Greg Kihlström advises Fortune 1000 companies on MarTech, CX, and Digital Transformation, and hosts The Agile Brand Podcast.
While the statistics range depending on where you look, it is safe to say that anywhere from a small to a large majority of companies are in some stage of digital transformation at the time you are reading this article. What happens, though, when a disruptive technology is introduced midway through the process of transformation? Unless you’ve got access to a time machine to travel back and forth, you and your team are likely grappling with how to incorporate artificial intelligence like generative AI into your plans.
Thus, you’re going to have to potentially make some tough decisions and prioritize areas that will have the greatest impact. In this article, I’m going to explore three key areas to focus on when it comes to AI in a digital transformation.
Key Area #1: Customer Experience And Lifetime Value
I talk a lot about customer lifetime value (CLV) being a key measurement and consideration in many areas, and this is no different. When looking at your digital transformation efforts and the most impactful ways to integrate AI into them, I recommend this as a starting point.
Incorporating AI into a digital transformation can help improve the customer experience in a number of ways. For one, AI can help organizations provide more personalized experiences to their customers. By analyzing customer data, AI-based tools can identify individual preferences and behaviors, which teams can use to tailor their messaging, offers and experiences to each customer. This can help to create a more engaging and satisfying customer experience, and can also help to increase the CLV by encouraging repeat business.
Another way AI can help to improve the customer experience is by providing better attribution of marketing efforts. By using AI to track and analyze customer interactions across multiple channels, organizations can gain a more complete picture of the customer journey and use this information to make more informed decisions about where to invest their marketing efforts. This can help to ensure that marketing efforts are being directed toward the most valuable customer segments, and can also help to increase the CLV by improving the effectiveness of marketing campaigns.
Finally, AI can be used to predict customer needs and preferences, which can help organizations anticipate and respond to customer needs more quickly and effectively—again, helping to increase the CLV.
Key Area #2: Operational Efficiency
Despite fears of AI replacing human roles and reducing the workforce, there is a lot that it can do to augment and assist existing employees and help them do better and more strategic work. Thus, operational efficiency is another key area where AI should be considered.
Incorporating AI into a digital transformation can help improve operational efficiency in a number of ways, including helping organizations to make more informed decisions about where to invest their time and resources. By analyzing large amounts of data, AI can identify trends and patterns that might not be immediately apparent to human decision-makers. This can help organizations make more informed decisions about where to allocate their resources and help optimize their operations for maximum efficiency.
Another way AI can help to improve operational efficiency is by helping organizations identify and target specific customer audiences. By analyzing customer data, AI can identify patterns and trends to help business teams understand their customers better. This can help organizations target their marketing efforts more effectively and allocate their resources in a way that is more likely to generate results.
Key Area #3: Augmenting Decision-Making
The best way to engage in a digital transformation is to take an agile, iterative approach, which means that you need the best possible information to decide where to focus and what lessons should be learned within your iterative steps.
Integrating AI into a digital transformation initiative can significantly augment an organization’s decision-making abilities. For example, AI can be used to identify patterns in customer data that can help organizations to determine where to invest resources, such as which products or services to develop or market. Additionally, AI can be used to analyze staffing and hiring data to identify areas where the organization may need to recruit new talent or develop existing employees. This can help organizations to make more informed decisions about where to allocate resources, which can ultimately lead to better outcomes.
AI can also be used to inform other investments in innovation, such as research and development, using data to gain insights into which areas are most promising. For example, AI can be used to identify patterns in data related to the success of different R&D projects, which can help organizations to determine which types of projects are most likely to lead to successful outcomes.
If you are months away from realizing some of your digital transformation goals, you likely can’t afford to wait until you start before adding AI-based tools into your processes and platforms. By focusing on some of these high-priority areas, you can make the most of your AI investments while not pulling too much focus away from your current in-progress transformation efforts.
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