Getting Started with AI Transformation
One of the biggest obstacles to any transformation is the lack of organizational readiness. As we look at AI adoption, we find companies often struggle with a lack of AI talent, data quality issues, trouble finding the right business use cases, and corporate cultures that don’t recognize the value of AI.
With billions of dollars spent on change initiatives, what are the common elements that prevent corporate growth?
Gap in AI talent and experience
AI is a complex technology that requires specialized skills and experience to implement successfully. Unfortunately, many companies lack the internal talent and expertise needed to execute an AI transformation effectively. This can be especially true for smaller organizations or those in industries that are slow to adopt new technologies.
Data quality issues and insufficient data
Another significant challenge companies face when it comes to digital transformation is data quality issues and a lack of sufficient data. AI relies on large amounts of high-quality data to train algorithms effectively. Unfortunately, many companies struggle with poor data quality or a lack of data altogether, which can impede their ability to leverage AI.
Generative AI models require large amounts of high-quality data to generate meaningful outputs. However, collecting and labeling this data can be time-consuming and expensive. Additionally, the data used to train Generative AI models must be diverse and representative to avoid biases in the outputs.
Difficulties in Identifying Applicable Business Use Cases
Another common challenge companies face when it comes to digital transformation is difficulty identifying applicable business use cases for AI. AI is a powerful technology, but it's not a magic solution that can be applied to any problem. It's crucial to identify specific use cases where AI can provide a significant business advantage.
Getting Started
“You may delay, but time will not.”
- Benjamin Franklin
It’s not uncommon for companies to juggle multiple challenges, and find themselves not able to address them at all once. So what then, can be done to about Generative AI adoption even if everything isn’t in place?
The answer lies in adopting a pragmatic and flexible approach to digital transformation. Let’s take a look at some potential approaches:
The Pilot
This approach involves walling off a small portion of the business to experiment with AI and test the waters without risking the entire organization's operations. By starting small, companies can build up their data sets gradually and identify areas for improvement without disrupting the broader business.
For example, Domino's Pizza launched an AI-powered pilot program in Australia and New Zealand to optimize its pizza-making process. The program uses computer vision to analyze images of pizza toppings and determine if they meet quality standards. By starting with a small pilot program, Domino's was able to test the technology and build up its data sets without disrupting its core business operations.
Sometimes a pilot may require creating a separate team responsible for the pilot project, which is free to experiment and innovate without the constraints of the larger organization. By creating a separate team, the company can avoid the internal resistance that often comes with implementing new technologies and can focus on the project's success.
For example, in the banking industry, many banks are struggling to implement AI solutions due to data quality issues and a lack of applicable business use cases. To address this, some banks have created innovation labs or digital garages, which are separate teams responsible for exploring new technologies and developing innovative solutions. These teams are given the freedom to experiment and innovate without the constraints of the larger organization, allowing them to identify and develop potential AI solutions that can be scaled up to the rest of the organization.
A problem-first approach
This approach involves starting with a specific business problem and then determining if AI can help solve it. By focusing on specific problems, companies can identify potential use cases and build a business case for AI.
One example of a company using a problem-first approach to implement generative AI is American Express. They wanted to improve their customer service and reduce the amount of time customers spend waiting on hold. To achieve this goal, they created a chatbot that uses generative AI to answer customer inquiries in real-time, 24/7. The chatbot is able to understand natural language and provide personalized responses, resulting in improved customer satisfaction and reduced wait times.
Minimum Viable Product (MVP) approach
This approach involves developing a basic version of the AI solution and testing it on a small scale before scaling up. This approach helps to identify the potential issues and limitations of the solution and provides valuable insights into how it can be improved.
For example, in the healthcare industry, many hospitals are facing challenges in implementing AI solutions due to a lack of AI talent and experience. To address this, some hospitals have adopted an MVP approach by starting with a small AI project, such as using AI to automate the patient scheduling process. By starting with a small project, the hospital can test the AI solution on a small scale, identify any issues or limitations, and build on that knowledge to improve the solution and expand it to other areas.
Fractional talent & external points of view
AI partners (like Human PIlots AI) provide expertise and insights into best practices, emerging trends, and innovative solutions in the AI space. Bringing in outside talent accelerates a company’s ability to adopt a new technology and navigate the emerging complexities of Generative AI to identify issues, limitations and provide guidance on to address these challenges.
For example, the retail industry struggles with implementing AI solutions due to a lack of AI talent and experience within the organization. To address this, some retailers have partnered with external consultants to help identify potential AI use cases and develop AI solutions. These consultants provide expertise and insights into the latest AI technologies and best practices, helping companies to develop and implement effective AI solutions.
Tailoring Your Approach
While these approaches can help companies navigate the challenges of AI transformation, there is no one-size-fits-all solution. Each company's situation is unique, and they need to identify the approach that works best for them. However, there are some common themes that emerge from successful AI transformations.
Strong commitment to change from senior leadership. For AI transformation to succeed, it is essential that senior leaders understand and recognize the potential value of AI and are committed to making it a priority within the organization. This commitment needs to be demonstrated through actions, such as investing in AI talent, providing resources for AI projects, and supporting a culture of innovation and experimentation.
Focus on the end-users of the AI solution. Successful AI transformation requires a deep understanding of the end-users' needs and how the AI solution can help them. This understanding needs to be incorporated into the development process to ensure that the AI solution addresses the end-users' pain points and is easy to use.
Building a data-driven culture. This involves creating a culture where data is valued and used to inform decision-making at all levels of the organization. A data-driven culture helps to ensure that the data used to train AI models is of high quality, and the AI solution is built on accurate and relevant data.
Developing AI talent within the organization. This includes providing training and development opportunities for existing employees and hiring new talent with AI expertise. By investing in AI talent, companies can develop the skills and knowledge needed to build and implement effective AI solutions.
AI transformation presents significant challenges for companies, but those that are able to navigate these challenges and build effective AI solutions stand to gain a competitive advantage in a very short amount of time.
AI Content Disclaimer: The human author who wrote this augmented the final post with Generative AI tools for transcription (Fireflies.AI), organization (ChatGPT 4), research (Perplexity AI) and image creation (Midjourney V5.2).
