Artificial intelligence is rapidly changing the way we do business. From customer service to marketing to operations, AI can improve efficiency, make better decisions, and personalise experiences. To reap the benefits of AI, here’s what to research and how to prepare.
Goals and expectations
Clear goals will govern AI adoption, whether increasing customer experience, operational efficiency, or decision-making. Identify appropriate use cases, which should be prioritised by their impact and practicality. Where can AI best benefit your company? Through tasks, processes, and operations that could use automation, predictive analytics, or data analysis?
Ensure you have clean data.
Clean data is essential for AI to work effectively, which means it must be accurate, complete, and consistent by identifying and correcting errors, standardising the data format, and filtering out irrelevant data.
For example, a business owner who wants to use AI to predict customer churn must collect clean customer data, including demographics, purchase history, and engagement with the brand. Once the data is clean, the business owner can train an AI model to predict customer churn.
How to ensure clean data:
- Use a data cleaning tool – a software application that can be used to identify and correct errors in data like typos, missing values, formatting, and duplicate records.
- Create a data quality policy.
- Train employees on data cleaning best practices.
Prepare your models
Preparing your models means picking the suitable algorithm, architecture, and hyperparameters for your business problem. The algorithm is how your model will use maths to learn from the data. The architecture is how your model is built, including how many levels it has and how many neurons are in each layer. The learning process is controlled by how the hyperparameters are set.
AI models can use various methods, architectures, and hyperparameters. Your business’s best choice will depend on the problem you are trying to solve.
Using the example of predicting customer churn, you would need a well-suited algorithm for classification problems. This architecture is deep enough to learn the complex patterns that can predict customer churn and tune the hyperparameters to optimise your model’s performance.
A single platform with security and governance built-in to enable both innovations and increased customer trust
Businesses may prevent data breaches and more easily comply with regulations using a single data storage and management platform. A single platform can also help business owners by creating AI regulations and auditing AI models to ensure they perform as intended, reducing AI risks and using this technology responsibly with a clear governance framework.
By using a single platform with built-in security and governance, businesses can store and manage data securely, increase customer trust by being honest about how they use data and AI, and protect customer privacy.
Ensure ethical use processes.
Ensuring ethical use processes is critical for businesses to use AI responsibly. Companies must advocate for the ethical use of AI by having clear policies and procedures in place for managing AI risks and be prepared to accept responsibility for any harm caused by AI by:
- Only collect and use data necessary for the specific purpose for which it is being collected and take appropriate steps to protect user privacy, such as anonymising data and using encryption.
- Monitor their models for bias and take steps to correct it.
- Be transparent about how you use AI and what data you collect, and allow users to control their data and opt out.
Plan for Scalability and Infrastructure
AI will very likely help you grow, so be on the front foot!
- Choose the proper infrastructure: This includes the type of hardware, the amount of storage, and the network bandwidth.
- Design scalable systems for future growth to handle increasing data and traffic.
- Automate infrastructure management by using tools such as cloud-based orchestration platforms.
- Monitor infrastructure performance: Use tools such as performance monitoring dashboards.
Pilot Projects and Proof of Concepts
Need more certainty before going all in? Pilot projects allow businesses to test AI solutions in a real-world environment and gather data on their effectiveness. PoCs are smaller-scale projects that can be used to test the feasibility of an AI solution before committing to a full-scale implementation.
Contact Oracle Tree to blend practical and efficient marketing results.
Although it may seem like a considerable undertaking, incorporating AI into your company is necessary to maintain a competitive edge. The success of your AI implementation is crucial to realising AI’s many advantages, and The Oracle Tree is here to guide and support you.
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