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According to this McKinsey report, generative AI could account for over 75% of total yearly Artificial Intelligence (AI) value, with high expectations for significant or disruptive change in industries. Additionally, the report states that generative AI technologies have the potential to automate work activities, improving employee productivity by 60-70%.
Your CEO and board have tasked you with creating the generative AI strategy for your company. I have met with several business leaders struggling in this situation this year. They were often selected because they had successfully deployed software automation that reduced errors, lowered costs, and improved customer experience. Sometimes, they are the most effective at using data to make better business decisions that deliver better results than their peers. However, they are all trying to determine the best way to mobilize their company's excitement and manage the hype and expectations of generative AI.
Organizations must address these four challenges as a starting point to create an executable generative AI strategy:
Establish a baseline of AI knowledge across the business, including understanding AI capability today and agreed-upon terminology. While generative creates the most excitement today, traditional AI is valuable to your company. Traditional AI focuses on performing specific tasks intelligently. It responds to a predefined set of inputs and makes decisions based on existing rules. Examples include voice assistants like Alexa and Siri and eCommerce recommendation engines. Generative AI is the newest generation of artificial intelligence. It goes beyond specific tasks and can create new original content, making it especially powerful in creating personalized sales, marketing, and customer communication. AI enablement needs to include all your executives, practitioners, and users. As AI capability snowballs and the hype cycle intensifies, all roles must acquire a baseline understanding of AI. The enablement content and delivery will differ for each role (executive, practitioner, user), but the foundational knowledge must be similar. My experience is that combining workshops, case studies, and office hours is the most effective and efficient delivery method for AI enablement. Establishing a baseline knowledge will improve participation communication while reducing the fear that AI will take my job.
Document the goals of AI use. AI is an exciting technology with seemingly unlimited uses that will result in many benefits: improved efficiency, reduced costs, new products and services, and better customer experiences, to name a few. I think it's important that you're reaching out to the priority of those outcomes for your business. The priorities may be different for each business line or function. I see some businesses deploying AI capabilities with marginal business value for the sake of being able to point to AI use. For example, customers have deployed Microsoft CoPilot for all their employees. Many need help to document a specific business benefit they have achieved. The time to think about the business benefit of AI deployments is before you deploy into production. Having the business agree to the priority of benefits will naturally focus the organization on the best projects. I recommend our Propel engagement to help our customers document their AI opportunities by the line of business and department with the defined expected business benefit.
Support Experimentation. Since AI technology is new to many business users and the art of the possible is evolving quickly, businesses need to support experimentation. Let's be honest: trying to stop employees from experimenting with AI will not work. The key is for the company to learn from the experimentation. Experimentation is great for learning the art of the possible, the challenges you will have deploying into production, and getting people closest to the task involved in learning how to improve it. To support AI experimentation, I recommend IT provide safe sandbox environments to business users with the preferred technology tools and necessary data sets. Often, to nurture experimentation, businesses will run Innovation or Hackathon contests. These contests provide an opportunity to identify and develop opportunities from the broad business user community. These sandbox environments enable business users to experiment with AI-enhanced business processes and validate business value without jeopardizing the exposure of sensitive data.
Engage risk management teams early, including legal, compliance, and governance teams. AI technology is outpacing our current laws, compliance, and governance practices. AI is a transformative business capability that has the power to harm your business's reputation. Implementing AI in production will require these teams' support. Including them from the beginning will allow you to establish the right balance between aggressive innovation and good governance while enabling you to implement faster.
In conclusion, the transformative business potential of generative AI cannot be overstated. As highlighted by the McKinsey report, this technology could contribute to over 75% of total yearly AI value, revolutionizing industries and driving significant change. The journey is exciting and challenging for business leaders tasked with creating a generative AI strategy.
Many of these leaders have already experienced success with software automation, witnessing reduced errors, cost savings, and improved customer experiences. Their proficiency in leveraging data for better decision-making sets them apart. However, the path to harnessing generative AI requires addressing critical challenges head-on.
Here are four essential steps to kickstart your executable generative AI strategy:
- Baseline AI Knowledge: Ensure that everyone across the organization understands AI capabilities and terminology. While generative AI generates excitement, traditional AI remains valuable. Traditional AI follows predefined rules and performs specific tasks. At the same time, generative AI creates original content, making it ideal for personalized sales, marketing, and customer communication.
- Inclusive AI Enablement: Involve executives, practitioners, and users in AI adoption. As the hype around AI intensifies, all roles must grasp its potential and contribute to successful implementation.
- Data-Driven Decision-Making: Leverage data to inform your generative AI strategy. Understand your organization's unique needs, identify use cases, and prioritize areas where generative AI can create the most impact.
- Manage Expectations: Generative AI is powerful, but managing expectations is essential. Set realistic goals, communicate transparently, and continuously evaluate progress.
By addressing these challenges, organizations can unlock the full potential of generative AI, driving innovation, efficiency, and customer satisfaction.
