Understanding How To Effectively Incorporate Digital Transformation And AI

Small BusinessUnderstanding How To Effectively Incorporate Digital Transformation And AI

Founder LifeWIRE Corp, |CEO Nova Insights |Digital Health Exec| Strategic Advisor| Fellow, HITLAB (Columbia University)| Inventor.

With Microsoft Bing amped up with its own generative artificial intelligence (AI) chat programming, there is little doubt that we are now in the era of AI being publicly part of our world. So what now? All the hype aside, will AI-type chat become as pervasive as we are hearing? I believe we will certainly get there, but there is still a long road ahead before it can be done successfully.

Digital transformation is the process of using digital technologies to fundamentally change the way a business operates, delivers value to customers, and achieves its objectives. With, at best, only 26% of digital transformation being successful, what can leaders do to make sure they succeed when incorporating new digital tools? The first step is to define the problem and lay out where you are in the process. Using AI is no different.

With that in mind, here are four points I would suggest leaders focus on to help ensure success with any form of digital transformation.

1. Vision And Strategy

A clear vision and strategy are essential to successful digital transformation. This involves defining your goals and identifying how digital technologies can help you achieve them. It’s important to align your digital transformation efforts with your overall business strategy and to have a roadmap that outlines the steps needed to achieve your vision.

2. Leadership And Culture

Digital transformation requires strong leadership and a culture that embraces change. Leaders need to champion the transformation effort and communicate the importance of the change to the entire organization, fostering a culture of experimentation and innovation. Transformation is an ongoing process, and the culture needs to be open to new ways of working and be willing to experiment and learn from failures so they can adapt to new technologies and changing customer needs quickly and keep pace with an evolving digital landscape.

3. Customer-Centricity

Digital/AI transformation should be centered around the customer and respective stakeholders. This involves understanding customers’ needs, preferences and behaviors and using digital technologies to deliver a better customer experience. This can involve everything from personalized marketing to new digital products and services.

Similarly, this means understanding that you also have customers (employees) within your organization who require information to best perform their work. They need to know what is being done, why and how it affects them.

4. Technology And Data

Technology is at the heart of digital transformation, and the right technologies and data infrastructure are critical to success. This may involve implementing new software, hardware or cloud-based solutions and ensuring that data is managed and analyzed effectively.

Along with these general aspects of digital transformation, integrating AI into a business requires additional important considerations. Here are some key points to keep in mind.

• Determine the potential value of AI.

As with any technology investment, it’s important to determine the potential value that AI tools can bring to your business. This involves identifying areas where AI technology can improve efficiency, reduce costs or enhance customer experiences.

• Assess your data.

AI algorithms rely on data, so it’s important to assess the quality and quantity of data available to train the AI models. In some cases, additional data may need to be collected or acquired to ensure that the AI models are effective and accurate to the addressable population.

• Choose the right AI technology.

There are many different types of AI technologies, each with its own strengths and weaknesses. It’s important to choose the right technology based on specific use cases and technical expertise within your organization. For example, you may want to use ChatGPT, which has a good user interface for engaging lay people in general or specific searches and writing. Or there are versions such as B.E.R.T. or BioGPT, which are much more specialized—perhaps even more robust—but may also have more of a learning curve.

• Develop AI models.

Developing AI models requires expertise in data science and machine learning. It’s important to have a team of skilled data scientists and machine learning engineers to develop and train the AI models. As the programming is only as good as the coding, you need a project team that is not only familiar with the particular AI tool but also understands the parameters and biases that need to be addressed for it to be of any value.

• Maintain and test your AI models.

AI models need to be regularly monitored, tested, refined and retrained to ensure that they continue to perform accurately and effectively over time. Update and maintain your model as needed; as most AI tech is designed to build upon the data of how it is used, you will need to monitor it to ensure it is being used as intended and that it operates within the desired parameters—such as NOT giving away its own protected code.

Testing and retraining are important for refining the AI model’s accuracy and reliability. This can be as simple as asking questions you know the answers to or that may not have an answer. You could also work with a focus group of industry experts who can ask questions that will uncover the program’s limits.

In Conclusion

Integrating artificial intelligence into a business may be hyped as a game-changer, but it does require careful consideration and planning to work. As with any digital transformation initiative, a clear vision and strategy are essential, and the right leadership and culture are critical to success.

When it comes to AI, businesses need to assess their data, choose the most fitting technology, and develop, test and refine their AI models for the most success. Additionally, ongoing monitoring and maintenance are crucial to ensure the AI models continue to perform accurately and effectively over time. By following these key points, your business should be on the road to more successfully integrating AI and leveraging its potential to enhance efficiency, reduce costs and deliver great customer experiences.

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