96% of IT pros use AI now: Their top 7 agentic applications and biggest implementation roadblocks
Source: ZDNet
ZDNET’s key takeaways
- Nearly all data and IT pros use AI, but few are heavy users.
- Many would give AI agents unrestricted data access.
- AI data preparation and validation take about 10 hours a week.
If you’re curious about what’s happening in the eye of the artificial‑intelligence storm, look no further than what the data analysts of the world are up to. They’re bullish on AI, of course, but they’re still using spreadsheets, and only a handful are working with real‑time data.
That’s the word from a new global survey of 700 data analysts and 700 IT leaders from Alteryx. While 96 % report using AI for their work, only half can be considered frequent users of AI tools—49 % say they use AI always or most of the time.
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Agentic AI is high on the agenda, with close to six in 10 respondents (59 %) predicting they will be actively employing AI agents within the next 12 months. At least half say they are willing to grant AI agents “unrestricted access” to their data.
The security implications of such access were not discussed in the survey report, but 44 % specified that it was critical to include human oversight as part of such access.
The most common agentic AI applications
The most common agentic AI applications now in production are drafting communications and scheduling workflows.
| Application | Percentage |
|---|---|
| Drafting standardized communications or summaries for stakeholders | 59 % |
| Scheduling or routing workflow tasks (e.g., alert triage, process automation) | 54 % |
| Generating standard reports or dashboards without manual intervention | 48 % |
| Monitoring key performance indicators and triggering alerts or actions | 45 % |
| Cleaning, preprocessing, or validating routine data sets | 45 % |
| Running routine statistical analyses or basic predictive models | 34 % |
| Automatically generating insights or recommendations from data | 23 % |
“Foundational data work” — cleaning and prepping data for ingestion by AI models or retrieval‑augmented generation platforms — still consumes a large chunk of analysts’ time. Respondents report spending close to six hours per week on such tasks, with 48 % spending six to ten hours weekly. The tools they use are:
- Spreadsheets – 61 %
- Business intelligence tools – 56 %
- Dedicated data‑preparation platforms – 51 %
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“The continued dominance of spreadsheets reflects a broader reality,” the survey’s authors suggest. “AI is layering on top of existing workflows rather than replacing them.”
Another surprising finding is that despite all the attention to real‑time responsiveness, few organizations truly have real‑time capabilities. Only 20 % report that moving from data analysis to a business decision can be done within a few hours, and a mere 5 % say they support real‑time decision‑making.
The biggest barrier to AI?
Explaining AI outputs to business decision‑makers is the top challenge, followed by a notable lack of analytical skills across businesses.
| Barrier | Percentage |
|---|---|
| Difficulty interpreting or explaining AI outputs to decision‑makers | 55 % |
| Limited analytical skills among business users | 54 % |
| Data not sufficiently clean, integrated, or governed | 50 % |
| Lack of clarity on ownership or accountability for decisions | 49 % |
| Technical limitations of AI tools or infrastructure | 45 % |
Generating insights from AI is not a one‑off exercise; it also consumes more of analysts’ time. Survey participants spend almost four hours per week validating or correcting AI‑generated outputs. One in six say they spend an entire workday (six hours or more) fiddling with AI results. Adding the six hours spent on foundational data work creates an AI “tax” of almost two days per week.
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This points to an emerging skill set that is becoming more valuable in the AI age: validating AI outputs. As the survey’s authors note, “while AI can accelerate work, organizations still need human oversight to ensure outcomes are consistent, explainable, and trusted.”