As a manager responsible for extracting business value from artificial intelligence (AI), how can you begin to understand, assess and deliver on the potential of this truly transformational technology?
Since the 2022 release of ChatGPT, generative AI (GenAI) chatbots have produced diverse expectations among boards, managers and employees. The rapid adoption of GenAI applications has been well documented, alluding to the role managers play in delivering on executive and board-level expectations. Most advice emphasizes the need to identify use cases to prove the value of AI. While this advice is generally helpful, it leaves managers with questions about how and where to start.
So then, what is the current state of AI?
In “AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference”, Arvind Narayanan argues that the technology is not yet as transformational as some claim. Similarly, the Gartner Hype Cycle for Generative AI 2024 suggests that GenAI is currently positioned in the “Peak of Inflated Expectations” phase; meaning, there is substantial interest and high expectations around GenAI, but it may still be ahead of its practical, widespread use. Gartner also suggests that “The hype surrounding GenAI can cause AI leaders to struggle to identify strong use cases, unnecessarily increasing complexity and the potential for failure. Organizations looking for worthy AI investments must consider a wider range of AI innovations.”
This current state suggests that although interest in GenAI remains high, expectations for it are becoming more realistic. This creates a great opportunity for managers to begin to deliver on GenAI’s promise in a structured manner. By employing a comprehensive GenAI strategy, managers can create enduring value throughout the enterprise.
Organizing AI Opportunities
Most managers’ experience with AI chatbots is limited to research or creative applications. However, it is important to recognize that GenAI holds immense value when deployed as a nontraditional technology, offering innovative solutions and transformative capabilities across business processes.
The challenge for managers in launching a program lies in identifying not all potential opportunities, but those that can significantly benefit the organization. By categorizing AI opportunities, managers can effectively organize use cases, employees, and tools to ensure productive use of resources. This categorization helps funnel the vast array of opportunities into streams of concurrently running projects, enabling managers to deliver AI projects with confidence while achieving realistic outcomes.
Developing a three-category strategy provides a way forward to organizing for AI success. Opportunities within an organization can be divided into three categories: enhancing individual productivity, improving processes, and driving transformative innovation. Each category has distinct characteristics and demands a specific approach to fully realize the potential of the capabilities within.
1. Enhancing Individual Productivity
GenAI chatbots serve to support research, offer recommendations, facilitate creativity, and provide other useful capabilities. Use cases vary by employee, but most enterprises have begun to experience adoption of GenAI chatbots by their workforces, typically through the curiosity and ingenuity of enterprising workers.
Think of these chatbots as personal assistants or knowledge partners that can be leveraged to reduce employee labor and provide benefits beyond the user’s capabilities. Examples of these tools include OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude. Microsoft, with early and highly advantageous access to GPT4, embeds versions of GenAI directly into their popular applications.
The best way to establish an individual productivity program is to leverage practices found in organizational learning, experiential learning, and professional development. Consider developing a program that incorporates communities of practice, champion programs, and centers of excellence. For instance, Procter and Gamble adopted communities of practice where employees from different departments share insights, best practices, and collaborate on improving the use of AI tools. Applying these practices early will ensure successful implementation of the program and support the sustainable use of GenAI by the workforce.
2. Improving Processes
Automated business processes typically require active human involvement at various points to complete each process cycle. The reason for this is historical: conventional software development has limitations, primarily in the ability for systems to duplicate the human cognitive abilities required to advance a process. This is changing as GenAI begins to demonstrate how it can process data, understand patterns, and generate human-like responses. This enables GenAI to take over laborious process components and free up employees to focus on higher value tasks that require the type of reasoning not currently available from GenAI engines. For example, Stitch Fix leverages this by interpreting textual feedback clients share about their preferences to help fashion stylists curate personalized selections more efficiently.
The most effective way to identify meaningful use cases is to understand the collaborative relationship between employees and systems within business processes. Business process improvement practices are well-established, and documenting workflows can pinpoint tasks that artificial intelligence can manage.
In this approach, artificial intelligence applications serve as extensions of existing software solutions, offering far greater capabilities than traditional technologies available to developers. By employing established program management methods and techniques, management can effectively prioritize use cases, enabling development teams to seamlessly integrate AI into existing software systems. As organizations experience the transformative power of AI, adhering to this approach will deliver a more efficient workforce and foster a sustainable culture of continuous improvement.
3. Driving Transformative Innovation
The opportunities in this category should be perceived not merely as providing individual assistance or enhancing existing processes, but as catalysts for true transformation. These opportunities transcend incremental improvements by creating entirely new processes and enabling capabilities that were previously unattainable. AI allows for the more precise application of individual characteristics from an expanded array of data sources. To give you an idea, Allstate uses GenAI to automate the transcription and analysis of customer calls to help identify common issues, thereby improving response times, and enhancing overall customer service. This effort would have been too time-consuming and expensive before the introduction of GenAI.
For these opportunities, embrace the mindset of “Fail fast, fail often, and fail forward,” which is particularly suited for high-impact, experimental projects. These opportunities are more valuable and less predictable than the previous two categories. Consider these as “moonshot” projects, where the path to success involves multiple iterations and learning from failures.
Realizing the Potential of AI
Managers that plan and execute a three-category program will enhance their ability to successfully deliver projects and articulate operational improvements. Each category should be pursued with equal effort and resourcing. This will accelerate progress broadly across the enterprise.
As the AI landscape continues to evolve, staying ahead of the curve is essential for sustained growth and competitive advantage. The promise of AI is immense, and those who realize this promise will lead their organizations into a future defined by business efficiency, technical innovation, and professional success.