A recent survey found that only 37 percent of businesses are prepared for AI. This means they risk being left behind as competitors embrace the technology.
We spoke to Richard Tworek, CTO at Riverbed about how organizations can embrace AI and how they can succeed in today’s rapidly evolving landscape.
BN: What is AIOps and what does it mean for businesses?
RT: AIOps — or Artificial Intelligence for IT Operations — uses AI to simplify IT system management. For teams overwhelmed by service issues, AIOps automate the detection, analysis, and resolution of problems, proactively identifying potential issues before they impact users and ensuring smoother operations.
By handling mundane routine tasks, AIOps frees IT teams to focus on more complex problems that require human insight. This shift accelerates problem resolution, boosts operational efficiency, and helps reduce burnout by clarifying priorities.
A critical function of AIOps is the collection of real-time relevant data such as network metrics, application performance, and device resources making the analysis meaningful and present false positives. The system will stream this data to analysis; event correlation to identify patterns and root causes; anomaly detection to predict issues; and automating corrective actions based on these insights. With proper tuning and AI, at least 95 percent of the anomalies received are ignored as routine or known issues such as a large backup starting that consumed resources creating capacity alarms.
Automated workflows provide the context and diagnostic data needed to resolve issues faster, without manual intervention.
For customers, this means seamless, personalized interactions, faster response times and improved service delivery. It also enhances accessibility and user engagement across platforms — so AIOps is a win-win solution.
BN: What are the main challenges preventing businesses from achieving AI readiness, and how can they start addressing these issues?
RT: Riverbed’s recent Global AI and Digital Experience Survey reveals that whilst there is widespread enthusiasm for AI with 94 percent of business and IT leaders say it’s a top C-Suite priority, only 37 percent of organizations are fully prepared to implement AI projects now, and 72 percent say it’s been a challenge to implement AI that works and scales effectively. This readiness gap is largely due to insufficient data quality, insufficient infrastructure, and a lack of AI expertise.
A major hurdle in closing the gap is the overestimation of AI’s current capabilities. Many business and IT leaders believe their companies are ahead of the curve, with 82 percent believing they are ahead of industry peers, but in reality, many AI projects stall due to a lack of readiness. This gap between perception and readiness can lead to strategic missteps such as the misallocation of resources and unrealistic expectations which may result in the inability to meet their goals.
To tackle this, businesses should adopt a more practical approach — beginning with a readiness assessment to test AI in action, before rolling it out on a larger scale. Businesses should start slow, prioritize productivity and go after the ‘low-hanging fruit’ use cases.
Data management also plays a crucial role. Many organizations fail to fully leverage their internal data, which undermines AI’s effectiveness. Companies must make data across departments available, invest in data observability, and ensure they are collecting real, high-quality data from all sources.
Fostering a culture of continuous learning and forming dedicated AI or observability teams is another vital step. These teams, for example, can take an active role in regularly auditing the infrastructure to identify weaknesses. By monitoring the entire IT environment, from end users to applications, these teams can provide real-time insights and ensure a superior digital experience.
With these more practical approaches to AI in place, we expect the next three years to bring rapid growth to businesses driven by AI. By 2027, 86 percent of leaders believe their organizations will be fully equipped to execute their AI strategies and projects.
BN: What is ‘good data’ and why is it important?
RT: ‘Good data’ is accurate, complete, and consistent data, that ensures AI models can make reliable decisions. For AI to be effective, organizations need data that is not only plentiful but also trustworthy. High-quality data — especially real data from actual operations — supports more accurate AI predictions and avoids the biases that can arise from synthetic or sample data. Without it, AI outcomes can be impacted and compromised.
The best approach is to understand which data elements are critical and not move data to a centralized data warehouse but to leave data in place and fetch only the data needed for problem analysis and resolution.
Many of the key obstacles holding back an organization’s AI success are related to data issues. In fact, 76 percent of organizations express concern about the use of synthetic data, highlighting the risks of compromised decision-making when ‘good data’ is not available. By developing a data collection strategy, investing in management tools and encouraging cross-departmental collaboration, businesses can elevate their ‘good’ data to ‘excellent’ data.
BN: As more businesses form dedicated AI teams, what skills and knowledge do they need?
RT: Organizations should equip their teams with diverse skills and knowledge. This includes AI strategy development, with a focus on providing comprehensive training to bring employees along on the journey. Expertise in AI-driven analytics is also essential, to improve both user experience and IT operations.
AI governance frameworks should be a key consideration, helping businesses address data security and privacy concerns as the technology grows, from compliance with standards such as ISO to industry-specific regulations that dictate how it should be deployed.
Interestingly, high-performing companies (those with 10.5 percent or higher revenue growth) are more likely to report that AI is being leveraged to its full capabilities in their organization to improve the user’s digital experience compared to low performers (67 percent vs. 45 percent). A significant reason for this is because high performers are likely to provide extensive training on how to use AI responsibly versus low performers (63 percent vs. 41 percent). This demonstrates the importance of training and building dedicated teams; it helps all employees use AI to transform the business.
BN: How do generational differences within an organization impact its AI readiness?
RT: Generational attitudes towards AI vary significantly. Riverbed’s recent survey found that the younger generations, such as Gen Z and Millennials, are thought to be most comfortable with AI. This comfort likely stems from their exposure to digital technology from an early age, making them naturally adept at using and trusting AI solutions. Millennials, having witnessed and adapted to major digital transformations during their formative and professional years, also display a strong familiarity with AI. As many Millennials now hold leadership positions, they often drive tech adoption within the workplace.
On the other hand, older generations like Gen X and Baby Boomers tend to exhibit more caution or resistance toward adopting AI. Their professional experiences have been shaped by pre-digital work practices, and integrating new technologies may seem disruptive or uncertain.
Overall, the goal is to create a collaborative, cross-generational approach where knowledge-sharing and strategic guidance enables the entire workforce to embrace AI effectively. Addressing this generational gap is imperative for companies to shift from the promise of AI to implementing AI that works, scales and grows the business. AI is not to replace years and decades of experience and knowledge but augments the team to relieve them of the mundane tasks allowing the opportunity for career growth and addressing the growing backlog of IT challenges.
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