Transforming enterprise operations with generative AI

How to minimize the risk while scaling generative AI

Iguazio is a leading AI and machine learning operations company acquired by McKinsey in 2023. Since its acquisition, Iguazio has been integrated into QuantumBlack, McKinsey’s AI arm, dedicated to driving innovation and experimentation in AI. To learn more about what QuantumBlack’s AI products can do for your enterprise, please contact us.

This blog post is based on a write up of the webinar “Transforming Enterprise Operations with Gen AI”, which was held with Dinu de Kroon, Partner and Operations Hub Lead, Nicola Unfer, Sr. Program Delivery Analyst, Davide Di Lucca, Research Science Analyst, and Yaron Haviv, Co-Founder and CTO, Iguazio (acquired by McKinsey).

You can watch the webinar recording here. The original blog post was published on the Iguazio blog in June 2024.

Enterprises are beginning to implement gen AI across use cases, realizing its enormous potential. Since we are all charting new technological waters, being mindful of recommended strategies, pitfalls to avoid, and lessons learned can assist with the process and help drive business impact and productivity. In this blog post, we provide a number of frameworks that can help enterprises effectively implement and scale generative AI while avoiding risk. We also include a number of use cases, from R&D to automotive to the supply chain. In the end, we list potential hurdles and how to overcome them.

Five Questions to ask and answer about generative AI in the enterprise

The evolution of AI began in the 1950s, but the advent of ChatGPT and other generative AI capabilities have created the “perfect storm” of AI. This revolution has been driven by the convergence of massive computing power, enabling data processing at unprecedented scale and speed; an abundance of data available through the internet for model training; and pre-trained transformers that empower us to efficiently work with unstructured data.

We recommend that enterprises ask themselves five questions about operationalizing generative AI:

1. Where do we stand on the journey to accumulate value from generative AI?
2. Are we ambitious enough with generative AI?
3. Where can we maximize the value of generative AI?
4. What does it take to scale AI/gen AI in operations?
5. How to stop using risk as an excuse?

Let’s dive into each one.

1. Where do we stand on the journey to accumulate value from generative AI?

Both generative AI and AI provide significant value. Each has its own strengths and integrating them also brings value.

2. Are we ambitious enough with generative AI?

Our recent research found that only 1 out of 10 organizations focus on business model reimagination. But the generative AI evolution is just at the beginning, and the art of possible has never been bigger. Organizations are beginning to tap into the value of generative AI, either capturing low-hanging fruit or realizing its full-blown potential.

Generative AI innovation is mostly present in consumer-facing industries. For example:

• Procurement: Automating supplier negotiations using internal requirements and external data
• Manufacturing: Using synthetic data to retrain vision system models and optimize workflow of the conveyor system
• Supply Chain: Answering complex supply chain questions from employees on messaging tools like Microsoft Teams, Slack, or Whatsapp
• Consumer Marketing: Producing videos to answer common customer questions, interact with customers, and even help them build their shopping lists

Overall, generative AI is expected to have a significant productivity impact across all industry sectors, from high-tech to retail to banking to energy to agriculture, and many more. Additionally, entire industries will be reimagined and reshaped.

Source: McKinsey report: The Economic Potential of Generative AI: The Next Productivity Frontier

Within industries and organizations, generative AI value comes mainly from operations, marketing and sales, and engineering. Each role type constitutes approximately one-third of the value.

It’s also important to remember that generative AI is only one piece of the pie and should be combined with additional technologies, like AI, AR/VR and Web 3.0. According to our recent research, the value potential of combining generative AI with traditional AI is $17–26 trillion, with generative AI constituting 20–40 percent of that value.

3. Where can we maximize the value of generative AI?

Generative AI offers value across entire domains and functions.

Spotlight 1: R&D Use Case

Generative AI can help optimize, automate, and innovate across multiple R&D steps, including:

• Automating steps in R&D development (e.g., new chemical compositions, circuit designs)
• Optimizing traditional part designs (e.g. component weight reduction)
• Accelerating coding and overall software generation
• Automating the conversion of code from one language to another
• Accelerating R&D process through R&D Virtual Expert, leveraging internal and external insights
• Improving product requirements from customer claims and regulatory updates

Spotlight 2: Automotive ROI

Based on an ROI analysis, we found that for a leading automotive OEM, generative AI is expected to lead to an estimated long-term efficiency potential of 21–25 percent in indirect functions, within the next few years. This includes research and development, indirect production and procurement. Across these functions, generative AI will affect 70–80% of all activities.

Additional use cases and productivity benefits were given in the webinar.

4. What does it take to scale AI?

While use case identification is important, organizations should also extend their thinking to the long-term, strategic gen AU vision.

Ask yourself:

• Do I consider generative AI an opportunity or a threat?
• What use cases do I have? Where do I start : operations, customer service, supply chain, something else?
• Is my organization ready? What are the tech considerations? What talent do I require?
• What is the long-term vision? How can I leverage this across my organization? How will this impact my operating model?

Answering these questions will increase the likelihood of generative AI success.

5. How do we stop using risk as an excuse?

Responsible AI is the framework for ensuring AI is developed and deployed in a safe, trustworthy, and ethical manner. As of now, we only partially understand how LLMs work. This means there are risks that need to be considered and handled.

Responsible AI consists of establishing policies, best practices, and tools to ensure:

• Human-centric AI development and deployment that embeds human oversight, includes diverse perspectives, and aligns with organizational values
• Fair, trustworthy, and inclusive AI to prevent bias and discrimination
• Transparent and explainable AI, enabling impacted individuals (e.g., developers, users) to understand how systems work and how decisions are made
• Robust data protection, privacy, and security measures to protect sensitive information (e.g., PII)
• Ongoing monitoring and evaluation of AI systems to ensure they continuously meet ethical, legal, and social standards

To convert theory into practice, see the original Iguazio post or the webinar recording for three use cases about the supply chain and procurement verticals.