Developing an effective enterprise AI strategy starts with clear business goals in mind. It also requires well-defined evaluation metrics to ensure that a curated AI solution addresses the right objectives.
Often, the best place to start with data and AI is process optimization projects. These typically yield impactful outcomes quickly and provide the ROI needed to fuel further initiatives.
Understand Your Business Problems
Many companies use AI to automate their processes and create new business opportunities. However, enterprise-grade AI is more than machine learning and predictive analytics – it requires a holistic approach to people, processes, and technology. This requires leadership commitment to digital transformation and enterprise-wide change management. It also requires a strategic approach to organizational structure and resources.
A common mistake is rushing into deploying AI without understanding your primary business drivers. The best way to do this is by identifying key metrics for your business and setting them as goals for the AI project. This way, you can ensure that the efforts of different operational teams are aligned with accurate ROI and deliver the desired results.
For example, AI solutions can help you sift through vast volumes of data to identify patterns that could indicate a health risk for a customer or detect potential fraud in a transaction. This can save time and improve customer retention by allowing you to focus on high-value prospects. AI can also reduce costs by helping you streamline and automate processes. This frees up resources to focus on other areas of the business.
But for AI to succeed, it needs access to clean, unified, current, and representative data. This is often a challenge in large enterprises with disconnected teams, disparate systems, and thousands of production applications. Choosing an AI platform that can accelerate and simplify integrating data from these sources for use by enterprise-grade AI applications is essential.
Develop Your AI Strategy
A successful AI strategy requires understanding how the technology is applied, the people involved, and how it aligns with the overall business goals. It also involves identifying the skills and talent needed to implement an AI solution and developing a plan for training. It’s also essential to ensure that the AI solutions are designed with the ability to adapt and evolve as the technology progresses.
Developing an effective enterprise AI strategy is challenging; a business leader must lead the project. Often, this person is the CEO, CFO or COO. This approach enables them to understand the impact of AI on the entire organization and how it can improve performance. Creating a culture that supports using AI technology within your company is also essential. This means educating employees on using AI and providing tools that help them work more productively. It’s also necessary to address any concerns your employees may have about the impact of AI on their jobs and the possibility of being replaced by the technology.
Finally, a good AI strategy will help you avoid the common pitfalls many companies experience as they deploy their first AI projects. Defining success metrics for your AI initiatives is vital, selecting best-of-breed technologies and ensuring the project is well-aligned with your overall business strategy. Otherwise, you could spend years trying to deliver the benefits of AI and failing miserably.
Identify Your Data Sources
Creating an AI strategy and implementing it successfully requires strong leadership and coordination from the top. The right tools must also be in place to promote and support data-driven decision-making. To succeed, it’s essential to understand the limitations of your current data and identify additional sources.
In addition, organizations must ensure that their data is sufficiently accessible and compatible for AI use. This requires assessing how data is stored, managed, and accessed across departments and identifying opportunities for improvement in those areas. Organizations must also consider their overall data governance practices and develop a plan for future initiatives, including how they will collect and manage new data.
To make the most of their existing data, enterprises must find ways to streamline the AI process and optimize existing systems to take advantage of AI capabilities. This is particularly important for capital-intensive industries, such as shipping or manufacturing, where AI can help automate and streamline operational processes to improve efficiency and reduce costs.
For example, an enterprise can leverage AI to optimize its supply chain management by incorporating IoT sensors to monitor and improve business operations and deliver exceptional customer experiences. This can save time and money by reducing waste, improving product quality, and boosting productivity. In turn, it can increase revenue and profit margins.
Identify Your AI Technology Options
Organizations need to identify the right AI technologies to implement enterprise AI applications that can help meet business objectives. These include chatbots and intelligent agents, robotic process automation that streamlines simple workflow processes, or deep learning visual recognition systems that recognize images or videos.
Once you’ve identified the appropriate technology for your use case, conducting a proof-of-concept pilot with the end users in mind is essential. This helps to ensure that the new AI applications are user-friendly and can be quickly adopted by employees. It also allows organizations to test different models and make necessary adjustments to the AI applications’ user interfaces or application logic over time.
Many businesses need help adopting enterprise AI despite the potential to drive productivity. A common reason is that companies focus on tactical AI projects, such as preventing customer churn or optimizing production, without addressing more integrated conceptual business practices. To maximize their return on investment, companies must create a full-circle strategy supported by strong leadership, tooling to facilitate change, and a shift in company culture that prioritizes data-driven decision-making.
In addition to these organizational changes, IT teams must invest in an IT platform that supports developing and managing enterprise AI applications. This includes a cloud-based model train and run infrastructure, data analysis and visualization tools, and a microservices-based architecture that provides a standardized API and catalog for users’ access to AI services.
