Written by 14:03 Tech News Views: [tptn_views]

How Companies are Struggling to Maximize Artificial Intelligence Returns

Certainly, the investment into Artificial Intelligence (AI) is accelerating, yet the transition from ‘project phase’ to ‘production phase’ remains riddled with challenges for several companies. One huge underlying problem? It’s all about data management. Let’s delve into the core reasons behind this widely faced struggle and what’s hindering businesses from enjoying their AI-infused returns on investment (ROI).

1. Disorganization: The Bane of Data Management

A comprehensive transition towards optimal AI application heavily relies on structured data. However, many businesses grapple with disorganized data, often throwing a wrench into the smooth functioning of their AI models. Dealing with ‘dirty’ data can be a significant setback, sidelining the overall evolution of the AI deployment phase.

2. Data Silos: Defeating the Purpose of Integration

Integration is at the heart of AI. Unfortunately, one of the biggest issues at hand is that most of the valuable data in many companies is siloed. In case you’re wondering, data silos refer to situations where only one group or department within an organization can access a set of data. This absence of cross-functional access inhibits the consistent training and tuning of AI models that need a diversified and balanced data influx.

3. Untapped Data: The Unoptimized Potential

Companies invest extensively in AI, yet they undermine the potential of data, leaving it under-utilized and unoptimized. The result? AI projects that can’t fully blossom, let alone deliver meaningful ROI. It’s as if companies have an extensive toolbox but are only using a fraction of the tools available – clearly, a missed optimization opportunity.

4. The Data Management Puzzle: An Unresolved Conundrum

Many pundits have touted the potential of AI, but getting AI-powered projects off the ground is not an easy feat. Indeed, the complexities of data management have proven to be a baffling puzzle, acting as stumbling blocks on the path to profitability. With unoptimized data, a good number of companies are caught in a catch-22: Implementing AI to improve processes but unable to do so properly due to inefficient data management.

Unraveling this puzzle evolves to become an essential step in not just successfully implementing AI but in unlocking its potential to elevate business performance and ROI. The case becomes clearer: Effective data management is not an option, it’s a must. It encourages companies to lean more towards using AI resources more effectively, ushering in the much-desired era of seamless AI application and potent profitability.

In conclusion, whether the goal is to integrate AI technology for minimalist applications or complex, cutting-edge processes, perceptive businesses need to address the issues delaying their AI project success actively. By investing in robust and efficient data management systems, companies can stride ahead towards attaining a significant ROI on their AI investments, taking a leap from scattered projects to full-scale production.

Credit: BBC. TechCrunch, Reuters