Data structuring for effective AI and IA adoption is gaining priority.
AI and IA technology advancements are allowing organisations to seek opportunities, improve customer experience, enhance processes, and increase revenues.
Organisations understand the need to have a well-defined data management strategy that allows them to provide an omnichannel and well-articulated personalized brand experience. Integrating valuable data allows brands to be in a competitive position, and leveraging AI and IA allows them to provide a seamless customer experience. Thus, data structuring for effective AI and IA adoption is gaining priority and the BPM-led RPA modernization of business processes is gaining momentum to deliver maximum efficiency and value.
However, this comes as no surprise. Organisations have been investing heavily in omnichannel redesign to enhance their CX and sales. In the CX Live 2021 survey, 58% of companies cite Data Analytics, IA and AI as top priorities for 2021.
Last year, Walmart redesigned its website to be more personalized allowing shoppers to be served with product recommendations based on past buying behaviour. According to Accenture, 58% of consumers are more likely to buy from an online retailer that eases their online shopping experiences via such recommendations. SmarterHQ found that when online retailers do not provide recommendations, 47% of people will go to Amazon. 1
Getting the right start – a structured approach to data
We are all familiar with AI and IA as valuable technologies to create seamless processes and enhance efficiencies. In order to achieve maximum ROI from an AI or IA implementation, leveraging data effectively to harness technology's incredible computational asset makes a powerful and enticing prospect. Also, data enhances an organization’s process efficiency. By improving analytics capabilities it carves the path to adopting advanced AI and Machine Learning (ML) models in the future.
While intense data collection and analytic techniques facilitate comprehensive learning that can be used in formulating a customer experience strategy. The key is leveraging these insights in creating personalised customer experiences. Based on a strong data foundation, it is possible to progress through increasingly advanced analytic capabilities where AI and IA solutions are integrated and adequately utilized, to create seamless “micro-moments” that enhance CX.
Risk factors - key barriers in developing an efficient data infrastructure for AI and IA implementation
A survey by Customer Experience Live of CX leaders in the Middle East suggested companies are struggling to analyse strategically valuable and actionable data, especially with the growth in their data sets and information collection capabilities during the pandemic. The challenges mire data management effectiveness by organisations, thus preventing them from achieving a holistic view of the customer. The top challenges organisations face when implementing AI and IA are:
Managing and integrating large data sets
Protecting data, enhancing security and privacy
Finding the right balance between technology and human touch
These issues result in the data foundation being incomplete, inaccurate, or sometimes seriously compromised in organisations.
The two challenges consistently found across companies are:
Data collection and analysis, and
Data protection
Even though these challenges are prevalent, it is also true that AI and IA adoption are dramatically higher today with companies actively addressing challenges surrounding the volume, quality, and security, of different data types to achieve desirable outcomes.
Where has it gone wrong? – reasons for a crumbling data foundation
The complexity of crafting a viable data infrastructure that is fit for purpose for AI and IA adoption cannot be underestimated, and let’s not forget most organizations are constantly leveraging historic data.
It is reasonable for any sizable business - enterprise or government institution to have volumes of data being added on a daily basis. Thus, ensuring the data being collected by organisations is relatable across multiple systems, and not sitting in silos (one system, process, department, etc.) is vital. The correlation of data across functions and processes allows an organization to achieve a holistic view of the customer. Thus, data structuring is an important choice organisations need to make when implementing IA and AI applications. However, complete data synthesis and visibility is an ongoing challenge for most organisations.
Another challenge facing organisations is that AI and IA projects require access to data that has been stored and structured differently from what most organizations are accustomed to. The current data composition means organisations need to work with unstructured data which was never planned to be used for in-depth analysis but simply record keeping. As such, companies need to invest in time-consuming and costly processes to prepare the data for analysis, whilst protecting the privacy and assuring security.
According to the survey CX Live conducted in April 2021, the most popular AI applications that require structured data include:
Data Analytics
Business Process Management
Machine Learning
So what are the steps to designing a strong data foundation for AI implementation? Read on.
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