5 Critical AI Strategy Mistakes Costing CEOs Growth—and How to Fix Them

The application of artificial intelligence can present business opportunities, yet the implementation of AI tools does not equate to AI strategies.

In terms of AI strategies, the more difficult task for CEOs would be determining where AI can add value, what initiatives are worth funding, and what capabilities are needed to achieve success. Without the determination, there is a possibility that AI activities can become disjointed from the overall business goals.

AI strategies begin from the prioritization of business considerations instead of technology. The organization’s data, systems, people, processes, governance requirements, and measurement capability are some things to take into consideration. It is important to note that not all business issues require an AI solution.

AI strategy mistakes could potentially generate big strategic holes:

• AI being perceived as a technology initiative rather than a business strategy

• Too many AI applications being chased without prioritization

• Overlooking data, technology, and organizational preparedness

• Scaling AI without proper governance and accountability

• Measuring AI efforts rather than business impact

Each of these issues can be corrected with discipline. Here’s how CEOs can recognize such AI strategy mistakes, fix them, and move forward on their journey from AI experiments to business results.

What an Effective AI Strategy Actually Looks Like

An AI strategy goes beyond the mere identification of technological tools and methods which an organization intends to invest in. It offers a systematic way for understanding how AI can enable certain business goals and what is necessary for the realization of such goals.

An effective AI strategy involves the linkage of five components:

Business goals → AI opportunities → Capabilities of the organization → Implementation → Measurement

The reason why the order of these components is important is that technology is supposed to be a means for achieving some business goal.

AI strategy is more than adopting AI tools

An organization can deploy generative AI, predictive modeling, intelligent automation, or AI assistants while lacking an integrated enterprise AI strategy altogether. 

Different departments can deploy different technologies in order to optimize their own processes and receive valuable outcomes, but there is still a need for leadership to decide if these solutions align with the organization’s goals.

An enterprise-wide strategy includes:

• What business challenges are we addressing?

• Why is AI a relevant choice to tackle this challenge?

• What are our capabilities?

• What are the risks or limitations?

• How will the organization evaluate the outcome?

That implies understanding that AI won’t be a solution in all cases. The AI RMF by the National Institute of Standards and Technology stresses the importance of considering if AI is a suitable solution for a specific task.

The CEO's role in AI strategy

The CEO doesn’t have to make every technical call when it comes to implementing artificial intelligence. The CEO does have to create the right strategic context for those calls to be made.

Leadership needs to decide on things like which business imperatives matter, where the organization should invest in AI, who “owns” each project, what level of risk is acceptable, and what constitutes success. The leadership team also sets the right context for expanding, redesigning, or stopping an AI project.

This ensures that AI strategy remains tied to business, rather than being allowed to get too far away from the business inside the technology department.

The five-question CEO test

Before committing to an AI initiative, ask:

1. What business problem does this solve?

2. Why is AI the right solution?

3. What capabilities and resources do we need?

4. What can go wrong?

5. What result would make this worth our while?

If you don’t have good answers to these questions, you might want to do some strategic thinking before proceeding to development.

1: Treating AI as a Technology Project Instead of a Business Strategy

Another significant AI strategy blunder involves beginning with the technology versus the business problem.

The technology-driven approach poses the question of: ‘Where can we apply AI?

The business-driven approach poses the question of: ‘What important business problems can AI help us address?’

While the two questions might seem to differ slightly, the implications of their answers are quite profound for the ways investments are selected, implemented, and assessed.

How it manifests

The technology-driven AI strategy might generate initiatives which, while technologically interesting, might be strategically out-of-place within the organization. These red flags may arise when:

• AI initiatives are chosen irrespective of the business priorities of the organization.

• Departments pursue their own AI initiatives without any coordination.

• Leadership emphasizes building the capability without specifying what outcome they want to obtain.

• Initiatives are greenlighted simply because AI has strategic importance even if there is no clear business use case.

• Teams monitor implementation or adoption without establishing what business outcome they seek to achieve.

None of these activities will necessarily guarantee failure of the AI initiative; the point is that they make it hard to determine its justification and success criteria.

Why strategic alignment matters

AI deployment takes up resources, which may encompass engineering resources, data preparation, security, processes redesigning, training, governance, integration, and monitoring. In the absence of a clearly defined business goal, it becomes difficult for the leaders to understand whether the resources are going into a good use.

Compare these two statements.

Technology-driven: "We would like to deploy generative AI to customer processes."

Business-driven: "We would like to cut down the amount of time it takes for our employees to deal with routine customer requests while ensuring the same service level. We will investigate how AI-driven processes may help us achieve this goal."

The latter statement provides better guidance regarding what needs to be done, what needs to be measured, and whether it is worth investing further.

How CEOs can fix it

Begin with the strategic imperatives of the organization and work your way down. For each potential AI project, specify:

Business imperative → Problem → Opportunity → Capabilities → Outcome → Metrics

This is not to say that a detailed business case needs to be developed before you even begin experimenting. Initial experimentation can stay fairly lightweight, especially if the experiment is about figuring out whether there is a viable technical/operational solution.

But the eventual experiments need to be anchored to some business problem that leaders can assess. If not, you can keep experimenting, without making any investment decisions.

Action items for the CEO

Before moving forward with an AI project, ensure that:

• Business problem identified.

• Business problems are linked to an organizational imperative.

• AI is the right solution.

• Outcome identified.

• Appoint a business owner.

• Identify a metrics approach.

Not to hamper innovation. Just to make experimentation better targeted.

2: Chasing Too Many AI Use Cases Without Prioritization

AI can be used in a wide range of different functions inside the company, leading to another strategic challenge of there being too many potential applications.

Without proper prioritization, companies may end up with experiments rather than with a coherent set of initiatives that should be scaled. Therefore, the challenge is not the lack of AI opportunities, but rather how to pick the right ones.

The AI “everything” challenge

A company may come up with potential AI initiatives in such fields as:

• Customer support

• Marketing

• Sales

• Finance

• Human resources

• Operations

• Software development

• Knowledge management

• Risk management

• Product development

The number of potential use cases does not mean that they all should be pursued at the same time. Each initiative is competing for the company’s attention, money, technology, and managerial effort.

With too many initiatives going on at the same time, it becomes difficult for the management to distinguish between important and interesting initiatives.

Why more AI projects do not necessarily mean more value

An extensive pilot list poses various issues. Groups may devote themselves to maintaining experiments of low strategic value while important experiments lack sufficient resources to advance into full operational stage.

Similar solutions may even be developed by different divisions without knowing that similar solutions have been developed elsewhere.

How to prioritize AI use cases

The CEO may create a scoring system based on the following six criteria for evaluating all proposed programs. These criteria include:

A score can be assigned to each program. This does not aim at achieving mathematical accuracy but rather developing a standardized approach to analyzing the opportunities.

Use an AI opportunity portfolio

Having scored the initiatives, categorize them as follows:

• Invest: Significant strategic value with good feasibility.

• Test: Significant potential value but lacking in evidence.

• Prepare: Attractive initiative but certain capabilities have to be developed.

• Stop/rethink: Poor alignment or lack of justification.

This will create an effective portfolio compared to letting each department follow their own agenda regarding artificial intelligence initiatives. The CEO will be able to make informed decisions about where to invest, experiment, and when to do nothing.

CEO’s to-do list

• Create an inventory of AI initiatives.

• Score initiatives based on a set of common criteria.

• Identify duplicates and overlaps.

• Allocate resources towards initiatives with significant potential.

• Establish criteria for scaling up/down or stopping experiments.

• Adjust priorities according to changes in business needs.

The point here is not to stop experimenting. It is to ensure that the experimentation will lead to the right decisions.

3: Ignoring Data, Infrastructure, and Organizational Readiness

Even a compelling business case will not ensure success in implementation. The AI solution may be appealing strategically, yet challenging to implement if the company fails to have sufficient resources in terms of data, technology, talent, and capacity to adopt the new system.

AI strategy goes beyond just a model

Some things companies might need to consider before implementing an AI solution are:

• Data availability

• Data quality

• Data accessibility

• Existing technology systems

• System integration

• Security

• Technical talent

• Talent within the organization

• Changes in workflow

• Governance

• Maintenance

Of course, it all depends on the case. Internal solution implementation may be entirely different from the readiness assessment of the AI system that impacts customer or operational decisions.

That’s why the AI strategy should consider the context of the technology and not the model itself.

The four dimensions of AI readiness

A readiness assessment may consider four key dimensions.

1. Readiness of data

Questions to answer include:

• What data does the program need?

• Where does the data reside?

• Does the organization have access to it?

• Is it reliable enough?

• What privacy, security, and governance issues must be considered?

These questions allow the organization to determine its data readiness to use it for the specified purpose.

2. Readiness of technology

Factors to think about are:

• Existing technology

• Integration

• Infrastructure

• Security

• API and data integration

• Monitoring

The issue is not whether the organization has the latest technology but rather whether it has the right technology to use it for the specified purpose.

3. People readiness

AI can have an impact on employee behavior, making organizational readiness another important component of the implementation strategy.

Think about such factors as:

• Who will use the system?

• What skills do people need to have?

• Whose workflow does the system take part in?

• What kind of training is needed?

• How will people give feedback?

• What tasks cannot be transferred to automation?

4. Process readiness

The new AI system may mean a certain alteration of processes that already exist in the organization. The leader has to think of the place where the system comes into operation, how the system handles uncertain results, output review procedure, exception handling, and performance monitoring.

Technically advanced systems may cause operational problems even if the workflow is not prepared for them.

How CEOs can fix the readiness problem

Prior to committing resources to a particular initiative, perform an AI readiness assessment based on its scores for:

Data → Technology → People → Processes → Governance

Low AI readiness score does not always imply that the project needs to be declined. In some cases, the low score indicates the need to complete some preparatory activities in the organization.

For instance, when the innovative project requires the information that is not easy to obtain, organize and maintain, the improvement of the corresponding data process may become the necessary preparatory step.

CEO action checklist

• Evaluate data requirements prior to implementation.

• Determine technical dependencies.

• Find out capability gaps.

• Determine affected people and processes.

• Define ownership.

• Tackle readiness gaps prior to scaling.

An AI strategy, thus, has to consider the context in which the technology operates.

4: Scaling AI Before Establishing Governance and Accountability

Scaling up an AI project can shift the risk dynamics of an AI project. As more AI is embedded into workflows, it’s critical for leaders to have a clear understanding of responsibility, oversight, risk management, and decision rights.

Why Governance is part of AI Strategy

It is not just a box ticking task that must follow development. Governance answers the following basic questions regarding an AI system life cycle:

• Who is responsible for the system?

• Who is approving deployment?

• What data does it access?

• How performance is being assessed?

• What happens when it generates an undesirable output?

• Who is able to alter or decommission the system?

• How often does the system need to be reviewed?

In NIST's AI Risk Management Framework, there are four main functions for the AI risk management: Govern, Map, Measure, and Manage. In addition, governance is considered a cross-cutting function influencing other processes in the AI life cycle.

This is important for CEOs because it makes responsibility of the governance part of the AI strategy rather than just an administrative function.

The danger of treating governance as a final-stage exercise

In cases where governance requirements are determined post development of the AI system, the organization will realize that it needs some modifications of its systems before implementing or scaling them.

It is advisable that organizations take into consideration governance requirements during planning and modify their oversight approach based on the context of the initiative. 

A low-risk internal productivity application will need a different oversight process from an AI system that is participating in a high impact business decision making.

The objective is not to develop similar control measures for all AI applications. The objective is to implement appropriate governance measures for each AI system.

Build governance into the AI lifecycle

Some ways of building governance include:

Idea → Assessment → Development → Testing → Approval → Deployment → Monitoring → Review

At each stage, establish your responsibilities and controls.

In the NIST's framework, emphasis is put on ongoing measurement and monitoring of AI. The framework takes into account pre-deployment tests and assessments as well as assessments done during operation of AI systems.

In such a way, management has a clear process through which it can determine whether a problem exists in the system and whether a system needs to be continued, modified or discontinued.

Create clear AI decision rights

The governance framework must provide clarity on responsibilities around decisions. Depending on the nature of the organization and the use case at hand, responsibilities may include:

• Executive management: strategy and investments

• Business owner: business outcomes intended

• Technical team: development and technical performance

• Security and privacy teams: applicable controls

• Legal or compliance teams: requirements

• Operational teams: implementation and user feedback

The precise configuration will be based on the organization's size, its operational model, risk profile, and use cases, rather than trying to follow someone else's governance model.

What CEOs can do about it

Create a proportionate governance model where, for each AI project, you have:

• Business Owner

• Technical Owner

• Requirements for approvals

• Risk assessments

• Testings requirements

• Monitoring requirements

• Escalation process

• Conditions for review or retirement

It enables accountability while not necessarily making each project go through a long approval process.

CEO action list

• Determine ownership before implementation.

• Set the right thresholds for approvals.

• Identify risks involved.

• Set testing and monitoring requirements.

• Determine escalation process.

• Review the system based on changing use and impact.

Responsible AI governance will enable the organization to benefit from the power of AI in proportion to the risks that may come from the initiative.

5: Measuring AI Activity Instead of Business Outcomes

It is possible for an organization to implement AI initiatives without being able to measure how much business value these initiatives are generating. This usually occurs in an organization where leaders tend to measure AI initiatives rather than business results.

The activity versus outcome dilemma

The following metrics are some useful operational metrics:

• Number of AI pilots

• Number of users of an AI tool

• Number of AI applications implemented

• Number of automated processes

• Frequency of use

But these metrics alone are not sufficient in proving that the business result was achieved.

The better question is: how did this AI initiative change change the business?"

What CEOs should measure instead

The proper metrics will vary based on the use case, but can be broadly classified in four ways.

Financial outcomes

Metrics related to a project can be:

• Revenue contribution

• Cost savings

• Margin effect

• Savings

• Return on investment

Operational outcomes

Such metrics can include:

• Time to process

• Cycle time

• Error rate

• Throughput

• Utilization of resources

Customer outcomes

Such metrics can include:

• Response time

• Conversion

• Retention

• Quality of service

• Customer satisfaction

Strategic outcomes

Some projects might relate to:

• Better decision-making

• New capability

• Development of product

• Organizational responsiveness

• Expanding into new areas

The metric needs to link back to the original business objective. For instance, a customer-service project should not be measured in terms of how many people opened up the AI application if the actual objective was to reduce response time or quality of service.

Build an AI value measurement framework

Structure is as follows:

Baseline → AI intervention → Target → Result → Evaluation → Scale decision

Begin by determining the baseline. Determine what impact would be achieved with the AI initiative and how it will be measured. Then after implementing the initiative, compare the result with the baseline and the target.

This way the decision about whether the initiative needs additional investment becomes more robust.

Measure before scaling

Not all good pilots lead to good deployments. Before scaling, leaders should consider the following questions:

• Has the initiative achieved its desired objective?

• Was the outcome compared to a real baseline?

• Is the outcome reproducible?

• What will be the cost of scaling?

• What additional risks arise?

• Is the business case still attractive?

The NIST approach is also centered around defining whether the AI system accomplishes its intended goal and desired objectives as part of the evaluation for further development and deployment.

Action checklist for CEO

• Define KPIs before implementation.

• Define baseline.

• Measure results and not just adoption.

• Consider costs.

• Regularly review the performance.

• Define scale, redesign, and stop conditions.

It is not necessary to demonstrate that every AI initiative produces tangible financial returns immediately. What is needed is sufficient data for making sound investment decisions.

The CEO's AI Strategy Fix: A 90-Day Action Plan

The above five mistakes can be dealt with using a systematic approach in terms of a review rather than the need to create a whole new program for AI.

Days 1-30: Diagnosis

This involves comprehending the existing AI portfolio. Create an inventory of:

• Existing AI initiatives

• Existing pilots

• Future pilots

• Business owners

• Technical owners

• Expected outcomes

• Current measurement metrics

• Dependencies

• Governance

After this, assess how each initiative relates to the current business priorities. Find out which ones are strategically aligned, duplicated, poorly resourced, unready or lack metric targets.

Output: AI strategy diagnosis This will involve getting visibility into the program. Leadership cannot prioritize without visibility.

Days 31–60: Prioritize

Assess initiatives on an apples-to-apples basis against the following dimensions:

• Strategic importance

• Anticipated business impact

• Feasibility

• Data readiness

• Organizational readiness

• Risk

• Scalability

And develop a portfolio of initiatives prioritized accordingly. The highest priority initiatives deserve better defined ownership, resource commitment, measurement, and implementation plans.

Other less high-priority initiatives may be left in their experimentation or preparatory phases without requiring the same level of effort.

Output: Prioritized AI Portfolio

Days 61 to 90: Implement

For the higher priority initiatives:

• Assign responsible owners.

• Define clear objectives.

• Determine data and technology needs.

• Define governance roles.

• Develop implementation milestones.

• Develop monitoring.

• Define scalability and exit points.

At this point, leaders will find a much clearer link between strategic priorities and AI investments.

Output: AI Implementation Roadmap

This 90-day framework is not intended as an implementation timeline. Rather, it represents a process through which executives review, prioritize, and decide upon the execution of AI.

CEO AI Strategy Checklist

Asking the following questions prior to approving or expanding an AI project can prove useful:

Strategic alignment

• Does this project address a well-articulated business problem?

• Does this project meet any strategic business priority?

• Is this the right problem for the application of AI technology?

Value

• What needs to change?

• What is the status quo?

• How can success be measured?

• Why should we invest in this project?

Readiness

• Do we have all the necessary data?

• Can our IT infrastructure meet the project’s requirements?

• Are people and processes ready?

• What capabilities are lacking?

Governance

• Whose project is this?

• Who owns the results?

• What risks need to be addressed?

• What testing and monitoring is necessary?

• What will we do if the system fails to deliver on its promise?

Scaling

• Has the project shown measurable value?

• Can the project result be reproduced?

• What resources will be required by the expansion of this project?

• When should we abandon it?

Final Takeaway: AI Growth Starts With Strategy, Not Tools

AI strategy blunders do not usually stem from the choice of an AI product only. They are primarily strategic mistakes that impact an organization’s choice of AI products and the ways it implements, manages, and evaluates AI projects.

An organization may explore new technologies while being unable to establish any link between its AI spending and its business priorities. 

It may run a multitude of pilots without having decided on those which should receive more funding, fund new AI systems without being ready for them, and roll out AI projects without assigning any accountability for them. 

It may even monitor the adoption rate without evaluating whether the business goal behind the project has been met.

These problems can be fixed through a more disciplined approach to AI spending. It includes business priorities before technology, proper selection of AI opportunities, evaluation of organizational readiness, proper management, and measures of success.

The process is simple:

Set business priorities → Define the right AI opportunity → Check readiness → Build governance → Track results → Scale successes

The aim here is not to implement as many AI applications or use cases as possible but rather to determine those situations where AI can add value and build the necessary environment for realizing that value.

This point of view is vital for CEOs. AI should be regarded as a business strategy empowered by technology, data, people, and governance instead of technology-driven strategy which is separate from business.

As long as AI initiatives are aligned with objectives and measured by tangible results, CEOs have more ground for making decisions on what needs to be created, improved, scaled up, and stopped.

Frequently Asked Questions About AI Strategy Mistakes

Which are the main AI strategy mistakes of CEOs?

There are five major mistakes in the strategies CEOs tend to apply: thinking of AI as a technological issue and not a business strategy, having too many use cases to pursue simultaneously, ignoring organizational readiness, scaling before proper governance measures, and measuring AI actions instead of the business results.

Why does an AI initiative not provide the desired business value?

An AI initiative could fail to generate any measurable value because of a fuzzy business goal set, poor prioritization of a use case chosen, lack of required data or capability in the organization, non-integration of the implementation process in workflows, or inadequate measurement against a relevant benchmark.

These are not factors of failure but rather elements making evaluation and realization of the desired value much harder.

How can CEOs prioritize their use cases?

CEOs are able to compare use cases based on a number of criteria like strategic alignment, expected business value, feasibility, readiness of data, organizational readiness, risk, and scalability. Having a uniform score system helps the leadership to evaluate the projects and prioritize them accordingly.

What do CEOs look at when measuring their AI investments?

It all depends on how AI technology is applied. For financial purposes, this could be any number of metrics including revenue impact, savings, or ROI, and for operational purposes could be time of processing, throughput, or error rates. 

Any customer-facing projects could use metrics that relate to quality of service, time to respond, conversions, or retention.

The key here is not to measure the implementation of AI simply because it is measurable but rather to measure what the original goal was.

How can companies scale their AI usage responsibly?

This could mean having defined ownership, understanding risks associated with AI, testing before deployment, monitoring, documenting decisions, and having an escalation plan. NIST’s AI Risk Management Framework includes a Voluntary Framework which includes Govern, Map, Measure, and Manage.

How much time does it take to develop an AI strategy?

It all depends. There is no standard process. It depends upon a number of things including company size, available technology, data readiness, AI maturity, company priorities, governance considerations, and the number of use cases.

A proper assessment at the beginning of the process would be helpful for leadership in deciding which aspects of the organization need immediate action and which aspects need more capability building.