Organization strategy

Three Ways to Improve Your Organization’s AI to Drive Great Results

Data has been both friend and foe to businesses since the early 2000s. Most executives understand the power of data, but the vastness of an organization’s data stores, which can easily reach the level of the petabyte, contributes to a general state of information overload. As such, traditional businesses are struggling, and common IT terms such as “data lakes” and “data warehouses” are now jokingly referred to as “data dumps”.

The challenge today is how to energize these “data dumps” into intuitive recommendations and predictions. This is where an artificial intelligence (AI) strategy comes in, providing a way to collect and unify relevant information so that it becomes actionable and insightful.

For example, AI can galvanize analysis of an organization’s supply chain management. Once a company has embarked on the AI ​​journey, it will need to embark on a broad data strategy. This includes collecting and unifying data from internal ERP and CRM systems, as well as data from external sources such as news and social feeds. Once all the data is collected and processed, it can provide the business with insightful raw material cost forecasts, help the organization automate resource planning, and even suggest optimal inventory based on demand forecasts.

Let’s look at three specific ways AI can propel a business toward true digital transformation.


The most visible evidence of the cost savings seen with IA-ifying systems is in operations and customer service. In the United States, customer service call centers charge $26 to $30 per hour— and more for specialists. In-person customer service can cost anywhere from 8 to 12 times that amount. But AI can help reduce these costs in several ways.

First, AI can power a call center system to detect early anomalies and patterns that may reside with, for example, product defects that can lead to problems, and either prevent problems before they occur. they do occur, or alert the consumer and the business in advance.

Second, conversational computing, perhaps better known as interactive chatbots— can help save time and money when organizations allow them to quickly answer common customer questions. Meanwhile, other customer service representatives can devote more time and energy to responding to more complex customer inquiries. Either way, customer satisfaction and retention are improved and the business can save up to five times the amount needed to acquire a new customer.

Finally, once a problem is detected, the AI ​​can quickly verify system inputs and outputs to determine the cause. Analytics offering predictions and recommendations can then be provided to the organization through its ERP systems, or even spreadsheets.


AI is the automation engine. It can optimize recommendations and search engines, turning them into powerful tools to bring relevant content to visibility. Computer vision (CV) instantly scans hundreds of pages of documents for relevant information. Additionally, experienced mobile app development teams can use CV tools to automatically detect UI differences from provided designs.

AI can empower organizations with data-driven projections by helping QA teams analyze inputs, outputs, and simulated data for anomalies. On the other hand, by using comprehensive data from multiple sources, AI helps predict business outcomes, providing actionable insights and rapid decision-making.

Consider using high-speed automated auctions. Ad platforms often use automated bidding to connect advertisers to mobile websites. When done manually, this process is tedious and inevitably inaccurate. But AI provides technological solutions through platforms that minimize processing time and increase execution speeds.


Business processes become more efficient with the application of AI. It wasn’t too long ago that you would find broadcast editors struggling to match the timestamps with the subtitles of a newly posted video. Now, with natural language processing and context analysis, AI can provide subtitles and generate near-perfect translations.

In healthcare, computer vision equipped with deep learning is increasingly used in diagnosis and imaging. A research firm notes that the use of AI can improve patient outcomes by around 40%, while cutting treatment costs in half!

AI can also help assess a patient’s condition levels. Thanks to videoconferencing, doctors can treat even the most serious cases immediately. This ensures that all patient conditions are handled appropriately through the appropriate channels, while saving time and avoiding confusion.

Integrating an AI and data science strategy can be challenging, but it has huge benefits. Using the right tools to collect, unify, and process data can save time in business processes. Data science, machine learning, and artificial intelligence, among others, provide data-centric directions that make innovation possible.

AI offers a host of possibilities, and successful execution can lead traditional businesses into a new era of digital transformation.