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.
Prediction #3: A Center of Excellence is Key for AI Implementation – Get the Right People and Right Expertise in One Place
The world is beginning to recognize the transformational power of AI; therefore, there is no doubt that the future of AI will be a significant part of the business strategy of forward-looking organizations in 2023. The entire life cycle of AI will become ever more sophisticated, with complex solutions that demand better interpretability to reduce implementation cycles and affordable price points.
Therefore, creating a Center of Excellence or COE is crucial when implementing an AI journey. That said, AI needs an “all hands on deck” approach as an AI implementation requires an organization to centralize and organize its data infrastructure. Building a COE with team members from multiple areas of an organization and outside vendors can be a windfall for AI transformation.
COEs can help an organization implement and succeed in its AI journey in the following ways:
• Creating the right team of dedicated experts from multiple disciplines and departments
• Provide the basis for organizing, analyzing, cleaning, and identifying the right data silos so that an IA implementation can commence.
• Drive digital class transformation with the COE’s buy-in of the AI goal so that the organization can make changes for the better.
Prediction #4: 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.
Prediction #5: Natural Language Processing and Computer Vision Will Play an Important Role
Enterprise adoption of automation of processes involving text or voice data using Natural Language Processing (NLP) and Computer Vision (CV) technologies will greatly enhance in 2023. Large language models with high complexity will increase the sophistication of NLP applications. For example, AI-based virtual assistants are becoming essential to most organizations’ customer service lifecycle and engagement strategies. This allows customers, vendors, and employees to ask questions that can be easily answered through automated processes, as in a chatbot. But there are more sophisticated uses as well. For instance, broadcast editors who used to struggle to match timestamps with subtitles for a newly posted video can now utilize NLP and context analysis to provide subtitles and generate near-perfect translations.
While designing a solution, recommendation and search engines are powerful tools in bringing relevant content to visibility. With CV and NLP, it is now possible to scan documents and retrieve relevant information instantaneously. AI has enabled quality assurance teams by analyzing inputs, outputs, and simulated data for anomalies. Based on wide data, data from multiple sources, AI can also help predict business outcomes, allowing companies to make rapid decisions.
In addition, NLP-based systems to help organizations to meet regulatory compliance requirements.