Machine Learning Initiatives Across Industries: Practical Lessons from IT Executives


In a recent project led by M-Brain, leading industry IT Executives shared their best practices implementing machine learning (ML) projects at their organizations.

Meet The Authors

Anna Räder                  

Anna, Senior Consultant at M-Brain Toronto, is responsible
for new business development in California as well as managing and
executing strategic analysis and advisory projects.



Irida Jano

Irida, Consultant at M-Brain Toronto, manages and
executes strategic analysis and advisory projects.




Machine learning is a practice which is growing exponentially within North American and Global organizations. Organizations across a number of industries, including Healthcare, Financial Services, Manufacturing, and Retail have been inspired to undertake such initiatives due to various business needs, predominantly improving predictive analytics capabilities and automation. Despite the differences that exist in use cases and business needs across not only different industries, but also between organizations within specific industries, there is one key takeaway shared by all – the implementation of machine learning into organizations has proven to have a direct (positive) impact on earnings and profits.

In addition, machine learning accomplishes thousands of industry and company-specific goals, such as automating services, modeling pricing and services, improving customer service and predictive aspects such as fraud, threats, and even temperature control in manufacturing facilities. IT executives, who are the key stakeholders involved in machine learning initiatives, have shared a number of lessons learned and best practices. The insights gathered in this project show that with a visionary, forward-thinking leadership team and trustworthy, knowledgeable partner, there is no limit to the potential of machine learning in any business or industry setting.

Recent M-Brain research included in-depth interviews with IT management of twenty large companies (1000+ employees) across verticals including financial services, manufacturing, healthcare, retail, and media & gaming (see Exhibit 1). CIOs, CTOs, VPs of IT and IT Directors from the interviewed organizations shared their experiences related to the tactical implementation of machine learning initiatives.

This article outlines a summary of key findings from this research, including key lessons learned, examples of use cases, business needs that have lead to the initiation of machine learning projects, involvement of different stakeholder groups, insights on outsourcing preferences and external vendors of technology and human work, key steps of typical machine learning projects, as well as the evaluation of key benefits.


Exhibit 1. Interviewed Respondents, by Vertical (n = 20)


Lessons Learned
CIOs and IT decision makers shared an extensive list of best practices that they would recommend to the organizations planning to implement machine learning projects.


• Define the need and objectives, put a framework into place and create a solid plan; Define ROI being sought; Go into the process with an open mind

• Have a forward-thinking mentality and build a framework that is effective to manage in the present, but is also agile and resilient to future modification

• Do research up front; Figure out key requirements and let those drive the project

Stakeholders & Teams:

• Have a visionary leadership team involved to support the process, as it takes time to realize value and is expensive

• Avoid thinking it is a one-man job; Ensure that the ML initiative is collaborative

• Consider the opinions of multiple stakeholders

• Keep the team involved and updated throughout the process


Evaluate ML vendors extensively and choose the proper tools to implement; Top requirements include:

• Reputable and trustworthy (50% of respondents who discussed the topic of vendors)

• Has client’s industry expertise (38%)

• Strong technology, including a proven business case on how vendor implements ML, IoT, etc. (12%)


• Monitor and validate data and ensure it is error free

• Clean up the data beforehand to save time during project


• Consider compliance and regulations, especially in certain industries like healthcare

• Do not be intimidated, e.g. by thinking technology is too complicated or difficult to learn, or afraid to ask questions

• Break process tasks down into smaller projects


Use Cases
There is a wide variety of application areas for machine learning technology. During interviews, use cases varied not only between the analyzed verticals, but also within each vertical (as illustrated by Exhibit 2).

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Examples of the discussed application areas included data analysis to support cancer and asthma treatments in healthcare, increased accuracy of insurance rate modeling in financial services, control of humidity and temperature in manufacturing facilities, prediction of future product demand to support inventory planning in retail, as well as recommendation engines in media & gaming.

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Exhibit 2. Machine Learning Use Cases, by Vertical


• Insurance membership plans

• Data models for HIV, cancer, oncology, asthma, congestive heart failure (CHF)

• Wearable devices

• Pulmonary, lung cancer research

• Predicting and understanding customer behavior

Financial Services

• Evaluating new lines of business (win/loss analysis)

• Insurance modeling

• Pricing strategies

• Facilities management

• Customer service analytics

• Fraud analytics

• Threat analysis

• Automatic account approvals


• Humidity and climate control

• CRM system

• Invoice scanning

• RFID tags

• Predict and monitor competitive threats

• Monitor materials to avoid shortfalls

• Automation, smart devices

• Improve IT operations


• Credit risk assessment

• Predicting product demand to improve supply chain

• Materials replenishment

• Fraud detection

• Customer behavior

• Pricing, costing


• Make company more agile, especially from customer service perspective

• Content delivery (recommendation engine)

• Forecasting (e-commerce)

• Identify trends and issues in customer service

Business Needs
Identified business needs that inspired machine learning projects varied significantly between the analyzed companies (see Exhibit 3). However, some common themes were discovered as well.

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Improving predictive analytics and processes and increasing automation were identified as key needs across all industries, as shown in Exhibit 4. Other common themes included the need for better data utilization, and improved customer service.

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Exhibit 3. Top Business Needs That Lead to Implementation of Machine Learning Projects, by Vertical


• Grow patient care

• Introduce predictive modeling

• Automate processes to free up resources and improve staffing

• Improve/streamline processes and data utilization

• Better understand customer behavior to help drive strategic planning

Financial Services

• Improve/streamline predictive analytics

• Reduce reliance on human intervention and manual review

• Provide improved service to customers to gain retention and wallet share

• Reduce support costs


• Improve facilities management (humidity and climate control)

• Improve/reduce manual processes through automated capabilities as part of a natural industry evolution

• Monitor market and consumer trends

• Improve service level
• Organize data


• Develop solutions for credit risk assessment

• Improve supply chain processes (i.e. demand and planning)

• Generate more sales

• Become more technologically innovative (i.e. through automation)


• Gain competitive advantage

• Eliminate manual efforts

• Collect and streamline data utilization

• Improve recommendation engine

Exhibit 4. Summary of the Top Business Needs Across Verticals


The IT department is the most involved in machine learning projects, based on the insights received from the respondents.

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Improving predictive analytics and processes and increasing automation were identified as key needs across all industries, as shown in Exhibit 4. Other common themes included the need for better data utilization, and improved customer service.

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Exhibit 5. Key Stakeholders Involved in Machine Learning Projects (n = 20)

Exhibit5vNote 1: 15% of the respondents did not provide an answer to this question

Note 2: Department Heads refers to leaders within key departments involved in ML initiatives; This differs by industry – examples include Strategy, Cardiology, Wealth Manager, Design Director, etc.

Note 3: Respondents could list several stakeholder groups

Outsourcing & Vendors
65% of respondents outsourced at least part of human work in machine learning projects to external vendors, as shown in Exhibit 6.

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46% of respondents that outsourced at least part of the human work or hire external vendors for consulting selected IBM as their vendor (see Exhibit 7), followed by Microsoft (15%) and Accenture (15%).

Companies that kept all work in-house argue that this decision helped them to keep all the knowledge gained during the project in-house.

As illustrated in Exhibit 8, financial services and manufacturing companies were more likely to keep the human work in machine learning projects in-house, compared to other verticals. All retail industry respondents used external vendors for more that 50% of the work.

With regards to the software and tools used for machine learning, 100% of respondents across all analyzed verticals said that they were using external software and tools in their machine learning projects. Microsoft (60%) and IBM (50%) were the most frequently selected vendors of machine learning software, followed by Google (30%). The specific names of the software used are listed in Exhibit 9.

60% of the interviewed companies relied on in-house data scientists for machine learning projects, 25% decided to outsource all data science work, and 15% used a hybrid model. Manufacturing was the only industry vertical that relied completely on their internal in-house data scientists (see exhibits 10 and 11). Companies that decided to keep the data science work in-house justified their decision by the fact that they already had sufficient in-house data scientist resources. Some of them may still decide to outsource for short-turnaround projects where more resources are required. When necessary, employees underwent special training to learn the required skills.

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Exhibit 6. Share of Respondents Outsourcing Human Work to External Vendors (n = 20)


Note 1: Software is not included in this analysis; see next page for an overview of external tools used.

Note 2: Interpretation of share of work outsourced is based on analysis of qualitative answers

Exhibit 7. Vendors Used for Outsourced Human Work and Consulting (n = 13)

Exhibit7Note: Respondents could list several vendors

Exhibit 8. Share of Respondents Outsourcing Human Work to External Vendors, by Vertical (n = 20)


Exhibit 9. Software / Tools Used in Machine Learning Projects (n = 20)


Note: Respondents could list several vendors / tools



Exhibit 10. Presence of Data Scientists in Machine Learning Projects (n = 20)


Exhibit 11. Presence of Data Scientists in Machine Learning Projects, by Vertical (n = 20)


Key steps in a typical machine learning project included the identification of the need and key stakeholders, building a business case around expected benefits, planning the project and clarification of key objectives, evaluation or vendor offerings, ending with approval and implementation.

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As illustrated in Exhibit 12, the key difference between verticals in the typical steps of a machine learning project is the timing of vendor evaluation. The insights show that healthcare companies were more likely to select vendors at a later stage of the project, as compared to other verticals.M-Brain research shows that the implementation of machine learning projects takes anywhere between 3 and 26 months (11 months on average). Most respondents who commented on this topic said that the pre-work phase took them up to 3 months, with 4 months being an average for the whole sample of responses. Please refer to Exhibits 13 and 14 for details.

Respondents felt that the most difficult step in machine learning projects was data collection and processing (25%), as well as finding the right vendor (20%), as shown in Exhibits 15 and 16. Healthcare and financial services companies were also concerned with compliance and regulatory requirements.

A quarter (25%) of the interviewed companies said that seeing the results and the final launch was the most successful and rewarding step in their project. Other successful steps mentioned included the initial project phases, data collection, and overall team collaboration during the project (see Exhibits 18 and 19).

The majority of interviewed respondents (55%) stored the data for the discussed projects in the public cloud. Several respondents already had data in the cloud before the project started. 20% chose to store their data on-premises, with majority of these companies being in financial services sector, as illustrated in Exhibits 19 and 20.

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Exhibit 12. Typical Steps of a Machine Learning Project


Note 1: The key steps are not necessarily identical in each company; this is a summary of identified similarities

Note 2: *Refers to vendors of software or human work

Exhibit 13. Implementation of ML Projects: From Needs Identification to Finalizing the Implementation (n = 20)


Note 1: 5% of the respondents did not provide an answer to this question

Note 2: Figures are based of the higher end of the provided ranges

Exhibit 14. Time Required for Pre-Work (n = 20)

Note 1: 35% of the respondents did not provide an answer to this question

Note 2: Figures are based of the higher end of the provided ranges

Exhibit 15. Most Challenging Steps in Machine Learning Projects, by Vertical


Vendor selection:
• Vendor lacking industry expertise in the medical field
• Selecting the most reputable provider for healthcare industry

• Meeting compliance requirements, incl. cloud security

Balancing efforts:
• Balancing between data mining efforts and actionable results

Financial Services

• Data cleaning
• Selecting tools for data collection and storage
• Data collection and model creation

• Challenges with meeting legal requirements at the project launch

• Creating a global framework that works across the organization


• Getting vendors to be completely transparent, incl. the downsides

• Data collection and cleaning

• Selecting the right toolsets

• Prioritizing the work
• Getting alignment from stakeholders


Vendor selection:
• Selecting the best vendor / solution

Technical skills:
• Debugging the code
• Having the technical expertise needed

Internal buy-in:
• Getting buy-in from stakeholders, selling to management


Understanding ML capabilities:
• Refining the need, understanding what the systems can do

• Data sourcing, aggregation, cleaning, building out the model


Exhibit 16. Summary of the Most Challenging Steps in Machine Learning Projects


Note: Unprompted; based on analysis on qualitative responses

Exhibit 17. Most Successful Steps in Machine Learning Projects, by Vertical


Results / launch:
• Seeing the results
• Delivering the product to end-users
• Showcasing the successful implementation to other departments

• Cross-functional collaboration during the project

Financial Services

Pilot phase:
• Deploying a demo / pilot before full implementation

• Data collection

Global practices:
• Building a global standardized set of practices


Mechanical steps:
• Connecting the APIs

• Collecting data

• Finding simple tools that are reasonably priced

• Voting on best solution / vendor as a cross-functional team
• Developing the internal process, bringing together a cohesive team


Early steps:
• Completing work before the debugging phase

Results / launch:
• Launching, seeing the solution work in reality


• Collecting data

• Testing and validating of a viable solution


Exhibit 18. Summary of the Most Successful Steps in Machine Learning Projects (n = 20)


Note 1: 10% of the respondents did not provide an answer to this question

Note 2: Unprompted; based on analysis on qualitative responses



Exhibit 19. Data Storage for Discussed Machine Learning Projects (n = 20)

Exhibit 20. Data Storage for Discussed Machine Learning Projects, by Vertical (n = 20)

All interviewed respondents said they have found their machine learning project initiatives successful.

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Many of the identified benefits of machine learning initiatives have direct impact on earnings and profits (see Exhibits 21 and 22). Key benefits included time and cost savings, increased revenues, better risk management, and improved quality of analytics.

As shown in Exhibit 23, 75% of respondents consider data science, analytics and machine learning high priorities in their organizations. This is due to the following factors:

• Data is needed to justify costs and operations, and often drives pricing and decision making at companies
• Data science, analytics, and machine learning enable company evolution, modernization, and digital transformation
• Business processes become more efficient (e.g. reducing margins of error and doubts, seeing trends through predictive analytics and understanding
customers better, etc.)
• Freeing up resources, resulting in saved money

15% of respondents consider data science, analytics, and machine learning low priority due to several reasons, including:

• The nature of the industry (e.g. mortgage servicing focuses on minimizing costs, and budget holders may not see value in such initiatives, especially
if current processes are working well)
• Benefits are not easily and quickly seen
• Key stakeholders may be narrow-minded and lack imagination

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Exhibit 21. Key Benefits of the Implemented Machine Learning Projects, by Vertical


• Automation of: Infrastructure, monitoring of medical equipment, monitoring of patients

Improved service:
• Improving the quality of care for patients
• Improving medical treatments

• Improving the quality of the analytics

Financial Services

Time savings:
• Time required to analyze the data reduced dramatically

Cost savings:
• Standardized business model

• Automation of data collection and analytics

Risk management:
• Avoiding potential incidents


Time savings:
• Faster data collection
• Faster response time

Cost savings:
• In procurement, when purchasing energy
Increased revenues:
• New stream of revenue generation

Data utilization:
• More actionable utilization of data
Risk management:
• Stopped certain issues from recurring


Increased revenues:
• Increased sales at higher prices; increased upselling
• More customers

• Better consumer trends insights that help to predict the inventory needs

Risk management:
• Increased accuracy of credit risk assessment


• More focused analysis
• More in-depth analysis of customer behavior


Exhibit 22. Summary of Key Benefits of the Implemented Machine Learning Projects, by Vertical (n = 20)


Note: Respondents could list multiple benefits

Exhibit 23. Priority of Data Science, Analytics and Machine Learning at Respondent Companies (n = 20)


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