Artificial Intelligence: Why the Gap Between Interest and Reality?
Business leaders still talk the talk about embracing AI, but they aren’t walking the walk. According to a survey of senior analytics and IT leaders, only 20% of AI applications are currently in production.
Why the Wide Gap?
The answer is multifaceted. Concerns around security and data privacy, compliance risks, and data management are high-profile, but there’s also anxiety about AI’s lack of transparency and worries about ROI, costs, and skill gaps.
Get a Handle on Data
“High-quality data is the cornerstone of accurate and reliable AI models, which in turn drive better decision-making and outcomes,” said Rob Johnson, VP and Global Head of Solutions Engineering at SolarWinds. “Trustworthy data builds confidence in AI among IT professionals, accelerating the broader adoption and integration of AI technologies.”
Today, only 43% of IT professionals say they’re confident about their ability to meet AI’s data demands. Given that data is so vital for AI success, it’s not surprising that data challenges are an oft-cited factor in slow AI adoption.
Take Ethics and Governance Seriously
With regulations mushrooming, compliance is already a headache for many organizations. AI only adds new areas of risk, more regulations, and increased ethical governance issues for business leaders to worry about. While the rise in AI regulations might seem alarming at first, executives should embrace the support that these frameworks offer, as they can give organizations a structure around which to build their own risk controls and ethical guardrails.
Reinforce Control over Security and Privacy
Security and data privacy concerns loom large for every business, and with good reason. Cisco’s 2024 Data Privacy Benchmark Study revealed that 48% of employees admit to entering non-public company information into AI tools, leading 27% of organizations to ban the use of such tools.
Boost Transparency and Explainability
Another serious obstacle to AI adoption is a lack of trust in its results. The infamous story of Amazon’s AI-powered hiring tool, which discriminated against women, has become a cautionary tale that scares many people away from AI. The best way to combat this fear is to increase explainability and transparency.
Define Clear Business Value
Cost is on the list of AI barriers, as always. The Cloudera survey found that 26% of respondents said AI tools are too expensive, and Gartner included “unclear business value” as a factor in the failure of AI projects. Yet, the same Gartner report noted that AI had delivered an average revenue increase and cost savings of over 15% among its users, proof that AI can drive financial lift if implemented correctly.
Set Up Effective Training Programs
The skills gap remains a significant roadblock to AI adoption, but it seems that little effort is being made to address the issue. A report from Worklife indicates the initial boom in AI adoption came from early adopters. Now, it’s down to the laggards, who are inherently sceptical and generally less confident about AI – and any new tech.
The Barriers to AI Adoption are Not Insurmountable
While AI adoption has slowed, there’s no indication that it’s in danger in the long term. The many obstacles holding companies back from rolling out AI tools can be overcome without too much trouble. Many of the steps, like reinforcing data quality and ethical governance, should be taken regardless of whether or not AI is under consideration, while other steps taken will pay for themselves in increased revenue and the productivity gains that AI can bring.
Frequently Asked Questions
Q: What are the main barriers to AI adoption?
A: Concerns around security and data privacy, compliance risks, and data management are high-profile, but there’s also anxiety about AI’s lack of transparency and worries about ROI, costs, and skill gaps.
Q: How can organizations overcome the barriers to AI adoption?
A: Organizations can overcome the barriers to AI adoption by getting a handle on data, taking ethics and governance seriously, reinforcing control over security and privacy, boosting transparency and explainability, defining clear business value, and setting up effective training programs.
Q: Why is data quality so important for AI success?
A: High-quality data is the cornerstone of accurate and reliable AI models, which in turn drive better decision-making and outcomes.
Q: How can organizations ensure the transparency and explainability of AI results?
A: Organizations can ensure the transparency and explainability of AI results by prioritizing the development of rigorous AI governance policies, investing in explainability tools like SHapley Additive exPlanations (SHAPs), fairness toolkits like Google’s Fairness Indicators, and automated compliance checks like the Institute of Internal Auditors’ AI Auditing Framework.
