Recent media reports have pointed to some growing skepticism around AI, especially where early promises of transformation haven’t materialized yet.
Gary Drenik is a writer covering AI, analytics and innovation.Big trees fall hard. The bigger they are, the harder they fall. There’s a reason there are multiple variations of this idiom in our lexicon: because it’s true.
And depending on who you’re listening to, you may think that’s where we stand when it comes to hype around AI. Recent media reports have pointed to some growing skepticism around AI, especially where early promises of transformation haven’t materialized yet. Look a little closer and you’ll see that the issue isn’t that AI can’t deliver. The issue is that many organizations have failed to lay the necessary groundwork – with data, IT operations, and realistic timelines – to convert potential into measurable results.which polled 1,200 business decision-makers, IT leaders, and technical specialists, shines some light on both the optimism and the obstacles shaping enterprise AI adoption and implementation today. “Companies are investing heavily in AI for IT because they understand the potential it has to transform operations in today’s working world,” said Jim Gargan, Chief Marketing Officer, at Riverbed. “However, our research shows that enterprises face several significant challenges as they attempt to move from the early stages of implementation to practical AI solutions that deliver a strong return on investment. Across the globe, Riverbed is helping organizations to improve user experiences and IT operations with safe, secure, and accurate AI. We’re focusing on what our customers need: full support for AIOps; a solution to the data gap with observability across all of IT; and fast, agile, secure AI data acceleration.”Optimism is unmistakable. Seventy-eight percent of organizations are increasing AI investment. Nearly two-thirds of executives say they feel confident in their AI operations strategies.But that optimism has its limits: only half of IT staff report the same confidence. And preparedness is lagging, with only 36 percent of respondents feeling ready for AI. Far more leaders feel prepared compared to just 25 percent of IT staff. These data points indicate a gap between optimism among leadership and the realities on the front lines. The good news: 86 percent believe they will be AI-ready within three years.Data quality stands out as a major challenge, with fewer than half of organizations rating their data as ready for AI initiatives. “Organizations are eager to embrace AI, but many underestimate the effort required to prepare their data and operations,” noted Richard Tworek, Chief Technology Officer, at Riverbed. “AI outcomes are only as strong as the real data behind them. Having accurate, secure, complete, reliable data is essential, and many organizations are now investing in data quality and real-time access to build a strong data foundation. Additionally, given the massive volumes of raw data across the IT stack, enterprises need to rethink how data is collected, implementing a centralized data store to identify and extract only the most relevant data in real-time for analysis.”, which finds that 29% of business leaders and 28% of employees believe that AI requires more disclosure and transparency regarding the data it uses. Furthermore, 39% of business leaders and 33% of employees believe that AI needs more human oversight.Priorities for AI Readiness The Riverbed Global Survey highlights four urgent priorities. First is tool consolidation. Almost every enterprise surveyed, 96 percent, plans to reduce the number of vendors and tools in IT operations. Companies manage, on average, 13 observability supplied by 9 vendors, creating inefficiencies. To streamline, 93 percent are considering new vendor partnerships, and nearly 80 percent expect consolidation to be complete within two years. Unified communication is another point of pain. Employees spend nearly half their work week on UC platforms, and 65 percent say these tools are essential. Even so, fewer than half of companies are satisfied with how they perform, and 43 percent report regular issues. About half lack the ability to monitor UC tools in real-time, which means IT teams can’t spot problems until they show up as helpdesk tickets. Data quality and network performance also remain top concerns. Ninety-one percent of organizations say the ability to move and share AI data is critical to their strategy. Over the next three years, they expect data storage to shift further toward the cloud and edge – public cloud use rising from 36 to 39 percent, edge from 9 to 13 percent – while reliance on on-premises data centers drops from 23 to 17 percent. “These shifts underscore the growing need for reliable data pipelines and strong governance practices,” said Richard Tworek, Chief Technology Officer, at Riverbed. “AI is only as effective as the infrastructure supporting it. Without high-quality data and resilient networks, AI initiatives are still at risk of delays, inaccuracies, and stalled adoption.” Finally, organizations will need to rethink their observability strategies. OpenTelemetry is quickly moving into the mainstream as an industry standard. More than half of companies expect it will be mandated within the next two years, and 94 percent see it becoming a cornerstone of automation.First step: closing the gap between technical and leadership teams. When the C-Suite is overly optimistic, they can sometimes take on more than their teams can realistically manage. Shared KPIs, joint governance, and regular project reviews are critical to keep everyone aligned and accountable. Second, get data in order. Fewer than half of companies rate their data as excellent for AI. There’s a real need to improve quality, centralize repositories, and do a better job of making information available in real-time. It’s the only way to establish a reliable foundation for AI. Next, get a handle on tool sprawl. Too many overlapping platforms slow teams down and make it harder to see the full picture. Consolidating vendors and moving toward integrated solutions helps IT spend less time managing tools and more time focusing on strategy and results. “My research has found that the majority of early adopters of AI have discovered that their IT observability tools are not fully prepared to manage AI infrastructure and AI traffic,” said Shamus McGillicuddy, vice president of research at Enterprise Management Associates. "They need better data and more of it. They also need tools that can recognize AI traffic and apply specific analytics to provide actionable insights for managing AI infrastructure. Moreover, IT operations teams are embracing AI to manage AI. They see value in leveraging AI capabilities from their tool vendors to optimize and automate operations for AI infrastructure.” Fourth: start to embrace OpenTelemetry, if you haven’t already. Embracing it now will give you a consistent framework for automation moving forward. Finally, organizations need to give unified communications the attention it deserves. Real-time monitoring and proactive diagnostics can cut down on outages and frustration. UC platforms may not be the most exciting part of an IT strategy, but they’re central to how people work every day. The bottom line: companies are clearly enthusiastic about AI, but optimism by itself won’t deliver results. But by putting some basic best practices in place, businesses can move past pilots and start realizing the real returns AI has been promising. Disclosure: The consumer sentiment study referenced above was conducted by my company, Prosper Insights & Analytics. This is the same dataset used by the National Retail Federation, and available from Amazon Web Services, Bloomberg, and the London Stock Exchange Group for economic benchmarking.
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