How Enterprise AI 2.0 is Changing the Way Businesses Deploy AI
Enterprise AI 1.0 — the last 25 years — was characterised by trial and error, and most projects saw little to no positive outcomes.
A 2019 VentureBeat article pointed out that 87% of enterprise AI failed to go into production due to data-related issues. And an IDC research revealed that even companies with enterprise-wide AI strategies saw a 50% failure rate.
But a major shift happened in the last year.
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Advances in AI that made it more accessible, increasing business needs and acceleration of digital transformation resulting from the COVID-19 pandemic ushered Enterprise AI 2.0.
Increase in the Global Adoption Rate of AI
According to IBM’s Global AI Adoption Index 2021, 43% of businesses reported that their company accelerated its rollout of AI as a result of the COVID-19 pandemic. The two main forces driving the need for AI adoption in enterprises are digital transformation due to the COVID-19 crisis and the technology becoming more accessible.
But the two top challenges organisations are facing are lack of AI skills and increased data complexity. The report also states that startups that can help overcome adoption and deployment barriers and tap AI and automation tools to tackle these challenges will be able to deliver value from AI in 2021.
A top reason why businesses are now using AI tools is for automation purposes — so as to enable greater efficiencies, cost savings and reducing work hours for employees. Among the businesses surveyed in the report, the most popular uses of automation tools are:
- Improving the clarity of provenance of training data
- Collaborating across organisational roles involved in AI model development and deployment
- Creating enterprise AI policies and guidelines
- Monitoring AI across cloud and AI environments.
How is Enterprise AI 2.0 different from Enterprise AI 1.0?
Business leaders are now trying to mitigate AI modelling and management challenges by:
- Improving the clarity of provenance of training data
- Collaborating across organisational roles involved in AI model development and deployment
- Creating enterprise AI policies and guidelines
- Monitoring AI across cloud and AI environments.
For executives across the business spectrum, this shift is a significant opportunity to learn and adapt from past failures and create concrete expectations for future uses of AI.
Businesses that want to make the most of the next-gen AI tools, have to:
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Identify the right business use cases:
this will generate the most value for the organisation — as well as hone in on use cases that will demonstrate proof of concept and generate value quickly.
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Create an air-tight data strategy:
if businesses want to have repeatable use cases for AI applications, they’ll need to harness their data. Business executives need to know how to find, analyse and leverage the organisational data that will fuel their enterprise AI projects.
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Build the right team:
enterprise-wide AI projects require resources that extend beyond a typical IT team. Without company-wide training and buy-in, AI projects cannot be successful. Since, lack of skills is the biggest barrier to AI adoption, leaders will need to re-skill and upskill their employees to optimise the chances of success of their enterprise AI projects.
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