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This month we focus on how IT leaders can take their organization’s analytics capabilities to the next level. How they can capture AI’s potential to boost productivity and innovation. But how ready are organizations to take advantage of AI in this way?   

You can view our conversation here, or in the YouTube player below:

In terms of data management and analytics, there are three areas in which IT buyers are thinking about AI.ÌýÌý 

1. The first is productivity.

Specifically, bringing AI automation into data management to improve operational efficiency. This includes automating repetitive tasks and natural language interactions with data.ÌýÌý 

2. The second is managing intelligence about your data.

Generating actionable insights from data and analytics.ÌýÌý 

3. The third is agentic AI.

The next phase of AI, bringing automation into data management in the form of autonomous action.  In effect replacing human activity with an agent.ÌýÌý

Levels of preparation are mixed. There are organizations whose processes, platforms, infrastructure, access to data and infrastructure make them ready to apply AI solutions and accelerate into the distance.ÌýÌý  

More often IT leaders tell us their organizations have challenges in those areas.ÌýÌý

The not-wholly-prepared cohort itself falls into two groups: those who need to fix the underlying infrastructure to apply AI; and those who hope to fix issues by using AI.ÌýÌý

If the path to perfection takes too long, can you add AI tools and platforms to generate actionable insights from even flawed input data? Should you skip the data preparation stage and jump all in with AI? 

This viewpoint is attractive because it solves other challenges companies face when preparing their data for AI accelerated analytics.ÌýÌý(See also: 4 agentic AI-savvy IT leadership strategies.)

Take ROI. Data preparation can consume a huge amount of time and resources finding, accessing, cleaning, transforming and sharing data. The increasing number and complexity of data sources, coupled with the need to access them across distributed ecosystems, demand significant resources and expertise. Can AI create a shortcut? Some IT leaders believe that is a bet worth placing.ÌýÌý  

IT teams are often overwhelmed by the rising requests for self-service data access and integration, while varying data requirements from different users complicate the process further. Again — an AI-supported data-platform could solve for this.ÌýÌý 

Data-related skill gaps further hinder the development of robust data-management plans. Agentic AI is seen as another area in which AI could help to winnow data into insights.ÌýÌý

With all these opportunities IT leaders must balance the questions of ‘can it be done’ and ‘should we do it’. Insights generated by AI from flawed data may not be all that insightful and will definitely introduce risks to be managed.ÌýÌý

There is a pressure to move quickly, but it doesn’t have to be an either/or thing. It’s unlikely that AI is ever ‘done’.  Smart IT leaders have a strategy of building future-proofed organizational IT infrastructure, whilst in the short-term extracting data insights using AI.ÌýÌý