Machine Learning and Its Impact on Organizational Culture

Define the 7 major ways in which Machine Learning (ML) can impact organizational culture and weigh the pros and cons of implementing AI in your business.


Machine Learning has had an enormous impact on the way organizations operate day to day and how their employees feel about their work and their company.

In optimal cases, the implementation of machine learning models and artificial intelligence (AI) has allowed companies to transform digitally, hence keeping them on the cutting edge of innovation.

In less than optimal cases, machine learning (ML) and AI have created tension and friction among departments where daily person to person communication is ingrained in businesses processes, and therefore, not so easily transferred to a technology system.

In this article, we’ll explore 7 major areas in which machine learning can impact organizational culture and we'll also highlight some of the pros and cons of these impacts.

Note: While reading, it's important to keep in mind that these impacts can vary dramatically based on specific factors like industry, business size, organizational structure, and more.

1. Innovative

Machine learning models give organizations the opportunity to take large amounts of data and business processes and replicate them in a repeatable, scalable way. As a result, this creates more time and opportunity for future innovation.

An optimized ML system can theoretically produce the same outcomes that a trained individual would. By doing so, it frees up time and human resources to develop and innovate in other areas of the business that require new human learning. In addition, these new, innovative learnings can be passed down and taught to machines over time.

This ability for companies to delegate repeatable work to systems allows them to make big strides forward, and as a result, it keeps them more competitive.

2. Aggressive

By default, increasing and driving innovation top-down in an organization makes that organization more competitive or ‘aggressive’ in its space.

There have been many studies about AI and how companies that do not adopt it may never catch up to their competitive counterparts while companies that take advantage of AI are positioning themselves to face future digital transformation.

There are many pros to being more aggressive in this space including but not limited to more efficient operations, more opportunity for growth, and more informed decision making.

The potential drawback is that by instilling a competitive culture, you may experience some negative sentiment from existing employees whose job functions or roles change or become non-essential.

3. Outcome Oriented

The science of data analytics in business is primarily based on predicting outcomes to help drive businesses forward.

Modern data and analytics allow machine learning models to produce data that is consistent with the findings of informed decision-makers. It empowers individuals in an organization to take more calculated actions when approached with familiar problems or challenges.

No longer do individuals have to play the guessing game of ‘What should I do,’ but instead they are able to act accordingly and within the best interest of the business itself.

If you enable your machine learning model to produce actionable insights, but you don’t give your team the proper tools to respond accordingly, then you have only solved a part of the problem and your ultimate outcomes will not be optimized.

4. Stable

How robust you can make your machine learning model is largely dependent on the training dataset and how stable the model can remain when the data inputs change over a period of time.

A well-trained, well-built system has the ability to provide dependable solutions and it can even become a single source of truth for multiple employees and departments.

When you develop your machine learning model, it’s important to account for where you currently are, and where you are going. A stable model in your organization is extremely specific, yet has flexibility to adapt if needed.

Keep in mind that if you change any significant systems that feed your data inputs, it could have significant impacts on your outputs.

5. People-Oriented

This section of organizational culture is always one of the most interesting to approach, because machine learning and artificial intelligence at the core are data driven, machine operating sectors of a business.

To say that ML makes businesses ‘people-oriented’ prompts two arguments:

Argument 1:

On the one hand, ML can create more opportunities for the right people to fulfill the right roles, maximizing their individual potential and allowing companies to scale, grow and develop more quickly and efficiently.

Argument 2:

On the other hand, with the introduction of technology in business, employees can develop negative sentiment about the idea of computers taking over their jobs or day-to-day tasks.

If you’re implementing systems that drive operational efficiency, security, and engagement, the health of your business will be better, and the people in your business will be happier and more successful in their work as a result.

Machine learning becomes people-oriented only when it complements human decision making or can act in the interests of people (for example, providing better customer service).

6. Team Oriented

Pre-implementation, there is a level of teamwork that needs to take place in order to access all of the necessary data to build an effective machine learning model.

Post-implementation, teams need to delegate who will be responsible for the maintenance, supervision, inputs, and tracking of the system over time.

In order for teams to work together, team leads, especially in departments like sales and marketing, need to take initiative and ensure that their teams are gathering and entering data correctly.

An example of this is a sales representative reporting a lead source in a CRM. The marketing team can then run reports on all lead sources entered in the CRM by all sales reps and identify trends in the data. This enables the marketing team to decide what initiatives they should take to ensure a steady and consistent lead flow for sales.

Get your teams on the same page with the collective goal of elevating your business, and you can expect a strong sense of teamwork as a result of implementing ML in your business.

7. Detail Oriented

Machine learning models by design can process big data and distill it down to repeatable processes and actionable insights.

With ML models, all of the information that was stored in a larger system or a person’s brain can be translated to systems of intelligence that empower larger teams and organizations to make better decisions on a more reliable basis.

The result is that maybe we are not as detail-oriented, but the small details naturally fall out of the equation so we can focus on the bigger pieces.

If a detail needs to be added as a critical data point into a machine learning model, it is easy to implement later on.


There is much to be argued about humans versus machines in the workplace about ‘who does what better.’ A non-negotiable fact is that AI and machine learning, in the right setting, have introduced a new level of efficiency for humans and business professionals to produce their best work and for companies to thrive, elevating organizational culture to new heights.

The 7 ways a Machine Learning Model can impact organizational culture explored in this article are not the only ways in which organizations can be changed, but collectively they represent some of the biggest impacts.

To explore other ways in which Machine Learning and AI solutions can positively (or negatively impact your culture), talk to a member of our team.