping reduce costs. These solutions could accelerate the completion of the software backlog, increasing efficiency and lowering the resources required to deliver. This comes at a time when cost optimization is critical to avoid rising cloud fees.

    How Are Companies Working With Low-Code?

    So, how are development teams actually working with these platforms? One common use case is improving data processes by streamlining integrations and having a centralized repository. In fact, 86% of CIOs and IT pros said low-code helped their organizations generate more accurate insights from their data. These data-driven insights have been used to help better serve customers and internal stakeholders.

    For example, ÖBB, an Austrian rail operator, leveraged a low-code platform to synchronize data and automate customer service inquiries with virtual agents. Other case studies report using Microsoft Power Platform for migration from legacy systems and reduced document processing lead time.

    Looking outside of the Microsoft report, however, not everyone is fully on board the low-code bandwagon. For example, the CEO of StackOverflow has expressed reservations because low-code platforms ultimately have a ceiling. With low-code, there’s a limit on customization, making it a less-than-ideal fit for experienced developers or technology-specific companies. Organizations must also mitigate the potential security repercussions of allowing business users to create applications, requiring new governance and policies. Low-code platform users may also fall victim to vendor lock-in and uneasy data retrieval—underscoring the need for open platforms with programmatic access.

    The Advantages of Low-Code Platforms

    Low-code solutions are a viable option to help clear items from the IT backlog, automate tedious processes and free up engineers to focus on more pressing matters. Some low-code/no-code tools also help democratize application development to those with different skill sets, thus bringing in new users to the development arena. And interest in low-code continues to pick up—Markets and Markets forecast a compound annual growth rate (CAGR) in the low-code development industry platform market of 28.1% to reach $45.5 billion by 2025.

    Naturally, the findings and case studies in this particular report are a bit skewed toward the benefits of using the Microsoft Power Platform. Nonetheless, it serves as an indication of the various advantages of low-code platforms. For more insights, you can read the full report here on the Microsoft Power Platform blog.

  • DevOps Adoption in Salesforce Environments is Advancing

    DevOps Adoption in Salesforce Environments is Advancing

    A global survey of DevOps professionals building apps in Salesforce environments found 44% of respondents reported their organization has a continuous integration/continuous delivery (CI/CD) platform in place, with another 39% planning to adopt one.

    The survey, conducted by Gearset, a provider of a DevOps platform for building custom applications in Salesforce environments, polled 1,254 DevOps professionals that are building and deploying custom applications on top of the Salesforce software-as-a-service (SaaS) platform. The survey also found nearly half (49%) implemented change monitoring, with another 33% planning to follow suit. More than half (52%) implemented source-drive workflows, with another 33% similarly planning to follow suit. A total of 41% of respondents noted that, over the last 12 months, they have more custom code to manage.

    Overall, the survey found 54% of respondents reported increased productivity, followed by improved quality and faster releases at 41% each. Nearly three-quarters (72%) reported they are able to deploy changes from one environment to another within four hours. Well over a third (39%) of this group are able to complete a deployment in less than an hour. Just under a quarter (23%) are also able to release code multiple times a week.

    However, only 18% can release a new feature to production in less than a day. Nearly half can complete the release within one day to a week. A total of 43% claimed that less than 10% of releases include a bug or error, with 46% reporting they can roll back a release in a day or less, if needed. Just over a third can restore a process in a day or less.

    The biggest obstacle to DevOps adoption is lack of experience (41%), followed by a third (29%) of respondents that reported difficulties with collaboration and another 29% that experienced limited buy-in from the rest of the application development team. Only 30% received DevOps training once a month, with 50% citing additional CI/CD and automation training as their biggest need.

    Despite these issues, a full 98% are generating a return on their DevOps investments, with 59% of teams reporting returns of over $10,000 per month and 40% reporting returns in excess of $50,000 per month. The top two priorities for the next 12 months are to consolidate systems and processes and improve flexibility, tied at 47% each, followed closely by 43% that noted that digital business transformation is a top priority.

    David Runciman, technical content manager for Gearset, said that while Salesforce applications are based on unique object architecture, it’s apparent that organizations are extending those applications using, for example, low-code tools more quickly. The issue that creates is, as organizations move to deploy these applications, they are encountering bottlenecks that conspire to slow the pace of innovation, he noted.

    The maturity level of DevOps teams building these applications will naturally vary. However, as the number of applications being built—especially by professional developers—increases, it’s only a matter of time before more organizations embrace DevOps best practices to bring order to the chaos, noted Runciman. While there may be some citizen developers building applications in Salesforce environments, the bulk of enterprise-class applications are built by professional developers that typically appreciate DevOps best practices, he added.

    The issue remaining is just how quickly those DevOps teams will be able to implement DevOps best practices as demand for additional applications grows.

  • How Will Low-Code and AI Coexist?

    How Will Low-Code and AI Coexist?

    Over the last few years, low-code solutions have become important to help organizations widen programming accessibility to a broader base of users and help fill development gaps. And these technologies are still gathering steam—Gartner estimated the market for low-code development technologies would grow to a total of $26.9 billion in 2023 and that 65% of applications would be developed using low-code by 2024.

    However, AI-based code generation is set to further level the playing field with advanced auto-completion and AI coding assistants. Take GitHub’s Copilot, a helpful AI pair programmer, or OpenAI’s ChatGPT, which can generate complex functions given natural language prompts. Other AI tools trained on UIs, like Galileo, can derive frontends for any project in a matter of seconds. So, are low-code development platforms still relevant in this new paradigm of AI-driven software development? If so, how will low-code solutions and AI coexist?

    Well, generic algorithms can only go so far in creating truly unique and innovative experiences that solve real-world business cases. And since most programmers don’t have the skills to train machine learning or deep learning models from their own datasets, this is one area where low-code solutions could lower the barrier, enabling users to tag unstructured data, generate models, run simulations and share reusable AI across departments.

    I recently chatted with Johanna Pingel, MathWorks AI product marketing manager, to discuss the role of AI in low-code programming. According to Pingel, low-code solutions can lower the barrier to AI functionality, both in quickly integrating AI-driven functionality into applications and seamlessly generating new AI algorithms based on unique datasets. Below, we’ll consider some additional use cases for AI and how low-code platforms may work alongside it.

    Use Cases For AI

    First off, what are application developers using AI for these days? Well, AI has an increasingly wide range of uses. It began with computer image and processing, such as identifying cats and dogs or postal codes. Now, voice recognition, voice-to-text and text-to-voice are commonplace, and AI is being used to create ever more sophisticated applications. One area that Pingel is excited about is applying AI to help with battery life management, predicting the lifespan of batteries for electric vehicles and consumer electronics.

    “Anywhere there’s data, there’s an opportunity to create models and make predictions based on that data,” said Pingel. This especially rings true for scenarios involving data and visualization. For example, consider visual inspection in a pharmaceutical production line to ensure there are no defects with any pills. Another use case is visual inspections of parts in automotive assembly lines. Applied to these scenarios, AI can remove grunt work and make production safer and more efficient.

    ChatGPT is another example of an AI application quickly becoming mainstream, not only for content creators but for scientific research. For example, ChatGPT can spin up MATLAB code and create functions and unit tests, which can be copied and pasted into IDEs. However, code written by ChatGPT should still be checked and verified by engineers and scientists to ensure its accuracy, said Pingel.

    How Low-Code Lowers the Barrier to Bespoke AI

    Low-code solutions can bring many benefits—these platforms help to speed up tedious tasks and level the playing field for those with different coding abilities and desires, said Pingel. Low-code platforms can also be a great tool for engineers looking to incorporate AI into their business systems. For example, having a low-code method could be helpful in generating code for machine learning algorithms and new models, which can help companies create their own algorithms and incorporate them into their own platforms.

    More people are interested in incorporating AI into their applications, but the use cases are growing increasingly more sophisticated. Businesses may start with simple needs, such as identifying faces or identifying text from handwritten letters. These rely on simple datasets, and algorithms already exist to accomplish most of these tasks. Off-the-shelf AI is helpful at the start, but eventually, companies tend to want to use their own data and settings far more specific to their applications, said Pingel.

    For example, a more niche case could be as specific as identifying cracks in a windshield. Or, in a financial industry, it might be analyzing mass amounts of loan applications to determine approval rates. In these situations, using your own internal data becomes necessary to create AI that understands the situation at hand. Yet, the hard part is assembling this data in a way that is accessible for a model to comprehend. Analysts estimate that upwards of 90% of data held within enterprises is unstructured, meaning it requires a heavy amount of tagging to become usable for AI/ML.

    Low-code is a perfect solution to avoid the point-and-click aspects of cleaning, cropping and structuring this data, said Pingel. This can introduce automation into the process of labeling data while still involving some human intervention to fix mistakes. Machine learning models can take a while to train and simulate, underlining the need for a fast way of iterating on models. A common platform could help companies develop custom machine learning algorithms and models and keep track of experiments. This helps to ensure that the most effective model is used for the final application.

    End Benefits of Democratized AI With Low-Code

    Overall, low-code platforms can provide a number of benefits that lower the barrier to unique AI creations. Low-code platforms like MATLAB and Simulink are useful for structuring and labeling data and quickly generating ML models. Low-code also makes it easier to bridge different programming languages and quickly generate code for a variety of different languages, such as C, C++ and Java.

    Additionally, low-code platforms enable users to collaborate easily with other users. But low-code doesn’t mean no-code, Pingel explained. In fact, “you can learn to be a better programming through low-code tools, too,” she said. For example, it could enhance understanding of the structure and syntax of code.

    In general, democratized AI is an important capability for businesses to take advantage of. Although AI-based solutions are coming to market to displace old tools and processes, there is still an argument to be made for low-code—namely in empowering non-technical users and lowering the barrier to creating custom AI-driven functionality.

  • No-Code Automated Software Solves a Host of Problems

    No-Code Automated Software Solves a Host of Problems

    There’s a huge disconnect in corporate America, and it’s costing companies big time. The disconnect involves software testing, or, more precisely, the lack of it. More than nine out of 10 CEOs are confident that their company’s software is tested regularly, yet roughly eight out of 10 product testers reported that up to 40% of their software goes out the door with insufficient testing. This dichotomy came to light in the latest Leapwork survey of 500 CEOs and software testers.

    The consequences of untested software are potentially dire and far from hypothetical. Flawed software that delayed the launch of new car models contributed to the dismissal of Volkswagen CEO Herbert Diess, while Facebook saw its market cap value plummet by billions in 2021 when a software glitch came to light. 

    It’s not as if anyone is unaware of such risks: A whopping 95% of CEOs surveyed admitted they’re concerned about losing their jobs if a software failure occurs, while 76% of testers shared that concern. Those feelings are certainly valid, as more than three out of four CEOs reported that software failures damaged their companies’ reputations during the past five years.  

    Despite these realizations, CEOs seem willing to gamble. A full 85% said they considered it acceptable to release software that wasn’t adequately tested, as long as it can be patched later. That attitude has made patching standard operating procedure. More than half the testers said their teams spend five to 10 days a year patching software. 

    Why are organizations willing to take unnecessary risks by releasing software that’s potentially not ready for prime time? There are several common reasons—and a solution that many are overlooking. 

    The Problem with Manual Testing

    One of the major problems is the continued reliance on manual testing, which requires time and resources companies can’t afford. Only 43% of testers said they use automation to some extent in their testing, proving that the majority of testing is still a manual process.  

    That significantly slows product development, an issue for businesses anxious to get their products to market before the competition. Nearly 40% of CEOs said the time-consuming nature of manual testing is one of the primary reasons they release programs that haven’t been properly vetted. Testers clearly feel the crunch, with about one in three saying faster development cycles don’t allow enough time for testing. 

    Given the choice between rushing through testing or delaying a release, companies often roll the dice and opt for the former.  

    Two recent pandemic-related trends have made the situation even more difficult. More organizations are embracing digital platforms to cope with the effects of COVID-19 on their businesses, increasing the need for IT talent. At the same time, upheaval in the labor market has resulted in widespread resignations and job hopping, contributing to a serious shortage of skilled IT personnel. 

    The Coding Conundrum

    Even in this chaotic business climate, experienced software developers remain a hot commodity, with many career options at their fingertips. The competition for talent has left many IT teams scrambling and understaffed, making it difficult to produce high-quality software at a breakneck pace.  

    Eliminating the need for manual testing could relieve the pressure to a great extent. By automating testing, organizations can reduce the burden on staff and minimize the risk of errors caused by deadline pressures. That would allow more testing of more software in less time, which would help move product out the door and into the hands of customers. 

    But automated testing isn’t without its challenges, as many organizations have discovered. Many solutions that are billed as “low code” still demand coding skills beyond the capability of typical business users and testers. That need for coding is an obstacle that can drag down the pace of testing and make it difficult for business users to participate in the process.    

    That challenge grows as the need for ongoing maintenance expands. The test engineers who came on board to set up an automation solution can find themselves bogged down in maintaining code. As a result, speed to market continues to suffer. 

    Democratizing the Test Process

    There is a solution: true no-code test platforms. This type of solution allows both testers and business users to design, run and maintain tests without the need for coding. Business users who understand how the software deliverables are supposed to grow business can be intimately involved in testing, which enhances the quality and functionality of the product and the satisfaction of customers. 

    It also promotes speed. When testing is largely automated and more people are involved, it’s possible to test more software in less time. This eliminates bottlenecks and addresses the issue of scale. The use of automation on a no-code platform has been shown to speed time to market by a factor of 10—an improvement that largely eliminates the need to rush potentially flawed software to market without adequate testing.   

    Organizations can position themselves to meet the growing demand for digital solutions by revamping the way they test software. By eliminating manual testing and adopting an automated platform that doesn’t require coding skills, they can afford to test all their products thoroughly. The end result: They can minimize the risk of bugs that could undermine their success and threaten job security.  

  • Salesforce Launches Low-Code DevOps Platform

    Salesforce Launches Low-Code DevOps Platform

    Salesforce made generally available today a low-code DevOps Center service on its infrastructure through which developers are provided an opinionated platform for building custom applications. The Salesforce DevOps Center service is based on the same object model Salesforce uses to construct its own applications.

    Karen Fidelak, senior director of product management at Salesforce, said after nearly two years of development effort, this platform will make it simpler for both professional and citizen developers to build applications consistently using a common Source Control repository. That’s especially critical for professional developers that may need to access source code previously created by a citizen developer, Fidelak noted.

    In 2023, Salesforce will embed more robust continuous integration/continuous delivery (CI/CD) capabilities within the platform to make it simpler to deploy custom applications on the Salesforce platform, Fidelak added.

    In general, the number of developers willing to embrace DevOps best practices to build Salesforce applications has been surprising, since the platform was initially launched in 2020, noted Fidelak. She noted that both citizen and professional developers are looking to adopt best practices that provide a set of guardrails that ensure high-quality applications are secure. Salesforce anticipated a much higher level of DevOps resistance, added Fidelak.

    The DevOps Center platform enables development teams to track and deploy changes to custom objects on the Salesforce platform as Work Items that can be integrated with other Salesforce Flows, a set of process management tools the company provides. As development teams make changes in their sandboxes, DevOps Center automatically tracks them. Developers can then point and click to promote their desired metadata components via custom pipelines they define.

    In addition, Salesforce also provides optional integrations with version control systems such as GitHub to centralize the management and governance of artifacts.

    Finally, Salesforce is committing to tightening integration between DevOps Center and multiple software-as-as-service applications such as Tableau and middleware platforms such as Mulesoft, said Fidelak.

    It’s not clear how much low-code is moving through DevOps pipelines these days, but the volume is increasing as more developers rely on these tools to accelerate application development. The issue that creates is citizen developers typically don’t have the expertise required to build and deploy secure applications at scale—at least not applications that anybody other than themselves really wants to use. Ideally, citizen developers should be supported by application development platforms and DevOps teams that ensure security guardrails are in place for building applications at scale. DevOps teams should also reduce the friction that a citizen developer will most certainly encounter when building any application.

    The fact is, in the absence of that support, most citizen developers—who typically have other tasks that require their attention—will simply abandon an application development project should projects become overly complicated. None of this means organizations shouldn’t be working toward building their own army of citizen developers. The increased reliance on citizen developers is part of an inexorable wave of the democratization of IT. The challenge is making sure those applications are of the highest quality possible regardless of who actually developed them.