The emergence of generative AI has impacted pretty much every industry. Tech professionals increasingly rely on generative AI tools such as ChatGPT and Google’s Gemini for everything from writing emails to generating code.

But do tech pros actually trust these tools to get the job done? According to data from Dice’s new Sentiment Report, 30 percent of tech professionals say they utilize generative AI tools at least once per week. Meanwhile, roughly a third of tech pros aren’t using generative AI tools at all.

(For the data folks: The Q2 2024 Technology Professionals Sentiment Survey was conducted online via email June 5 – 27, 2024. The survey generated 520 qualified responses from fully employed technology professionals residing in the U.S.)

Among those who don’t use generative AI, what’s holding them back? Only eight percent of respondents said they don’t use these tools because their companies restrict the use of generative AI for work-related tasks. “This avoidance appears to be mostly voluntary,” the Sentiment Survey added. “This hesitation is consistent with other reports we have been seeing; in their AI sentiment survey, the company ReTool found that 51 percent of tech professionals consider AI to be overrated.”

Younger tech professionals are more likely to have adopted AI as part of their workflow (among those between the ages of 18 and 34, some 38 percent had used the technology at least once per week); meanwhile, half of respondents who said they were over 55 years old also reported they’d never used generative AI. Perhaps some of these respondents are waiting to see how AI technology evolves before committing to changing their existing workflows.

Of the one-quarter of tech professionals in the Survey who are responsible for developing or implementing AI solutions or systems, just over half feel the projects they work on are strategically valuable to their company. Additionally, one-third indicated that their AI-related projects are primarily used to show stakeholders the company is doing something with AI.

Naturally, the AI professionals who feel that the projects they work on are strategically valuable are also more likely to be satisfied with their current role.

Putting Generative AI into Practice

Here’s a list of how generative AI is finding its way into contemporary tech workflows:

• Software Development:

  • Code generation: Developers are using AI to generate code snippets, entire functions, or even complete programs based on natural language prompts.
  • Code completion: AI-powered code completion tools suggest the next line of code, accelerating development speed and reducing errors.
  • Debugging: AI can analyze code for potential issues, providing insights into error causes and suggesting solutions.

• Data Science:

  • Data exploration: AI can quickly generate summaries of large datasets, identify trends, and suggest potential correlations.
  • Feature engineering: AI can automate the creation of new features from existing data, improving model performance.
  • Model building: AI can assist in selecting appropriate algorithms, tuning hyperparameters, and evaluating model performance.

• Cybersecurity:

  • Threat detection: AI can analyze vast amounts of data to identify potential security threats and anomalies.
  • Incident response: AI can automate routine tasks during security incidents, freeing up analysts to focus on critical issues.
  • Vulnerability assessment: AI can scan code for vulnerabilities and suggest remediation steps.

• Design and UX:

  • Design generation: AI can generate design concepts based on user requirements and preferences.
  • User testing: AI can simulate user interactions to identify potential usability issues.
  • Content creation: AI can generate marketing copy, social media posts, and other content.

In order to effectively use generative AI for any of this, you’ll need to understand the technology, identify the right tasks for it, and experiment until you achieve the desired results. Companies that will succeed in the AI space will ensure the quality of data used for the inputs and the outputs—and they’ll prioritize the privacy and security of data throughout the process.