By Anand Mahurkar, CEO, Findability Sciences
As traditional businesses undergo digital transformation, many companies understand the importance of AI but struggle with its adoption and deployment. Properly and efficiently embedding AI into existing infrastructures requires companies to custom-build AI integrations, which can be a paralyzing challenge, even for some of the largest enterprises.
Another challenge is that most organizations do not have a sound data architecture in place for successful AI implementation. In fact, the latest research has shown that up to 60% of AI projects fail. Organizations require that data is cleaned, analyzed and organized, but too often, enterprises are struggling with data overload and crippling silos that can hinder digital transformation.
Below are three predictions for how traditional enterprises should approach AI technologies that will help enterprises understand the importance of getting ready for AI.
Organizations Must Focus on Getting the Data Fabric in Place or Risk AI Project Failure
As more enterprises look to implement AI projects in 2023 to increase productivity, gain better insights and have the ability to make more accurate predictions regarding strategic business decisions, the challenge will be for traditional enterprises to establish a robust data framework that will allow their organizations to leverage data effectively for AI purposes. To succeed, organizations must have the correct data infrastructure architecture (IA) in place.
The issue is that most companies do not have a sound data infrastructure and will struggle to maximize the value of their data unless their data fabric is in place. Additionally, the data is often unorganized, uncleaned, and unanalyzed and could be sitting in several systems, from ERP to CRM.
In 2023, organizations must utilize data in the same way that oil firms use crude oil and farmers use their land and crops to generate profit: identify the sources, plant the “seeds,” extract the impurities, refine, store, and pipe them, build the infrastructure for distribution, nurture, cure, safeguard, and yield it. AI solution providers can work with enterprises on these obstacles and implement frameworks that will strengthen the infrastructure architecture (IA) so that it can more successfully implement AI.
The first order of business should be how to collect data which includes widening the data by adding external features – both structured and unstructured data along with more focus on the quality and availability of the data required for developing an AI solution versus just volume. When finding answers to “what will happen,” enterprises need various data sources. Once all the data is collected, it can then be unified, processed, and ultimately presented as the AI output to iterate predictions and other information enterprises need and then all three ROIs like strategy, capability and financial ROI rather than only financial ROI to be focused.
ERP Systems Need to be “AI-ified”
While ERP systems are strategic for entering, storing, and tracking data related to various business transactions, CIOs, COOs, and business analysis teams have struggled over decades to extract, transform, and load data from ERP systems and utilize it for AI/ML applications. As enterprises spearhead digital transformation journeys and look to implement AI, the demand to connect to enterprise data across the organization has never been more paramount.
In 2023, the market is starting to support the concept of AI micro-products or toolkits that can be used to connect to ERP systems through middleware. These middleware toolkits must have the ability to link to data both within the organizations from the ERP systems as well as CRM or HR platforms and external data (such as news or social media). The middleware can then feed into the leading AI platform to develop, select, and deploy ML models to provide highly accurate predictions and forecasting.