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Three challenges impacting your team’s AI productivity gains

AI is becoming a critical part of software development — but there are growing pains. Learn more about common roadblocks and how to address them.

January 23, 2025 5 min read
Sabrina Farmer
Sabrina Farmer Chief Technology Officer

Software development is at a turning point. AI promises to transform development workflows, but many organizations are discovering that integrating AI effectively requires more than just adopting new tools. A GitLab research study revealed that while executives are confident about AI adoption, 25% of individual contributors report their organizations aren’t providing adequate training and resources to help them use AI.

AI can help teams tackle increasingly complex challenges, from code generation and security vulnerability detection to automated testing and project management. When implemented thoughtfully, AI allows developers to focus on innovation rather than repetition, leading to improved code quality. More importantly, AI’s ability to analyze vast datasets of code, builds, and deployments helps teams make informed decisions that accelerate delivery while reducing risks.

However, as AI technology becomes more integrated into software development processes, organizations encounter three key challenges that can hinder these potential productivity gains.

1. The AI training gap

The executive/developer perception gap isn’t surprising: While executives focus on AI’s strategic potential, development teams face the day-to-day reality of integrating these tools into their workflows. The disconnect often stems from organizations viewing AI as a potential replacement for software engineers, rather than a tool that enables more creative and strategic human-centered work. Software leaders should supplement their investments in AI with investments in training and development resources that allow software development teams to build momentum and motivation over time.

It’s important to call out here that your teams will need a grace period to determine how AI best fits their processes. Initially, productivity may decline as they adjust to new workflows. However, your teams will build trust in their new tools by testing how AI can best fit into their day-to-day workflows and see better results.

2. AI-powered toolchain sprawl

One major factor that can detract from developer experience and impact overall productivity is toolchain sprawl, or having multiple point solutions across the software development process. GitLab’s research found that two-thirds of DevSecOps professionals want to consolidate their toolchain, with many citing negative impacts on developer experience caused by context switching between tools.

Toolchain sprawl has additional drawbacks, such as adding cost and complexity, creating silos, and making it more challenging to standardize processes across teams. It also creates security concerns due to expanding attack surfaces and unnecessary handoff points. AI-powered point solutions compound these issues. In fact, GitLab’s research found that respondents whose organizations are currently using AI were more likely to want to consolidate their toolchains than those not using AI — even though there wasn’t a significant difference between the two groups in the number of tools respondents reported using.

Rather than attempting to integrate AI into unwieldy, complex toolchains, adopt consistent, strategic best practices that minimize your teams’ context switching and cognitive load while reducing your organization’s total cost of ownership. Before incorporating new AI development tools, evaluate your existing toolchains to determine areas where you can streamline or eliminate disparate tools to avoid the strain of integrating excess tools with AI-powered solutions.

3. Unclear productivity metrics

Developer productivity is a top concern for the C-suite. While measuring developer productivity has always been difficult, AI has compounded the challenge. You might agree that measuring developer productivity can help business growth, but most leaders aren’t effectively measuring productivity against business priorities. GitLab’s research revealed that less than half (42%) of C-level executives currently measure developer productivity within their organization and are happy with their approach.

Many organizations struggle to quantify the impact of AI-powered tools on developer productivity or other real-world business outcomes. Traditional metrics, such as lines of code, code commits, or task completion, are often insufficient when assessing development’s impact on a business’s bottom line.

The best approach to modernizing measurement practices begins with consolidating quantitative data from throughout the software development lifecycle with insights from software developers on how AI is supporting or hindering their daily work.

Making AI work for your teams

Successfully implementing AI in software development requires closing the gap between executive expectations and developer realities. Start where your team feels the most friction today— whether that’s providing proper training, consolidating toolchains, or rethinking traditional productivity metrics. Taking action now allows your teams to realize meaningful productivity gains, rather than just adding new tools.

Build the right AI approach for your software innovation strategy

Generative AI is transforming how teams develop software, and staying competitive means evolving your development practices to recruit and retain top talent. Continuous upskilling, adoption, and support are critical to safely, securely, and responsibly unlocking AI’s power. Download our guide for enterprise leaders to learn how to prepare your C-suite, executive leadership, and development teams for what AI can do today — and will do in the near future — to accelerate software development.

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Key takeaways
  • AI can increase software development productivity by automating tasks, identifying insights from large datasets, and reducing time spent on repetitive tasks. However, there are challenges to achieving these productivity gains.
  • Organizations may face challenges such as an AI training gap, toolchain sprawl, and appropriately defining productivity metrics. Addressing these can help ensure the effective utilization of AI in software development.
  • To evaluate AI's effectiveness, organizations should measure ROI based on user adoption, time to market, revenue, and customer satisfaction metrics. Evaluation of the right metrics can help organizations better understand AI's impact on business outcomes.