SURGE IN AI DEMAND
The demand for AI intervention in the retail business processes has ramped up significantly due to the massive disruption in customer behavior and purchasing patterns, customer product affinity, e-com sales, and in-store operations.
This has encouraged retailers to invest in digital transformation to reshape their legacy business processes with AI. According to Meticulous Market Research®, a leading market research company, “Artificial Intelligence in Retail market is expected to grow at a CAGR of 34.4% from 2020 to 2027 to reach USD 19.9 billion by 2027.” However, many enterprises find incorporating AI challenging for various reasons.
CHALLENGES WITH AI ADOPTION
Widespread AI adoption faces multiple challenges.
Despite investments and its obvious benefits, AI’s adoption in most retail organizations has remained siloed and short-lived. Six key challenges prevent widespread adoption of AI:
5.Finding right data– Identifying‘quality’ data without any bias and drift often becomes a challenge for data scientists and analysts. This often degrades value delivery and the time to market of AI solutions, causing business sponsor dis-satisfaction.
6.Insufficient talent – Many organizations are hampered by insufficient in-house talent required to embark on an AI-ML journey. Missing fully digitized and dynamic skill development programs often restricts AI-ML adoption within an organization.
THE FOUNDATION FOR AI OFFICE
Over the last few decades, the growing emphasis on data office has helped organizations to become more data-driven and enabled data foundation for AI experimentation.
However, AI maturity improvement and adoption remains too complex due to the absence of a strong AI maturity improvement framework driven by focused function like AI office, hampering the widespread adoption of AI by data-driven retailers.
These are the different levels of AI maturity, based on observations and market research.
3A (assess,analyze,advice) framework for AI office will improve the AI maturity of retailers. The framework is a knowledge-based recommendation engine to improve AI maturity. This module is powered by a knowledge repository, managed by the governance team, and enriched by ‘crowdsourced’ market research data. The repository has two key segments—survey questions and recommended actions—and supported by the following functions:
The engine recommends AI maturity improvement actions based on survey response. It can help stakeholders at different levels, like CDO (chief data officer), CAIO (chief AI officer), PO (product owner), managers, and associates of organizations to understand AI maturity gaps and the improvement actions required. Stakeholders can run a survey using a survey screen and get persona-based recommendations driven by knowledge repository and recommendation engine of the 3A framework.
One sample outcome for a retail store operations team is shown below.
AI OFFICE ECOSYSTEM CRITICAL FOR RETAILERS’ GROWTH
The presence of artificial intelligence in the retail industry is bound to grow in the years to come.
This makes it increasingly important for retailers to build a sustainable ecosystem for AI maturity improvement. An AI office ecosystem powered by the 3A framework can play a critical role in fulfilling the aspiration of retailers in accelerating AI adoption across organization and business processes.
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