Building generative AI applications is difficult for many developers in a specific, documented sense: enterprise respondents reported gaps in development processes, trust and governance work, and practical tooling. The evidence is a January 2025 IBM-sponsored survey of more than 1,000 U.S. enterprise AI developers, conducted by Morning Consult—not a measure of every developer or proof that one product solves the problem. [IBM’s survey summary]
What the survey says is making AI development difficult
IBM’s January 8, 2025 summary of the Morning Consult survey puts process and trust work at the top. One-third of respondents cited a lack of standardized AI development processes; the same share cited the challenge of developing an ethical, trusted lifecycle with transparency and traceability. These are tied findings, not evidence that one issue is universally the biggest obstacle.
InfoWorld’s account of the survey reports several other challenges, with figures referring to respondents who cited each issue:
| Challenge | Share |
|---|---|
| Customizing AI for enterprise context | 32% |
| Keeping up with rapid technology change | 31% |
| Infrastructure complexity | 29% |
| Governance and compliance | 28% |
InfoWorld’s report and IBM’s summary describe the same survey; these percentages should be read as respondents’ reported challenges, not as an independent audit of project failures.
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Why the challenges differ by role
Confidence in generative AI expertise was not uniform across the roles surveyed. IBM says 24% of application developers rated themselves generative AI experts; AI developers and data scientists were the only surveyed role groups in which a majority did so. That suggests a skills and collaboration issue inside enterprise teams, rather than a simple claim that developers in general lack expertise. Self-rated expertise also does not establish demonstrated proficiency.
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What developers want from their tools—and why it remains hard
Respondents identified performance, flexibility, ease of use, and integration as the most essential qualities of enterprise AI development tools. IBM says more than a third also described these qualities as among the rarest.
| Tool quality | Respondents who called it essential |
|---|---|
| Performance | 42% |
| Flexibility | 41% |
| Ease of use | 40% |
| Integration | 36% |
These figures are useful as an evaluation checklist, not as a ranking of products. A team comparing options can ask whether a tool meets its performance needs, accommodates its models and workflows, is manageable for the intended users, and fits its existing systems. Documentation, cost-effectiveness, community resources, openness, governance, and fit with the organization’s context also matter in the survey coverage. The findings do not establish which vendor performs best on any of these dimensions.
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Tool sprawl adds coordination work
IBM reports that 72% of surveyed respondents used between five and 15 tools to create an enterprise AI application, while 13% used 15 or more. The categories describe respondents’ reported tool counts; they do not identify which tools were used or show how much time switching among them consumed.
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Even without a measured productivity cost, a broad toolchain gives teams a practical question to investigate: which tools are needed for development, integration, evaluation, deployment, and governance, and where are responsibilities duplicated? Consolidation may reduce operational friction, but replacing tools can also sacrifice capabilities or create migration work. The survey does not test either outcome.
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Coding-assistant savings are self-reported
IBM says 99% of respondents used coding assistants in some capacity for AI development. Among the reported time-saving responses, 41% said they saved one to two hours per day and 22% said they saved three or more hours. These are respondents’ estimates, not independently measured time savings or proof that coding assistants caused a productivity increase. The figures also describe the surveyed U.S. enterprise group in 2025, not current usage across the industry.
Agent development brings oversight and trust questions
IBM reports that 99% of respondents were exploring or developing AI agents. InfoWorld’s account highlights concerns around trustworthiness, attack vectors, compliance, and systems becoming so autonomous that human oversight is reduced. That combination helps explain why process and lifecycle questions matter: a team must consider not only whether an agent can perform a task, but also how its behavior is monitored and governed.
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What the findings can—and cannot—tell you
The survey offers a snapshot of more than 1,000 U.S. enterprise AI developers, conducted by Morning Consult and summarized by IBM on January 8, 2025. IBM sponsored and published the findings. The source material does not establish detailed questionnaire wording, field dates, sampling method, or weighting, so the numbers should not be generalized beyond that respondent group as precise industry-wide rates.
IBM’s summary recommends its own offerings, including watsonx.ai, watsonx Code Assistant, IBM Application Integration solutions, Granite models, and the watsonx Developer Hub. Those are vendor-proposed options, not independent comparative evidence that they resolve the reported challenges. Any organization evaluating them—or alternatives—should assess them against its own requirements and the same tool qualities respondents prioritized.
The narrow conclusion is that these enterprise respondents described generative AI application development as demanding because it combines fast-changing technology, fragmented tooling, skills differences, and substantial process, trust, and governance work. The survey identifies the pressures; it does not show that all developers face them equally or establish a universal remedy.
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