What Successful AI Projects Have in Common: ROI, Metrics, and Execution

 

 Artificial intelligence is becoming a more integral part of day-to-day technology. Currently, it's the job of the CIO to convert the business investment of Artificial Intelligence into profitable business advantages. Executive boards across industries have shown more interest and allotted more funds to AI initiatives, but organizations have shown little advancement in achieving better AI initiatives.

Often, it can be even more complicated than AI technology.

Defining the value of the project to the business, establishing success metrics, and executing to plan the fundamentals often result in the failure of AI projects. When integrated with disconnected, siloed subsystems, even the most creative AI ideas are doomed.

What can we learn about the pillars of success from the AI projects that are operational and successful? Let's take a look.

1. The Objective is the Business Outcome, Not the Technology

Organizations that have the philosophy of adopting AI technology because it is the latest trend are common.

Truly successful CIOs have a business philosophy. They focus their resources, in this case AI technology, to a defined and specific business challenge.

Instead of a gauntlet of ideas like:

"What are all the ways AI can be added to our processes?"

The question is framed like:

"What is the business challenge we are positing to our customers that is resulting in the most lost revenue?"

As an example, the challenge could be better processes to predict the needs of our customers (forecasting), increased efficiency and reduced costs of doing business (operational), or better overall service to the customers (customer satisfaction). All outcomes from this process and the answer rather than the question are preferred.

The support for the AI project is naturally garnered because the outcome is aligned with the defined priorities and goals of the business.

When AI projects are designed to serve the goals and outcomes of the business strategy, not only is there better support from Executives, but the teams engaged in the projects have a clearer understanding of what success is.

2. They Define ROI Before Implementation

Setting early expectations for ROI is one of the most underrated factors in the successful implementation of AI.

Often, initiatives are set in motion without the agreement of how successful outcomes will be assessed. This results in confusing stakeholders as to the success of the project months down the line.

Acceptance of the ROI of AI projects begins with setting the metrics of success.

The following may be considered:

The time the current state consumes.

• What is the current state of the costs and how are they incurred?

• What are the current state error rates?

• What potential revenue is being lost?

• What are the potential customer experience improvements?

• What are the customer experience pain points?

AI ROI goes beyond the savings it produces, and includes:

• Improved employee productivity

• Faster, better decisions

• Lower operational risk

• Increased revenue

• Improved customer retention

• Greater accuracy in forecasting

The top organizations view ROI in both the financial and the strategic sense.

3. They Use Meaningful KPIs

Success cannot be assessed without measurement. Defining KPIs will ensure AI implementation is successful, and teams remain focused and aligned.

The appropriate KPIs must be selected.

AI performance metrics can be categorized into different groups which include:

Operational KPIs

1. Processing duration measures

2. Level of tasks automated

3. Decrease in repetitive task performed

4. Error occurrence measures

Financial KPIs

1. Savings accrued

2. AI influenced sales increase

3. Shorter ROI period

4. Improved profit margins

Customer KPIs

1. Customer satisfaction measures

2. Service duration measures

3. NPS

4. Customer retention measures

Adoption KPIs

1. Employees’ rate of using AI systems

2. Engagement measures

3. Rate of completed AI training

4. AI systems used for decision support

These metrics provide objective measures and offer leaders a chance to make changes to avoid minor problems escalating to major failures.

4. They Eliminate Siloed Systems

For most CIOs, the most difficult hurdle to AI adoption is a siloed system.

In most cases, different departments rely on disparate applications, different data sources, and inconsistent data reporting. Sales may use one system while operations use different systems, and customer data may be stored elsewhere.

Without integration, AI systems are inadequate.

With data on different systems, organizations may face the following:

• Poor insights

• Wasted resources

• Unreliable predictions

• Delayed actions

• Low confidence on AI systems

Integration is a priority for successful organizations before broadening the use of AI.

Connected systems provide leaders with an enterprise view instead of disparate evaluations.

5. They Consider Execution a Never-Ending Process

Most strategies fail at the implementation stage.

AI systems beyond the model launched. Successful organizations focus on:

Change Management

Employees know that the introduction of AI strengthens their work.

Governance

Accountability within the structure ensures AI technologies are used responsibly.

Continuous Optimization

Models of AI are monitored, and both the AI and the environment are adjusted as necessary.

The most successful CIOs know that the use of AI technologies never stops.

AI Implementation: Maintaining Competitive Advantag

AI technologies are the first and foremost decision support tools.

The advantage of AI technologies rests in the ability to leverage sophisticated decision support for the organization.

By combining advanced data analytics and AI, organizations are able to derive insights before their competitors, allowing leaders to make confident decisions.

The adoption of AI decision support technologies leads to improved flexibility and adaptability in the organization.

In a rapidly changing business environment, the organization with the most advanced AI technologies will enjoy a significant competitive advantage.

AI Facilitated Decision Making

One clear competitive edge is the ability to make better decisions.

A truly competitive edge goes beyond having the best technology or the best talent. It is the combination of all the above with the ability to derive insight and make better decisions faster than competition.

In the world of business, faster and better decisions equal greater competitive advantage.

Insufficient understanding of the iterative and perpetual nature of AI technology stifles organizational growth.

AI Implementation

Successful AI projects are based on clear business goals, measurable returns, clear KPIs, integrated systems, and dedicated teams. NIWs focusing on this core foundations are the ministries leading their organizations to derive long-term and sustainable value from their AI investments.

We help large organizations move beyond the AI experimentation trap. We help clients integrate 'next best-action' AI solutions with measurable project goals.

Book an executive AI briefing to explore AI use cases for your business.