The executive’s AI playbook
Potential total annual value of AI and analytics across industries
$9.5T - $15.4T

Use cases

Need to up your AI IQ?
Value & Assess
Size the opportunity and determine data needs
Explore
Execute
Learn best practices to realize the value
Explore
Beware
Know the warning signs of AI program failure
Explore

This data visualization shows the potential business applications and economic value for a range of analytics and artificial-intelligence (AI) techniques. It is based on a study of more than 400 use cases, covering 19 industries and nine business functions.

For each use case, we estimated a range of the annual value potential of applying AI and other analytics across the entire economy based on the structure of the global economy in 2016. We aggregated these values at the industry, function and domain levels and display the high end of the value range in this visualization. Note that figures may not sum to total due to rounding.

We define traditional AI and analytics and advanced AI as follows:

Traditional AI and analytics
Traditional machine learning (eg, clustering) and statistical techniques (eg, basic regression)
Advanced AI
Deep learning neural networks (eg, convolutional neural networks)

Our library of use cases, while extensive, is not exhaustive, and thus it may overstate or understate the potential for certain sectors. See “Notes from the AI frontier: Applications and value of deep learning” for more details on our methodology.

Learn best practices to scale AI

Applying AI and analytics across an organization requires much more than the latest technologies and modeling techniques. Through a survey of more than 1,000 companies globally and our work with clients, we’ve identified the practices that better position companies to achieve full scale and value from AI and analytics.

- Click each category to explore winning practices -
1
Aligning on strategy
2
Building tech, data & people capabilities
3
Completing the
last mile

To reap the value from AI and analytics, organizations need to plug these technologies into critical strategic areas of the company, which typically cut across business functions (eg, customer experience). Doing so requires a clear, coordinated approach and focused investment.

Organizations achieving better scale and value, what we call “breakaways,” are more likely to engage in the practices below.

2Xmore likely to obtain strong executive alignment
% of breakaways
61%
% of others
28%
3.5Xmore likely to execute 3+ use cases across the organization
% of breakaways
52%
% of others
15%
13Xmore likely to spend >25% of their IT budget on analytics
% of breakaways
65%
% of others
5%
2.5Xmore likely to plan to spend more on analytics over the next 3 years
% of breakaways
75%
% of others
33%
4Xmore likely to devote more of analytics spend to embed analytics into organizational DNA (the last mile).
% of breakaways
87%
% of others
23%

Executing a successful, full-scale strategy requires establishing the building blocks of effective AI and analytics, which include data, processes, technologies, and people.

Organizations achieving better scale and value, what we call “breakaways,” are more likely to engage in the foundation-building practices below.

Data

2.5Xmore likely to have sound data strategy
% of breakaways
67%
% of others
27%
2Xmore likely to have strong data governance
% of breakaways
63%
% of others
32%

Analytics methodologies

2.5Xmore likely to have a clear methodology
% of breakaways
63%
% of others
24%
2Xmore likely to use challenge and test system
% of breakaways
66%
% of others
33%

Talent and organization

1.5Xmore likely to have deep data and analytics expertise
% of breakaways
58%
% of others
35%
2.5Xmore likely to employ more data and analytics talent (>25 per 1,000 FTEs)
% of breakaways
89%
% of others
34%
3Xmore likely to have well-defined analytics roles and career paths
% of breakaways
60%
% of others
22%
2Xmore likely to use cross-functional, agile teams
% of breakaways
58%
% of others
31%

One of the keys to unlocking the value of AI and analytics is completing the last mile—in other words, enabling decision makers in the organization to regularly and naturally make analytics-driven decisions. The outcomes of these decisions essentially create the value.

Organizations achieving better scale and value, what we call “breakaways,” are more likely to devote time and resources to the last mile through the practices below.

4Xmore likely to devote more of analytics spend to embed analytics into organizational DNA (the last mile)
% of breakaways
87%
% of others
23%
2Xmore likely to prioritize top decision-making processes
% of breakaways
55%
% of others
31%
2.5Xmore likely to establish decision-making rights and accountability
% of breakaways
56%
% of others
22%
1.5Xmore likely to achieve quick, continually refined decision making
% of breakaways
57%
% of others
36%
Read the in-depth article

Know the warning signs of AI program failure

We’ve detected what we consider to be the ten red flags that signal an AI and analytics initiative is in danger of failure. In our experience, business leaders who quickly respond to these alerts will dramatically improve their companies’ chances of success in as little as two or three years.

1
The executive team doesn’t have a clear vision for its AI and analytics initiatives.
First Response
The CEO, CAO, or CDO—or whoever is tasked with leading the company’s analytics initiatives—should set up a series of workshops for the executive team to coach its members in the key tenets of advanced analytics and to undo any lingering misconceptions.
2
No one has determined the value that the initial use cases can deliver in the first year.
First Response
Companies in the early stages of scaling analytics use cases must think through, in detail, the top three to five feasible use cases that can create the greatest value quickly—ideally within the first year. This will generate momentum and encourage buy-in for future analytics investments. These decisions should take into account impact, first and foremost.
3
There’s no AI and analytics strategy beyond a few use cases.
First Response

There are three crucial questions the CDO or CAO must ask the company’s business leaders:

What threats do technologies such as AI and advanced analytics pose for the company?

What are the opportunities to use such technologies to improve existing businesses?

How can we use data and analytics to create new opportunities?

4
AI and analytics roles—present and future—are poorly defined.
First Response
The right way to approach the talent issue is to think about AI and analytics talent as a tapestry of skill sets and roles. Each thread of that tapestry must have its own carefully crafted definition, from detailed job descriptions to organizational interactions. An immediate next step is to inventory all of those currently with the organization who could meet those job specifications. And then the next step is to fill the remaining roles by hiring externally.
5
The organization lacks analytics translators.
First Response
Hire or train translators right away. Hiring externally might seem like the quickest fix. However, new hires lack the most important quality of a successful translator: deep company knowledge. The right internal candidates have extensive company knowledge and business acumen and also the education to understand mathematical models and to work with data scientists to bring out valuable insights. As this unique combination of skills is hard to find, many companies have created their own translator academies to train these candidates.
6
Analytics capabilities are isolated from the business, resulting in an ineffective analytics organization structure.
First Response
The C-suite should consider a hybrid organizational model in which agile teams combine talented professionals from both the business side and the analytics side. A hybrid model will retain some centralized capability and decision rights (particularly around data governance and other standards), but the analytics teams are still embedded in the business and accountable for delivering impact.
7
Costly data-cleansing efforts are started en masse.
First Response
Contrary to what might be seen as the CDO’s core remit, he or she must not think or act “data first” when evaluating data-cleansing initiatives. In conjunction with the company’s line-of-business leads and its IT executives, the CDO should orchestrate data cleansing on the data that fuel the most valuable use cases. In parallel, he or she should work to create an enterprise data ontology and master data model as use cases become fully operational.
8
Analytics and AI platforms aren’t built to purpose.
First Response
In practice, a new data platform can exist in parallel with legacy systems. With appropriate input from the chief information officer (CIO), the CDO must ensure that, use case by use case, data ingestion can happen from multiple sources and that data cleansing can be performed and analytics conducted on the platform—all while the legacy IT systems continue to service the organization’s transactional data needs.
9
Nobody knows the quantitative impact that AI and analytics are providing.
First Response
The business leads, in conjunction with translators, must be the first responders; it’s their job to identify specific use cases that can deliver value. Then they should commit to measuring the financial impact of those use cases, perhaps every fiscal quarter. Finance may help develop appropriate metrics; the function also acts as the independent arbiter of the performance of the use cases. Beyond that, some leading companies are moving toward automated systems for monitoring use-case performance, including ongoing model validation and upgrades.
10
No one is hyperfocused on identifying potential ethical, social, and regulatory implications.
First Response
As part of a well-run broader risk-management program, the CDO should take the lead, working with the CHRO and the company’s business-ethics experts and legal counsel to set up resiliency testing services that can quickly expose and interpret the secondary effects of the company’s AI and analytics programs. Translators will also be crucial to this effort.
Read the in-depth article

The executive’s AI playbook

It’s time to break out of pilot purgatory and more effectively apply artificial intelligence and advanced analytics throughout your organization. Our interactive playbook can help.

Value & Assess
Size the opportunity and determine data needs
Execute
Learn best practices to realize the value
Beware
Know the warning signs of AI program failure
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