Capgemini: scaling AI initiatives to improve efficiency
The report conducted by the has examined the pace of enterprise Artificial intelligence (AI) adoption over the last three years.
Within that time, more than half (53%) of organisations have moved beyond AI pilots, an increase of 17% since which reported 36%. In addition the latest research also reports 78% of leaders are continuing to progress their AI at scale initiatives at the same pace as before COVID-19, while 21% have increased their deployment pace.
In contrast 43% of organisations struggling due to COVID-19 have pulled their investments into the technology, while 16% have suspended aii AI initiatives due to high business uncertainties due to COVID-19.
Capgemini’s report titled ‘’ revealed that the successful implementation of AI at scale delivers significant topline benefits including: 79% of leaders seeing more than 25% increase in sales of traditional products and services; 62% of leaders seeing at least a 25% decrease in the number of customer complaints; and 71% witnessed at least a 25% reduction in security threats.
When it comes to the adoption of AI, Capgemini’s life sciences and retail organisations are making up 27% and 21% of AI at scale leaders respectively, followed by automotive and consumer products (17%) and telecommunications (14%). Comparing sectors that have suspended or pulled their investment in AI due to COVID-19, only 38% of life sciences organisations have had to suspend or pull their investment, compared to insurance (66%), banking (64%) and utilities (64%).
Capgemini notes that “this reflects in today’s context, where virtual assistants, contact tracing apps and chatbots are proliferating as organizations, like the World Health Organization, launch AI-based tools to gather as well as provide information during the ongoing pandemic.”
The essentials for scaling AI technology
Trusted and quality data
Leaders in scaling AI rank ‘improving data quality’ as a number one approach to generate more benefits from their AI systems. Having a strong data governance ensures that AI teams have the right quality of data and helps to improve the trust executives have in data. “Establishing the required technology platforms, such as a hybrid cloud architecture and democratising the data access, serve as core building blocks for scaling AI,” noted .
Having a dedicated AI lead
The research conducted by Capgemini reported that 70% of organisations find that a lack of mid to senior level talent a major challenge when scaling AI. More than half of leaders scaling AI (58%) have appointed an AI head, lead or chief AI officer to provide development teams with a vision, as well as establish guidelines relating to priorities, ethics and security, and harmonise the use of platforms and tools for developing AI.
“Organisations also need to focus on a wide range of skill sets for scaling AI applications, beyond pure AI technical skills, to include business analysts and change management specialists. However, there is currently a significant gap between demand and supply in important disciplines like machine learning or data visualisation,” added Capgemini. As a result, training and upskilling are critical in order to address the gaps.
Ethical AI interactions
Regardless of whether a company has a strong consumer and regulatory focus on ethical AI, Capgemini’s research found many organisations are not actively addressing issues that require an empowered ethics team. The report highlighted 29% of struggling organisations compared with 90% of AI leaders agreed that they have a detailed understanding of how and why AI systems produce the output they do.
“This is important for business executives to be able to trust organisational AI systems. At the same time, it is impossible to establish consumer trust if the customer-facing employees lack trust in the models or data organizations use.”
Four principles for successful AI
Within the report Capgemini also highlighted four principles it recommends businesses looking to scale their AI to focus on:
- Empowerment: building strong foundations to provide easy access to trusted, quality data via the rights tools and platforms
- Operationalise: deploying AI with the right operating model, prioritise initiatives and ensure well-balanced governance and ethics
- Nurture: develop talent and collaborate with ecosystems and partners
- Monitor and amplify: continuous monitoring of the models accuracy and performance to deliver and amplify business outcomes
“In light of the recent COVID-19 crisis, while organisations are looking at data and AI to bring resilience to their operations, there is an even stronger need for connections between tactical and strategic business objectives and implementation in order to achieve scale,” says Anne-Laure Thieullent, Artificial Intelligence and Analytics Group Offer Leader at Capgemini.
“Our research highlights that the most successful organisations combine efforts to rationalise and modernise their data landscape and data governance processes, focus on bringing new agile tools from partners ecosystems as well as approaches like DataOps and MLOps to develop and deploy AI solutions, nurture teams from diverse backgrounds, and set up balanced operating models.”
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