Capturing Value with Generative AI in Operations

Introduction

Generative AI is an emerging, transformative technology with immense potential to enhance operational efficiency, productivity, decision-making and innovation across industries. By leveraging the power of large language models, machine learning and natural language processing, generative AI systems can understand complex information, identify patterns, generate insights and ideas, and even create novel content. As generative AI rapidly advances, organizations that effectively harness this technology will likely gain significant competitive advantages.

This article will explore the transformative impact generative AI can have on business operations, best practices for capturing value from the technology, key metrics to measure success, and real-world case studies of generative AI in action. By understanding the current state of the art, strategic considerations, and early examples of generative AI deployments, business and technology leaders can make more informed decisions about if, when and how to leverage generative AI to drive operational improvements.

The Potential of Generative AI

At its core, generative AI excels at analyzing large datasets and "generating" novel outputs, whether those are insights, ideas, copy, code, images, or other digital assets. This has significant implications and use cases for enhancing operations:

Insight Generation - Generative AI can rapidly process and synthesize information from diverse structured and unstructured data sources. This enables surfacing novel insights that may have been missed by human analysts or traditional analytics. Generative AI can help identify emerging trends, risks, and opportunities more quickly.

Idea Generation - By training on successful past examples, generative AI can suggest creative ideas for new products, services, marketing campaigns, process improvements, and more. This "augmented creativity" helps expand an organization's idea funnel and surface concepts that build on proven best practices.

Content Generation - From marketing copy to financial reports to employee communications, generative AI can dramatically accelerate content creation. Organizations can automatically generate first drafts in seconds that capture key themes and talking points. While human oversight is still needed for fact-checking, editing and finalizing high-stakes content, generative AI can improve content velocity and consistency.

Code Generation - For technology and product teams, generative AI models trained on codebases can rapidly generate code snippets and templates. This can help developers be more productive, quickly build out boilerplate components, find and fix bugs, explain complex code, and more.

Decision Support - By analyzing data and best practices, generative AI can provide evidence-based recommendations to guide strategic and operational decision-making. Generative AI decision support tools can serve up relevant context, surface risks and opportunities, and suggest potential actions - ultimately leading to faster, more data-driven decisions.

Workflow Automation - Many operational workflows involve laborious human processes related to data collection, aggregation, analysis and generation of documents and assets. Generative AI can automate and augment many of these steps, helping knowledge workers be more efficient and focus on higher-value activities.

Customer Experience - When integrated with products and customer touchpoints, generative AI can power more intelligent, personalized customer experiences. This includes everything from AI-powered chatbots and virtual agents that can engage in human-like dialogue to predictive models that can recommend highly relevant content, products and offers in real-time.

In summary, generative AI has wide-reaching potential to enhance the speed, quality and efficiency of many core operational processes. But to realize this value, organizations need to take a strategic approach to experimenting with and scaling the technology.

Best Practices for Implementing Generative AI

Implementing generative AI in operations is not as simple as flipping a switch. To capture maximum value from the technology while navigating risks and limitations, consider the following best practices:

Start with High-Value Use Cases

Focus initial generative AI efforts on use cases that have a clear path to value - whether it's reducing manual effort, improving the quality and consistency of outputs, or enabling better decision-making. Good candidates are operational activities that are time-consuming, repetitive, and dependent on historical data and knowledge. Prioritize "quick win" pilots that can demonstrate value and gain buy-in before scaling.

Build Robust Data Infrastructure

The power of generative AI models depends on the quality and quantity of data they are trained on. Organizations need robust data infrastructure and governance to support generative AI development. This includes aggregating relevant structured and unstructured data, ensuring data quality and consistency, and implementing appropriate access controls and security measures. A solid data foundation is a prerequisite to operationalizing generative AI.

Combine Human and Machine Intelligence

Despite rapid advancements, today's generative AI systems are not perfect. They can sometimes produce inaccurate, biased, or nonsensical outputs. Human oversight is still essential for high-stakes use cases to validate outputs, provide feedback to improve models, and manage risks. The most effective generative AI deployments augment human workers, not fully replace them. Design human-in-the-loop workflows that leverage the speed of generative AI and the judgment of human operators.

Implement Appropriate Guardrails

Generative AI systems raise novel risks that need to be proactively managed. These include the potential for biased or factually incorrect outputs, plagiarism, malicious use, and unintended consequences. Organizations must put policies and technical guardrails in place to mitigate these risks. Techniques include careful prompt engineering to align outputs with intended use cases, content filtering to catch inappropriate outputs, and clear disclosure when AI-generated content is used. Implement governance structures to oversee responsible development and use of generative AI.

Monitor and Continuously Improve

Generative AI models are not "set it and forget it" systems. They need continuous monitoring and improvement as they are deployed. Track metrics related to usage, output quality, efficiency gains and business impact. Use this data to identify opportunities to fine-tune models, expand use cases, and measure ROI. Treat generative AI implementations as ongoing programs, not one-off projects.

Invest in Skills and Training

To leverage generative AI, organizations need to build the right skills and capabilities. At a minimum, this includes AI/ML engineering skills to develop and deploy models, as well as domain experts who can define use cases, validate outputs, and apply generative AI to operational workflows. More broadly, organizations should invest in data literacy and AI skills for the workforce at large. Employees across functions need to understand the basics of how generative AI works, its potential and limitations. Provide training and change management to help employees adapt to AI-enhanced ways of working.

Balance Efficiency and Innovation

Generative AI can drive step-change efficiency improvements in operations through automation and augmentation of human work. But the technology's greater value may lie in supporting innovation. Leverage the "augmented creativity" of generative AI to expand your organization's idea funnel, discover novel solutions to problems, and create differentiated products, services and experiences. Striking the right balance between efficiency and innovation is key to capturing the full value of generative AI.

Key Metrics to Track

To gauge the impact of generative AI and optimize deployments over time, organizations should track a range of metrics:

Adoption & Usage - Active users, frequency of use, API calls, user feedback

Efficiency - Time savings, cost reduction, productivity gains, tasks automated

Quality - Error rates, accuracy, human evaluation of outputs, A/B testing

Business Value - Revenue growth, profitability, innovation (new ideas generated), customer satisfaction

Model Performance - Training steps, inference times, retraining frequency, computational requirements

Risks & Governance - Inappropriate outputs, security breaches, bias incidents, adherence to policies

Organizational Readiness - AI/ML skills, data literacy, employee sentiment, change management

Real-World Case Studies

A number of organizations are already leveraging generative AI to transform aspects of their operations. Here are a few examples:

JPMorgan Chase - The bank is using generative AI to write research reports, summarizing complex financial data into market commentary and analysis. Hundreds of thousands of hours of human work is saved annually. By enabling analysts to generate reports 10-15x faster, they can spend more time interfacing with clients.

Visa - Visa is leveraging generative AI to automatically write code for identity verification systems used in their payments platform. ML engineers provide requirements in natural language, and generative models write secure, efficient code to spec. Visa has seen a 40% reduction in development time and fewer bugs.

Progressive Insurance - The insurance provider has deployed generative AI in their claims process. Using natural language models trained on claims documentation, they can automatically generate initial incident summaries to help adjusters more quickly understand cases and make decisions. By extracting key data points and suggesting next steps, the system is improving the speed and efficiency of claims handling.

Airbus - The aerospace manufacturer is applying generative AI to optimize aircraft design. Engineers can describe design requirements and constraints in natural language, and generative models propose novel aircraft component geometries optimized for desired performance characteristics. These AI-generated designs serve as a starting point to inspire new engineering concepts.

Salesforce - The CRM giant has infused generative AI across its platform to help sales and marketing teams be more productive. Capabilities include automatically generating email and chat responses, social media content, product descriptions, meeting summaries and more. Salesforce claims its generative AI can make teams up to 2x as productive.

Fidelity Investments - The financial services firm is leveraging generative AI to enhance its chatbot for customer support. By using large language models that draw on a knowledge base of financial topics, the chatbot can engage in more human-like dialogue to understand and resolve customer queries. Fidelity has seen improvements in customer satisfaction and first-contact resolution rates.

While still early, these examples highlight the diversity of generative AI use cases being deployed across industries and functions - from financial analysis to software engineering to product design to customer service. As more organizations pilot and scale generative AI, additional best practices and success stories will undoubtedly emerge.

Challenges and Limitations

Despite the immense promise of generative AI, the technology still has significant challenges and limitations to navigate:

Hallucination - Even the most advanced generative AI models today can sometimes produce content that is inaccurate, inconsistent, biased or nonsensical. Careful human oversight, fact-checking and editing is essential, especially in high-stakes domains.

Safety and Security - If not implemented with proper safeguards, generative AI systems could be used to create spam, fraud, misinformation, and other harmful content at an unprecedented scale. Technical and governance controls are needed to mitigate misuse.

Intellectual Property - Training generative AI models on copyrighted data raises thorny questions around ownership and fair use. The legal implications of AI systems that can closely replicate patented or trademarked content are complex.

Workflow Integration - Realizing the full value of generative AI requires rethinking legacy operational workflows to incorporate AI capabilities. This may require substantial process re-engineering and change management.

Explainability and Trust - The "black box" nature of complex generative AI models makes their outputs difficult to interpret and explain. For highly regulated industries and domains where accountability is essential, this opacity can hinder trust and adoption.

Job Displacement - While generative AI will undoubtedly augment and enhance many human jobs, it could fully automate others. Organizations will need to responsibly manage the impact of AI on the workforce through reskilling and job transition programs.

Long-term Implications - It's difficult to predict how the continued advancement of generative AI could reshape business and society in the coming decades. Proactive governance and Ethics will be essential to ensuring the technology develops in alignment with human values.

Conclusion

Generative AI represents a major breakthrough in artificial intelligence with transformative potential for business operations. From accelerating insight and idea generation to automating rote tasks to enhancing decision support, organizations across industries are already realizing early value from the technology.

But implementing generative AI in operations is not a trivial undertaking. It requires strategic planning, strong data capabilities, thoughtful human-AI collaboration, proactive risk mitigation, and ongoing monitoring and improvement. Organizations that get it right will be able to move faster, work more efficiently, and drive innovation.

As generative AI continues to advance at a breakneck pace, now is the time for leaders to educate themselves, experiment with high-impact use cases, and develop roadmaps for wider adoption. The future competitive landscape will likely be defined by the institutions that most effectively leverage AI. Generative AI will be central to that shift.

While challenges and uncertainties remain, the potential of generative AI is immense and the technology is here to stay. Learning to harness its power in responsible, robust ways will be one of the great business imperatives of the decades ahead. Fortune will favor the organizations that adapt their operations and strategies for an age of ubiquitous generative AI.

In summary, generative AI has the potential to be one of the most transformative technologies of our time, with far-reaching implications for how organizations operate and create value. But realizing this potential will require a strategic, human-centered approach to AI development and deployment.

As Kevin Kelly has noted, "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Leaders across industries would be wise to explore how they can take the core of their business and enhance it with generative AI. The greatest gains will likely come not from small efficiency improvements, but from reimagining entire processes and business models in an age of AI.

Of course, the rapid advancement of generative AI also raises profound societal questions that we will need to grapple with. As the technology grows more capable, how do we ensure it remains a tool that empowers rather than replaces human ingenuity? What guardrails do we need to put in place to mitigate malicious use and harmful unintended consequences? How might generative AI disrupt labor markets, and what policies and social safety nets do we need to support impacted workers? Answering these questions will require deep collaboration between technologists, business leaders, policymakers, and ethicists.

One thing is clear: generative AI is here to stay, and it's advancing quickly. We are in the early stages of an intelligence revolution that will reshape every domain of human endeavor in the coming years and decades. The organizations that adapt their operations to harness this incredible new tool will be best positioned to thrive in a generative AI-powered future. But we must steer its development deliberately and responsibly, with human values at the center. Nothing less than the trajectory of human progress is at stake.

References

Agarwal, R., & Dhar, V. (2014). Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research. Information Systems Research, 25(3), 443-448.

Bechmann, A., & Bowker, G. C. (2019). Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media. Big Data & Society, 6(1), 2053951718819569.

Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities. MIS Quarterly Executive, 19(4), ix-xxi.

Benlian, A., Kettinger, W. J., Sunyaev, A., & Winkler, T. J. (2018). Special Section: The Transformative Value of Cloud Computing: A Decoupling, Platformization, and Recombination Theoretical Framework. Journal of Management Information Systems, 35(3), 719-739.

Broussard, M. (2018). Artificial unintelligence: How computers misunderstand the world. MIT Press.

Brynjolfsson, E., & Mcafee, A. (2017). The business of artificial intelligence. Harvard Business Review, 7, 3-11.

Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute.

Davenport, T. H. (2018). The AI advantage: How to put the artificial intelligence revolution to work. MIT Press.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.

Furman, J., & Seamans, R. (2019). AI and the Economy. Innovation Policy and the Economy, 19(1), 161-191.

Garbuio, M., & Lin, N. (2019). Artificial intelligence as a growth engine for health care startups: Emerging business models. California Management Review, 61(2), 59-83.

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.

Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.

Mccarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174-1182.

Metcalf, J., Keller, E. F., & Boyd, D. (2016). Perspectives on big data, ethics, and society. Council for Big Data, Ethics, and Society.

Muro, M., Whiton, J., & Maxim, R. (2019). What jobs are affected by AI? Better-paid, better-educated workers face the most exposure. Brookings Institution.

Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), 403-414.

Plastino, E., & Purdy, M. (2018). Game changing value from Artificial Intelligence: eight strategies. Strategy & Leadership.

Ransbotham, S., Khodabandeh, S., Fehling, R., LaFountain, B., & Kiron, D. (2019). Winning with AI. MIT Sloan Management Review, 61180, 1-17.

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210.

Susskind, R. (2018). AI, work and 'outcome-thinking'. Journal of Industrial and Intellectual Property Law, 17(5), 5-21.

Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Knopf.

Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education. European Union.

Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: humans and AI are joining forces. Harvard Business Review, 96(4), 114-123.

Wooldridge, M. (2020). The road to conscious machines: The story of AI. Pelican.