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Discussion paper - McKinsey Global Institute
Notes from the AI frontier: Applications
and value of deep learning
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Ten red flags signaling your analytics program will fail
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Ten red flags signaling your analytics program will fail
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Business
leaders
Business leaders lead analytics transformation across organization
Delivery
managers
Delivery managers deliver data-
and analytics-driven insights and interface with end users
Analytics
translators
Analytics translators ensure analytics solve critical business problems
Workflow
integrators
Workflow integrators build
interactive decision-support tools
and implement solutions
Visualization
analysts
Visualization analysts visualize data and build reports and dashboards
Data
scientists
Data scientists develop statistical models and algorithms
Data
engineers
Data engineers collect, structure,
and analyze data
Data
architects
Data architects ensure quality
and consistency of present and future data flows
Data
engineers
Data
architects
Data
scientists
Visualization
analysts
Workflow
integrators
Analytics
translators
Delivery
managers
Business
leaders
Data
engineers
Data
architects
Data
scientists
Visualization
analysts
Workflow
integrators
Analytics
translators
Delivery
managers
Business
leaders
Technology skills
Analytics skills
Business skills
High-performance data analytics demands a range of actions across
the organization, including the careful definition of roles—and making the
right hires to fill them.
Teamwork
Discussion paper - McKinsey Global Institute
Notes from the AI frontier: Applications and value of deep learning
Dive deeper
Dive deeper
% of use cases by applicable techniques
15%
Full value can be captured using
non-AI techniques
16%
69%
AI alone enables value capture
AI augments value captured
by other analytics techniques
But there’s a catch: you have to already be a sophisticated player in data analytics. That’s because AI’s primary value is to improve the performance of existing analytical techniques.
Force multiplier
Discussion paper - McKinsey Global Institute
Notes from the AI frontier: Applications and value of deep learning
Dive deeper
Dive deeper
11.6%
Travel
10.2%
High tech
7.1%
Insurance
6.9%
Media and entertainment
6.4%
Transport
and logistics
6.3%
Telecom
6.1%
Pharmaceuticals
and medical products
5.7%
Retail
5.2%
Banking
5.3%
Advanced electronics/
semiconductors
4.9%
Consumer packaged goods
4.0%
Automotive
and assembly
3.7%
Healthcare systems and services
3.7%
Agriculture
3.2%
Aerospace
and defense
3.1%
Basic materials
2.3%
Chemicals
1.9%
Oil and gas
1.4%
Public and
social sector
Impact of AI as % of industry revenues
12
10
8
6
0
2
4
It depends which industry you’re in, but AI is a big deal in almost every sector.
Huge
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How does AI work best? As a multiplier
for data analytics.
Use the force
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