Survey: Generative AI Makes Tasks Simple, But Developing That AI is Anything But

Survey: Generative AI Makes Tasks Simple, But Developing That AI is Anything But

Developers across industries have seen their job descriptions expand to include a common phrase: "generative AI." As enterprises rush to adopt the technology, they are tasking their developers...

Developers across industries have seen their job descriptions expand to include a common phrase: "generative AI." As enterprises rush to adopt the technology, they are tasking their developers with building, customizing, testing and deploying generative AI applications.

Yet enterprises underestimate the complexity of the AI stack and development lifecycle. Underneath every sleek, intuitive AI application is a complex and often cumbersome technology stack.

Now, a new survey sponsored by IBM and conducted by Morning Consult explores that complexity - and unveils the challenges developers are facing when it comes to skills variance, vast and complicated toolsets, and ensuring accurate and trusted results from these systems. Our survey interviewed more than 1,000 enterprise AI developers in the U.S. who are building generative AI applications for the enterprise. Survey participants span a range of roles, including application developer, software engineer, and data scientist.

The survey paints a vivid picture: Enterprise AI developers are building tools to make their colleagues' workflows simpler… yet developing these tools is often anything but. The survey also offers a window into technologies and approaches that can help address this problem.

The challenges: skill gaps and tool sprawl

Our survey revealed that generative AI skill levels vary significantly among surveyed developers. A majority of developers who identify as "AI developers" or "data scientists" view themselves as experts in generative AI - but a minority of the seven other developer demographics do. App developers in particular rarely view themselves as generative AI experts, despite being on the front lines of generative AI adoption.

Less than one quarter (24%) of application developers surveyed ranked themselves as "experts" in generative AI.

This speaks to the skills gap in the generative AI space. For many developers, this is new terrain with a steep learning curve - and fast innovation cycles mean new technology is constant. Compounding the skills gap is a lack of clarity when it comes to reliable frameworks and toolkits. Survey respondents listed the lack of a standardized AI development process as a top challenge, along with prioritizing transparency and traceability.

"Lack of a standardized AI development process" and "Developing an ethical and trusted AI lifecycle that ensures transparency and traceability of data" are tied as top challenges in the development of generative AI applications among those surveyed (33%, a plurality of respondents).

Developers are also frustrated with the tools at their disposal. The most important tool qualities for building enterprise AI are also the rarest, respondents said, hampering the development process. Meanwhile, developers must juggle a roster of tools.

Performance (42%), Flexibility (41%), Ease of Use (40%), and Integration (36%) are the four most essential qualities in enterprise AI development tools, according to those surveyed. Yet over a third of those surveyed also said those very same traits are the rarest.

A majority (72%) of those surveyed use between five and 15 tools to create an AI enterprise application. A notable number -- 13% -- use 15 or more tools.

The upshot is clear: Developers are facing real complexity challenges in the AI stack - which has real consequences. Enterprises are investing in generative AI for a competitive advantage. An overly complex AI stack saps this investment and ripples out to other systems.

These challenges will only become exacerbated as the industry pushes further into agentic AI, which promises greater power and autonomy - but also hinges on trust and integration with broader IT systems.

Almost all developers surveyed (99%) are exploring or developing AI agents - and the top concern reported for agentic development is trustworthiness.

It's clear that now is the time to address the AI complexity challenge.

The solution: simplify the stack (with an assist from AI)

Our survey results shed light on what we can do to help address the complexity of AI development, as well as some tools that are already helping. First, given the pace of change in the generative AI landscape, we know that developers crave tools that are easy to master.

Only one third of those surveyed are willing to invest more than two hours in learning a new AI development tool - signaling that simplicity and user experience is key when it comes to introducing new technologies to aid in the AI development process.

When it comes to developer productivity, the survey found widespread adoption and significant time savings from the use of AI-powered coding tools.

99% are using coding assistants in some capacity for AI development. And most commonly, developers said these tools saved them 1-2 hours per day (41% of developers) with some saying it saves them 3 hours or more (22% report 3 hours+ time savings).

Simplifying the AI stack and AI development lifecycle is a key focus for IBM. We know that with the right approaches and tools, developers can harness generative AI and set their enterprise up for success.

We do this with big-picture strategies, like IBM's support of open-source AI. An open-source AI stack can mean a more transparent, trustworthy, and innovative AI stack. We also do it with specific products. IBM's watsonx.ai - our studio for end-to-end AI application development - offers a broad set of tools, frameworks, and integrations to help simplify and streamline the development lifecycle. IBM watsonx Code Assistant grants developers extra firepower when writing enterprise-ready AI applications. And IBM's Application Integration solutions help developers create and manage APIs to modernize their AI applications across hybrid environments. Meanwhile, IBM Granite models provide an open-source foundation for enterprise development of trustworthy AI.

The AI development stack doesn't receive a lot of attention in the broader generative AI conversation. Yet it can play an outsized role in the technology's impact. Let's make the AI stack as simple and intuitive as the applications it produces.

For developers looking for resources to support their generative AI projects, check out our new watsonx Developer Hub - a centralized repository which includes the latest quick starts and guides to help with everything from models to chat, RAG, tool calling, and more.

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