Sustaining AI Excellence: Spearheading Successful Initiatives

Here’s a sad truth: Most AI initiatives fail. In fact, according to some estimates, the rate of failure is as much as 80%.

With the rise of tools like ChatGPT, many businesses are increasingly turning to AI. But too often, they don’t employ this powerful technology effectively.

Why do so many AI initiatives fail? And what can business leaders do to improve the odds of success?

Why Do Many AI Initiatives Fail?

Lack of Clear Objectives

Perhaps the biggest reason why so many AI projects fail is that businesses rush into them without having clear goals in mind. Many people want to hop onto the AI bandwagon simply because it seems like everyone is embracing it and they don’t want to be left behind. 

But not all initiatives are AI-worthy. If you’re looking to use the technology, consider how it aligns with your business strategy and how it can further your goals. 

“AI and ML have the capacity to be misunderstood, so much that it has the potential to become ‘yet another technological gadget’ that replaces critical thinking,” says Ella Atkins of Virginia Tech College of Engineering. 

This is the crux of the matter: Rather than making AI your entire strategy and replacing existing initiatives, consider how it can be a tool to aid you in furthering the objectives you already have in the pipeline.

Data Issues

Data access, quality, and management also dictate the success of your AI model. If you don’t have access to plentiful and diverse datasets, the information could be incomplete and biased. 

Nearly one-third of executives say data-related challenges are among their top three concerns regarding AI initiatives, according to a Deloitte study. High-quality, complete data is fundamental to building any AI model because its processes depend on sound information. Without the necessary data, projects may be faulty—or fail entirely.

Unrealistic Expectations

Many of us expect AI to be an all-powerful entity. We assume it will always make ethical decisions and serve us beyond the boundaries of human capacity. But in reality, artificial intelligence is limited by human capabilities. It is only as accurate as the data the model is fed. 

Real-world applications are rarely as impressive as expected. In fact, according to a survey by Upwork’s research institute, while 96% of C-suite executives expect AI tools to increase productivity, 77% of workers say that they have decreased their productivity and increased their workload.

Because of unrealistic expectations, many business leaders make overpromises about AI models, leading to decreased morale and a lack of buy-in.

Talent Shortage

Any AI project demands a high level of expertise. It takes far more than the work of one person—it requires the skills of specialists, including AI/ML engineers, data scientists, domain experts, and others. Many organizations lack the necessary talent and niche skill sets.

Given that AI is still relatively novel in its current iteration, it can be difficult to find a high degree of expertise in this domain. Building out a team capable of carrying out quality projects is challenging. Given the rapid evolution of artificial intelligence and its various applications, scaling initiatives—including moving beyond the PoC stage to real-world implementation—is often difficult as well.

Strategies for Succeeding with AI Initiatives

Align AI with Business Objectives

AI initiatives should solve specific business problems. They need to augment your overall strategy—not complicate it.

We’re all familiar with use cases like Netflix’s recommendation engine, Uber’s matching algorithm, and Mastercard’s fraud detection. But not all AI initiatives are flashy or even forward-facing. 

For many businesses, the more promising use cases are internal. For example, they can automate key processes or make supply chains more efficient. While these initiatives may not seem as exciting as, say, self-driving cars, they can do wonders for smaller or new businesses that are beginning to tap into AI.

Prioritize Data Readiness

Nearly half of data leaders in a 2023 survey said data quality was their biggest challenge to utilizing the full potential of generative AI in their organizations. It’s essential to ensure proper data quality, governance, and architecture before beginning any AI project. The quality and preparation of your data play a pivotal role in the success of your AI model.

In addition to working with skilled data experts, there are several best practices you should adhere to ensure data readiness:

• Profile your existing data.
• Establish frameworks for data governance and standardization.
• Define data roles.
• Cleanse data.
• Utilize data quality processes and tools.
• Establish data quality metrics.
• Continuously monitor and validate all data.
• Build a culture of data awareness.

Set Realistic Goals

When it comes to innovative (and infamous) technologies like AI, expectations are lofty—and, at times, unrealistic. As a business leader, you need to manage expectations not just for stakeholders but for yourself.

“Successful programs focus on potential, not productivity: experimentation and iteration with the tools at the team level that allow people to make work better,” Brian Elliot writes in TIME. “That requires investments of time and energy, training, and support—investments that executives need to be realistic about, including frank conversations at the board level.”

Follow an established framework, such as SMART, to ensure your AI goals are realistic and achievable. In addition to formulating overall project objectives, break the initiative up into smaller components and create milestones to help you measure progress. Remember, too, that many projects don’t show transformative results immediately. Devise a system for measuring and tracking results so you can continue to adjust as needed.

Invest in the Necessary Talent

Specialized talent is essential for persisting through AI initiatives. One important factor in building an AI team is diversity.

IBM’s Institute for Business Value report stresses the importance of varied backgrounds and perspectives in AI, noting, “A diverse workforce becomes a safeguard for improving trust and brand equity.” It’s important to bring in a range of voices to curb bias and innovate more successfully.

Often, the right AI talent isn’t at your doorstep. By sourcing top AI/ML developers from other regions, such as Latin America or Europe, you can tap into niche skill sets and diverse perspectives. 

AI initiatives require the skills of professionals beyond AI/ML developers. You’ll need to create cross-functional teams consisting of engineers, data scientists, business intelligence analysts, and others. Bear in mind that every role contributes to the bigger picture and has a stake in the project’s success.

The Future of AI in Business Initiatives

Making AI initiatives successful requires ensuring their value, first and foremost. Start small, considering how your projects will contribute to your larger business goals. Take incremental, calculated steps to scale the models once they’ve proven to have a positive impact.

At the same time, consider long-term potential. To foster a culture of innovation, you should attain buy-in from stakeholders and employees and continuously monitor and iterate.

Remember, the work is never done. AI will continue to evolve—and so must your strategy.