Artificial intelligence (AI) is no longer a mere futuristic mirage hovering on the horizon; it's firmly embedded in the operational landscapes of the vast majority of public and private sector organizations. However, a new study by Splunk, a cybersecurity and observability leader, paints a nuanced picture of AI adoption – one where rapid deployment is juxtaposed with persistent trust concerns.
The research unveiled that all respondents reported either actively using, testing, planning or investigating AI technologies, which emphasizes the pervasive belief in its potential. This widespread adoption spans diverse applications, with organizations leveraging AI for tasks ranging from cybersecurity threat detection to customer service optimization.
Despite this enthusiasm, trust remains a critical roadblock on the path to broader and deeper AI integration. Concerns around the reliability and transparency of AI-powered systems emerged as the top obstacle for public and private sector leaders. These anxieties are particularly acute when it comes to AI employed in cybersecurity tools, where compromised trust could have dire consequences.
So, what may contribute to the trust deficit? One could say bias and discrimination embedded in AI models, a lack of human oversight and control and opaque decision-making processes. The reason is that these anxieties typically fuel fears of unintended consequences, algorithmic unfairness and even weaponization of AI.
There is also a disconnect between the perceived risks and the actual realities of AI deployment. While concerns about job displacement and the erosion of human autonomy linger, AI can complement and augment human work, creating new jobs and empowering employees with additional capabilities.
“For both the public and private sector, purpose-built AI solutions can help improve an organization's resiliency,” said Bill Rowan, Vice President of Splunk Public Sector, Splunk. “However, the push and pull between eagerness to innovate and hesitancy to venture blindly into the unknown will continue to hinder AI innovation until we have a clear body of general principles and rules for AI technology use and adoption.”
The study did point to positive examples of AI in action. The study showcased its ability to enhance cybersecurity defenses, streamline operations and drive innovation. For example, AI-powered fraud detection systems are credited with thwarting billions of dollars in attacks, while chatbots powered by AI are delivering personalized customer service at unprecedented scale.
The Splunk research paints an optimistic picture of the future of AI, despite the challenges of trust. With thoughtful approaches to mitigating risks and building trust, organizations can utilize AI to tackle complex challenges and unlock new opportunities.
Edited by
Alex Passett
