What are the warning signs of AI program failure?

BUSINESSES have been chasing artificial intelligence (AI) projects for a while now.

It’s a top priority for businesses on their digital transformation journey and serves as a key differentiator if executed well. AI is the technology that helps truly leverage big data and allows organizations to make automation of jobs and skills easier.

There’s hardly anyone today that isn’t trying to harness the power of AI for their business.

However, there’s a high chance that many of them will fail — which means, managers must be vigilant for the warning signs of AI program failure and find ways to overcome them. Here are some suggestions from McKinsey:

# 1 | The executive team doesn’t have a clear vision for its AI and analytics initiatives

When the company’s leaders don’t understand the power of AI and analytics, it’s difficult for them to form a clear vision for the AI project at hand.

In order to overcome this, the team tasked with the implementation of the AI project should also be tasked with organizing training sessions for senior leaders to help them understand what AI does and how it can help meet business goals and objectives.

# 2 | No one has determined the value that the initial use cases can deliver in the first year

Prototyping and pilot projects are easy and are successful in most cases. They deliver the promised results and help the business better visualize the returns that a particular use case can bring.

However, when it comes to scaling up, it’s important for the organization to line up all its use cases, measure the impact that each will make, and then invest in a few that will make the biggest impact and deliver the greatest value.

According to McKinsey, doing so will generate momentum and encourage buy-ins for future analytics investments.

# 3 | There’s no AI and analytics strategy beyond a few use cases

Although this seems quite unlikely, there are several organizations that explore a few use cases that are “pitched” to them by different vendors and solutions providers.

The company trials those and depending on the results, decides to plunge into AI — without really assessing whether doing so is really going to help the overall business.

McKinsey suggests posing the following questions to business leaders before making any AI investments in order to stimulate a conversation about the right AI and analytics strategy for the business:

• What threats do technologies such as AI and advanced analytics pose for the company?
• What are the opportunities to use such technologies to improve existing businesses?
• How can we use data and analytics to create new opportunities?

# 4 | AI and analytics roles—present and future—are poorly defined

Before scaling AI and analytics across the organization, it’s important to ensure that every department and division within the business has the right people to help leverage these new-age tools and solutions.

McKinsey emphasizes the importance of the right people in the right roles in the organization in order for AI and analytics to really help transform the organization.

To counter this, it’s important to define the roles that each team needs within the business in order to leverage AI and then match internal executives to those positions.

Further, the organization should be encouraged to hire externally for roles that demand skills the organization doesn’t currently possess.

# 5 | The organization lacks analytics translators

“Hire or train translators right away,” is McKinsey’s message. However, the company cautions against hiring externally as those outside the company lack the most important quality of translators: deep company knowledge.

McKinsey suggests finding the right internal talent that not only understands the company and possesses business acumen, but also the education to understand mathematical models needed for AI and analytics to function.

Given the nature of the role, it’s important to think about such positions sooner rather than later.

---

---

---

---