Top 5 IT challenges leaders are facing (and solutions to them)

Amid budget constraints, labor shortages, and the need to do “more with less,” CIOs and IT leaders are facing common IT problems that transcend industries. From poor data accessibility to changing customer expectations, IT leaders are turning to generative AI (GenAI) as an answer to their problems.

Continuous investments in GenAI promise companies new ways to solve key business problems and build revenue-generating streams. But for most, the key to reaping the benefits of GenAI is hidden in plain sight: data. Data is at the heart of IT innovation, but most businesses today aren’t using their data to its full potential. Investing in a robust data foundation is critical to leverage GenAI to optimize business workflows and innovate. Read on to discover what other challenges IT leaders are facing.

1. Difficulty getting insights from data

A substantial 69% of C-suite executives and decision-makers cite the inability to use data continuously — in real time and at scale — as a significant hurdle contributing to their company’s business challenges. The result is a lack of real-time insights that forces leaders to rely on their intuitions rather than evidence. This hinders decision-making and stifles growth and efficiency. Operationalizing data isn’t a one-time job. You need tools that can grow as your data does while giving you visibility into your systems.

“We have data silos across the business and are not able to consolidate [them to] have a single pane of glass to make decisions,” explains a telecommunications C-suite executive.

The feeling is supported by data: 60% of organizations are unsatisfied with the data insights they have today with only 35% leveraging data insights daily for business decisions. The inability to make real-time, data-driven business decisions is due to underlying data challenges, with 98% of leaders struggling with some combination of data problems. Notably, 67% of organizations are struggling with separate data solutions for different environments, and in most cases, this is due to inefficient data management. This is partly caused by a lack of adequate tools to manage disparate systems and software — another challenge IT leaders face today.

Solution:

Getting insights from data is resource-intensive. It requires time, expertise, and clear objectives and must be integrated into IT development processes. Once you’ve collected relevant data, it takes data analytics and analysis, often with GenAI, to get actionable insights. Actionable insights offer specific measures and steps that can help you achieve a goal by telling you what to do based on your data. With the precision of search and the intelligence of AI — including machine learning (ML) and natural language processing (NLP) — you can transform raw proprietary data into actionable insights to accelerate your business outcomes.

2. Lack of adequate tools

Traditionally, organizations have continued to invest in tools that serve a specific purpose based on the needs of the business. However, this conventional technical investment process leads to unplanned isolation and/or duplication of data, information, work, and costs. The result of tool sprawl further inhibits cross-functional collaboration, disables end-to-end visibility of your current environment, and overall creates organizational silos.

Legacy systems can also play a part in tool sprawl. Organizations must balance the cost of phasing these systems out with the cost of keeping them active. And because phasing them out can prove much more expensive, companies remain reliant on legacy systems. As a result, their teams might get stuck with tools that aren’t the most performant and useful for their use cases today. This may mean that all the tools don’t “connect” and speak to each other, ultimately hindering access to real-time, relevant information and digital transformation.

In the case of observability and security — practices that share data — redundant work and disparate tools can be detrimental to operations, compromising productivity and security while negatively impacting revenue.

Bottom line: inefficient tools and processes create bottlenecks, leading to slower workflows, wasted resources, and increased operational costs.

Solution:

In response to this challenge, 56% of C-suite executives prioritize investment in data tools and technology as a top solution. More specifically, you have everything to gain from consolidating your tools and investing in ones that can democratize access to data from multiple environments across organizational silos.

3. Too much time spent on manual work and analysis

“If data cannot be processed and analyzed quickly, it can lead to delayed decision-making, affecting critical aspects like customer service, product development, and marketing strategies,” explains a technology company C-suite executive. Inefficiencies hinder productivity and even slow down innovation while IT departments bear the brunt of tool sprawl and data silos.

Without the right easy-to-use tools and processes, teams often spend a lot of time on excessive manual work and analysis to get the output they need. Not only does this stifle efficiency and productivity, but it also often hinders innovation.

You hire the best people — why keep them stuck doing inefficient tasks instead of innovating? If teams had the right tools, they could save time on manual routine tasks and instead focus on more value-added activities that drive business growth. Repetition and inefficiencies can often lead to burnout and can exacerbate valuable talent. Building solutions and tools that allow teams to quickly approach laborious tasks and integrate with existing workflows can lead to better employee satisfaction, retention, and business efficiency. Using tools that do not support your teams can lead to a loss of productivity, reputation, and revenue.

Solution:

Taking a people, processes, and technology (PPT) approach to investing in technology and tools can help you build better workflows that prioritize automating repetitive tasks, ultimately leading to increased efficiency, cost savings, and a more agile, innovative organization. By analyzing and redesigning workflows, organizations can identify bottlenecks and inefficiencies, creating streamlined processes that are documented and standardized for consistency.

Selecting the right tools that integrate seamlessly with existing systems and leveraging advanced technologies like GenAI and machine learning further optimize automation capabilities. This approach not only improves accuracy and reduces costs but also enhances organizational agility and employee satisfaction^[1], ultimately providing a competitive advantage in the market.

4. Lack of operational resilience

Outages are a business’s worst nightmare — especially considering the average cost of downtime can be as high as $9,000 a minute.^[2] Operational resilience helps businesses weather disruptions by minimizing downtime and preventing potential crises. Resilient companies adapt faster to market changes and outperform competitors during and after a crisis.^[3] In other words, operational resilience is good for business.

Successful data management and practices are at the heart of operational resilience, yet establishing it is a challenge for many businesses. Without the proper tools, practices, and experts, business data is a burdensome anchor rather than a sail. As a result, organizations are vulnerable to frequent disruptions, delays, and downtime, which impact resilience, increase business risk, reduce productivity, and drive up costs.

Solution:

Without the ability to proactively get ahead of disruptions and outages, organizations are locked in a reactive stance and forced to play catch-up. AI can put you ahead of the game with predictive resilience models. By analyzing trends in your data, it can spot potential issues before they occur. Putting out fires big and small ultimately affects end-user productivity and revenue from customer-facing services.

Achieving operational resilience begins with a robust data foundation rather than a disparate collection of fragmented tools and systems. By prioritizing data infrastructure, you can empower your teams with actionable, real-time insights to take on a proactive approach that drives business growth and ensures that your revenue-generating applications are up and running.

5. Not able to effectively mitigate cybersecurity threats

GenAI has many potential advantages, but it has also fostered the rise of a new generation of cyber threats. The use of GenAI in both official and unofficial capacities has also intensified and fueled these cybersecurity threats. Often understaffed in the security domain or underskilled in the face of rapidly evolving AI technologies, organizations see negative business impacts: reactive measures lead to high-risk exposure, financial loss, legal issues, reputational damage, and lost customer trust.

Effectively mitigating these cybersecurity threats requires specialized skills that are in high demand and very difficult to come by. Organizations must also update security monitoring practices to reach across data silos and offer security teams a 360° view into their systems and operations.

Solution:

So, while GenAI may be exacerbating the challenge of keeping up with new threats, it may also be the solution to mitigating them more effectively. More than half (59%) of leaders have already invested in AI and ML-driven security automation technologies, and 96% believe that using GenAI security assistants that can proactively detect and remediate network issues and threats will drive value to their organizations. Generative AI has the potential to help close the expertise gap in the security sector and fill security roles when applied to a robust data infrastructure.

Ultimately, it all comes down to data. Leaders are dealing with data challenges — from sprawl and silos to a lack of adequate tools and an insufficient workforce — which compound observability, security, and resilience challenges. It’s no wonder then that C-suite executives and leaders are prioritizing GenAI solutions and data analytics tools as their top technology investments.

Take the data and AI assessment

Leaders across many organizations struggle with similar business and data challenges, all while looking to AI and GenAI for new opportunities. To identify areas of improvement and investment, reflecting on existing challenges and understanding your competitors is the best place to start to develop a strategic plan to stay competitive.

See how you stack up against your peers in AI investments, business challenges, and opportunities.