4 Ways Successful Enterprises Are Driving Success In AI

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Deloitte’s recent study researched the question: “What have companies that have successfully incorporated AI done differently?

This article offers a summary of its findings to help you decide whether you should invest time to read it.

The companies that have garnered the most success in AI do well in the following categories:

  1. Strategy: Companies have a higher probability of achieving their goals if they adopt a top-down approach to strategy and incorporate a bold vision.
  2. Operations: Companies should design their operations with the aim to address risks, inculcate trustworthiness and ensure consistent quality. (see AI Can Be Dangerous—How To Reduce Risk When Using AI)
  3. Change Management and Culture: When establishing a culture, management should focus on a data-driven, agile culture of trust.
  4. Ecosystems: Companies that create diverse and dynamic ecosystems to gain an edge over competitors get more success.

Let’s discuss each of these in detail:


A large number of companies are now taking their first steps in testing AI in different divisions. However, this is not enough. It is imperative to roll out a clear and bold strategy with a top-down approach. The AI strategy needs to be aligned with the overall vision and mission of the business.

Management should ensure that AI strategy is communicated to all relevant stakeholders. This not only helps clarify goals but will also attract talent that wants to be involved in the new and exciting initiatives being introduced. Lastly, it helps employees at all levels understand changes taking place within the company and if and how they will impact their jobs. (see Covid Has Changed How We Work. With The Rise Of AI, Is Your Job At Risk?)

Leadership in collaboration with IT and data scientists should be the driver behind the corporate AI strategy. Companies that have poor results from AI often delegate it to tech specialists.

The optimal AI strategies help companies create new products, enter new markets, improve customer satisfaction, and achieve other growth-related goals. You should ask yourself this key question before making your AI strategy: “How does this help create a sustainable competitive advantage?”


Phil Thomas, executive vice president at Scotiabank has said: “Look at the organizational structure because that can really facilitate the change.” This quotation embodies the spirit of the change needed to truly operationalize AI across companies.

Corporate strategy should always be the driver behind AI strategy. For successfully executing the AI strategy, a close partnership is needed between management and technical development teams.

Here, management will need to learn the intricacies of AI. On the other hand, the technical teams need to understand the business implications of their policies and procedures. This critical factor will ascertain whether your AI strategy succeeds or not. A good practice is to create liaison positions to ensure clear two-way communication to avoid ambiguity or miscommunication between the business and technical teams.

Business workflows will most likely change. The new workflows need to be driven by the business owners. Most businesses and employees are changing averse and will resist it. Thus, only introducing AI will need buy-in from employees along with education on its impact on business.

Developing Ai solutions will inevitably require process changes for the technical teams. The business needs to adopt machine learning and development operations processes (MLOps). This requires close cooperation across technical teams including but not limited to application developers. It is the management’s responsibility to ensure that MLOps are in place.

Lastly, the technical and business teams need to focus on ensuring a data-driven culture to achieve acceptance and drive change.

Change Management and Culture

AI is worrying for most employees because of the possible implications on their jobs. (see other jobs?). Thus, to ensure success, it is necessary to lead with change management efforts. This will improve the AI systems’ probability of success.

Another key factor in change management is trust. Companies should focus on relationship-building, cooperation, and training of employees to promote a culture of trust. Companies that have successfully introduced AI help their employees comprehend that, in most cases, AI replaces tasks and not jobs. These companies are augmenting jobs with AI and employees working together. If, however, the AI will replace jobs, management needs to be upfront with employees and offer opportunities to upskill or reskill.

Another key driver that will assure successful AI adoption is ensuring employees are data-literate. The better these skills, the more employees will trust the results of AI-based systems.

The employees in most companies that have adopted AI are three times more likely to trust AI than their intuition in comparison to low-success companies. This is because of the more trust in data being fed into the models.

Furthermore, employees who will be working in an AI-driven environment require upskilling to become proficient in working with AI.  The better they understand the fundamentals, the more they will be willing to work with AI. It is not enough to only encourage employees to think differently. Management needs to educate, share information and incentivize them to change their beliefs.


Companies need AI ecosystems and partnerships to access, build and license the various elements to execute their AI strategy.

High-performing companies have two or more ecosystem partners. On the other hand, low-performing companies often have only one or two partners. Focusing AI efforts on a small number of partners can lead to over-dependence and vendor lock-in; situations that are best avoided. 

When companies have a larger number of partners, they have higher flexibility, differentiation, and a broader perspective to achieve better results. Companies that have a transformative vision of AI usually have a more diverse set of partners.

Management should carefully assess the components of its AI systems that need to be built in-house vs. what needs to be outsourced. (see How To Succeed With Enterprise AI: Buy Vs. Build). It is important to ensure that both management and the key stakeholders from technical teams are overseeing all partners.

Although it will be some time before companies see significant results in their bottom line from AI. Nonetheless, the best practices on how to achieve these results need to be identified and adopted today.

This is a summary of the original Forbes article. To read it, click here.