• Key Technical Challenges Faced by Software Practitioners in 2025

• Key Challenges in AI/ML Adoption

• AI Infrastructure Priorities for 2025

• Cloud-Native Technology Adoption

• Top Architectural Challenges in Modern Systems

• Challenges in Managing Databases and Data Engineering Efforts

• Database and Data Engineering Technology Adoption

• Challenges in Delivering High-Quality Software

• Audience Demographics (available upon request)

*Total survey respondents = 191

Key Technical Challenges Faced by Software Practitioners in 2025

The latest InfoQ reader survey reveals several recurring technical challenges that are top of mind for senior software engineers, architects, and technical leaders in 2025. Across 191 survey respondents, a few clear themes emerge—legacy modernization, technical debt, AI integration, scalability, and cost management dominate the landscape.

1. Legacy Systems & Technical Debt - The most frequently cited challenge is the burden of legacy systems, often intertwined with technical debt. Respondents highlight difficulties in maintaining aging codebases while simultaneously adopting modern frameworks, tools, and architectures. Many are caught in a cycle of delivering new features while lacking the time or resources to refactor or retire outdated systems. This tension is exacerbated when legacy architectures create bottlenecks or architectural inconsistencies that hinder scaling, deployment speed, and integration with modern services.

2. AI Integration & Adopting AI — especially Generative AI — is both a promising opportunity and a growing pain point. Practitioners report challenges in integrating AI/ML into existing workflows, managing AI/ML model accuracy and training, and understanding the actual ROI of AI initiatives. Others express concerns about keeping up with the fast pace of AI advancements, upskilling their teams, and avoiding hype-driven decision-making. The application of GenAI across the software development lifecycle (SDLC) is still experimental for many, and organizational alignment around it remains immature.

3. Scalability, Reliability & Complexity - Engineering teams are increasingly grappling with the scalability and reliability of distributed systems, often in the context of cloud-native and microservices architectures. These concerns extend to observability, deployment reliability, and the management of cross-team dependencies. As applications become more modular and interconnected, ensuring fault tolerance, consistent system performance, and coherent team collaboration is an escalating challenge.

4.Cloud Complexity and Cloud costs, infrastructure sprawl, and cost optimization are major concerns. Teams are balancing the need for speed and flexibility with the pressure to control operational expenses. Respondents frequently mention the difficulty of managing multi-cloud environments, monitoring cloud spend, and implementing effective FinOps practices.

5. Skill Gaps and Organizational Alignment - Beyond technical hurdles, many cite lack of skilled developers, internal silos, and misalignment between business and engineering as barriers to success. Upskilling, retaining talent, and maintaining high-quality developer experience were also prominent challenges—especially in relation to newer tech like AI and evolving cloud platforms.

Key Challenges in AI/ML Adoption

Organizations face a range of barriers when adopting and operationalizing AI/ML technologies, with skills shortages standing out as the top challenge. Nearly half (48%) of respondents cited a lack of skilled AI/ML professionals as their primary hurdle. This highlights an ongoing talent gap that continues to limit organizations' ability to scale AI efforts.

Another major obstacle is the difficulty in measuring and demonstrating ROI from AI/ML initiatives, selected by 38% of respondents. This suggests that while many companies are experimenting with AI/ML, proving its business value remains elusive, potentially slowing down broader investment and adoption.

Additional significant challenges include:

• Integration complexity: 35% report struggles with embedding AI/ML into existing systems and workflows, reflecting technical and organizational silos.

• Pace of change: 32% say that keeping up with evolving technologies is a persistent issue, underlining the rapid innovation cycles within AI/ML ecosystems.

• Data hurdles: 30% identify difficulties with obtaining and preparing high-quality training data, and another 30% point to the high costs of infrastructure and tooling.

Managing model performance in production (29%) and addressing ethical concerns and bias (18%) were noted but ranked lower compared to operational and technical barriers, suggesting that while ethics is recognized, immediate practical concerns dominate current adoption efforts.

Interestingly, 10% of respondents indicated that none of these challenges significantly affect their organizations, hinting at either higher maturity levels or more limited AI/ML initiatives.

Overall, the survey reveals that successful AI/ML adoption is as much a people and process challenge as it is a technical one. Organizations aiming to lead in AI will need to invest in skills development, ROI measurement frameworks, and modernizing infrastructure to overcome these barriers.

AI Infrastructure Priorities for 2025

Survey data reveals strong momentum toward cloud-based AI adoption across organizations:

Cloud-based AI platforms (59%) such as AWS SageMaker, Google Vertex AI, and Azure ML are the top priority, establishing the cloud as the foundation for AI development and deployment.

AI development environments like Jupyter Notebooks and AI-enhanced IDEs (46%) are also widely used, supporting experimentation and model iteration.

Adoption is more measured for hybrid and on-premises approaches:

20% plan to implement hybrid AI infrastructure, combining cloud and on-prem solutions—indicating a need for flexibility in data governance and performance.

On-premises AI infrastructure (17%) and AI model hosting platforms (20%) are less common, suggesting more targeted or advanced use cases.

Edge AI adoption remains limited (7%), with only niche applications driving current interest.

Notably, 13% of respondents report no plans to adopt AI infrastructure in 2025—reflecting either organizational constraints or a wait-and-see approach.

Key Insight: Cloud-native AI platforms and development tools are becoming the default, while hybrid, on-premises, and edge deployments remain specialized strategies driven by specific operational or regulatory needs.

Cloud-Native Technology Adoption

Survey respondents indicated strong adoption of foundational cloud-native technologies across their organizations:

• Containerization (87%), microservices (81%), and infrastructure as code (70%) are firmly established as standard practices.

• Kubernetes is widely adopted (68%), though 21% of respondents do not plan to use it—signaling potential complexity or suitability concerns.

• Serverless computing (61%) and event-driven architectures (72%) are gaining traction, reflecting demand for scalable and loosely coupled systems.

Adoption is more cautious for advanced or specialized technologies:

• Only 29% currently use service mesh, with over half (52%) not planning to adopt—highlighting complexity as a likely barrier.

• Cloud-native continuous delivery tools (e.g., ArgoCD, Flux) are in use by 50%, with another 19% planning to adopt—pointing to growing momentum behind GitOps practices.

Key Insight: Core cloud-native patterns are now mainstream, while more complex components like service mesh and CD tools are still emerging, with adoption driven by organizational maturity and operational readiness.

Top Architectural Challenges in Modern Systems

Survey data highlights the top architectural challenges technical leaders are facing today:

Managing complexity is the most critical concern:
Managing dependencies and reducing coupling in complex systems was selected by 54% of respondents, making it the most cited challenge.

Design trade-offs between scalability and simplicity are a major focus:
50% reported difficulty balancing simplicity with scalability, and another 50% emphasized aligning architecture decisions with business goals and timelines.

Adaptability and modernization are ongoing priorities:
41% are focused on maintaining flexibility for future changes and technology adoption, while 38% are transitioning from monolithic systems to microservices or other modern architectures.
Integration of legacy systems remains a notable hurdle for 37%.

Operational resilience and cloud strategy are secondary concerns:
Ensuring high availability and reliability was cited by 27%, while designing for multi-cloud or hybrid cloud environments lags behind at 17%.

Key Insight:
Architectural leaders are primarily grappling with managing system complexity, making pragmatic design decisions, and future-proofing their systems—while operational and multi-cloud challenges, though important, are comparatively lower priorities.

Challenges in Managing Databases and Data Engineering Efforts

Survey respondents highlighted the following concerns and challenges associated with managing databases:

• The top concern is managing costs for storage and database infrastructure (62%), followed closely by optimizing query performance and reducing latency (55%).

• Ensuring data consistency and accuracy across distributed systems (42%) and integrating and maintaining data pipelines across diverse systems (40%) reflect the complexity of operating in modern, multi-environment architectures.

• Scaling databases to handle increasing data volume and user traffic (36%) and adopting and scaling new technologies like AI/ML for data processing (36%) are also prominent challenges.

• Ensuring robust data security and preventing breaches was selected by 34% of respondents.

Overall, the data shows that organizations are balancing cost management, performance optimization, system complexity, and technology adoption as they scale their data operations.

Database and Data Engineering Technology Adoption

On the database adoption front, InfoQ survey respondents showed strong current usage of foundational technologies, with growing interest in emerging solutions.

• Key-Value Stores (e.g., Redis, Amazon DynamoDB) are the most widely used, with 76% currently using them. An additional 7% plan to adopt within the next 12 months.

• NoSQL Databases (e.g., MongoDB, Cassandra, DynamoDB) show similarly high adoption, with 72% currently using and another 7% planning to adopt in the next year.

• Streaming Data Platforms (e.g., Apache Kafka) are currently used by 60%, with 10% planning to adopt within 12 months and 5% beyond that.

• Cloud-Native Databases (e.g., Amazon Aurora, Google Spanner) are currently used by 59%, with 7% looking to adopt within the next year.

• Data Analytics Platforms (e.g., Databricks, Apache Spark, Tableau) are used by 57% today, and show the highest short-term growth potential, with 13% planning adoption within 12 months and 7% beyond.

In contrast, several specialized database types show significantly lower current adoption but are gaining attention:

• Document-Oriented Databases are used by 26%, with 15% planning adoption in the future.

• Graph Databases are used by only 21%, but 11% plan to adopt within 12 months and 8% beyond.

• Vector Databases, a newer category (e.g., Pinecone, Weaviate), have 19% current usage, with 14% planning near-term adoption—highlighting early momentum in support of AI/ML workloads.

Overall, the data suggests continued investment in established platforms alongside growing interest in technologies enabling real-time processing, AI/ML, and advanced analytics.

Challenges in Delivering High-Quality Software

Respondents identified several key challenges in delivering high-quality software at their organizations.

The most significant issue was managing technical debt while innovating on new features (69%), highlighting the ongoing tension between maintaining existing systems and driving forward progress.

Half of the respondents also pointed to ensuring effective testing and minimizing bugs in production (50%) as a major hurdle, while keeping up with rapidly evolving tools, frameworks, and technologies (46%) remains a persistent challenge.

Other commonly reported difficulties included:

• Integrating legacy systems with modern technologies (41%)

• Managing cloud infrastructure costs and complexity (39%)

• Achieving consistent deployment frequency and reliability (35%)

Additionally, adopting and scaling DevOps practices across teams (31%) and scaling and optimizing software to handle increased user demand (26%) were seen as notable obstacles.

Overall, the data reflects that while innovation is a top priority, technical debt, system integration, and operational complexity continue to place significant demands on software teams.

Conclusion

In 2025, senior software leaders are focused on modernizing legacy systems, managing technical debt, and figuring out how to use AI effectively. While cloud-based AI platforms are becoming the norm, many teams still struggle with the skills, tools, and processes needed to make AI work in the real world. Core cloud-native technologies like containers and microservices are widely adopted, but newer, more complex tools are seeing slower uptake. Across the board, teams are balancing the need to innovate quickly with the realities of cost control, system complexity, and a constant need to learn and adapt.

Audience Demographics

The survey also included specific demographic questions on:

• Professional Focus / Role in Organization

• Company Size

• Seniority (e.g. Do you manage/supervise others?)

If you are a software vendor that would like to see the complete results from this survey (including data from the ‘audience demographics’ and ‘commercial product adoption’ questions), please email us at sales@infoq.com.

These topics have also been pivotal to the success of a number of recent sponsored campaigns on InfoQ:

- The best performing sponsored asset so far this year has been HPE’s white paper “Exploring enterprise AI: An Introduction to LLMs, vector databases, and more,”

- In June, Harness sponsored the InfoQ Live Roundtable “Developer Experience in the Age of Generative AI” which proved to be a popular topic amongst readers

- In July, InfoQ published the “Practical Applications of Generative AI” eMag. Sponsored by Composable Prompts, the eMag generated a record number of leads in the first two weeks

Editorial Coverage on InfoQ

The topic of GenAI has also been a big part of our recent editorial and conference coverage on InfoQ.

In her QCon London presentation, The AI Revolution Will Not Be Monopolized (summarized by Daniel Dominguez) Ines Montani argues that - in order to avoid heading into a world of big tech monopolization of LLMs - we need to get behind open-source initiatives that can help us democratize AI technology, offering transparent, extensible tools that empower users.

At the inaugural InfoQ Dev Summit Boston, Justin Sheehy of Akamai delivered an opening keynote on being a responsible developer in the age of artificial intelligence hype. The talk was aimed at software practitioners who might be feeling overwhelmed by the rapid developments and inflated expectations surrounding AI.

Lastly, Alex Cruikshank recently discussed where GenAI is likely to have the greatest impact, steps to manage this change, and ways to leverage the shift to AI mediated work to better understand business processes.

Here are the Top 10 AI-related content pieces published on InfoQ so far this year:

• Slack Combines ASTs with Large Language Models to Automatically Convert 80% of 15,000 Unit Tests

• Mistral AI's Open-Source Mixtral 8x7B Outperforms GPT-3.5

• Google Announces 200M Parameter AI Forecasting Model TimesFM

• Meta Open-Sources MEGALODON LLM for Efficient Long Sequence Modeling

• OpenAI Releases New Embedding Models and Improved GPT-4 Turbo

• Retrieval-Augmented Generation (RAG) Patterns and Best Practices

• OpenAI Launches AI Text-to-Video Generator Sora

• Cloudflare AI Gateway Now Generally Available

• Maximizing the Utility of Large Language Models (LLMs) through Prompting

• Meta Releases Llama 3 Open-Source LLM

Of the approximately 90K respondents to this year’s Stack Overflow 2023 Developer survey, 43K respondents participated in the professional developer series of questions, which marks a 16% year-over-year increase in professional survey respondents.

With the advent of artificial intelligence (AI) tools like ChatGPT and GitHub Copilot, one of the big themes emerging from this year’s survey was how developers perceive and actually use AI across the development lifecycle.  

Professional developers use - and perception - of AI-assisted technology

The 2023 survey reveals that a significant portion of professional developers are utilizing AI tools in their development processes, with 44% already using AI tools, and an additional 26% planning to adopt them soon. However, of the 21 AI-assisted tools currently on the market, “a vast majority are just using the two popular products: ChatGPT (83%) and GitHub Copilot (56%),” according to Stack Overflow survey author, Erin Yepis.  

While the ‘learning to code’ cohort of the survey is more actively using AI technology for software development, the numbers indicate that professional developers are less trusting of the tech’s accuracy, with “only 3% highly trusting of the results and 6% highly distrusting them.”

They survey also examined the adoption of AI tools through a global lens, revealing that perception and actual usage vary greatly across different parts of the world.  

“Professional developers from India, Brazil, and Poland are most likely to use or plan on using AI tools as part of their development workflow—all responding at 70% or higher,” according to Yepis. Meanwhile, “UK, French, US, and German developers are more likely to say they don’t plan on using AI tools (responding at 36% or higher).”

With the ever looming fear of copyright infringement, constantly evolving GDPR laws and regulations, and limits to data access, professional developers in the US and EU are perhaps taking a more cautious approach when using large language models to aid in software development, according to the report. 

AI in practice

While ChatGPT and Copilot certainly aren’t the panacea for rapid application development, AI technology is already a proven part of developer tooling, increasing productivity by handling many of the mundane tasks related to coding, debugging, and even monitoring (AIOps), freeing developers up to focus on the more creative aspects of software engineering.

When asked about their primary use cases for using AI tools today, “the overwhelming majority” of survey respondents indicated that they use it “to write code (83%) or debug code (49%).”

In terms of the perceived benefits of AI tools, 33% of survey respondents believe increasing productivity is the most important benefit, followed by speeding up the learning process, increasing efficiency, and improving accuracy in coding.  

Additional highlights from the 2023 Stack Overflow survey

• 27% of respondents are 5-9 years into their professional careers.

• Most Professional Developers report having CI/CD, automated testing, and DevOps available at their organization. 16% of organizations have AI-assisted technology.

• 49% of professional developer respondents report having Microservices within their organization

• Over 60% report having a DevOps function in their organization

• While PostgreSQL (49%) and MySQL (MySQL) were the top two databases used by professional developers, other databases that appeared in the top 10 list were MongoDB (26%), Redis (23%), and MariaDB (18%)

• While AWS (48%) remains the most used cloud platform for all respondents (professional and non-professional), “you can see the inroads that Azure has with organizations—twice as many Professional Developers are using Azure compared to people who are learning to code (30% vs. 15%),” according to Yepis. 

•  ~14K AWS developers —a little less than half—want to develop in Google Cloud or Microsoft Azure next year.

Since the rapid ascension of ChatGPT, an artificial-intelligence (AI) chatbot developed by OpenAI, some are predicting that in about five years, an unemployed human race will be staring back at a sentient, ChatGPT-powered robot army that is ready to shed its mammalian overlords.  Others predict that these same robots might eventually ‘liberate’ us from our jobs as we ring in the 0-day work week, introduce a universal basic income, and edge closer to a Star Trek-like world where human subsistence is no longer a concern.  

In the meantime, cooler heads are advising that we not panic about widespread job losses and economic upheaval being presaged by AI pessimists.  On the contrary, AI-powered tools like ChatGPT could very well help us augment the nature of human work by automating the tedious and repetitive aspects of our jobs, allowing us to focus on more creative work.  

“While the launch of large language models such as ChatGPT, the premium service ChatGPT Plus and the enhanced GitHub Copilot have resulted in headlines about jobs that are at risk of being replaced by AI (with programmers at the top of some lists), the question of how real the threat is, and how the potentially displaced adapt, is very open at the moment as it is early days in the evolution of the tools,” says Shane Hastie, InfoQ’s Lead Editor and Podcast Host for Culture & Methods, in a recent InfoQ report.  

Even in these early days however, AI-technology has already become an integral part of developer tooling, increasing overall productivity by handling many of the mundane tasks related to coding, debugging, and even monitoring (AIOps), freeing developers up to focus on the more creative aspects of software engineering.  

“Based on our research, programmers spend 35% of their time on understanding code, 5% on writing code, 10% on other coding-related activities, and 50% on other non-coding activities,” says Nikita Povarov, Data Analytics and Machine Learning Team Lead at Jetbrains. “Helping programmers perform small tasks more efficiently is a vast area for AI usage: AI can help to complete the code, to teach the user to utilize new features, and to search in the code and beyond.”

But can tools like ChatGPT ultimately take over the entire task of creating complex software systems, thereby replacing the need for programmers?  We decided to ask ChatGPT what it’s true intentions are:

“ChatGPT is not intended to replace programmers. While ChatGPT is capable of generating code snippets, it is not a full-fledged programming language and cannot create complex software systems or solve programming problems on its own.

Programmers are still essential in the development of software and technology. They have the ability to think critically, understand business needs, design software architecture, and write efficient and secure code.

ChatGPT, on the other hand, can assist programmers by suggesting code snippets, helping with syntax and language-specific knowledge, and even identifying potential bugs in the code. However, it cannot replace the human creativity and problem-solving skills that are necessary in software development.”

(Response from ChatGPT when asked “Will ChatGPT replace programmers?”)

A Brave New (AI) World for Marketers

Surely, if AI-powered tools like ChatGPT can augment ‘thinking-type’ work like programming, the opportunities for marketing professionals are limitless. 

For B2B marketers in particular, lead nurturing is an important part of the pipeline building process.  We decided to ask ChatGPT how it can help on this front:

“Overall, ChatGPT can help improve lead nurturing by providing personalized, timely, and automated responses that improve the customer experience and increase the chances of conversion:

Personalization: ChatGPT can be used to create personalized content and messaging that speaks directly to the needs and interests of each lead. By analyzing data on the lead's behavior and preferences, ChatGPT can generate messages that are tailored to their specific interests and pain points.

Timely Follow-Up: ChatGPT can be used to send timely follow-up messages to leads based on their actions and behavior. For example, if a lead downloads a whitepaper, ChatGPT can send a follow-up email with additional resources on the same topic.

Automated Responses: ChatGPT can be used to respond to leads automatically, even outside of business hours. By using ChatGPT to answer common questions and provide basic information, leads can receive immediate responses, improving their experience and increasing the chances of conversion.

Qualification: ChatGPT can be used to qualify leads by asking questions and collecting information about their needs and interests. This information can then be used to prioritize leads and provide more personalized and relevant content.

Lead Scoring: ChatGPT can be used to assign scores to leads based on their behavior and engagement with marketing materials. This can help sales teams prioritize leads based on their likelihood to convert, allowing them to focus on the most promising opportunities.

By using ChatGPT to analyze data and automate responses, B2B marketers can spend more time focusing on high-value activities, such as developing relationships and closing deals.”

(Response from ChatGPT when asked: “How can ChatGPT improve lead nurturing?”)

Looking Forward 

“The companies working on software development tools are quickly developing the ability to productize AI-powered solutions for small tasks, so we expect more such solutions to emerge in the near future,” says Povarov. 

“Computers can now write code, but we don’t see this as an industry transformation. The ability to write code is not sufficient to replace a human programmer. It turns out that the ability to consider complex interactions of software components is critical, and AI is not there yet,” he adds. 

In the sales and marketing domains too, AI cannot replace the much needed skills of developing customer empathy, storytelling, and adding context to the cold, hard metrics that our systems generate.

The survey generated 529 responses.

The following report summarizes some of our key findings, including readers’ biggest technical challenges along with their technology adoption plans over the coming 12 months in the areas of cloud-native development/architecture, database/data engineering technologies, and software delivery.

[Note: If you are a software vendor that would like to see the complete results from this survey (including data from the ‘audience demographics’ and ‘commercial product adoption’ questions), please email us at sales@infoq.com.]

Key Findings

• 50% of survey respondents self-identified as “Architects”, and another 26% self-identified as “Developers”

• 39% of respondents decide which technologies/software products their company will adopt

• 80% of respondents are currently building and running Microservices, with another 8% planning to within the next 12 months

• 63% of respondents are currently using Kubernetes, with another 13% planning to within the next 12 months

• 77% of respondents currently have a Monitoring / Observability solution with another 13% planning to within the next 12 months

• When asked about their biggest technical challenges, the top five, most frequent responses were related to:

  • Cloud Computing - Including cloud adoption, migrating to the cloud, cloud cost reduction, and moving to a hybrid cloud

  • DevOps - Including improving team culture, best practices, improving software delivery

  • Application Security - Including shift-left security, cloud security, Zero-Trust Security and DevSecOps

  • Observability - Including service monitoring, distributed tracing, observability of distributed systems

  • Test Automation

Biggest Technical Challenges

In what areas are you facing the biggest technical challenges? What are your top 3 challenges? (439 respondents)

Role

Of the survey respondents:

• 50% self-identify as "Architects"

• 26% self-identify as "Developers"

• 9% self-identify as "DevOps / Operations & Infrastructure"

Which of the following roles do you strongly identify with?

Technology Adoption

Cloud-Native Technologies / Architectural Approaches

Of the survey respondents:

• 80% are currently building and running Microservices, with another 8% planning to within the next 12 months

• 71% are currently using Cloud Databases, with another 9% planning to within the next 12 months

• 63% are currently using Kubernetes, with another 13% planning to within the next 12 months

• 56% are currently employing Event-Driven Architectures, with another 20% planning to within the next 12 months

• 48% are currently using Serverless , with another 14% planning to within the next 12 months

• 31% are currently employing Reactive Architectures, with another 14% planning to within the next 12 months

• 26% are currently using Edge Computing, with another 11% planning to within the next 12 months

• 23% are currently running a Service Mesh, with another 20% planning to within the next 12 months

Is your organization currently using or planning to use any of the following Cloud-Native technologies / architectural approaches?

Database / Data Engineering Technologies

Of the survey respondents:

• 68% are currently using a Key-Value Store with another 8% planning to within the next 12 months

• 67% are currently using Data Analytics with another 13% planning to within the next 12 months

• 64% are currently using a Document Store with another 9% planning to within the next 12 months

• 48% are currently using Machine Learning with another 15% planning to within the next 12 months

• 44% are currently using a Column-Oriented Database with another 10% planning to within the next 12 months

• 41% are currently using a Stream Processing Platform with another 16% planning to within the next 12 months

• 20% are currently using a Graph Database with another 12% planning to within the next 12 months

• 19% are currently using Data Mesh with another 20% planning to within the next 12 months

Is your organization currently using or planning to use any of the following database / data engineering technologies?

Software Development / Delivery

• 90% of InfoQ readers are currently doing Manual Testing

• 77% of InfoQ readers are currently doing Monitoring / Observability with another 13% planning to within the next 12 months

• 77% of InfoQ readers are currently using an Authentication and Authorization platform with another 9% planning to within the next 12 months

• 84% of InfoQ readers are currently doing Test Automation with another 9% planning to within the next 12 months

• 59% of InfoQ readers are currently doing API Management with another 19% planning to within the next 12 months

• 38% of InfoQ readers are currently doing Testing in Production with another 13% planning to within the next 12 months

• 30% of InfoQ readers are currently doing Zero-trust security with another 24% planning to within the next 12 months

• 10% of InfoQ readers are currently doing Chaos Engineering with another 21% planning to within the next 12 months

Is your organization currently using or planning to use any of the following software development / delivery technologies?

Audience Demographics and Commercial Product Adoption

The survey also included specific demographic questions on:

• Company Size

• Seniority (e.g. Do you manage/supervise others?)

• Role in the adoption of new technologies / software products

…. as well as questions on commercial product adoption, in the following three areas:

• Observability / Monitoring Tools

• Databases (NoSQL / NewSQL / SQL)

• ServiceMesh Vendors / Products

If you are a software vendor that would like to see the complete results from this survey (including data from the ‘audience demographics’ and ‘commercial product adoption’ questions), please email us at sales@infoq.com.

As various functions across the software development lifecycle - such as coding, testing, and deployment - become increasingly automated through machine learning algorithms, Jewell predicts that - in the not too distant future - “coders working with IDEs” will become “model and machine learning artisans.”

Humans [will] shift their role from developers to directors, taking responsibility for describing system intent, managing the robotic infrastructure that builds and maintains the software, and providing the data + models needed to arm and improve the machines.

These are largely the skills and expertise embodied by data scientists today. A large majority of the 40 million professional developers that will exist in 2040 will undergo a reskilling in order to adopt and embrace these techniques. Everyone will be scrambling to revisit their undergraduate statistics classes; I know I will.

-Tyler Jewell, Dell Technologies Capital

Jewell’s report is quite expansive, covering past and current trends and providing economic forecasts across a wide range of DevOps tools and technologies. From a developer marketing perspective, here are some key sections of the report that may be of particular interest:

• The supply of developer developer talent is not meeting the demand for new digital services

• Two decades of increasing software complexity have limited developer productivity gains

• Developer Relations (DevRel) Has Emerged As An Extension Of Marketing, Support, and Product Organizations Employing 5000 To Further Enable Developer Users And Buyers.

• Fragmented Tooling, Infrastructure, and Platform Drag Productivity As Developers Spend Increasingly More Time Evaluating And Adopting Technologies Which May Not Exist In Five Years.

• DevOps Automation, Which Abstracts And Shifts Complexity, Improves Developer Productivity

Diagrams of interest from the report:

• 3 Waves of Software Supply Chain Industrialization

• Shades of DevOps Roles (“Patrick Debois of Snyk maps the roles of DevOps teams over time”)

A trend the QCon San Francisco Program Committee is focusing on this year is the rise of the Staff+Engineer. In many large organizations, as technical team leads and other senior engineering staff move into management roles, they often find that this comes at the cost of losing their ‘technical chops’ as their new roles often require them to take on more traditional business and management functions.

However, organizations are now paving the way for technical team leads to grow into a new kind of leadership role: the Staff+ Engineer. While there are many variations of the role, the Staff+ Engineer typically remains very close to the technology, drives the technical direction of the product, and empowers other software engineers - and teams - to collaborate and grow within the company.

It’s also a very influential role when it comes to adopting new technologies and influencing software purchasing decisions within the organization.

“Usually, Staff+ Engineers are responsible for providing the technical perspective on many organization decisions, including software purchasing,” says Fabiane Nardon, QCon San Francisco 2022 Track Host, Data Scientist, Java Champion & CTO @tail_oficial. “Before acquiring new software, a Staff+ Engineer will be called to evaluate it.”

It’s uncommon for an organization to acquire a new solution if the technical team has advised against it, says Nardon.

Marketing your product to Staff+ Engineers

Staff+ engineers are focused on unblocking their teams, facilitating collaboration, and driving product innovation. In order to empower them to do their jobs, marketers need to be direct and transparent about their products’ features and limitations.

“Staff+ Engineers care for things that work and solve their problems and they will reject products if they feel there is more marketing than real functionality,” says Nardon.

Ensuring that your software has good documentation, a vibrant community around it, and real world use cases that you can point to, are all key things that Staff+ Engineers look for when researching new solutions for their teams and organization, adds Nardon.

Fostering technical leadership

In order to continue driving innovation within the industry, it’s important that top technical talent within the company isn’t simply ‘poached’ into managerial positions, where better compensation and decision making power usually reside, says Nardon.

More than a traditional manager, the Staff+ engineer may in fact have greater influence and impact on fellow software engineers through a combination of technical skills, deep understanding of the product, and career mentorship.

Many marketers would agree that an important measure of success for their demand gen efforts is the number of Marketing Qualified Leads (MQLs) that have been generated across a wide range of activities, which might include content syndication, email and newsletter promotions, and even display ads.  Various MQL criteria - which define how engaged or ‘purchase-ready’ a lead is - can help the sales team prioritize their outreach efforts.  

However, while MQLs are certainly an important “leading indicator” of commercial growth, focusing purely on MQL volume can sometimes result in short-sighted marketing investments, enable “bad behaviors”, and minimize marketing’s contribution to overall revenue, argues Gartner Analyst Rick LaFond, in his recent blog post “Beware of the MQL Trap.”

MQL-generation can become a bit of a self-fulling prophecy as marketing activities become squarely focused on bringing in more MQLs while potentially overlooking the longer term impact of brand and content marketing initiatives.  

“By merely communicating how many times you provided your sales team with a lead for reps to convert, you risk positioning marketing as the sales team’s trusty sidekick—not as a strategic commercial growth leader,” writes LaFond. 

ABM, The Buyer’s Journey, and Focusing on Pipeline

Anadelia Fadeev, Senior Director of Demand Generation at Teleport, agrees that while it’s important for marketers to track funnel metrics - like MQLs - at the end of the day, pipeline is what should matter most to both the sales and marketing teams.  

“Are the activities that you're doing across sales and marketing producing the pipeline that you need for your business? If the answer is no, then you need to start digging into each funnel stage to understand where the issues might be, and try to resolve that,” says Fadeev. “Emphasizing MQLs doesn't really show the full picture.”

According to Fadeev, one of the primary reasons that account-based marketing (ABM) has become so popular is that organizations have realized that the buyer’s journey for enterprise software is not linear.  There is no one metric - like MQLs - that can capture an organization’s product needs or buying intent.  

“When you're selling enterprise software, chances are that you have more than one person influencing the deal. You might have someone doing the research, somebody that influences the deal, and an economic buyer,” says Fadeev. “You have multiple touch points across several people in the company and MQLs simply don't show the full picture of that buyer's journey.”

“MQLs really are a way to prioritize leads for sales and potentially gauge the quality of those leads - the reality is that it's not a perfect system,” says Fadeev.

LaFond concludes by suggesting that marketers should continue to track MQLs and MQL volume but “prioritize other metrics that are more closely connected to revenue outcomes,” such as marketing contribution to revenue, MQL conversion rates, and cost per acquisition.