How to Organize Your Enterprise for a Successful Relationship with AI

Businesses everywhere are recognizing the power of AI to improve processes, meet customer needs, enter new spaces, and, above all, gain a sustainable competitive advantage. With this recognition has come an increased adoption of—and investment in—AI technologies. What are the critical elements that managers need to foresee to build a lively and effective organization which will adopt AI comprehensively?

1. Establish a center of excellence in the field.

A center of excellence is key to success when implementing any new technology, but especially so with AI. This team should be responsible for researching and testing new applications of AI within the company, developing Standards Operating Procedures (SOPs) for its use, and training other employees on how to best use AI technologies. The center should also be kept up to date on the latest AI advancements to continue to bring value to the company.

Establishing a center of excellence can be difficult, as it requires dedicating both time and resources to the effort. However, with the right team in place, your company will be able to maximize the potential of AI technologies.

2. Set business priorities and identify opportunities where

One of the essential steps in using AI is determining where it can be used within your business. This involves understanding your company's priorities and then looking for areas where AI can help you meet those goals.

Some typical applications for AI in businesses include:

• Improving worker/employees/teams productivity (example: algorithms that can optimize production)
• enhancing customer engagement and experience (example: chatbots providing recommendations or personalized experiences, or using AI to improve the design of products/services)
• entering new markets or spaces (example: developing a custom AI solution for a specific industry or creating a new business model that incorporates AI)
• reducing costs (example: automating manual tasks or using predictive analytics to optimize resource allocation)

3. Select and commit to a limited number of projects.

Once you have identified the opportunities where AI can be used, it's crucial to select a limited number of projects and commit to seeing them through. This will allow you to focus on delivering value rather than spreading resources too thin.

It's also important that these projects align with your company's business priorities and be achievable within the desired timeframe. Here are a few methods for prioritizing AI initiatives:

• Use a scoring system to rate projects based on factors such as impact and feasibility
• group initiatives into categories (such as improving worker productivity, enhancing customer engagement, entering new markets, etc.) and rank them within each category
• conduct a 'business case' analysis of individual projects to determine their financial benefits

The most important thing is to be realistic about what can be accomplished with AI in the short term and focus on initiatives that will have the most significant impact.

4. Assign executive-level project sponsors.

Executive-level project sponsors are critical to the success of any AI initiative. These individuals are responsible for ensuring that projects remain on track, meet business objectives, and have access to the necessary resources. They also act as advocates for AI within the company, helping to ensure that everyone is on board with its implementation.

Project sponsors should be individuals already familiar with AI and its potential applications within the company. They don't need to be experts in the technology, but they do need to understand how it can be used to achieve business goals. Before you approach project sponsors, it's beneficial to prepare:

• An executive summary of the project that highlights its objectives, benefits, and proposed implementation
• A detailed project plan with timelines, resources required, and potential risks
• A business case for the project that outlines its financial benefits (example: return on investment, cost savings, increased revenue)
• Data strategy for the project, including how data will be collected, processed and used
• Roadmap with AI models (machine or deep learning algorithms) which will be leveraged in the project
• Model training strategy which helps to identify the input which is required to train AI models
• Customer (internal/external) gain. Gains describe the outcomes and advantages that your consumers are looking for. Customers may value any number of things, but some are necessary, anticipated, or desired by them. Some might surprise them. Functional value, social benefits, positive emotions, and cost savings are all examples of gains categories.

The more information you can provide project sponsors, the better. This will help them understand why AI is important and how it can improve business outcomes.

5. Determine and fill any skills gaps.

One of the challenges of implementing AI is that not everyone within the company will have the necessary skills to work with it. This can be a significant obstacle, especially if you're looking to use AI for tasks that currently require human intervention.

To overcome this, it's essential to determine and fill any skills gaps within your organization.

This can be done by:

• Identifying the skills required to implement and use AI within your company
• Creating a training program or initiative to fill these gaps
• Hiring employees with the necessary skills
• Partnering with third-party providers who can help you to develop and/or execute AI projects

The last option is especially important, as it can be difficult for companies to keep up with the rapidly changing field of AI. Partnering with a third-party provider can help you get started quickly without having to invest in resources or staff members who are already familiar with the technology.

Once these gaps have been filled, everyone within the company will be able to work effectively with AI and help to improve business outcomes.

6. Deal with your data

Large amounts of data are coming from various sources and must be handled through the creation of technical infrastructure that is capable of gathering, cleaning, moving, and storing all that information while also delivering it to AI systems at the proper time and speed.

"You can't start with a bunch of Excel files and build out an AI solution," Deloitte's Ammanath said. "You have to enable a robust and reliable data infrastructure."

The following are some key considerations when dealing with your data:

• Data governance. This is essential for ensuring that data is collected, processed, and used consistently and responsibly. It also helps to protect against unauthorized access or use of data.
• Data quality. This is important for two reasons: first, bad data can lead to inaccurate results, and second, it can be costly to fix data quality issues once they've been identified.
• Data security. This is critical for protecting against unauthorized access or use of data. It's also essential to ensure that data is stored safely and securely.
• Data integration. This helps to ensure that data is collected, processed, and used consistently and efficiently.
• Data movement. This helps to ensure that data is collected, processed, and used promptly.

Once your data infrastructure is in place, you'll need to determine which AI models will be used and how the data will be accessed and delivered to those models. This can be done by creating a roadmap with AI models and data flows.

7. Address security, privacy, regulations, legalities, ethics,

There are several challenges to overcome in order to apply AI, such as significant security, privacy, regulatory, and compliance concerns, as well as legal issues and ethical problems.

Many of these challenges are still being worked out, and it can be challenging to know how to proceed when there are so many unknowns. However, it's essential to address these concerns head-on and develop a plan for dealing with them.

This can include:

• Developing a data governance framework
• Creating a privacy policy
• Addressing regulatory and compliance requirements
• Drafting a code of conduct or ethics
• Creating a data retention policy

Many companies are still in the early stages of exploring AI, so it's essential to address these challenges now. Doing so will help you overcome any potential roadblocks down the line and ensure that your company is ready to take advantage of the benefits that AI offers.

8. Establish parameters for acceptable AI performance

It's important to establish what level of performance is sufficient for AI systems and define success metrics.

This can help ensure that expectations are appropriately managed and that AI projects are aligned with business goals. Additionally, it can help prevent unrealistic demands from being placed on AI systems.

Defining success metrics also helps to track the progress of AI projects and ensure that they are meeting expectations.

There are various factors to consider when establishing performance parameters for AI systems, such as accuracy, speed, latency, scalability, and robustness.

It's also essential to establish how these factors will be measured.

Once performance parameters have been established, it's essential to track how AI systems perform against them. This can be done by regularly measuring the performance of AI models and comparing it to the desired level of performance.

Conclusion

Organizing your enterprise for a successful relationship with AI can be daunting, but it's essential to address the challenges head-on. Businesses are rapidly coming closer to the day when artificial intelligence is supposed to independently and regularly illuminate creative and strategic possibilities, allowing managers to escape from narrow viewpoints. If a business is unprepared for these changes, it will quickly fall behind.