AI success for organizations

 I know how important it is to have a clear and effective AI strategy for your business. AI can help you achieve your objectives, such as improving customer experience, increasing productivity, growing revenue and enhancing employee experience. But how do you get started with AI? Here are some steps that I recommend you to follow:

1. Define your business objectives and how to measure their value. Before you dive into AI, you need to have a clear vision of what you want to achieve and how you will track your progress. For example, do you want to improve customer satisfaction, retention or loyalty? How will you measure these metrics? What are the key performance indicators (KPIs) that matter to you?

2. Identify and priorities AI use cases that support your goals. Once you have your objectives and metrics, you need to find out how AI can help you reach them. You can start by exploring some of the generative AI use cases that Microsoft offers, such as creating content, designing products, generating insights or enhancing experiences. You can also look at how other businesses in your industry or domain are using AI to solve similar problems or create new opportunities. You should priorities the use cases that have the highest impact and feasibility for your business.

3. Build a portfolio management plan to guide your investments. After you have a list of potential AI use cases, you need to decide how much time, money and resources you are willing to invest in them. You should also consider the risks and benefits of each use case, as well as the alignment with your overall business strategy. You can use a portfolio management framework, such as the one proposed by Microsoft, to help you balance your AI portfolio and maximize your return on investment (ROI).

4. Determine whether to buy, modernize or build applications. Depending on your needs and capabilities, you may choose to buy ready-made AI solutions, modernize your existing applications with AI features or build your own custom AI applications from scratch. You should evaluate the pros and cons of each option, such as the cost, speed, flexibility and quality of the solution. You should also consider the availability and compatibility of the data sources that you need for your AI applications.

5. Assess whether you have the infrastructure for AI applications to access data securely, quickly and at scale. Data is the fuel for AI, so you need to make sure that your data is accessible, reliable and secure for your AI applications. You should also consider where you want to store and process your data: on premises or in the cloud. Cloud computing offers many advantages for AI, such as scalability, performance, reliability and cost-efficiency. However, you may also have some constraints or preferences regarding data sovereignty, privacy or compliance that may affect your choice of cloud provider or service.

6. Ensure your cloud infrastructure is built to run large AI workloads and deliver reliability at scale. If you decide to use cloud computing for your AI applications, you need to make sure that your cloud infrastructure is optimized for AI workloads. This means that it can handle large amounts of data and compute power, as well as provide high availability and resilience for your applications. You should also look for cloud services that offer specialized hardware and software for AI, such as GPUs, TPUs or frameworks like TensorFlow or Py Torch.

7. Evaluate your organisation’s Zero Trust security posture. is a crucial aspect of any IT system, especially when it comes to data and AI. You need to protect your data and applications from unauthorised access, misuse or theft. You should adopt a Zero Trust security model, which assumes that no one and nothing is trusted by default, and requires verification for every request or action. You should also use AI to enhance your security capabilities, such as detecting and responding to threats, enforcing policies and procedures or monitoring incidents.

8. Explore how to use AI for improving security. AI can not only help you secure your data and applications, but also help you improve your security posture in general. For example, you can use AI to automate security tasks, such as patching vulnerabilities, updating configurations or auditing logs. You can also use AI to analyse security data, such as network traffic, user behaviour or threat intelligence. You can also use AI to generate security insights, such as identifying anomalies, risks or opportunities for improvement.