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Briefing Paper | Keys to Successful Innovation through Artificial Intelligence
Keys to Successful Innovation
through Artificial Intelligence
H I G H L I G H T S
More and more companies are
using artificial intelligence (AI) and
machine learning (ML) for everything
from boosting productivity to
enhancing customer experiences
and satisfaction to making better
decisions faster. These technologies
are also used to generate new
revenue opportunities and improve
operational efficiencies.
Implementing AI/ML successfully
requires more than just great
technology and mountains of data.
It also requires rethinking silos and
fragmented legacy systems, adding
capabilities, and retooling the
company culture.
While the details of implementing a
successful strategy for AI/ML vary
depending on each business and
the specific project, the steps to get
there are universal.
Artificial intelligence (AI) and machine learning (ML) are
playing an increasingly outsized role in strengthening and
transforming industries around the world. The global AI
market value is expected to reach $267 billion by 2027,
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and the technology is expected to contribute $15.7 trillion
to the global economy by 2030.
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While the technology
is still in its infancy in some industries, other sectors are
testing the waters, and still others are already reaping the
benefits from their AI/ML transformation and charting a
path for their industries.
More and more companies are using AI/ML, for everything from boosting
productivity to enhancing customer experiences and satisfaction to making
better decisions faster. These technologies are also used to generate new
revenue opportunities and improve operational efficiencies.
As the world economy moves from a time when every industry operates
from a different set of core competencies to one shaped by data and analytics,
everyone from C-suite executives to IT teams must understand how to use AI/
ML strategically and efficiently to achieve their organizations' transformational
and strategic goals.
But the road to a successful AI/ML implementation is not always a straight
or smooth one. The journey may come with detours and require constant
reiteration and reevaluation to stay on track and produce the intended outcome.
Defining business goals is the first step to figuring out what sort of strategy
is needed. "In every large corporation, you have somebody screaming, 'You
need to do more AI!' The question from smart people is, 'For what reason?,'
says Mark Maenner, head of data transformation for the BMW Group.
Implementing AI/ML successfully requires more than just great technology
and mountains of data. It also requires rethinking silos and fragmented legacy
systems, adding capabilities, and retooling the company culture, Marco Iansiti
