Developing an effective enterprise AI strategy requires an organizational culture and structure. Without a shared roadmap and clear prioritization, businesses will focus on tactical AI projects (like reducing customer churn) instead of transforming their operations.
Form a multidisciplinary team to assess how the AI strategy will meet objectives for key internal stakeholders. Identify the areas where data and AI can provide the most impactful benefits.
Define AI Objectives
When companies launch an AI initiative, executives must define what constitutes success and how that relates to business goals. For example, a successful AI project may improve productivity or reduce customer churn. Or, it might create a more effective predictive maintenance system by providing the right alerts to technicians at the right time.
An effective enterprise AI strategy will also address what is and isn’t acceptable performance. For instance, it might be acceptable for a chatbot to direct a caller to the wrong customer service representative, but it wouldn’t be in a self-driving car. Establishing parameters in advance is important so teams can design a project accordingly.
Gartner analyst Whit Andrews said an effective AI program requires a team of professionals with technical expertise and business savvy. This includes business analysts, data scientists, IT leaders, and risk management experts. Ideally, this multirole team will work on all projects to ensure consistency and collaboration. This will help organizations avoid the problem of siloed AI projects that don’t align with other company initiatives.
Assess Your Data
As you look to establish a data strategy and AI initiatives, you must make decisions about the data you want to use. This will include identifying the specific goals of each project and assessing whether the data you’re working with can be used for other purposes in the future.
Large amounts of data can be overwhelming, but AI tools can help you organize and analyze this information for better business results. For example, automated data orchestration tools are useful for managing large data sets and providing insights for AI models. This will also allow you to scale your data analytics efforts across the organization easily.
It’s important to remember that a robust Enterprise AI strategy takes coordination and buy-in from multiple areas of your organization. For example, you may need to train employees on new technology skills and adjust workflows as part of a larger digital transformation effort. A strong leader can help guide the process and ensure all relevant departments are on board. They can also serve as the communicator-in-chief and create enthusiasm for your AI project.
Select the Appropriate AI Models
One of the key challenges with AI is that it requires a lot of data. This is why it’s important to make sure your data management strategy can scale and also includes steps like data preparation, cleaning, and augmentation.
Once you’ve defined your objectives and assessed your data, it’s time to select the appropriate AI models. The right AI models will allow you to solve your business challenges more effectively and drive real value. Evaluating your AI projects based on business impact rather than machine learning metrics such as accuracy is also important.
AI tools that provide a centralized and simplified user interface can manage data wrangling, mining, visualization, training, machine learning, deployment, monitoring, and compliance with policies. This makes it easier to distribute the power of data science across departments and ensure everyone can contribute to an effective enterprise AI strategy.
Integrate AI with Your Existing Systems
As with any other project, AI integration requires an effective plan to ensure successful implementation. This involves identifying AI’s most valuable use cases, planning and scheduling the necessary resources, and establishing clear goals.
It is also important to consider ethical considerations and data democratization. Lastly, the company must be ready to invest in training and upskilling existing staff or hiring outside experts.
The first step is establishing a dedicated business unit to oversee and orchestrate AI initiatives. This will help to standardize practices and communicate across departments and lines of business. Some companies have already established AI Centers of Excellence to manage their AI programs.
It is also important to set clear measures for success and determine how the project will be measured. This will help ensure the project results align with the company’s overall objectives. For example, AI projects can be measured regarding customer satisfaction or ROI (representing profit, cost savings, or time savings). This will allow the business to identify its most successful use cases and improve current systems.
Measure AI Success
When deployed across an enterprise, AI must deliver tangible benefits in terms of cost reductions, new revenue streams, and enhanced customer satisfaction. This is why the three most important steps for implementing a successful enterprise AI strategy are establishing clear objectives, ensuring adequate data, and deploying intelligent systems that can be easily monitored.
To measure success, executives should identify areas where AI can add value and select three to five processes ready for pilots and proof of concepts. They should then assess the potential returns each project will generate and designate those with the highest returns as top priorities. This assessment should also include people and labor impacts, including how AI will impact the organization’s talent pool.
Finally, they must establish a dedicated business unit to oversee all AI initiatives within the company and ensure that the projects progress toward their objectives. This is called an AI Center of Excellence and can be a great way to standardize practices and facilitate communication.