7 Essential AI Digital Transformation Steps for Enterprises

Artificial intelligence can fundamentally alter how a large organization works, but purchasing AI tools is not equivalent to transforming the organization itself. An organization may initiate pilots, offer employees access to AI software, spend millions on technology innovations while maintaining the same process, fragmented data, and decision making systems.

The distinction here is important since AI digital transformation is more of a business transformation than a technology endeavor and can impact areas such as strategy, operations, customer experience, workforce, technology architecture, governance, and investments.

So, for a large organization, the task is greater than finding out how it could benefit from AI. The organization needs to identify areas where it can actually gain from using AI, whether it has required capabilities for doing that, risks involved, and what measures should be taken to successfully roll out initiatives.

Here is a guide on seven crucial steps for taking on AI digital transformation. You will learn about alignment of AI with business strategy, data and technology infrastructure, prioritizing scalable use cases, moving from pilots to production, establishing accountability, putting governance into implementation process, and measuring business results.

Why AI Digital Transformation Is Different in Large Enterprises

AI digital transformation is even more complicated in large-scale organizations. Large businesses have many business divisions; diverse technological environments, legacy systems; scattered data sources; existing business processes; and thousands of employees with different duties.

That diversity is the reason why an AI solution that works in one business division may not work automatically within the entire organization. The data may be kept differently, the processes will be varied, and the risks related to the application will depend on how and where it is used.

That is why AI adoption and AI digital transformation must not be considered as identical terms.

AI adoption may refer to employees’ usage of AI assistants; implementation of an AI system by some business division; or testing of some new AI model. AI digital transformation includes something more. 

This process consists of changes in ways of performing an important activity, supporting an important decision-making process, connecting technologies, and ways of generating value for the organization.

The discussion in the boardroom should focus not on, "Where could we use AI?" but on a more relevant question, "Where could AI make a difference in an important business result?"

1. Align AI With the Enterprise Business Strategy

The process of transformation through AI must be driven by business strategy, not technology procurement.

Otherwise, you may end up solving the problems which do not matter from the strategic point of view. In other words, you should begin with your existing business priorities and then figure out how AI can help with them.

Start with Business Priorities

Think about the goals your company aims at now. Perhaps, they include enhancing customer experience, boosting efficiency, developing new products, risk management, forecasting, entering into new markets and so on.

What matters is whether AI can contribute to those goals.

If there is an objective to reduce any delays in some complicated process of yours, the corresponding AI solution can relate to forecasting, processing documents, making decisions, workflow automation and so on. In such a way, you use technology as a tool for solving a specific business problem, not the goal itself.

By the way, such an approach simplifies discussion of investments into AI technologies too. When a certain system is “innovative”, the question is what does it actually change.

Identify Where AI Can Create Meaningful Value

A good progression is:

Challenge faced by the business → Opportunity created by AI → Capability needs → Investment → Outcome measured

Each step is important.

The challenge sets up the justification for action. The AI opportunity reveals where intelligent solutions can add value. The capability assessment reveals what data, technology, skills, and governance is needed. The investment brings the money. Finally, the outcome measures whether the effort generated the intended value.

This process allows leaders to separate real opportunities from AI opportunities that sound good on paper but lack strategic validity.

Ask the Right Questions in the Boardroom

Prior to investing in an important AI project, leadership should be able to answer a number of basic questions.

What is the business challenge? Why does the AI offer the right solution? What outcomes do we want to measure? What capabilities are needed? What obstacles could derail the project? What would happen if we delayed the investment?

The questions don’t demand that board members become AI experts. But it does ask that the board keep its strategic oversight.

The result will be a balanced portfolio of AI projects aligned with the overall strategy of the firm rather than a list of competing initiatives.

2. Build the Data and Technology Foundation

After establishing the priorities of the strategy, the next issue is the capability of the business to handle AI solutions.

AI algorithms depend on the availability, quality, security, and context of information to work properly. In most cases, enterprise AI works not as standalone software but is integrated into other systems and applications.

Make Enterprise Data Ready for AI Usage

The big organizations usually have the data of high value that is distributed throughout all the departments and systems. The issue here is not the mere presence of the data but its quality, governance, accessibility, and relevancy.

Data ownership must be identified and ensured. The quality issues must be recognized. The access must be appropriate and controlled according to information sensitivity and usage purposes.

It is important because advanced AI models are not able to deal automatically with such enterprise data that is unreliable, inconsistent, ungoverned, or inaccessible.

It is necessary before scaling up the AI initiative to find out if such data exists, who owns it, how it can be accessed, and what restrictions are applied.

Connect AI to Existing Technology

The reality for large organizations is that a clean slate from a technology perspective is rare.

AI may have to interface with enterprise resource planning systems, customer solutions, data warehouses, cloud services, business intelligence platforms, internal applications, and legacy automation.

This means that integration is a critical component of transformational efforts.

Integration doesn't always mean replacing all legacy components. For many organizations, it will make more sense to look at how AI can integrate with current technologies while overcoming technology limitations that prevent scale.

Architecting a solution must take into account security, reliability, accessibility, monitoring, and potential changes in future AI technology.

Strengthen the Foundation Before Scaling

The technology readiness has to be evaluated based on the intended use cases that the organization is aiming at.

One does not have to make the whole technology stack current before embarking on an AI  digital transformation journey. One has to be clear about where the shortcomings may prevent some important projects from being implemented reliably in production.

It is the difference that helps to avoid unnecessary technology modernization without any outcome in mind.

3. Prioritize AI Use Cases That Can Scale

Now that you have grasped the strategy and foundational concepts, the issue becomes where to allocate your resources.

A big company could list out hundreds of potential uses of AI technology. The issue isn’t coming up with ideas; it’s figuring out which ideas need more resources and which ones will remain as pilots.

Create an Enterprise AI Use-Case Portfolio

Do not let each department pursue its own projects separately. Develop a standardized process for analyzing opportunities.

Every single use case must be evaluated with the whole organization in mind. An initiative that generates value for one department might also be something useful for others to reuse. Other initiatives might demand heavy investments without adding much strategic value.

Using a portfolio perspective highlights these differences.

Evaluate Business Value

First, determine whether the use case represents a relevant business priority.

An important opportunity would imply a relationship with something that matters to the business, either in terms of improving an important process, supporting better decision making, improving the customer experience or developing a new capability.

The value would need to be measurable rather than some general statement of “improving efficiency”.

Assess Feasibility

Even an appealing use case can be impossible to pursue without proper data, technology, skills or maturity of processes within the enterprise.

Feasibility evaluation should take into account the current state of affairs rather than what is theoretically possible with the help of AI.

One also needs to think of the sustainability of the solution after implementation. It can be very hard to keep up with an AI project requiring continuous manual effort.

Evaluate Risk and Scalability

Value has to go hand-in-hand with risk.

What would happen if the system were generating a wrong output? Who will be impacted? What kind of information will the system have access to? Which decisions might be influenced by the system?

There is the aspect of scalability as well. If a pilot is successful but not scalable in any way, then its value of transformation is going to be compromised.

The ones with the most possibilities will be those who offer business benefit, feasibility, low risk, and scalability.

4. Move From AI Pilots to Enterprise-Scale Implementation

One of the most critical transformations in AI digital transformation happens from pilot to production.

The pilot will show that something will work in a controlled setting. An enterprise transformation needs much more than that.

Why Pilots Are Not the End of the Line

The pilot question really is:

"Does this work?"

A production deployment question is totally different:

"Will this work securely, reliably, economically, and consistently?"

This is a huge difference.

While a demo will be successful by a select few managing the process, a production deployment will run under business-as-usual, interfacing with other systems and real people.

Define Production-Readiness Criteria

But prior to expanding AI initiatives, the following criteria should be considered: reliability, security, integratability, quality of data, user adoption, cost of operations, performance tracking, and accountability.

A case for a business initiative should also be reconsidered because assumptions that were made initially might have changed due to new performance data.

This is when transformation management comes into play. It would be wrong to invest unlimited funds in a promising pilot just because it was enthusiastic.

Develop Criteria for Scale or Stop Initiative

Every big AI initiative should have its decision points.

In case the performance proves successful, more money could be invested. In case it is mediocre, the initiative will require redesigning. In case the value does not meet expectations, a stop would be the right thing to do.

This strategy provides room for experimentation while preventing experimentation from becoming the end goal itself.

The task is not to succeed with every AI pilot. The task is to figure out what is worth being scaled.

5. Create an AI Operating Model With Clear Accountability

AI digital transformation ultimately becomes an organizational issue.

Someone has to decide what gets funded. Someone has to be responsible for business outcomes. Technical groups have to be responsible for system performance, while security and risk have to have proper oversight.

Without proper accountability, AI digital transformation projects can easily speed through experiments and then become hard to control when they go into production.

Who Is Responsible for AI Digital Transformation?

The board must provide oversight instead of trying to manage each implementation of AI.

Leadership must be responsible for aligning transformation with business strategy. The technology leadership usually oversees the infrastructure and technical capabilities, while the business leadership remains accountable for the outcomes of the projects in question.

Data, security, risk, compliance, legal, and HR may have key roles depending on the use case.

It does not matter which specific organizational model an enterprise chooses. What is important is who makes decisions.

Balance Centralized Governance With Business Execution

A completely centralized AI capability is consistent, expert and standardized. It could be out of touch with specific business requirements.

A completely decentralized approach promotes innovation and ownership by businesses. It results in duplicate investments, inconsistency and lack of uniform technology.

A middle ground solution would be a combination of centralized standards and centralized capabilities with execution by the business units.

An organization could develop common governance standards, security, architecture and risks while having each business unit determine its appropriate application.

Make Ownership Obvious

Each and every major AI initiative must have a clear owner who will be responsible for its business results, performance, budgeting, risks, compliance and continuous monitoring.

It is particularly true in case an AI solution impacts on the process crossing several business units.

This would ensure that key decisions will not become the responsibility of everybody and, consequently, nobody.

6. Build AI Governance Into Transformation From the Start

Governance should not come at the end of the development process once the system has already been developed.

Rather, it should run throughout the process cycle.

According to the NIST AI Risk Management Framework, there are four functions for AI risk management that include Govern, Map, Measure, and Manage where governance is considered a cross-cutting function.

Establishing Risk-Based AI Governance

Each use case of AI technology does not pose an equal risk.

An internal productivity assistant needs to be governed differently than an AI system that facilitates important business, financial, employment, or operational decision-making.

Therefore, your governance program should be appropriate to the context and the risk profile of each AI use case.

Ensure Major Governance Aspects Are Covered

According to various applications, there can be a variety of aspects that governance needs to cover including data privacy, security, model performance, human involvement, compliance, vendor management, documentation, and monitoring.

The OECD AI Principles also highlight various aspects such as human involvement, transparency, robustness, security, privacy, and accountability through the AI lifecycle.

The idea here is not creating paperwork but ensuring sufficient visibility and control.

Keep Humans Accountable for Important Decisions

It can facilitate decisions without always being the decision-making component.

The right review, escalation, override, and accountability systems need to be set up for higher-stakes use cases.

It is important that the level of human involvement depends on the case and the risk at hand. What does not have to vanish is organizational responsibility.

Use Governance as an Enabler

Good governance does not have to preclude innovation.

With good governance guidelines, it will be easier for employees to know what applications are permissible, what controls are needed, what data needs to be secured, and when there is a need for extra reviews.

This way, they will be able to innovate within certain boundaries and will have fewer surprises in the future.

The NIST framework was intentionally created as a voluntary framework for addressing AI risks and building trustworthiness into AI throughout its lifecycle.

7. Measure AI Digital Transformation by Business Outcomes

AI digital transformation becomes hard to govern when leaders cannot determine if their investments are making any real difference.

This doesn’t mean measuring everything. This means measuring the results that matter.

Don't Confuse AI Activity With AI Value

The number of AI applications is evidence of investment activity. It doesn’t mean that the organization has been transformed by it.

Likewise, employee training numbers, pilot numbers, or AI interaction numbers are operational data points that don’t equate to business impact.

Hence, such metrics are to be considered only supporting and not conclusive ones.

Measure Outcomes That Matter

Different use cases require different metrics.

When it comes to an operational use case, then it could be cycle time, error rate, throughput, or cost.

When it comes to a customer-related use case, then the business metric could be service performance, customer experience, retention, or anything else appropriate.

Decision support systems would require decision quality metrics, among others.

What's important here is having the measurement approach established up front and not later trying to prove value.

Connect Investment to Realized Value

An important progression is:

Investment → Implementation → Adoption → Business Impact → Value

That avoids an AI project being deemed a success simply by virtue of going live.

Management needs to know how much has been invested, if anyone is really using the system, how operations have changed, and whether or not that change translates to value.

Show the Board a Transformation Dashboard

Board-level reporting must turn technical information into business information.

A useful dashboard may cover:

Strategy → Finance → Operations → Adoption → Risk

The goal is not to swamp the board with model-level metrics. The goal is to give the information necessary to ensure the transformation is still strategic, finance-worthy, operational, and properly managed.

The Enterprise AI Transformation Roadmap

The seven stages can be made simpler to implement when seen as a continuous transformation cycle.

Stage 1: Assess

Begin with analyzing the strategy, data, technology, skills, current AI use, and the governance context within the organization.

The idea is to create a realistic starting point.

Stage 2: Prioritize

Pinpoint AI potential based on the business value, feasibility, risks, and scalability of the situation.

Not all potentials have to be pursued.

Stage 3: Prove

Conduct targeted projects that have specific goals and measurements.

The idea is to create evidence rather than showcase the technology.

Stage 4: Scale

Transition the successful projects to operational activities following their requirements for performance, integration, security, governance, and business value.

The World Economic Forum has similarly pointed to well-developed foundational capabilities and preparedness of the workforce as key factors enabling scalable implementation of AI.

Stage 5: Optimize

Implementation should not be the endpoint of the AI transformation.

Keep tracking the performance, cost, adoption rate, risk, and business impact. With changing technology and business environment, some of the systems may need upgrading, replacement, scaling up, or retirement.

5 AI Digital Transformation Mistakes Large Enterprises Should Avoid

Beginning With Technology Rather Than Strategy

Purchasing AI technology due to its technical sophistication could result in fragmented investments. Begin with the desired business outcome and then proceed to select appropriate technology.

Assuming That All Pilots Succeed

The pilot project is an experiment, not a promise to provide value to the enterprise. Define evaluation criteria for initiatives and do not continue initiatives which fail to show their value.

Overlooking Data Quality

An AI strategy based on poor data access, quality, and governance will be limited by factors that cannot be overcome by using a better algorithm.

Excluding Employees from Transformation

Artificial intelligence affects workflows, responsibilities and sometimes skillset needed to perform work. Therefore, employees should know what systems imply for them in terms of changes in workflows and expectations.

Employees' readiness for transformation is viewed as an essential aspect of AI transformation. According to OECD AI Principles, building human capacities and preparing people for the change brought by AI is crucial.

Measuring Deployment Rather Than Business Value

Deployment of the system does not imply transformation. Keep focused on whether the initiative influences business outcome and delivers value.

What Boards Should Ask About AI Digital Transformation

The board does not necessarily have to be in control of the technical intricacies involved in each and every artificial intelligence effort within the organization. The board only has to have sufficient visibility in order to question the premises and guide the organizational direction.

The following questions may provide some guidance:

Which business strategies are supported by our AI transformation?

Which AI efforts have shown any tangible results?

Why cannot our pilot programs scale successfully?

Are our technological and data foundations suitable for what we plan to do?

Who is responsible for our key AI efforts?

What are our key AI risks?

How are we measuring the business value created?

What competencies do we need for our AI strategy?

Such questions keep the focus of the boardroom on strategy, value, competency, and accountability.

Turn AI Investment Into Enterprise Transformation

AI digital transformation does not consist in how much AI tools the enterprise utilizes. It consists in how much impact from this investment was produced, in terms of the change in how the enterprise functions, decides, serves clients, manages resources, and generates value.

In large companies, this implies much more than just choosing the right technology. Strategic alignment, usable data, proper infrastructure, priority of use cases, operating model with proper accountability, governance according to the risk, and measurement linking AI investment with outcomes are all required.

The task of the board is not managing particular models or defining the way in which technologies should be utilized. The board task is making sure that there is a proper strategy, the organization’s risks are understood, resources are allocated properly, and there is proof of progress.

If this is done right, AI is used not just as a tool for experiments. It is used as a way of improving the performance of the enterprise.

This is the goal of AI digital transformation: not just utilizing AI in the enterprise, but creating an organization able to do it properly.