AI Digital Transformation: 7 Essential Boardroom Steps

Artificial Intelligence is making the move from experiments to becoming part of business processes. In this case, there is more than the selection of specific technologies to consider at the board level in larger organizations. 

Boards and senior executives need to define their AI vision, priorities, risks, and transformations in order to generate business value.

The application of AI digital transformation goes beyond IT. The process has the potential to affect operational models, organizational competencies, the customer experience, data strategies, technology architecture, risk management, and investments.

The question for boards is that of offering strategic guidance on this topic, without interfering in the process of implementation of the initiative. 

This article offers seven critical steps to oversee AI digital transformation, including the definition of the ambition and priority use cases, governance, preparation of staff, measurement of the business value created, and constant oversight by the board.

Why AI Digital Transformation Belongs in the Boardroom

AI has traditionally been talked about in terms of a technology problem. Yet, for large businesses, it is becoming important to look at AI as a business transformation problem.

The use of AI could have implications on the way work gets done, the way people engage with the company, decision-making, information flows between departments, and resource allocation. All these could be outside the technology function.

That makes AI important for the board as the whole process of enterprise transformation is related to strategic and competitive issues.

AI Changes More Than Technology

Investing in an AI platform is not necessarily a step towards digital transformation.

This happens when technology becomes an inherent component of an enterprise’s operations that generate value. The enterprise can adopt the AI assistant without modifying its process design, or redesign a process, where people, data and AI systems operate differently.

It makes a difference.

Therefore, a board should not focus on what model a firm uses or what vendor provides a certain AI platform. Much more strategic questions are:

What are our strategic challenges?

Where could the application of AI add value?

What skills do we need?

What are the potential risks?

How will our employees’ tasks change?

How can we assess if the investment is valuable?

Such questions put AI into the broader perspective of corporate strategy.

The Board's Role Is Oversight, Not Implementation

Boards do not have to choose models, create prompts, or be involved with individual AI initiatives.

Instead, they should ensure proper oversight of significant strategic choices and risks that may arise.

These may include questioning management’s assumptions, reviewing key investments, considering the organization’s AI risk profile, and establishing accountability structures within management.

It is the responsibility of management to execute decisions.

Such an approach avoids two extremes: too little attention from the board on a crucial strategic decision and too much intervention from the board in technical matters that management should address.

1. Define What AI Digital Transformation Means for the Enterprise

Before approving large AI investment, the board and senior leadership team need a common understanding of what transformation entails for the organization.

An AI digital transformation can mean different things. One organization might want to automate certain processes. Another could wish to apply AI to customer service, software development, finance analytics, or knowledge management. A third could use AI to change its products, services, and operational model.

None of these strategies is automatically a fit for any organization.

The foundation should be based on the organization’s strategy.

Start With Strategic Needs, Not AI Options

The first step shouldn’t be to ask, “How can we apply AI?”

Rather, one should ask, “What strategic issues can be solved through AI?”

The objective is not about technology but about value creation.

Objectives can include better customer experience, eliminating redundant human tasks, helping to perform quick analyses, making knowledge easily available, improving forecasting practices, or creating new products and services.

It isn’t assumed that AI alone solves such objectives. Every application needs to be assessed in the context of the business reality.

Connect AI Priorities to Corporate Strategy

AI initiatives need to align with existing corporate priorities rather than create their own agenda for technology.

Such a framework is:

Corporate priority → Problem in business → AI opportunity → Capability required → Measure

So, if an enterprise seeks to enhance its customer service, the management could first identify problems in customer service and only then think whether AI can solve the issue.

This way, the board would be able to differentiate between strategic investments and interesting experiments.

Establish the Ambition of Transformation

The organization should establish what level of transformation is necessary.

What was the vision for transformation that required automation, AI-based decision making, AI-based products, full-scale deployment across the organization, or even an overhaul of the business model?

Each of these would involve different degrees of work and capabilities.

Having established the ambition of transformation, the board would have a baseline for assessing if individual AI initiatives contribute to the overall transformation strategy.

2. Build an Enterprise AI Strategy Around High-Value Use Cases

Once the goal of transformation is articulated, the question of how the organization will focus its efforts arises.

Large organizations will easily accrue a variety of different AI experiments conducted by various departments. Marketing could test generative AI; customer service could test conversational systems. Software developers may use coding assistants. Financial operations could test automated analysis.

While these individual ventures may all be valuable in their own way, they can become a fragmented technology environment without being coordinated.

Enterprise AI strategy becomes such a coordination mechanism.

Create an Enterprise AI Use-Case Portfolio

The management of an organization needs to look for potential use cases throughout the whole organization, instead of each department forming an independent AI strategy.

Potential areas include, but are not limited to:

Customer service

Sales

Finance

Human Resources

Supply chain

Marketing

Software development

Risk and compliance

Knowledge management

Analysis

The goal is not to apply AI to every area of the organization.

Management needs to compile a portfolio and determine what ventures are worth pursuing further.

Prioritize Use Cases Systematically

A practical evaluation framework might have five factors to consider:

Business value – What business impact can be accomplished with the initiative?

Feasibility – Is it feasible to implement and operate it?

Data Readiness – Is it feasible to have access to required data?

Risk – Are there any operation, security, privacy, legal, etc. risks?

Scalability – Is it possible to scale the solution to work in the required part of the company?

This way we don't allow technology enthusiasm to become the only factor of approval of the project.

Even a good use case requires an adequate business case and operating environment.

Experiments vs. Strategic Investment

Experiment proves if the idea works.

Production Deployment includes integration of the solution into the existing business process.

Strategic Transformation is the change in the way the company works through the new capability.

That's different stages.

Successful experiment does not mean that an organization is ready to deploy the solution company-wide. Scalability may require better security, integration, data management, training of employees, monitoring, etc.

It is important for the board to know where each initiative stands in this hierarchy.

3. Strengthen the Data and Technology Foundation

The AI digital transformation requires more than models and applications. Big companies will also require an environment where it will be possible to access data in an appropriate way, make different systems communicate, keep the security level high, and monitor AI applications.

This does not mean that all companies have to change their whole technology infrastructure.

This means that top managers have to understand whether the current environment can support their needs for AI.

Enterprise Data Readiness Assessment

Before implementing any AI solutions, it is necessary for top managers to know the data their project depends on.

Some questions to think about are the following:

Where is the critical business data stored?

Who is the owner of this data?

Is it accessible?

Is it consistent across all systems?

What sensitive data does it include?

What are the rules of using it?

How is the data quality controlled?

The question of data readiness is especially important in cases when AI systems are integrated into the internal business information systems.

Modernize the Technology Architecture Where Necessary

Depending on the transformation objectives, the organization would need capabilities that include:

Cloud or hybrid infrastructure

Data platform

APIs and system integration

Identity and Access Management

AI Infrastructure

Monitoring

Security

Model Management

The architecture required would be based on the existing infrastructure environment of the organization and its business objectives.

The board's task would not be to dictate a particular technical architecture but to ensure that the management is aware of architectural requirements, dependencies, investments needed, and risks involved in the transformation process.

Plan for Interoperability and Flexibility

With rapid changes in AI technologies, enterprises must plan how to make their architecture more flexible in accommodating changes in models, providers, and applications.

Interoperability can be achieved through modular design, appropriate data portability, interfaces and understanding vendor dependencies.

The goal is not to avoid all dependencies.

The objective is to understand the nature of these dependencies and if they are in line with the strategic goals of the organization.

4. Establish AI Governance, Risk and Accountability

Governance is one of the key board-level elements in AI digital transformation.

Different levels of risk can arise from the use of AI applications based on the information being processed, their level of autonomy and the consequences which might arise from mistakes made.

The NIST's Artificial Intelligence Risk Management Framework divides AI risk management into four activities: Governance, Mapping, Measurement and Management. This framework is supposed to help organizations to consider risks throughout the entire AI lifecycle.

Define AI Decision Ownership

AI governance may become complex without clear ownership.

In such situations, the enterprise should establish how responsibilities will be distributed between the board of directors, its committees, the CEO, technology managers, data managers, risk and compliance staff, legal department, business unit heads and AI specialists.

The structure may be slightly different across enterprises.

What is important is that the person responsible for important decisions should be appointed.

There should be a person responsible for the decision about the application's appropriateness, for the material risks of the project and for monitoring the performance after deployment.

Create an AI Risk Classification Approach

Not all AI uses the same level of governance.

An AI used for low impact internal assistance would carry a different set of risks compared to an AI that involves making significant customer, financial, employment or operational decisions.

Considerations organizations could take into account include:

Business impact

Customer impact

Sensitivity of the data

Regulatory risk exposure

Level of automation

Impact of erroneous output

Vendor dependency

Higher-impact uses may need more comprehensive testing, documentation, human oversight and monitoring.

Address AI Risks

Board supervision will take into account the way the organization manages risks including:

Privacy

Cybersecurity

Data quality

Bias & fairness

Accuracy & reliability

Explainability

Intellectual property

Third party risk

Model changes

Human oversight

Some of the characteristics related to trustable AI according to the NIST AI RMF include validity and reliability, safety and security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful bias addressed.

The idea is not to manage all risks. The idea is to manage the material risks.

Build Governance Into the AI Lifecycle

Governance must start prior to deployment.

A practical process is one which involves the following sequence:

Idea → Evaluation → Design → Testing → Approval → Deployment → Monitoring → Evaluation → Retirement

Governance controls at every step may differ depending on the significance and risk profile of the application.

It is much better than having governance as just an exercise done post-deployment of the system.

5. Prepare the Workforce for AI-Enabled Operations

Technology may alter the capabilities of an organization, yet transformation will impact the people and processes within an organization.

AI may impact how employees conduct their tasks, the manner in which collaboration occurs, the skills that will be required and human judgment that will still matter.

It is thus imperative for board-level AI oversight to consider workforce readiness.

The current guidelines issued by boards in relation to AI also touch on issues related to the workforce.

Roles and Workflows to Be Altered

Rather than just considering which jobs may be automated through AI, executives need to analyze the tasks themselves.

Questions to Consider

What tasks can be enhanced?

What processes may be redesigned?

Where does human judgment play a critical role?

Which skills will be increasingly important?

Who requires training?

Which tasks need alteration?

This approach provides a much better insight into organizational transformation.

The adoption of AI can lead to automation of certain tasks and greater human involvement in other tasks.

Develop AI Literacy Across Leadership

There is no need for board members and executives to become AI experts.

But they must have sufficient knowledge to ask the right questions.

The leadership needs to be able to talk about:

What the AI system can and cannot do

What data it needs

Where it could go wrong

What are its operating costs

What risks are involved

What kind of controls are necessary

How its performance will be assessed

Such a degree of literacy will enable the leaders to challenge the proposal without having the responsibility for implementing it technically.

Include Change Management in the Transformation Plan

Implementing technology alone does not mean that behavior is going to change.

Employees may require training, new procedures, new job descriptions and guidance regarding how AI is supposed to be used.

Therefore, change management must be incorporated into the transformation plan from the start.

It is important to communicate not only the technology that is being implemented but why it is being implemented.

6. Create an Investment and Scaling Model That Measures Business Value

Eventually, every board will have to ask a simple but vital question:

"What value are we getting for our AI?"

In order to answer this, one needs to do more than just project counting.

Go Beyond AI Pilots Counting

Counting helps understand how many initiatives the company pursues. It does not say anything about transformation success.

More sophisticated analysis can look at such factors as:

Adoption

Usage

Performance of the process

Outcomes for customers

Operational outcomes

Risk

Total cost

Impact on business

Metrics will depend on the nature of the initiative.

Customer service will have different metrics from the AI that supports software development or finance.

Define Key Performance Indicators Before Scaling

The management should set proper metrics before going into the scaling phase.

When talking about customer service, some examples of metrics would be resolution time, escalation patterns or customer experience metrics.

Regarding software development, it could consider such metrics as development cycle metrics or quality metrics.

As for finance, some examples could be processing time, reconciliation efficiency and error rate.

The main idea is to connect the AI initiative with the result that it should have.

Create Stage-Gates for Investment

An example of how a structured investment process can advance initiatives through various phases would be:

Explore → Validate → Pilot → Scale → Optimize → Retire

Criteria should be established for each phase.

An initiative could get into the explore phase due to its potential to solve an important issue. The initiative is allowed to proceed if there is proof for the next investment decision.

With this method, there is no risk that the enterprise will continue funding the initiative since the company has already invested in it.

Total Cost Consideration, and Not Just the Technology Cost

An AI investment goes way past just the model/software cost.

The total cost would include:

Infrastructure

Usage of model

Data preparation

Integration

Security

Compliance

Training

Change management

Monitoring

Maintenance

7. Build a Continuous Board-Level AI Oversight Model

AI digital transformation does not stop once the system is deployed.

The model could be updated. The business requirement could shift. The regulation could change. New risks could appear. The employees could use the system in ways different from the way it was intended.

This implies that board monitoring needs to be ongoing.

Create an AI Monitoring Cadence

Management reporting can give the board a consistent perspective on four aspects.

Strategy: How are AI projects aligned with corporate strategy?

Value: What has been accomplished?

Risk: What are the material risks, incidents or controls issues raised?

Capability: Does the organization build the capabilities for its strategy?

The frequency of the reporting can depend on many factors.

Ask Better AI Questions

Some questions that board members can use to enhance oversight include the following:

Which business results are we trying to achieve through our AI investments?

Which AI activities are strategic to the business?

What makes us believe they should be scaled up?

Whose responsibility is each key risk?

On what data does each critical system rely?

Where do we need human oversight?

How do we measure business value?

Under what circumstances will we kill or revise an AI activity?

This line of questioning helps shift discussions from technological demonstrations to enterprise-wide decisions.

Look at the Portfolio, Not Just at Individual Activities

A board needs to understand how individual activities fit into the picture as well.

Different departments may be spending on similar competencies. Different business units may be using different technologies to solve similar issues. Several activities may rely on the same data and technological platform.

Portfolio-level oversight is useful for finding these interconnections.

The goal is a coherent enterprise AI strategy, not a loose collection of independent experiments.

Common AI Digital Transformation Mistakes Boards Should Watch For

Organizations that make huge investments in AI may face problems even when transformation is not linked to the organization’s strategy and governance.

AI as an IT Project

AI digital transformation is linked with technology, but it may also have an impact on business processes, people, risk and strategy.

It may be treated only as an IT project and this will reduce visibility of executives about its broader impact on the organization.

Scaling Before Governance Is Prepared

Just because a pilot succeeded, it does not necessarily mean that the AI system is prepared for scaling.

Scaling needs to take into account security, data, monitoring, accountability, workforce readiness and other controls.

Selecting Technology Before Defining the Problem

Technology should be used to solve a certain business problem.

The reverse situation, where you start from a technology and try to find the problem, may lead to difficult-to-justify investments.

Ignoring Data Readiness

Information that may be required for AI solutions could be fragmented, untouchable, ungoverned or inconsistent.

Understanding data needs at an early stage will make sure that management identifies these problems before they become significant.

Measuring Activity Rather Than Outcomes

The number of AI projects, users or outputs may demonstrate activity, but it does not mean that business value was achieved.

What really matters is whether the solution helps achieve the planned outcome.

Underestimating the Change Management

AI can impact workflow and duties of employees.

An employee who does not know how to incorporate a specific system in his activities is likely to find it difficult to utilize the system properly.

A Practical Boardroom Checklist for AI Digital Transformation

Use the following list to determine if the enterprise has all the basic building blocks of an effective AI digital transformation strategy.

Strategy

AI priorities are aligned with corporate strategy

Use cases are well-defined from a strategic perspective

Transformation ambition is described

Technology and Data

Important data resources are known

Technology dependencies are known

The architecture supports planned scaling

Governance

AI ownership is defined

Important risks are categorized

Control points are present through the AI life cycle

Workforce

Skills needed for success are identified

Employees have been appropriately trained

AI-related workflows are being addressed

Value

KPIs have been defined

Investment milestones are clear

Business results are tracked

Board Oversight

Management reports on AI regularly

Important risks are escalated

Portfolio is assessed against strategy

Frequently Asked Questions About AI Digital Transformation

What Is AI Digital Transformation?

AI digital transformation is the embedding of artificial intelligence within the organization’s processes, decision making, products and services, and organizational models as part of larger organizational transformation. This is more than just deploying AI technologies; it involves aligning technology with organizational strategy and impact.

Why Should the Board Oversee the AI Digital Transformation Journey?

There is a need for oversight because AI will affect strategy, investment, operations, skills of the workforce, data management, cybersecurity, and risk management. This helps ensure that all AI-related major decisions are linked to enterprise objectives and risks are appropriately managed.

What Questions Should a Board Ask When It Comes to AI Strategy?

There are numerous questions that a board should be asking regarding the AI strategy and its implications. Among these questions are those on strategy, applicability, objectives, cost of investment, readiness of data, responsibility, readiness of workforce, scale, and risk management.

How Can Companies Scale AI Responsibly?

There are some ways for large companies that can help them to do it, namely: structuring priorities, having proper governance, good data management, preparing people, making phased investments and monitoring.

How Can Companies Measure AI Digital Transformation?

There is one important aspect when it comes to measuring AI digital transformation — companies should always relate to the objective of every initiative. This will depend on the case and might be related to productivity, customer success, adoption, efficiency, quality, cost, risk or something else.

Turn AI Strategy Into Enterprise Transformation

AI digital transformation cannot be seen merely as a quest to introduce additional AI-based tools.

It is even more challenging and complex for a big organization to define and implement a coordinated linking strategy, use cases, data, technology, governance, people, and results.

A crucial part of the process should be played by the boardroom. Boards are capable of making sure that all the significant AI initiatives are linked to the overall organizational strategy, have the right level of accountability and material risks are identified as artificial intelligence becomes more and more integrated into the organization.

These seven steps can serve as a helpful guideline:

Define what does the term "AI digital transformation" mean for your company;

Select high-value use cases;

Develop the necessary data and technology foundation;

Create proper mechanisms for governance, risk management, and accountability;

Enable your workforce for AI-enabled operations;

Measure the value delivered by artificial intelligence and scale appropriately;

Provide continuous board oversight.

What the objective is not is a simple introduction of artificial intelligence to an existing company. What it is - is an attempt to find out where exactly artificial intelligence should fit in and develop the corresponding capabilities.