7 Proven Steps to Scale AI Transformation

Running an AI experiment is not too challenging anymore. The hard part starts if your promising experiment needs to be scaled across the whole organization.

One might have already demonstrated that an AI project will be able to enhance specific business processes, support workers, analyze data or automate something. But scaling an experiment into a widespread solution raises a lot of new questions.

Does this technology integrate well? Is our data good enough? Who is the owner of this solution after implementation? How will our workers interact with it? What kinds of governance should be used? And the most important question – can we show any business value from such a pilot project?

That's why AI transformation is not only about technology. To scale up AI solutions requires an organization to align business strategies, data, technology, governance, workflows, people and measurement into an aligned operating model.

The idea here is not to transform every successful experiment into an enterprise-wide deployment. The idea is to find out the potential AI solutions that can bring value and prepare the organization for implementing and scaling them.

The next seven steps serve as an outline of how one can do it.

Why AI Pilots Rarely Become Enterprise AI Transformation

A successful AI pilot addresses a relatively specific question:

Can the technology be used to address this particular issue?

Transformation of the enterprise raises an altogether more general question:

Can the company deploy and use this AI tool to enhance its business processes?

These are very different questions.

An AI pilot could entail a small team of employees, a limited dataset, a single process flow and a well-defined goal. Adjustments are easier due to the limited scope of the project. But once you scale the project throughout the departments or business units, the complexity is bound to rise.

The AI solution has to interface with existing applications, deal with larger datasets, be able to comply with various security needs and corporate policies, accommodate varying tech skill levels among staff members, etc.

A Successful Pilot Is Not the Same as Enterprise Readiness

The pilot shows promise. But it does not show capability.

In order to deploy your AI initiative beyond just piloting it, you must know whether you have the technology, data, infrastructure, governance, and processes necessary to run it.

The reason this is important is that an organization can spend a great deal of money building a number of successful pilots without building the ability to run them in production.

Pilot-to-Production Gaps

There are a number of problems that come up when an organization attempts to scale an AI pilot project.

It can become apparent that ownership of the project is no longer clear once the experiment is finished. The data which worked well on a small scale is no longer available company-wide. 

The AI application is not easily integrated into existing systems. People do not know how new technology alters their job descriptions. And governance issues arise once the organization starts to think about scale.

But these are not indications of failure on the part of the pilot project.

Scaling Requires a Different Mindset

AI Transformation in organizations requires shifting from experimentation to capability building.

The question leaders need to ask themselves is not just “Does the AI work?” but:

Can we deploy the AI solution?

Can we embed the AI solution in our process?

Can we govern the use of the AI solution?

Can we support the users of the AI solution?

Can we measure the results of the AI solution?

Can we maintain the AI solution?

Can we scale the AI solution?

Step 1: Define the Business Outcomes Before Scaling AI

The first step in scaling AI is determining what the organization truly hopes to accomplish.

Technology is there to serve business goals; not vice versa. Otherwise, AI projects can become a collection of unrelated trials vying for limited resources.

Start With Business Challenges, Not AI Possibilities

Instead of asking "Where can we use AI?" ask questions like:

What processes cause the most friction?

Where do employees spend their time doing repetitive or information-rich work?

What decisions could improve with more information or analysis?

What customer or employee experience improvements are possible?

How could strategic priorities benefit from AI?

How would the organization know if progress had been made?

This approach allows the organization to identify areas where AI belongs as opposed to fitting AI to processes for which it was designed.

Connect AI Investments to Strategic Priorities

The AI effort within an organization must have a clear link with its overall objectives.

In case the objective of the firm is customer experience, possible AI initiatives could be customer service processes, knowledge discovery, personalization, or decision support.

In case the objective of the firm is efficiency, areas to work could be process automation, document processing, forecasting, or workflow assistance.

The particular use case will be different for different organizations. What is important here is that the AI effort has a business justification.

Create a Business Case for Scaling

Prior to scaling up from pilot implementation to wider use, consider the value in relation to the cost and difficulty of implementation.

Take into account:

Business impact

Effort of implementation

Data available

Integration considerations

Security and governance requirements

User adoption

Operating expenses

Scalability to other workflows or groups

This avoids scaling up the AI solution merely because the pilot demonstrated a cool demo.

Step 2: Prioritize AI Use Cases That Can Scale

With the objectives having been identified strategically, the next step will be to assess the AI projects that merit more investments.

All good pilots do not necessarily need to become corporate-wide solutions.

Assessing Use Case for Scalability

Where the application of artificial intelligence becomes scalable, there is a need for the examination of the same from different angles.

What outcome that has business value can be affected by this initiative?

Is there feasibility in terms of the company’s ability to adopt it?

Does it have access to all the relevant data required?

How complicated is the integration with existing applications and processes going to be?

Does it have any risks associated with security, privacy, operations, regulations, and reputation?

Is it likely that people will actually adopt it?

Can it scale beyond its initial scope?

This combination provides a much more realistic picture than a pure assessment of technical success of a pilot project.

Separate Strategic AI From AI Experimentation

The process of experimenting is still useful since it helps organizations gain knowledge.

Experimentation and AI transformation are two separate concepts.

A successful experiment might answer some crucial technical or business questions but fail to become a capability of the company. This doesn't have to be considered a failure. This just means that the organization has gained useful experience and has not wasted extra money yet.

What is a misconception is counting the number of pilots as a measure of transformation.

Build an AI Portfolio Instead of a Collection of Projects

Using such a portfolio helps the leaders understand what stage each of the AI initiatives is at.

Explore: Test the hypothesis and see whether it works technically or commercially.

Validate: See whether the solution provides any significant results under realistic conditions.

Scale: Implement the initiatives in broader production.

Optimize: Optimize the existing AI capabilities constantly, depending on their performance and other requirements of the business.

Step 3: Build the Data and Technology Foundation for AI at Scale

Scaling AI reveals flaws that could remain undetected in a small-scale pilot.

An approach that works well with a smaller volume of data or streamlined workflow could become very complicated when it is supposed to interact with enterprise-level systems and greater volume of data.

Enterprise Data Readiness Enhancement

Data is key in many AI applications just because there is a lot of data doesn't mean that it is enterprise-ready.

You should consider:

Data quality

Accessibility

Consistency

Ownership

Security

Privacy

Metadata

Governance

Right level of access control

You should know where crucial data comes from, who the owner of it is, how it can be used, and whether it is of good quality enough for your AI application.

Integration with Enterprise-Level Systems

In most cases, the full potential of AI cannot be achieved without its integration with existing tools and systems.

Depending on use case, your AI capability could require integration with CRM platforms, ERP systems, analytics suites, knowledge management, customer service software, enterprise databases or workflow systems.

Therefore, your goal is not to implement yet another AI application in your technology environment, but to integrate AI with your existing processes.

Design for Scale From the Beginning

Enterprise AI architecture has to do more than just take care of the first deployment.

Think about things like infrastructure capacity, model choice, APIs, security, monitoring, deployment procedures, and cost management.

The architecture should enable future deployments to be easier, when that makes sense, rather than forcing every AI project to be built from scratch.

Don’t Build Every AI Capability From Scratch

There are times when a company needs a really customized system.

And there are times when what they have available on existing platforms or APIs works better.

That depends entirely on what the business requires in terms of its own data, risk, technical skills, and strategy.

The key is not to add extra technical difficulty just because building it internally sounds fancy.

Step 4: Establish Governance That Enables Enterprise AI

Now that AI is beyond just experimentation, it is time for governance.

But governance does not need to be seen as a hindrance between organizations and innovation. If done right, it gives the necessary structure to enable scaling up AI with more confidence.

Make AI Accountability Clear

There needs to be accountability for all significant AI capabilities.

This may involve decisions on:

Use case approvals

Data

Risk

Modeling

Security

Monitoring

Incident management

Human oversight

Performance

While different organizations may have different structures, responsibility should never be unclear.

Develop AI Policies for Enterprises

Organizations should lay out their expectations about the development and use of AI.

These may cover areas such as:

Data

Privacy

Security

Acceptable AI usage

Human oversight

Modeling

Documentation

Vendors

Monitoring

Risk escalation

Policies should be easy to understand by employees and easy to use by teams in practice.

Build Risk Controls Into the AI Lifecycle

Governance should come before deployment and not after the fact.

Here's one way that a lifecycle could work:

Identify → Assess → Develop → Test → Deploy → Monitor → Improve

There is always an opportunity in each of these phases to incorporate technical, operational, security, and business concerns.

Control Versus Velocity

This doesn't mean developing an endless series of approvals.

Consistent assessments, consistent controls, accountability, and risk management can help businesses create a process that provides more predictability between conception and deployment.

Once the expectations regarding their work are set by the team, the team will not waste too much time on determining the expectations of the new AI project.

Step 5: Redesign Workflows Around AI and Human Collaboration

Placing an AI application over an already inefficient workflow may not yield substantial improvements. The workflow itself needs to change.

Don’t Just Put AI on Top of an Existing Workflow

Rather than doing that, consider asking yourself the following question:

How would this workflow look like if AI was part of the process since the very beginning?

The question opens up possibilities which may be hard to spot when AI is used as yet another tool.

For instance, an organization could alter the workflow and make it such that the assistance provided by AI would come in the form of information retrieval, draft creation, information classification, analytics, or recommendations when the process reaches the appropriate stage.

Decide What Particular Tasks AI Should Perform and Which Ones Humans Should Take on

All tasks cannot be automated by AI.

Depending on the workflow and risks associated with it, the AI system could be used to:

Make recommendations that would be approved by humans

Pre-classify information prior to humans reviewing it

Perform research

Help make decisions

Automate repetitive workflow processes

Escalate exceptional cases to employees

Redesign Jobs Around Higher-Value Work

AI can affect employee time allocation.

In addition to just looking at the aspect of automation, companies can think about using AI to make sure that employees spend time on tasks that require reasoning, strategizing, problem solving, relationship building, creativity, and exception handling.

This kind of view will also influence the adoption discussion, since now employees are not just learning another tool, but adopting a new way of working.

Process Owner

Every process that scales AI needs to have an explicit owner.

This person/team is supposed to be aware of the business goal, measure performance, collect feedback, and coordinate any changes according to changing requirements.

A technically correct solution can become difficult to control without its owner.

Step 6: Drive Enterprise-Wide AI Adoption

Technology alone is not capable of transforming organizations unless people start using it in their work.

In this sense, adoption should be seen as an integral part of AI transformation and not as an activity following technology implementation.

Make Employees Understand the Need for Adopting AI

The reasons for the development of new capabilities and their implications for work should be made clear for employees.

It should be explained:

Which problem AI is meant to solve

How it is supposed to be used by the employee

What tasks will still fall upon him or her

What assistance is provided

By which parameters performance will be measured

Otherwise, adoption becomes inconsistent even if the technological base works properly.

Provide AI Competences to Various Categories of Employees

Education in terms of AI should cover various competences related to different responsibilities.

For executives, this might be the understanding of the strategy, investments, governance, and risks associated with AI.

For managers – the re-design of workflows, adoption, and performance measurement.

For technical people – additional skills in architecture, deployment, security, evaluation, and monitoring of AI.

For business people – practical knowledge about incorporating AI into the workflow.

One training session will not be able to cover every need.

Create Internal AI Champions

Those employees that understand not only the technology but also the workflow within the organization can serve as a bridge between the AI team and the departments.

They will be able to help find appropriate use cases, notify the team of changes in workflow, highlight problems, and ensure proper adoption.

Communicate Change Clearly

AI implementation might bring up some issues related to job description, accountability, precision, privacy, and monitoring.

Managers need to answer those questions openly instead of expecting employees to get them by themselves.

Open communication provides a better basis for sustainable implementation.

Step 7: Measure AI Transformation and Continuously Optimize It

The last step in all of this is determining how you will measure the effectiveness of your AI transformation efforts.

More deployment of AI isn’t a measure of progress by itself.

Beyond AI Activity Metrics

Measuring AI activity metrics like the number of pilots, AI applications, AI models, or AI-enabled training of employees can be an important measurement tool for operations, but doesn’t necessarily measure business value.

Leadership needs to tie AI initiatives back to the goals that need to be achieved.

Measure Business and Operational Impact

Depending on the use case, some potential metrics could include:

Process cycle time

Cost

Quality

Error rates

Customer experience

Employee productivity

Adoption

Operational impact

Model impact

Again, there is no silver bullet for measuring AI success. It all comes down to which process and what the goal is.

Monitor AI After Implementation

Just because the AI has been implemented doesn’t mean its life cycle is over.

It’s important to monitor AI after implementation because the environment changes. The data changes. User behavior changes. Requirements change. Costs change. The underlying model or technology even changes.

Create a Continuous Improvement Loop

A mature AI operating model should follow a cycle of:

Measurement → Learning → Improvement → Re-deployment → Repeat Measurement Cycle

This changes the AI change effort into one of constant competence and not just information technology.

What an Enterprise AI Transformation Roadmap Can Look Like

The process of implementing the seven steps becomes easier when applied within the framework of an AI transformational roadmap.

Step 1: Assessment

Start by knowing the current context.

Check out the existing AI pilots, business considerations, data readiness, technology infrastructure, governance, skills of employees, and constraints of the organization.

The goal here is to determine a realistic point of departure.

Step 2: Prioritization

Evaluate the feasibility, risks, and potential for implementation of any initiatives that may be considered.

The best initiatives are those with a defined business case and an achievable plan to go into production.

Step 3: Productionization

Take the best pilots and get them to production environments.

This phase covers integration, security, governance, ownership, testing, monitoring, and user experience.

The focus moves from the proof of concept to reliability.

Step 4: Scale

After a capability proves that it can run successfully, think about how you can scale it.

The scale could include more departments, work streams, business units, or customer journeys, based on the specific use case.

It should not happen automatically but thoughtfully.

Step 5: Optimize

Make use of operational data, feedback from employees and customers, and evolving priorities to improve upon the whole AI portfolio.

Some projects require additional investments, whereas others require changes or termination.

This is one of the most important aspects of advanced AI management.

Barriers That Prevent Companies From Escaping The Pilot Stage

Awareness of these barriers is what will allow them to solve problems before it gets too costly..

Too Many Pilots at Once

Many pilots can lead to fragmentation of technology, duplication of efforts, and conflicting priorities.

What you want to do is to prioritize the opportunities with the most promise, not have as many pilots as possible.

Considering AI As Solely An IT Issue

AI influences processes, roles of employees, experiences of customers, and decision-making.

Both tech teams and business teams play equal roles here.

Neglecting Data and Integration

No matter how sophisticated your model is, it cannot fix issues with data access and integration problems.

Measuring Technology Instead of Outcomes

Deployment numbers can make things look good on paper but say nothing about whether the company is really getting better.

The results of the business should still be the focus of any measurement system.

Underestimating Change Management

Even when a system works well, people might not know how to use it properly unless they’re trained.

This training process needs to be included in the overall transformation strategy.

Neglecting Governance Early On

When thinking only about deployment, there can be problems around security, privacy, and oversight.

These need to be taken into account earlier on when selecting use cases.

How Leaders Can Accelerate AI Transformation Without Losing Control

Executives do not need to trade off between speed and discipline.

The key is to set up an operation system that makes responsible scaling easier.

Establish One Enterprise AI Direction

Individual business units might have their own agendas, but it is important for the enterprise as a whole to share what its AI goals and priorities are.

This is an alignment mechanism which does not inhibit individual units from solving particular business challenges.

Standardize What Should Be Standardized

Governance, security, technical architecture, evaluation, and deployment approaches can be reused, thus avoiding redundant effort.

Standardization is especially useful if multiple teams work on the same challenge without coordination.

Give Business Leadership Ownership

AI efforts should have leaders who know both the business challenge and the technical side of the solution.

This will help to avoid projects being disconnected from the real business challenge and focused only on technical progress.

Fund Capabilities, Not Only Projects

To ensure sustainable AI transformation, the effort should be funded not just at the project level.

It may require an investment in such areas as data capabilities, infrastructure, talent, governance, change management, and operations support.

Such capabilities will make future AI efforts easier to deliver and scale.

The Future of Enterprise AI Is Built on Scale, Not More Pilots

The strategic challenge for companies today is shifting.

It is no longer about being able to prove the ability of AI to work in a laboratory setting. Rather, it is about being able to consistently leverage the power of AI to achieve actual business impact.

To do so, more is needed than advanced analytics models alone.

Prioritization of business outcomes, use case scaling, quality data, technology infrastructure, governance, workflow design, adoption, and measurement are all required.

The seven points are interrelated in sequence:

Business outcomes → Use-case prioritization → Data and technology → Governance → Workflow redesign → Adoption → Optimization

Each point solves a different aspect of the problem of scalability.

A company lacking in one of the points above will be able to implement AI, but not without increased challenges in replicating its successes.

From AI Pilots to Enterprise Transformation

Deploying AI goes well beyond just moving from pilot to production.

The point is not to have the largest possible deployment of AI solutions. The point is to recognize where AI adds value, create the right environment for its responsible use, and build organizational capabilities that will allow scaling the right things.

This is all about tying strategy to execution.

You need business leadership that understands the right outcomes, technical capability that builds the necessary solutions, governance that will govern it all properly, people that know how to operate the new technology, and measurement capability that will demonstrate whether or not you actually got the transformational impact you intended.

The journey is easy to describe in one phrase:

Pilot → Production → Adoption → Scale → Optimization

The companies best placed to generate significant business value with AI won't be the ones that experiment with the technology most intensively. They'll be the ones that master the art of scaling up their success through governance and measurement.

In this sense, the transformation of an organization through digital means by AI is not a project, but a capability which grows every single day.

Frequently Asked Questions About Scaling AI Across the Enterprise

What is scaling AI across the organization?

Scaling AI across the organization means more than simply running isolated pilots and proving technology feasibility but implies incorporation of proven AI capabilities into related business processes, systems, people, and workflows.

Scaling goes beyond the technology itself as it implies the need for governance, data readiness, employee engagement, operational ownership, and measurement.

Why do AI pilots not scale?

AI pilots may fail to scale when there is lack of clarity regarding ownership, data availability, complexity of system integration, lack of governance, poor employee engagement, poor business case, or lack of transition plan from experimental phase to production environment.

Pilots may show the technical feasibility without solving organizational issues.

How do you transition AI pilots into production?

Translating an AI pilot into production usually means that there should be validation of business cases, assessment of technical and data readiness, security and governance considerations, integration into existing workflow and systems, testing in a realistic environment, allocation of operational ownership, and implementation of measuring processes.

What is an AI Transformation Roadmap?

An AI Transformation Roadmap is a defined process of moving the company from an experimental phase to AI adoption on a larger scale.

The road map may cover everything starting from capability assessment, prioritization of use cases, production of selected initiatives, scaling of proven solutions and continuous optimization of the entire AI portfolio.

How can organizations measure their AI transformation success?

The success of the AI Transformation can be measured through metrics which will be associated with the outcome of the AI initiative on the business level.

It will depend on the use case and may vary from process efficiency, quality, customer experience, employee productivity, adoption, operational performance, costs and model performance.

What is the most difficult thing about scaling enterprise AI?

There is no difficulty that applies universally to all organizations.

Scalability of the enterprise AI typically involves multiple components such as technology, data, governance, business processes, employee adoption, leadership and measurement.