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What are the traits of Fast Learners?

There are clear indications that organizations are using machine learning (ML) to significantly improve their performance across the breadth of their operations. The benefits they are seeing are substantial and span the entire organization.

A survey conducted by The Economist Intelligence Unit and written in discussion with SAP identifies organizations that are already benefiting from ML as Fast Learners. Five key traits emerge from the research that are important to their success.

1

C-level, strategic
priority

2

Increased competitive
differentiation

3

New revenues
and profitability

4

Key processes
close to home

5

Enterprise-wide
strategy

C-level, strategic
priority

 
Fast Learner organizations benefit from their senior-most management seeing the strategic value of ML.

Fewer Fast Learners suffer from a lack of strategic clarity about ML. And fewer are plagued by organizational resistance to change.
agree that productivity improvements enable a reduction in headcount
Fast Learners have fewer strategic issues
Challenges experienced implementing ML
% of Fast Learners
% of organizations not yet benefiting from ML
28
18
Lack of external AI and ML expertise
28
30
Lack of internal AI and ML expertise
22
17
Lack of funding
20
14
Poor data quality
17
21
Lack of understanding of how AI and ML applications work
16
22
Organizational resistance
16
26
Lack of clarity on strategy
14
18
No leadership from senior management
13
18
Difficulty proving return on investment
75%
expect to retrain employees to perform more interesting and higher value tasks
75%
expect to retrain employees to perform more-interesting and higher-value tasks

Benefits will grow over time

Distribution of benefits among Fast Learners
% of Fast Learners that have realized the benefit
% of Fast Learners that have realized or
expect to realize the benefit by 2020
48%
64%
Increased
profitability
41%
52%
Higher customer
satisfaction
34%
47%
Cost savings
36%
41%
Faster business
process speed
34%
45%
Higher quality and fewer errors

Increased competitive
differentiation

 
Fast Learners see ML as a way to
stand apart.

They are looking to bring about fundamental rather than incremental change, believing ML's potential in business model innovation to be enormous.
of Fast Learners say ML has already resulted in business model or business process innovation.
AI and machine learning impact the business model in a much more significant way than . . . any of the disruptions we've seen in our lifetimes.
— Cliff Justice, Principal for Innovation
and Enterprise Solutions, KPMG

New revenues
and profitability

 
Fast Learners have realized that ML can increase profitability and have a positive impact on new revenue streams.
48
%
cite increased profitability as the top benefit gained from ML
48
%
48
%
of Fast Learners expect revenue growth of more than 6% in 2018 and 2019
30
%
30
%
of those that have not yet begun to generate benefits from ML expect revenue growth of this magnitude

Key Processes
Close to Home

 
Fast Learners are already spending more today on business functions sourced locally than they are in low-cost regions – and they expect that trend to continue.

For Fast Learners, this means that business relevance and customer value will increasingly take precedence over cost in important decisions on sourcing priorities.
Fast Learners source more processes locally
Breakdown of spending on business processes
% of Fast Learners
% of organizations not yet experiencing benefits from ML
58%
39%
Mostly local sources
Mostly sources
in low-cost regions
22%
29%
Evenly split
20%
32%
These technologies will have a profound impact on the way organizations make sourcing decisions.
— Stanton Jones, Director and Principal Analyst, ISG

Enterprise-wide
strategy

 
Fast Learners are more likely to implement ML initiatives enterprise-wide – an approach that is more likely to benefit from synergies across different functions.

Fast Learners have done more than other organizations to integrate ML use into key customer-facing and product development functions, such as contact centers, marketing, data processing and analytics, and R&D.
Enterprise-wide implementation of ML yields faster benefits
36%
of Fast Learners are implementing ML enterprise-wide
26%
of those yet to realize benefits are implementing ML enterprise-wide
The broad approach Fast Learners apply to ML could help explain why
say that its use is translating into higher levels of customer satisfaction.
Tips for starting
on the ML journey

Organize an ML boot camp

Plan training sessions for your executive committee to help business unit heads understand how ML can help grow the business.

Identify external sources of
ML knowledge

Heads of business processes should canvass open innovation platforms where expertise and ideas are shared about applying ML techniques. Have them also gather and analyze examples of other organizations' ML initiatives.

Pilot, but not for too long

The first ML initiatives should be piloted in small sets of processes where risks are relatively low. Once proven, the scope of ML techniques should be steadily widened across business processes.

Manage the message

Have your organization's marketing and communications teams produce a handbook for directors to use to answer internal questions about why ML is being adopted and what it will mean for their teams.

Review sourcing practices

Long-term offshoring arrangements will need to be reassessed for business relevancy. Create a task force to understand which processes should be localized after ML applications are implemented.
Making the Most of Machine Learning
5
Lessons from
Fast Learners
This research was conducted by The Economist Intelligence Unit and written in discussion with SAP.