By Anand Mahurkar, Founder & CEO, Findability Sciences

Prediction #1: 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.

Prediction #2: AI White-Labeling Levels the Playing Field for Traditional Enterprises

Many traditional organizations 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. Outsourcing one-off solutions has sustained enterprise companies so far, but the demand for a quickly deployable and repeatable solution continues to increase as more and more automated and data-focused business approaches are introduced every day.

In 2023, as AI becomes a “need to have” versus a “nice to have,” the ability for an organization to utilize “white-label” AI to create configurable and customizable solutions can lead to a capability differentiator for enterprises, allowing for these companies to gain an AI-edge over their competitors and peers.

Newer products that allow enterprises to embed AI processes into their existing products– products that harness Computer Vision, Machine Learning (ML), and Natural Language Processing (NLP) – will power companies with AI at the back end to deliver a smarter, enhanced, and seamless experience in the solution’s native environment for end-users. These AI solutions can be utilized for price optimization, prediction and forecasting, segmentation and targeting, sales prospecting, customer service, and more.

As businesses leverage new insights and make actionable data-driven decisions, they free up the operational bandwidth to successfully innovate.

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