New TechPoint survey shows how AI is changing productivity, training and entry-level work
Artificial intelligence is already changing how members of Indiana’s tech community work, learn and prepare talent. A new TechPoint survey finds that daily AI users are reporting significant productivity gains, even as employer-provided training remains limited and expectations for entry-level workers continue to shift.
AI at Work in Indiana: Early Signals from Indiana’s Tech Community is based on 221 voluntary responses collected from TechPoint’s network in May 2026. The findings are not intended to represent every Indiana employer or worker. Instead, they offer an early look at how AI adoption is unfolding within a highly relevant segment of the state’s economy.
TechPoint reviewed the survey data and identified four key takeaways related to AI usage, productivity, training, entry-level work and the emergence of more autonomous workflows. Explore the interactive dashboard for the complete results or download the accompanying research brief.
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1. AI is Now a Daily Productivity Tool
The vast majority of respondents have made AI a core part of their workflow. Seventy-eight percent use AI tools daily and 93% report being more productive compared to a year ago.
The gains are not marginal. Forty-three percent describe themselves as significantly more productive, or 2x+, while the remainder report moderate or slight gains. Only 3% report no meaningful change or a decline.
That level of daily use far outpaces national workforce benchmarks. Gallup’s February 2026 workplace survey found that only 13% of U.S. employees use AI daily in their role. This gap reinforces the early-adopter nature of TechPoint’s sample and suggests Indiana’s tech community may be previewing patterns that become more common as workplace AI adoption matures.
Usage frequency is the strongest predictor of outcome. Daily users report 2x+ gains at five times the rate of non-daily users, 52% compared with 10%. The productivity dividend from AI is real, but it accrues primarily to those who use it habitually, not occasionally.
Current AI Use
Share of all respondents who use AI for each purpose
Because this question allowed multiple answers, percentages add up to more than 100%.
Why It Matters
AI adoption has crossed from experimentation into daily work. The productivity upside is real, but it is not evenly distributed. Daily users are seeing the largest gains, making AI fluency and workflow integration a growing competitive advantage.
2. Learning is Mostly Informal
Despite widespread adoption and significant productivity gains, only 17% of workers receive employer-provided AI training.
Among the 65%, or 143 respondents, who answered the learning question, AI skill development is occurring primarily outside formal education and training systems.
This mirrors national research from the New York Fed, which found that only 15.9% of employed respondents said their employer currently offers AI training, even as 38% said training on AI tools is important to them.
Coding and technical AI users are especially likely to rely on experimentation and external content. They are less likely than non-coding users to report learning from peers or employer-provided training. That may suggest technical users are moving quickly on their own, ahead of formal organizational learning.
Overall Learning Methods
Share of respondents who answered the learning question (n=143)
Because this question allowed multiple answers, percentages add up to more than 100%.
Why It Matters
AI adoption appears to be moving faster than formal training systems. Early adopters are learning by experimenting, following external content and relying on peers, but that approach may not scale to the broader workforce.
3. Entry-level work is changing
The data reinforces TechPoint’s experience gap research published in February. Across all respondents who answered the question, 58% say AI is increasing expectations for entry-level roles or reducing the number of entry-level roles.
Among those using AI for coding, concern is highest. Twenty-eight percent believe AI is already reducing entry-level roles in their field, nearly double the rate among non-coders at 15%. This group has the clearest direct view of what AI can replace in technical work.
Of the 47 respondents who said AI is reducing the number of entry-level roles, 33 came from organizations with fewer than 50 employees. Larger organizations with 1,000 or more employees were more likely to be unsure or to see AI creating new roles.
Many technical entry jobs are now management jobs. For many tasks, delegation to AI and managing/correcting output are table stakes.
— Executive leader at a small technology services company
Why It Matters
The responses do not point to a simple story of mass entry-level job loss. Instead, they suggest that AI may be changing what it means to be ready for an entry-level role.
Early-career workers may need more than technical knowledge. They need the judgment to use AI well and take responsibility for the work it helps produce.
4. Autonomous workflows are emerging
Fifty-eight percent of respondents indicate AI output requires moderate editing or refinement before use. Another 9% report the output needs significant correction or rewriting.
Only 10% describe their workflows as autonomous or orchestrated, where AI operates within defined workflows or manages multiple workflows with minimal human direction.
This feedback signals that AI has not replaced human judgment in most professional contexts. The ability to critically review, refine and take responsibility for AI-generated work is becoming a core professional competency.
The most productive workers are not simply prompting and publishing. They are prompting, evaluating and owning the final result.
The coding cohort appears further along the AI maturity curve. Eighteen percent of respondents using AI for coding or technical development report autonomous or orchestrated workflows, compared with 3% of non-coding users.
That suggests technical users may be more likely to move beyond one-off tasks toward defined workflows, integrations and AI-supported development processes.
AI is like an overconfident, under-skilled assistant. Its output needs considerable review, proofing and iterations.
— Mid-level manager at a large technology-enabled company
Why It Matters
AI is speeding up work, but human judgment still matters. Most respondents need to review or correct AI output before using it, making evaluation, refinement and ownership core workplace skills.
The coding cohort may show the direction adoption is heading: toward more integrated workflows, not less human responsibility.
Explore the Full Results
AI is everywhere right now and it can be difficult to separate sweeping predictions from what is actually changing inside organizations.
That is why TechPoint conducted this survey: to move beyond broad assumptions and get a real-time look at how Indiana’s tech community is using AI at work.
The takeaways highlighted here represent only part of what the data reveals. Explore the dashboard, examine the results through the lens of your own work and consider what the findings may mean for your organization.
Explore the Full Results
See how Indiana’s tech community is using AI at work. Dig into the interactive dashboard or download the complete research brief.
