What to look out for when working with AI-powered tools
9 Nov 2023
According to the latest Gartner trend predictions, more than 80% of tech companies will have embedded AI capabilities in their enterprise applications by 2026. While advancements in AI-powered technology unlocks incredible potential for businesses to transform how they work, there are key factors to keep in mind in order to maintain a high standard of safety, security, and quality.
Our upcoming whitepaper explores AI sentiments across our Elastic Team community. While respondents are generally aware of AI technologies, there are still reservations when it comes to full adoption. Before we launch the full report, we’ve gathered a sneak peek of top concerns that are holding developers back from embracing AI completely.
Before implementing any AI strategy, business leaders should be aware of the risk factors in order to properly plan and prepare around these potential obstacles.Why bring DevOps to IoT?
1. Security and privacy
As enterprises integrate AI into their strategies at the c-suite and board levels, Gartner reports that only 3% of executives cite security and privacy concerns as a barrier to AI adoption. However, 41% of the same organisations from the study reported to have previously had a known AI privacy breach or security incident.
Elastic Team community developers have a deep understanding of AI’s potential risks, stating in our survey that security and privacy were indeed among their top concerns regarding the use of AI-powered tools. They referenced the following risks:
AI tools often require access to data which might compromise privacy.
Potential for introducing security vulnerabilities in the code.
Concerns about intellectual property violations.
2. Code quality and accuracy
For developers who are particularly diligent about the cleanliness of their code, AI-powered tools pose a potential risk by generating code that is incorrect or buggy, or that might not fit the use case. There is also concern regarding code originality and maintainability. Best practices must be applied to always double check and review any code that’s powered by AI in order to maintain consistent levels of quality.
3. Dependency and skill degradation
For the developer community, there’s a weariness that a heavy reliance on AI might lead to atrophying skills and knowledge. For developers who are starting out on their learning journey, there’s risk for dependency, which may hinder their growth. For a simple change in perspective, developers should be encouraged to see AI as a tool and an asset, not as a replacement of their own growing knowledge.
4. Bias and misinformation
In McKinsey’s report, “The state of AI in 2023: Generative AI’s breakout year,” a third of respondents considered equity and fairness to be risks of leveraging AI but only 16% of organisations are actively working to mitigate those risks. For the Elastic Community, respondents echo this concern around bias, especially with potential outdated techniques or information that may be pulled in by an AI model.
Taking a nuanced approach is the best way to integrate AI into workstreams
For any leader, new technology is an exciting opportunity to maximise productivity and transformation. AI offers powerful solutions that continue to grow and develop today. As we move into the future, businesses that choose to leverage AI will have to keep in mind the risks associated with this new technology. That doesn’t mean organisations should let these potential pitfalls keep them from growing with AI. There's potential to build new practices and put in place the structures necessary for successful AI adoption.