Data efficiency concerns how effectively people or organizations store, process, and use information. Parties considering making relevant improvements with AI should follow some best practices to increase their likelihood of success.
1. Use AI to Support Human Skills
One of the most effective ways to apply artificial intelligence tools for better data efficiency is to evaluate how the available possibilities could augment human expertise and streamline some of the most time-consuming parts of their roles. In one example, a Singapore-headquartered bank deployed such solutions to its customer service officers. Those employees handle more than 250,000 queries monthly, requiring them to maintain high efficiency.
The AI tool assists the team members by helping them access relevant data faster and transcribes what happens during each exchange. The company’s statistics revealed that this change has allowed workers to spend approximately 20% less time on customer tasks, freeing up more time for complex ones.
Take inspiration from this application by getting feedback about which tasks or workflows take the most time or are most error-prone. Then, investigate AI-based data efficiency tools to address some of the mentioned pain points.
2. Connect Data Efficiency to Broader Organizational Aims
Those considering using data efficiency tools should examine the best ways to maximize utilization of AI, including by applying it to tasks that may not immediately come to mind. For example, automated information-gathering and analysis tools can improve productivity and accuracy when nonprofits apply for grants.
The parties assessing those applications appreciate data-driven insights concerning how enterprises would use funds if awarded to them. Some grant application processes are incredibly in-depth and highly competitive. However, when nonprofit workers can quickly retrieve data or use AI to identify specific trends, they will be well-equipped to make strong applications that get noticed.
Another option is to use AI to find and flag instances of duplicate data. That can be an incredibly beneficial application for those using cloud storage services since providers often base the associated monthly fees on the data stored. Plus, once the duplicates are gone, that space becomes reusable, preventing the need to pay for more capacity. Such exercises can become excellent opportunities to revisit how businesses use the cloud, ensuring they get the best value for their money.
3. Develop AI-Based Searching and Analysis Strategies
Some of the most common and significantly disruptive challenges stem from people’s difficulties locating and using internally maintained resources. Some associated outcomes are merely frustrating, but others create business-compliance friction. For example, some countries’ data protection laws require the deletion of customers’ data upon request. Complying is much more difficult if representatives from the entities holding that information cannot find it to abide by a person’s wishes.
However, artificial intelligence tools can solve many data efficiency problems, especially since they allow people to enter natural-sounding queries. Some do not even require text strings to work. One AI sourcing engine for small businesses enables users to input words or images to find necessary supplies.
This extensive tool incorporates 1 billion product pages and documents associated with industries from more than 100 markets. Its AI capabilities generate human-like answers to queries, helping them find relevant information faster. Decision-makers should strongly consider how artificial intelligence could help workers retrieve data faster, especially as the overall amount they work with rises.
A related possibility is to create AI analysis applications for better data efficiency. Artificial intelligence excels at processing gigantic quantities of information much faster than humans. However, people still need to verify the conclusions such tools make. In contrast, some of the newest AI applications are agents built to perform specific tasks autonomously. Additionally, these options learn through ongoing usage, gradually improving their output. They can analyze dense information and suggest potential actions, improving leaders’ confidence.
AI Supports Data Efficiency Efforts
The best artificial intelligence-based applications to enhance data efficiency combine high-tech advancements with practical changes. For example, training staff to handle incoming data could reduce duplicate files or other human-caused errors. Additionally, even if an AI tool has all the latest security features, employees still need ongoing guidance to keep data safe. These tools can help people get optimal results as they explore applying these emerging offerings to their workflows.
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