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Tag: software development

AI, the New Hero of Software Development … or Anti-Hero?
Artificial intelligence (AI) can be a hero or an anti-hero in software development, depending on your implementation approach. According to the 2024 DORA Accelerate State of DevOps Report, AI adoption shows promise with 75% of respondents reporting positive productivity gains, but also reveals unexpected challenges, including a 7.2% reduction in software delivery stability.

The key is to position AI as a supportive partner. AI should be utilized to enhance human expertise rather than replace it.

AI Is Promising, but Let’s Stay Suspicious

AI has been rapidly infiltrating software development for almost two years now. Coding assistants, automated testing tools, and AI-powered analytics promise to transform how we build applications, and some early adopters have shown impressive results. When AI first entered development workflows, it tackled well-defined problems such as code completion, simple test generation, commenting, and draft documentation with reasonable success.

But that initial enthusiasm has given way to more complex realities. Modern software development involves intricate systems, multi-cloud deployments, complex security requirements, and accelerating delivery expectations. AI has shown both remarkable capabilities and concerning limitations in this environment.

After hearing customer stories about dozens of AI implementations, I’ve found the story isn’t as binary as “hero” versus “villain”—it’s more like “hero” versus “erratically-troubled-if-ultimately-victorious anti-hero.” The reality of AI is nuanced—and much more interesting, depending very much on the implementation approach, governance scheme, and where organizations choose to apply AI technologies.

The Toil Problem Is Real and Measurable

Every developer knows the frustration of “toil”—those manual, repetitive tasks that consume time without adding much value. Toil isn’t just a minor annoyance. The 2024 DORA report confirms that this challenge remains persistent, with developers continuing to struggle with time allocation between valuable work and repetitive tasks.

Recently, I had the chance to discuss AI heroism and anti-heroism with the Chief Product Officer of CloudBees, Shawn Ahmed. CloudBees is an enterprise DevOps solutions provider and was co-founded by Jenkins creator Kohsuke Kawaguchi, so it has been in a good position to observe the thousand ways AI has been injected into software development. Their internal research indicates developers spend about half of their time triaging test failures, waiting on build times or addressing security issues—and as little as 20–30% of their time writing code. As Ahmed pointed out to me, this inefficiency seems like it wouldn’t be tolerated in most other industries; it is like accepting a manufacturing line that’s only 20–30% effective.

An example of CloudBees’ approach to incorporating AI productively and helpfully is their continuous integration (CI) capability. It analyzes historical pipeline data to identify patterns in successful deployments and uses AI to provide suggestions for future deployments. This eliminates hours of configuration work, thanks to AI’s ability to base pattern recognition on thousands, if not millions, of data points.

From CloudBees’ perspective, AI in software development isn’t a replacement technology in dev but rather what Ahmed characterizes as a “companion.” AI’s best role is handling routine tasks to preserve and expand the human developer’s time performing creative and strategic work.

Three Implementation Models, Three Different Outcomes

It’s clear that not all AI implementations are created equal. I see three distinct approaches emerging, each with dramatically different results:
1. The Replacers: Some organizations approach AI to reduce headcount by automating development tasks. This model typically delivers poor results, fundamentally misunderstanding both AI capabilities and the creative nature of software development. The DORA research supports this concern, showing that while AI adoption is widespread, 39.2% of respondents reported having little or no trust in AI-generated code, indicating that successful implementations require human oversight.
2. Tools First!: These organizations adopt individual AI tools without a cohesive strategy or governance framework. This approach delivers mixed results—productivity improves in specific areas, but in many cases at the cost of quality, security, or long-term maintainability. The DORA findings highlight this challenge, showing that despite AI’s positive impact on individual productivity and code quality, it can lead to decreased software delivery performance.
3. The Augmenters: The most successful approach treats AI as an amplifier of human capabilities rather than a replacement. The DORA research validates this model, showing that AI adoption leads to meaningful improvements: when AI adoption increases by 25%, individual productivity increases by 2.1%, flow improves by 2.6%, and job satisfaction rises by 2.2%.
CloudBees is built on this augmentation philosophy. According to Ahmed, AI handles pattern recognition and repetitive tasks while humans make decisions on strategic matters. Their DevSecOps solution demonstrates this approach by using AI to identify security vulnerabilities while preserving human authority over remediation strategies. CloudBees’ Value Stream Analytics uses AI to spot workflow inefficiencies while leaving process improvement decisions to the teams who understand the organizational context.

Making AI the Hero of Your App Dev Story

Here are five critical success factors that determine whether AI becomes a hero or an anti-hero for you:
1. Target high-toil activities first. Successful implementations start with well-defined, repetitive tasks where AI can deliver immediate value. CloudBees’ recommended targets include test generation, pipeline optimization, and code reviews, including areas where AI can significantly reduce manual effort.
2. Establish governance first, deploy AI second. Organizations seeing positive results implement governance frameworks before widespread AI adoption, defining, for example, appropriate use cases, verification requirements, or clear boundaries for AI autonomy.
3. Invest in developer education. Contrary to some narratives, successful AI implementation requires more skilled developers. Top-performing organizations invest in training programs that help developers understand both AI capabilities and limitations.
4. Integrate rather than isolate. Rather than adopting disconnected point solutions, effective organizations implement AI capabilities that integrate throughout their delivery pipeline. CloudBees’ platform takes this approach, connecting AI capabilities from requirements analysis through production monitoring.
5. Measure meaningful outcomes. The most effective implementations have moved beyond measuring code generation metrics. Instead, they focus on quality improvements, security posture enhancements, and business impact indicators that reflect the true value of AI adoption.
AI Is Your Enthusiastic Partner, Not Your Replacement

Successful AI implementations in app dev seem to share the common characteristic of using it as a complementary force instead of as a replacement. AI is adept at the mundane, repetitive tasks that drain motivation and attention among developers and increase their toil. It lets humans focus on innovation, customer experience, and ultimately, business value.

According to Ahmed, CloudBees envisions a future where AI becomes increasingly personalized to individual developers. He predicts that within two years, developers will likely work with multiple AI companions tailored to their specific preferences and working styles. This personalization aspect represents an important evolution beyond today’s more standardized AI tools.

A particularly noteworthy perspective I got from research and vendor discussions is the relationship between AI adoption and team scaling. While some organizations initially approach AI to reduce headcount, the data suggests the opposite effect. As Ahmed noted in our discussion, the more effective approach isn’t using AI to reduce a 10-person team to 5, but rather leveraging AI to assist that 10-person team in achieving what would have previously required 15 people. This is the proactive, business-building “scale up, not down” perspective that can help propel an organization to its goals.

I’ve concluded that today, the hero of software development isn’t AI. Not AI alone, but it is no longer purely human expertise either. The true hero is the partnership between them, working in concert to create outcomes neither could achieve independently.
June 17, 2025
Navigating the Complexity of Hard Choices in Software Development
There are many reasons why software development feels more challenging today than a few decades ago. However, a significant factor is the overwhelming amount of choice across all layers of the tech stack.

Consider all the options available in programming languages, frameworks, databases, tools, platforms, cloud services and more. Not to mention the various architectural styles, system design patterns, agile methodology approaches, etc.

The latest CNCF report and the MAD (ML, AI & Data) Landscape further underscore the dizzying array of choices available to developers.

While we often view having a lot of choice as a good thing, in practice, the allure of unlimited choice can be deceptive.

Although having options gives developers more ways to solve specific project requirements, an excessive number of choices can be overwhelming, leading to indecision, procrastination, suboptimal selections or even complete inaction.

The impact of choice paralysis extends beyond the mental strain on the individual

—which is significant in itself. A compromised decision-making ability impacts a core engineering skill — architecting software — and hinders developers from making their most significant contributions to a project.

Indeed, effective system design hinges on a series of interconnected decisions that define the high-level conceptual structure of a system, including its major components and their interactions. This encompasses all aspects of the system, such as software, hardware, data, interfaces and user interactions.

This blog post aims to explore the factors influencing our choices and to present some strategies for making more informed decisions.

Anatomy of a “Hard Choiceˮ

When A is better than B, the decision is straightforward — you choose A.

When A and B are equivalent, the choice remains simple. If they are equally good, you might as well flip a coin.

However, when A is better than B in some ways, but B is better than A in others, youʼre faced with a hard choice.

The complexity of a “hard choice” arises from how the alternatives compare: in an easy decision, one option is superior, or they are so similar that the choice becomes inconsequential.

In a hard choice, you must perform a trade-off analysis, weighing the pros and cons of each option, knowing that neither is absolutely superior to the other. As philosopher Ruth Chang would describe it, the alternatives are “on a par“: the choice can significantly impact the outcome, yet one option isnʼt universally better than the other.

This scenario is common in software architecture, where there is no single “correct” way to design a system — all choices are valid as long as they meet the product and technical requirements and the team is aware of the trade-offs and potential technical debt.

Randy Shoup, VP of Engineering at eBay and WeWork, captures this sentimen t perfectly: “There is no perfect architecture for all scale, for all phases of evolution, for all problem domainsˮ.

The Layers of Decision-Making

Understanding human decision-making requires considering a multitude of factors, including the physiology of the human brain, psychological influences (such as cultural biases, emotional triggers, past experiences, internal and external expectations and peer pressure) and external environmental factors.

Just to illustrate the complexity of all the factors that influence our choices, consider these studies:
- An average human can hold 7±2 items in their short-term memory.
- According to Millerʼs Law, on average, weʼre good at comparing one variable at a time with a maximum of 7 choices. For instance, choosing between seven apples based on price alone is manageable. However, our performance decreases as the number of variables — such as color, weight, shape, taste, etc., increases, complicating our ability to perform complex trade-offs and make optimal decisions.
- Comparisons change how we evaluate things. Our choices are influenced by the arrangement of options. When selecting wine, a consumer is likely to choose the middle-priced option from a set of three respectively priced at 8$, 27$ and 33$. Introducing a higher-priced fourth option (e.g., 57$ can make the previously highest price of 33$ seem more reasonable, thus shifting consumer preference. Behavioral economist Dan Ariely TED lists other external forces to which weʼre susceptible.
- We place a disproportionately high value on things we create ourselves. This phenomenon, known as the “IKEA effectˮ, and explored in a 2011 study, shows that people are willing to pay up to 63% more for furniture they assemble themselves compared to pre-assembled items. However, marketers knew well before this study that “the more effort someone puts into something, the more they will value itˮ. For example, this is the same psychological driver that forced General Mills to revisit their failing “just-add-waterˮ cake mix to “just-add-an-eggˮ. The simple act of replacing the powdered egg with a real egg made the creation of the cake more fulfilling and sales rebounded.
- The time of the day affects your choices. This is called the “Hungry judg e effectˮ and it was named after a 2011 study that revealed that judges are more likely to grant parole in the morning than in the afternoon, due to decision fatigue. Although this particular study was later revisited, subsequent research supports the idea that our decision-making capacity diminishes throughout the day, leading to more impulsive and less confident decisions.
- When our prefrontal cortex is busy, our emotional brain takes charge. The 1999 “Heart and Mind in Conflictˮ study found that when our prefrontal cortex is overloaded — such as when trying to memorize seven digits — we are more prone to make suboptimal decisions, often favoring emotion-driven decisions (choosing cake over fruit) due to reduced impulse control.
- Incorrect incentives might have counterproductive outcomes. The Cobr a effect exemplifies how misguided incentives can backfire. When the British government in colonial India offered a bounty for every dead cobra, it inadvertently encouraged the breeding of snakes for profit, ultimately exacerbating the problem when the program was cancelled and the captive snakes were released. In practice, we might choose an option because of the immediate apparent benefit, even though the end result might be counterproductive for our end goal.
The Paradox of Choice

Barry Schwartz vividly illustrates in his TED Talk, “The Paradox of Choice”, why an abundance of options can lead to decision paralysis and diminish our productivity.

In the era of post-industrial capitalism, the concepts of choice, individual freedom and self-determination have been elevated to an ideal. We often associate freedom with the ability to choose, underpinning the belief that “more choice equals more freedom, which equals more happiness.ˮ

However, this ideology also has a darker side, marked by an increase in anxiety, indecision and perpetual dissatisfaction.

The two primary negative effects of excessive choice are:

1. Choice Paralysis: With a plethora of options available, the decision-making process can become so overwhelming that it becomes difficult to make any choice at all.

2. Dissatisfaction with Decisions: Even after making a choice, we often look back and compare our decision against the options we didn’t select. Each disappointing aspect of our chosen option can breed regret. Moreover, the sheer volume of available choices escalates our expectations for a “perfect” fit, leading inevitably to disappointment when these lofty expectations are not met—even if the outcome is objectively good.

Best Practices for Complex Choices

There are countless strategies, philosophies and books dedicated to enhancing decision-making skills. After all, this skill is crucial not only in software development but in many other disciplines such as finance, research and data analysis.

Here are some best practices that I have personally found effective:

1. Check Your Assumptions Before You Decide

Before making a decision, review and challenge the constraints and requirements that will guide your choice. Ensure there is clear alignment on what is essential versus what is desirable among all stakeholders, and understand the boundaries (financial, regulatory, SLAs, etc.) within which you need to operate. This helps prevent misalignment and the need for backtracking later.

A notable example of the importance of details is the loss of NASA’s $125-millio n Mars Climate Orbiter in 1999 due to a metric conversion error, illustrating how critical accurate information is in decision-making.

2. Employ the Explore-Exploit Trade-off

In their book “Algorithms to Live By: The Computer Science of Human Decisionsˮ Brian Christian and Tom Griffiths propose to use computer strategies to help with decision making.

The explore-exploit trade-off is particularly useful when balancing the choice between trying something new (e.g. a new architectural style) and sticking with a known quantity (e.g. modular monolith).

If facing a tight deadline or a quick release cycle, itʼs advisable to “exploit” by leveraging existing knowledge and choosing the familiar option. Conversely, if the timeframe allows, “exploring” (i.e., experimenting with a new architectural style) may yield valuable insights that improve future decisions.

3. Ask for Support From Your Community

Decision-making does not have to be a solitary task. As psycho-economist Sheena Iyengar points out in her talk “The art of choosingˮ involving your team or community in your decisions can be beneficial. Relying on the judgment of trusted and respected colleagues does not diminish your competence; it underscores the importance of collaboration and recognizes individual limitations—after all, software architecture is a team sport.

4. Donʼt Waste More Time Choosing Than Doing

The 2003 overhaul of the Linux scheduler from an O(n) to an O(1) scheduler illustrates this point beautifully. The former ranked each task individually, often spending more time on task prioritization than on task execution. The switch to the O(1) scheduler that grouped tasks into a limited number of priority buckets increased performance by being less precise but executing in a constant time.

This example highlights that no choice is perfect. Surprisingly, it can sometimes be more effective to approximate a decision and complete a task, moving on to the next one, rather than spending excessive time researching and prioritizing options. Statistically speaking, the optimal stopping point for your research is a t 37% of the way through your options, anyway.

Conclusion

In conclusion, navigating the vast array of choices in software development is no trivial task.

No choice is perfect, and importantly, very few choices are irreversible. Opting for a suboptimal solution or accumulating architectural technical debt simply signals the need for continuous evolution and improvement in your systemʼs architecture. This ongoing process necessitates adapting and making more informed decisions as your system and its requirements evolve.

Itʼs unrealistic to expect that one can consider every option or predict every potential challenge or evolution in requirements. Even adhering to the best decision-making strategy does not guarantee a flawless outcome. A practical approach is to balance the costs of potential errors against the delays that indecision might cause. This mimics the way computers handle complex problems — by simplifying them, removing constraints, or permitting approximations, all aimed at making the most sensible choice in the least amount of time.

Ultimately, the goal is to find a balance that empowers developers to make meaningful decisions without becoming overwhelmed. By implementing strategic decision-making processes, we can provide the necessary framework to reduce choice overload, thus enhancing productivity and satisfaction in software development environments.

Further Reading

Here are some interesting books, if youʼd like to continue your research on this topic:

The Tyranny of Choice (Big Ideas) by Renata Salecl

The Paradox of Choice: Why More Is Less by Barry Schwartz

The Art of Choosing by Sheena Iyengar

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian and Tom Griffiths
June 11, 2025
How AI Is Transforming the Future of Software Development with Wing To

Digital.ai CTO Wing To delves into how the way code is written, deployed and managed will fundamentally change in the artificial intelligence (AI) era.

Cutting through the extremes, AI won’t erase entry-level dev jobs, nor will it fully replace senior developers. Instead, it’s changing what both groups focus on. Entry-level devs will need to learn how to effectively prompt, evaluate and refine AI-generated code, while senior devs may shift toward mentoring, reviewing AI-assisted output, and ensuring software quality. The fundamental knowledge of how systems work still matters—AI doesn’t remove the need for strong foundational skills.

But AI does change the coding landscape. Developers are writing less and reading more. Reviewing AI-generated code—often verbose, not elegant—has become part of the job. And while some fear that the code is harder to debug, it may just be different: more mechanical, less idiosyncratic.

From a DevOps perspective, AI introduces new challenges and opportunities. DevOps engineers are facing a surge in code volume and speed—often without a corresponding bump in quality. Automation, golden paths and better processes are essential to handle the influx of AI-assisted code. AI can also help streamline repetitive work: writing scripts, maintaining configurations, generating tests or spotting compliance risks.

Eventually, DevOps teams may evolve into ecosystems of human engineers and collaborative AI agents. That vision’s still emerging, but orchestration between tools and agents is the next hurdle. The goal? To let developers and engineers focus more on creative, value-adding tasks—and less on toil.

Still, AI must be used thoughtfully. It’s not a magic fix. It’s a power tool—one that needs skill, discipline and a good understanding of when (and how) to use it.

June 10, 2025
DevEx Got You This Far: What’s Next for True DevSecOps Maturity?

The journey toward integrating security into the fast-paced world of DevOps has seen significant strides, largely thanks to a much-needed focus on the developer experience (DevEx).

Collectively, the AppSec community and industry have moved mountains in making security tools less intrusive, integrating them into developer workflows, and acknowledging that developers need security as a help, not a hindrance. A focus on DevEx has been crucial, acting as the bridge to bring security considerations earlier into the development lifecycle while simultaneously enhancing the developers’ daily work.

The numbers don’t lie: Checkmarx’ recent DevSecOps Evolution 2025 report reveals that a majority of the organizations we surveyed—58% to be exact—are at Stage 2 maturity, a transition stage primarily focused on DevEx: They’ve integrated tools into IDEs, offer formal training, and the relationship between security and development teams is generally positive. While these developments signify important progress, this DevEx-focused stage represents a critical foundation rather than the ultimate destination in achieving mature DevSecOps.

DevEx Isn’t the Destination

Organizations haven’t yet achieved true DevSecOps maturity. They haven’t yet embedded security through shared culture, governance and aligned goals.

And while developer experience is essential, it’s just one brick of the DevSecOps foundation. To achieve the ultimate goal—high-performing, secure code delivered at the pace of business—organizations need to move beyond DevEx to a true culture of shared responsibility and trust.

Despite better tools and training, developers still report spending a significant chunk of their week—often over 17 hours—on security tasks. Trust in tool results is often hampered by false positives impacting velocity, and a lack of shared goals that align development and AppSec priorities means security can still feel like a separate concern rather than a shared objective. We’ve improved the experience; but haven’t fully embedded security seamlessly and at scale.

The Scale – and Stakes – Are Growing

The need to mature beyond DevEx is especially urgent given the accelerating complexity and scale of modern application development. Enterprises are managing sprawling codebases and adopting tools that increase both speed and risk. Checkmarx alone scans over 450 billion lines of code every month, and that figure is only rising.

At the same time, the software supply chain is expanding at an unprecedented pace: approximately 700,000 new NPM package versions are introduced monthly, each one a potential point of exposure.

Add to this the growing adoption of AI-assisted coding tools and agentic AI systems that generate or modify code autonomously, and the attack surface multiplies further.

On the other hand, development productivity requirements have never been higher, with organizations tracking DORA metrics and looking for AppSec solutions like Checkmarx’ that deliver on security while optimizing MTTR and other DORA metrics.

Achieving truly mature DevSecOps—Stage 3, where only 30% of organizations currently reside—requires moving beyond just making security palatable for developers. It demands a deeper, more strategic integration built on shared culture, robust governance, trusted automation, and aligned success metrics.

Checkmarx’ Five Pillars of DevSecOps Maturity

At its heart, DevSecOps is a cultural evolution. It’s about merging the “move fast” ethos of development with the “protect everything” mandate of security to achieve a common goal of delivering secure, high-performance code at the speed the business requires. To progress from Stage 2 (DevEx-focused) to Stage 3 (Mature DevSecOps), organizations need to build on their DevEx foundation with five key elements:

1. Shared Metrics

You can’t improve what you don’t measure, yet our research shows a surprising lack of consensus on DevSecOps metrics. While many organizations we surveyed track top vulnerable applications (52.1%) and mean time to detect vulnerabilities (51%), only 28.3% measure mean time to remediate—arguably the most critical metric for balancing security and velocity.

True DevSecOps maturity requires security and development teams to align shared metrics that measure how effectively they work together, not just how many vulnerabilities they find.

2. Collaborative Governance

In mature DevSecOps, security and development teams jointly establish policies, standards and processes. Our data shows this remains rare—when asked to describe the relationship between security and development in one word, respondents most frequently chose “trust,” “policy,” and “risk,” indicating an evolving but not yet fully collaborative relationship.

For 88% of organizations, the absence of shared metrics, policies and governance suggests security and development teams are aligning but are still struggling to form a fully collaborative governance.

3. Advanced Security Education

While essentially all developers have access to training according to our study, the model needs to evolve. Traditional formal training programs still have value, but the premium on developer time makes standalone training challenging.

Mature DevSecOps organizations are pivoting toward just-in-time training that delivers contextual guidance when and where developers need it. Providing a step further is in-the-moment training with inline feedback from within the IDE, allowing developers to learn while maintaining their workflow.

4. Trusted Automation

Our study showed that one in five (20%) of the organizations we surveyed have no AppSec automation in place. Even among the remaining 80%, adoption of key automation capabilities remains low—only 32% have automated security testing during change management, and just 30% auto-populate security tickets with remediation guidance.

The far-from-optimal adoption rate of automation cannot be attributed to a lack of technological solutions but rather appears as a matter of trust and culture gaps between security and development teams. Without shared governance and metrics, attempts to “automate everything” typically fail because there’s no foundation of trust on which to build automations that both teams will embrace.

5. Velocity-Security Balance

The ultimate goal of DevSecOps is to deliver high-performing, secure code quickly. Yet 66% of developers report that false positives impede their velocity, and 28% say their AppSec teams lack the tools or resources to help them fix security issues efficiently.

Mature DevSecOps organizations recognize that speed isn’t just about how quickly tools scan—it’s about how quickly developers can remediate issues without disrupting their workflow. This requires prioritized, actionable results with clear remediation guidance, not just faster scanning.

The Role of Leadership

Making the transition to DevSecOps takes more than just good tooling—it requires strong leadership. Security leaders must be willing to trade control for collaboration and build systems based on partnership, not policing. Engineering leaders must recognize that security is essential to velocity, not an obstacle.

This shift requires executive buy-in and strategic commitment. It means moving beyond budget conversations centered on compliance and instead investing in the long-term health of your codebase and your teams.

The Road Ahead

As the DevSecOps Evolution 2025 report shows, most organizations are still in the middle of the journey. That’s not a failure. It’s an opportunity. DevEx helped bridge the gap between developers and security, and now it’s time to build the foundation that inspires them to run together. By moving beyond DevEx as the sole focus and embracing these pillars of mature DevSecOps, we can finally embed security seamlessly at scale, achieving the ultimate objective of delivering high-performing, secure code without compromising speed.

The transformation won’t happen overnight. It requires sustained investment in building trust between security and development teams. But organizations that successfully make this journey will gain the significant competitive advantage of more secure and advanced applications built with less friction and lower costs.

As we look to 2026 and beyond, the most successful organizations will be those that move beyond seeing security as just another feature of the development process. Instead, they’ll embrace it as an integral, collaborative aspect of delivering high-quality software in a digital world with ever-rising security risks.

At Checkmarx, we believe the future of software development is secure by design—and it starts with mature DevSecOps. Let’s keep moving forward.

June 9, 2025
How Benchmarking Can Help Software Development Teams Achieve CISA’s “Secure by Design”

In April 2023, the Cybersecurity and Infrastructure Security Agency (CISA) launched its Secure by Design initiative, directing technology companies to ‘prioritize the security of customers as a core business requirement, rather than merely treating it as a technical feature’.

Since then, more than 300 of the world’s top software manufacturers have signed CISA’s Secure by Design pledge, committing to ‘built-in’ protection that incorporates the seven goals of the pledge: Increasing multifactor authentication (MFA); better enabling customers to do their own patching; reducing default passwords; decreasing vulnerabilities; publishing a vulnerability disclosure policy (VDP); expanding vulnerability transparency reporting in every common vulnerabilities and exposures (CVE) record for products; allowing customers to collect evidence of intrusions affecting products.

It is very encouraging to see such widespread support. Technology providers are committed to integrating defense as a fundamental component of the entire software development life cycle (SDLC), ensuring robust protection from the start, while also complying with regulatory guidelines.

The support arrives at a time when the software development industry faces considerable challenges, as developers too often rely on poor coding patterns and vulnerable components to meet deadlines. Nearly two-thirds of developers say they find it difficult to write code that is free from vulnerabilities, according to our research. About half admit to leaving vulnerabilities in their code. As artificial intelligence (AI) tools have become de facto coding assistants, the potential for security compromises has only increased, with the majority of software and security team members conceding that insecure AI suggestions are common.

To be clear, developers should not solely bear responsibility here. They often work under significant pressure to produce more code within increasingly tight deadlines. Moreover, there is no standardized method for them to assess their security proficiency or compare their practices against peers.

That is why pursuing industry-wide benchmarking is essential as part of a commitment to Secure by Design. It helps set standards for success that lead to a perfectly balanced state of protection and productivity. Ideally, such benchmarking will foster a ‘security-first’ mindset among developers, making security an inherent part of their approach to coding. This will result in impactful risk mitigation and the continuous quantification of their ability to safeguard the products they produce.

Currently, development teams participate in various training sessions and mandated compliance programs to boost these skills. However, these approaches are fragmented and not comprehensive. As a result, they cannot provide a standardized roadmap for the industry-wide defense of software products, which benchmarking could achieve.

The measurement of benchmarks — across learner distribution (how individual learners are distributed in terms of skill level) and skill attrition (a reflection of knowledge retention) — provides critical metrics that enable organizations to evaluate their internal practices against industry peers and the state of the industry overall. As a starting point, organizations should assess their developers’ security skill sets in areas such as language proficiency, contributions to secure code reviews, identification and remediation of vulnerabilities (i.e., structured query language [SQL] injection, cross-site scripting) and implementation of secure coding practices (e.g., proper authentication mechanisms).

Organizations can more readily achieve CISA’s Secure by Design through benchmarking by implementing the following best practices:

Aligning Benchmarking With Industry Standards and Measuring Progress

Establishing a standardized approach to evaluate and improve security practices against proven benchmarks is essential. Teams should incorporate recommendations from organizations such as the National Institute of Standards and Technology (NIST) and the Open Worldwide Application Security Project (OWASP), while maintaining the flexibility to customize practices to meet their specific organizational and industry needs. These teams must then assess their progress based on specific criteria to measure the effectiveness of their secure coding and encryption throughout the entire SDLC.

Enhancing Protection Across the Board
When effectively executed, benchmarking initiatives elevate teams in multiple ways. They help identify and address vulnerabilities, thereby reducing overall risk. They encourage organizations to regularly assess and continuously upskill their developers’ security capabilities, ensuring that teams stay informed about evolving threats and how to mitigate them. Moreover, they foster a culture where security is viewed as an integral part of daily efforts toward continuous improvement, not an afterthought.

Staying Ahead of Regulatory Requirements
Benchmarking supports organizations as they work to satisfy a wide range of regulatory conditions by providing structured documentation of security practices and skill levels. It also demonstrates a commitment to reducing or eliminating risks, aiming for a state of optimal defense.

When you think about the phrase ‘secure by design’, it means that defense comes first and is always a given. It is an innate, foundational part of the product, not something that is merely ‘tagged on’ and/or perceived as a bothersome ‘checklist item’.

By expanding developer team members’ knowledge of threats and measuring their ability to defend against them, benchmarking elevates organizations to new levels of ideal protection, regulatory compliance and continuous improvement. This not only fulfills CISA’s pledge but also builds greater trust with customers.

May 12, 2025
Developing Software That Solves Real-World Problems – A Technologist’s View

From automation of financial transactions to telemedicine, contemporary software architecture is critical to solving various challenges in finance, healthcare, automotive, education and many other sectors.

As a technologist, I have seen how well-designed software can determine the success or failure of a project. What makes one technology more effective than the other? It often depends on the answer to this: Are we creating a product that will be useful, or are we creating a product just because we can?

This is where a lot of digital initiatives go wrong: Before writing code, we need to define the problem to be solved. One of the most challenging requirements I have come across is the ‘a system should work for everything’ concept. If I try to design a software system that works for ‘everything’, it surely will become very complicated. If we apply the Pareto Principle, about 20% of the features would be most valuable to 80% of its users. In high-risk sectors such as healthcare and finance, where compliance is a major issue, not only does the software have to work properly, but it also has to be developed properly.

For instance, a hospital’s challenge to enhance the patient’s waiting time and the accuracy of the diagnosis is not only a technical issue but also a human one. Developing software for that environment implies understanding clinical workflows, patient activities and legal regulations. It also means that you are likely to be working with people who are not technical and may not be able to speak the same language as developers do. This is where software architecture converts business requirements into a structured and scalable system.

The Importance of Software Architecture in the Achievement of Real Solutions

Software architecture is not just a technical plan but a way to turn an idea into reality. A good system can model users’ behaviors and usage, expand to meet demand, secure data and combine well with other systems. It takes the concepts of distributed systems, APIs, security layers and front-end interfaces into one cohesive and easy-to-use product. I have been involved with building APIs that are crucial for the integration of multiple products to provide a consistent user experience to consumers of these products. Along with the group of architects, we played a crucial role in breaking down these complex integrations into manageable components and designing easy-to-implement API interfaces. Also, using cloud services, these APIs were designed to be highly resilient.

For instance, in the financial industry, developing a mobile banking application is not only about designing the user interface but also focuses on data protection, preventing fraud, handling a large number of transactions and fulfilling legal requirements across different countries. A good architectural base allows all those layers to be integrated without introducing risk or complexity to the end product. We were migrating some of our applications from on-prem data centers to cloud services. These applications have considerably basic security implemented. While planning for migration, we established clear guidelines to secure the application by implementing advanced authentication, web application firewall, rate limits for APIs and distributed denial of service (DDoS) prevention.

We have observed that microservices, event-driven architecture and cloud-native approaches help maintain and update the systems without disturbing the product. But architecture is not just selecting popular technologies or cloud platforms; it is creating a system that meets the specific business needs of the organization and the specific issues it is trying to address.

Fintech has changed the way customers experience things with the help of software. Other financial apps teach users how to manage their money, while banks prevent fraud with real-time alerts. These innovations are supported by strong data systems that enable organizations to ingest, process and analyze large quantities of data within milliseconds. The architecture is for agility and security simultaneously, something that can only be achieved through proper planning.

Software in healthcare includes the ability to conduct remote consultations and AI diagnosis, among others. However, the issue here is not just the issue of access but also trust. Patients have the right to their information. Doctors need tools that simplify their work, not ones that add unnecessary complexity. Successful platforms focus on interoperability (HL7, FHIR), privacy (HIPAA) and user experience. This is achieved by making architectural decisions that consider privacy, performance and usability as equal factors.

Automobile manufacturers are now shifting toward electric and autonomous vehicles, and this is due to software. From infotainment to diagnostic to predictive maintenance, software is the new wheel. The real-time communication between sensors, cloud platforms and driver interfaces is a complex architecture with high performance and low latency. Many current vehicles are equipped with over-the-air updates, which would be impossible without highly modular and resilient software systems that can efficiently deliver changes without affecting the essential product features.

Balancing Innovation With Responsibility

One of the most important lessons I have learned as a technologist is that just because we can build something does not mean we should. While working on a project related to financing a car, we were able to collect personally identifiable information (PII). Initially, we had it stored for a long duration. However, we were unaware of the implications. When we discussed the situation with the architecture and security teams, we found out that we don’t have ownership of the data and it was very risky to store that data for a long period. We mitigated the risk by reducing the data retention period to what will be useful to users. It is important to consider the ethical implications, security and future maintainability of innovation. The best solutions are not always the most complicated; they are the ones that serve a purpose and do it effectively, robustly and sustainably.

Other issues include what data to collect, how to use AI ethically or how to handle the edge cases in a critical system. Early decisions in a project have lasting effects, which is why mature software development cultures value business input, user feedback and technical excellence equally. For example, while working on applications that collect PII. To ensure privacy and data security, we decided early in the design process that all information must be encrypted at all stages. Additionally, logs were masked to further protect sensitive data. We also put in robust monitoring so that the development team gets alerted in case of any leakage.

The Human Factor in Software Design

At its core, software is a product of human activity. We develop it for people to make their work, decisions and lives better. Code coverage and performance metrics are important, but so is empathy. Technologists need to know what it means to create something that is going to be used in the real world. Whether it is a doctor using a clinical decision support tool or a student trying to access learning materials on a shared device.

Solving real-world issues with software is not only about creating the biggest and the best system, but creating the right one. Combining user understanding, technical expertise and architectural planning is important. In our contemporary world, digital products are almost everywhere, and technologists have a responsibility to design with purpose, clarity and effect.

The future of software includes staying abreast of the problems we aim to solve before writing code. When we get that right, we don’t just build better systems; we build a better world.

May 8, 2025
GenAI in Software Development with Nathen Harvey

Nathen Harvey, DORA Lead at Google Cloud, delves into how generative AI is rapidly reshaping the software development landscape, presenting both exciting opportunities and complex challenges.

Harvey reflects on his own journey, from early web operations and his time at Chef to leading the DORA program after its acquisition by Google. DORA (DevOps Research and Assessment), now in its 10th year, continues to be a definitive source for understanding how technical teams improve performance and value delivery. The program’s annual reports, especially its latest focused on generative AI, offer research-backed insights that are widely used across the industry.

The conversation dives deep into this year’s findings, starting with the rapid and widespread adoption of AI. Nearly 90% of organizations surveyed are prioritizing AI from a top-down perspective, while over 76% of practitioners report already using AI tools daily. This simultaneous push from leadership and uptake by developers indicates that AI isn’t a passing trend—it’s a permanent shift.

But the data reveals nuance. While AI tools are helping improve areas like documentation, productivity and developer satisfaction, there’s also a measurable decrease in software delivery stability and throughput. Harvey suggests that this may be due to larger and more complex change sets generated by AI tools, which clash with workflows optimized for smaller, more iterative updates. In essence, we’re accelerating one part of the pipeline while other parts can’t yet keep up.

Harvey emphasizes the importance of interpreting DORA findings in context—using them as hypotheses to test within each unique organization. He also raises critical questions about workforce development, particularly how AI might reduce opportunities for junior developers to gain real-world experience and grow into senior roles. The future, he notes, will require balance: leveraging AI as an amplifier while still investing in people and process.

The takeaway? AI is transforming DevOps, but not without growing pains. To navigate this shift, organizations need to blend data-driven research with community collaboration, continuous experimentation, and a commitment to developing talent at every level.

April 23, 2025
Balancing AI Workers of the Future With Human Talent of Today in Software Development

Artificial intelligence (AI) is becoming pivotal across industries and the app development market is no exception. Every app in the world is on track to be reinvented with AI. Even the most basic, single-purpose apps in Windows, such as Notepad and Paint, are now infused with AI to enhance productivity. These longtime staples, once just for quick notes and basic image editing, are evolving with intelligent features that help users work faster and smarter than ever before.

This surge of AI is not only manifesting in consumer-facing app features but is also being integrated into every aspect of software development. This shift is resulting in discourse among developers who are wary of how the technology will affect their roles.

It can’t be denied that AI is transforming how developers work; however, it is not intended to replace jobs but to enhance their effectiveness. Rather than replacing developers, AI enables developers to solve bigger problems much faster, which means that they can deliver more value in less time than ever imagined. Companies aren’t looking to cut development teams; rather, they’re looking to supercharge them with AI-driven efficiency. The real opportunity isn’t in resisting AI — it’s in embracing AI to do more and push the boundaries of what’s possible.

As companies look to integrate AI, it’s up to the tech leaders and developers to make sure that they are using the appropriate tools and methods to incorporate AI into their workflows. The capacity for digital product teams to provide genuine value is expanded when AI is viewed as a collaborator rather than a substitute.

Here are three ways developers can work with AI to enhance efficiency, improve user experience and drive innovation.

Merging AI Insights With Human Intuition for a Successful Business Strategy

With the vast amount of data and information floating around in organizations, it can be hard to gather a clear picture of what’s working and what’s not. Developers often jump between manuals, internal wikis, code review comments, Q&A sites and user feedback, consuming up to 25% of coding time.

That’s where AI can help quickly scan all those data points and pull insights so that developers can better understand how apps are performing. However, what AI is not yet capable of is using these insights to adjust architecture design or to determine the single, best path forward.

Software development teams can utilize the holistic insights generated by AI’s rapid processing capabilities to inform strategic decisions. This may include deciding what features to prioritize or rethinking certain design elements. The anomalies and patterns identified by AI will help developers and project managers optimize their larger business strategies and approach the market with more confidence.

Streamlining Essential – But Heavily Manual – Tasks

To develop an app that stays up to speed with consumer demands and trends, numerous time-consuming, mundane tasks go into the back end of development. These almost mindless routines consume valuable time that could otherwise be utilized for innovative creation or strategic planning.

Developers can offload these tasks to AI agents so that they can devote more time to the areas of development that demand human nuance. With AI, tasks such as the creation of basic user interfaces, the development of data models, or the creation of unit tests can be automated. Moreover, employees can utilize AI agents to assist in creating classes or lines of code that have already been created, instead of wasting time constantly entering the same code.

With all these manual checkpoints taken off their hands, developers can then focus on problem-solving, creative solutions and enhancing app users’ experiences.

Solving the Knowledge Gap With ‘Pair Programmers’

AI agents are capable of more than automating repetitive tasks when it comes to code. More and more businesses are leaning on what they refer to as on-demand ‘pair programmers’ or AI agents to assist their internal developers in bridging knowledge gaps.

These pair programmers can assist any level of developer, from junior to senior, to ensure work is up to standard and minimize mistakes. Nobody is all-knowing and researching how to accomplish certain nuanced or complex tasks can take hours. AI serves as the missing link, giving every developer of every skill set a coding partner that can ensure satisfaction.

As digital expectations rise among consumers and AI is integrated into consumer-facing experiences, having an internal AI-driven coding partner will prove instrumental to creating the smooth, unique app experience that users expect.

This use of AI also helps in onboarding junior developers faster, allowing them to get up to speed with unfamiliar codebases at a rate they would not have been able to achieve previously.

As AI continues to permeate every industry, teams need to learn how to balance automation with deeper-level oversight. The future will not be about how AI will take over for humans, but about the capabilities that AI will unlock for humans to use. In development, AI will become the brain, but developers will remain as the heart, guiding businesses with their creativity, intuition and experience — all now supplemented with AI insights.

April 23, 2025
Top 4 Considerations to Keep in Mind When Vetting Developers

Implementing new technology, be it a mobile app, complex AR/VR application, backend system or anything else, is challenging to say the least. Software development can be challenging, especially if you are not as fluent in it as you’d like to be. As a result, finding the right technology partner you can trust can make or break your project.

As the all-encompassing war on talent continues to grow, the discovery and selection process for the perfect developer can seem overwhelming. Having the information that you need will help you compare quotes effectively, secure approvals and ensure your project’s ongoing health and ultimate success. But what should you be looking for in your evaluations?

When shopping for the right development partner, below are four things to keep in mind:

Defining Your Scope

You must know exactly what you want. Any written project description you can provide will ensure that the developer provides a scope based on the requirements. The right conversations will help refine your project description. Once the scope is defined, make sure it includes all feasible options, challenges and workaround decisions considered ahead of time. Ensure that your scope and expectations are clearly documented as it goes through revisions. A well-defined scope will also help avoid scope creep and make sure you secure the best value. You want a partner who can not only deliver an MVP but is also aligned with your long-term goals and vision.

Understanding the Technical Nuances of Your Required Skills

If you are not technical or if you are building a new product, it can be hard to understand the more complex components. Here is where you need your development partner to walk you through the technological choices that will be made on your behalf and the process behind bringing your project to life. Make sure that the partner you are working with demonstrates a fluent understanding of your industry, potential roadblocks that you may encounter and solutions available to you.

Relevant Experience

Most projects require a team with a diverse set of skills and expertise. If a development firm has experience working on similar projects, it will be able to identify and avoid potential risks more accurately. It is important that your partner has the competence and depth of experience to understand both the technical and business considerations of your project. Ask for case studies on similar projects and client testimonials. Established companies with long-term employees typically have more defined processes and risk mitigation strategies.

Cost vs Quality

Regardless of how comparable the project scope and estimates of hours/effort are, prices quoted can vary wildly between companies. Project cost can depend on a variety of factors, including the size and location of the company, where the development team is based, whether you will be billed at a flat or an hourly rate or a blended or role-specific rate, the number of resources assigned to your project, inclusions and exceptions made in the scope and many other factors. While it can be tempting to go with the company that offers the lowest price, it is good to weigh costs with other aspects that determine the ultimate quality of your product.

The goal of choosing the right development partner goes beyond the project alone. An ideal partner is one who understands the larger mission of your company, is aligned with your future goals and collaborates with you as a ‘partner in crime’ to achieve these goals and realize your vision. As in any relationship, a strong developer relationship is characterized by transparency and trust in all aspects of project development, efficient ongoing communication and clarity around expectations at every stage.

March 19, 2025
Your Test Data Service is Outdated – Here’s Why (And What to Do About It)
It is 2025 — a year destined to be marked by AI and quantum computing breakthroughs. Yet, one of the most persistent challenges facing enterprises remains the same: Data. Some constant struggles I still see in almost every enterprise are lack of data availability and difficulty in complying with data regulation. The common denominator? Outdated test data practices continue to hold back organizations, creating bottlenecks in delivery and compromising software quality.

Why Should Enterprises Care?

Put simply, outdated test data practices are detrimental to your productivity, competitiveness and reputation.

Last year, Curiosity Software innovated the notion that software delivery is constructed of an inner and an outer loop. The idea highlights a critical gap: While most organizations heavily invest in their inner loop — development processes — the outer loop is often neglected. This outer loop, encompassing requirements, testing and data, plays a pivotal role in ensuring successful and efficient delivery.

One of the biggest culprits in this is test data. When test data is unavailable, insufficient or misunderstood, it creates a ripple effect:
- Quality suffers — teams cannot thoroughly define, develop and test critical scenarios.
- Productivity plummets — developers and testers waste time searching for, waiting on or manually manipulating the data they need.
- Agility is lost — teams want to develop quickly and not wait for other teams. Having test data available immediately allows teams to work in parallel.
The result? Delays, inefficiencies and a fragmented delivery pipeline — something modern enterprises cannot afford to ignore in today’s landscape where virtually every organization competes on software.

A fourth and critical consequence has recently emerged: Failure to adopt AI — something entirely dependent on having high-quality, unified, well-understood and accessible data.

What are the New Challenges of Test Data?

Test data isn’t a new industry —it has been around since the early 1980s. My co-founder, Huw Price, has founded five test data companies (yes, you read that correctly — five!), each addressing a unique technological need as the industry evolved. Over the past four decades, technology has transformed radically. We have moved from Mainframe copybooks to relational databases and NoSQL and now operate in a world dominated by APIs. Today, data is transmitted via complex message payloads where backend datastores are often black boxes to the teams that consume their data.

The rise of AI has introduced yet another wave of complexity, creating new challenges, demands and, the scary word, regulation. The simple truth? The old world of test data practices no longer meets the needs of modern technology and enterprise demands.

Here are the key challenges we see enterprises facing going into 2025:

1. Data is Bigger Than Ever

Data volumes are growing exponentially, with organizations often managing billions of rows of data. These massive datasets are becoming harder to process, understand, manage and provision effectively. Traditional test data tools simply can’t handle the scale and complexity promptly.

2. Increasingly Complex Application Landscapes

A typical enterprise today runs hundreds — if not thousands — of applications across hybrid ecosystems, including SaaS platforms, legacy systems, NoSQL and SQL databases and APIs. This fragmented landscape makes understanding, managing and governing test data a monumental challenge. This puts organizations at risk for costly security and compliance breaches, quality issues and release delays.

3. Unifying Data to Enable Successful AI Adoption

AI thrives on clean, unified datasets, but most organizations struggle to bring disparate data sources together in a compliant, usable way. Without unified data streams, businesses risk falling behind in their AI initiatives.

4. Meeting Compliance and Regulatory Standards

The growing web of data privacy regulations — such as GDPR, CCPA and the emerging AI Act — requires organizations to not only protect sensitive data but also govern how it is is used. Compliance failures are costly, both financially and reputationally, making it essential for test data solutions to meet stringent regulatory requirements and auto-detect new potentially sensitive data as it comes onstream.

5. Reactive Centers of Excellence (CoE)

Test data teams often operate reactively, firefighting as they are parachuted into projects. Working within silos, they must get up to speed quickly while navigating fragmented information and unclear business terminology. This constant reactive state limits their ability to drive proactive, high-value solutions.

6. Tightened Budgets, Heightened Expectations

The era of ‘growth at all costs’ is over. Organizations are under pressure to achieve a higher ROI with leaner teams and reduced budgets. Capital is increasingly being reallocated from traditional software projects to AI-driven initiatives, reflecting a shift in priorities toward innovation. Test data teams are facing the impossible challenge of adapting to these changes, proving their value while doing more with less, and balancing resources to meet risk reduction, productivity and delivery quality goals.

7. Increased Demand

AI has accelerated development processes, enabling teams to write and deliver code faster than ever before. The downside to this is it puts enormous strain on the rest of the delivery (the outer loop). This speed creates a critical demand for high-quality test data to be delivered efficiently and ahead of time. Without this, bottlenecks emerge, resulting in slow delivery times and compromised quality.

8. The Human Factor

Test data often requires deep expertise — not only in specific industries (e.g., payments standards like SWIFT or ISO 20022) but also in foundational test data processes. Unfortunately, central test data teams often lack sufficient training, resources or support to fully leverage modern platforms. As such, these teams often struggle to create demand and build the case for more projects, as it is hard to demonstrate the value being delivered.

Organizations must understand that simply establishing a test data service isn’t enough. Like running a business, test data teams need internal marketing. This includes educating stakeholders, enabling teams and consistently proving ROI to attract projects and ensure the service becomes an indispensable part of the organization’s delivery pipeline.

The New World of Enterprise Test Data

What does all this mean? Put simply, modern test data practices are no longer just about provisioning datasets or anonymizing sensitive information. Teams must work faster, smarter and with confidence. Here are the key capabilities test data teams need to thrive in 2025 and beyond.

Comprehensive Data Discovery and Cataloging

Enterprises can no longer afford to operate without a clear understanding of their data and application landscape. Visibility into data and application landscapes is essential for cataloging, tracking and auditing data across diverse ecosystems. Without this, creating reliable test environments becomes nearly impossible.

Proactive Test Data Creation

Defining data requirements early in the development lifecycle eliminates bottlenecks. Techniques like visual modeling help teams map business requirements to actionable data requests. This proactive approach also means teams spend less time firefighting, and more time driving high value outcomes.

Holistic Data Coverage

Generating comprehensive test data ensures scenarios — including edge and negative cases — are automatically created. Enterprises must move away from the tendency to rely on production data — which typically covers only 20% of requirement variations — as this directly impacts software quality.

AI-Powered Efficiencies

Automating understanding of your estate (your data) using interactive AI allows you to explore, enhance and iterate, to prevent and remove bottlenecks in software development. If you can understand where you are (data at rest), define where you are going (a process) and validate (test data validation) in a fraction of the time, then you can truly harness the power of AI.

Centralized Infrastructure for Test Data Teams

Centralizing test data processes across fragmented systems reduces operational overhead and ensures consistency and scalability. Gather up all your current processes, enhance and add state-of-the-art data activities and expose them via APIs, dynamic models and portals.

Monitoring and Governance

Real-time insights into compliance, data usage and schema and API changes help teams mitigate risks, prove ROI and enable proactive governance.

ROI Demonstration

Dashboards showcasing efficiency gains, cost savings and delivery acceleration make the case for continued investment in test data solutions.

Closing the Gap Between Data Needs and Delivery

The shift to modern test data practices is more than an operational upgrade — it is a strategic initiative. As enterprises navigate increasingly complex challenges, outdated approaches can no longer keep pace. High-quality, well-understood and accessible test data is not just a nice to have, it is a crucial component in an organization’s ability to adopt AI.

Enterprises must invest appropriately in outer loop activities to meet rising software quality and delivery expectations and future-proof their delivery process. Neglecting the outer loop often results in unavailable or misunderstood test data, which leads to quality gaps, delays and a fragmented pipeline — ultimately impeding the agility modern businesses require. Insufficient test data also undercuts AI adoption by failing to provide the high-quality, unified and accessible data it needs. By adopting a step-by-step approach to seamless, secure and intelligent test data solutions, organizations can bridge the gap between data needs and delivery demands, positioning themselves to thrive in a fast-evolving, data-centric and AI-driven world.
January 31, 2025