Leveraging AI to Enhance Efficiency, Foster Innovation, and Uphold Ethical Standards Across Global Business
At 1ArtificialIntelligence, Lee Bogner, Global Chief Enterprise Architect and Master AI Engineer at Mars, delivers a detailed and insightful presentation titled "Enabling AI in the Enterprise: How Large Organizations Are Integrating AI Technologies." With his extensive experience at Mars, a multinational company renowned for its confectionery, pet care, and food services divisions, Bogner outlines the practical steps large organizations must take to embed AI technologies across their operations. His presentation provides a clear roadmap for integrating AI in a manner that aligns with organizational values, optimizes performance, and maintains a strong ethical foundation.
AI as an Extension of Mars’ Strategic Vision
Bogner begins by framing Mars’ approach to AI through the lens of the company’s five guiding principles—freedom, responsibility, efficiency, quality, and mutuality. These principles drive every aspect of Mars' operations, from its internal decision-making to its external relationships with customers, partners, and communities. Bogner emphasizes that Mars' integration of AI is a natural extension of these values, aimed at enhancing the company’s long-standing commitment to operational excellence and ethical leadership.
Mars, which operates as a global leader in both the confectionery and pet care industries, generates more than $50 billion in annual revenue and has been privately held for over 110 years. Bogner shares that Mars’ adoption of AI is not only about improving business processes but also about aligning AI innovations with the company’s vision of creating value for its customers, associates, and the broader ecosystem.
“We see AI as a tool for making our business easier, faster, and more efficient while upholding our responsibility to society,” Bogner states. He notes that Mars' commitment to its principles has guided the company’s AI journey, ensuring that technological advancements are applied in ways that benefit all stakeholders.
The POST Framework: A Strategic Approach to AI Implementation
One of the key frameworks Bogner introduces during his presentation is the “POST” methodology—People, Objectives, Strategy, and Technology. This structured approach ensures that Mars’ AI initiatives are aligned with the company’s broader business goals and are implemented with purpose and clarity.
Bogner underscores the importance of starting with people, particularly understanding the needs of those who will be impacted by AI, including employees, customers, and partners. He warns against the common mistake of allowing technology to lead the conversation. Instead, organizations should first define clear objectives—what business problems need solving? What goals is the company aiming to achieve?
“Too often, companies lead with technology and then look for problems to solve. That’s the wrong approach,” Bogner explains. “At Mars, we start with the people and the problem. We then align our objectives and strategies before selecting the right technology.” AI at Mars is not deployed as a one-size-fits-all solution; it is tailored to address specific business challenges and create measurable value across various operations.
Real-World AI Applications Across Mars’ Business Units
Mars applies AI technologies across its four major business segments: confectionery, pet care, human food, and veterinary services. Bogner highlights several key areas where AI is delivering tangible results:
- Manufacturing and Automation: AI plays a pivotal role in streamlining Mars’ manufacturing processes. The company uses AI to automate factory operations, reduce manual data entry, and improve production efficiency. AI-driven analytics provide real-time insights into operational performance, allowing Mars to scale its manufacturing capabilities across multiple regions without sacrificing quality or efficiency.
- Supply Chain and Logistics Optimization: AI enhances Mars’ global supply chain by improving demand forecasting, optimizing logistics, and streamlining distribution channels. By leveraging AI to analyze vast amounts of data, Mars can make more informed decisions about inventory management, shipping schedules, and regional product distribution. This results in more precise planning, reduced costs, and faster delivery times for Mars’ products.
- Marketing and Consumer Engagement: AI helps Mars personalize its marketing efforts across diverse markets. Bogner shares a particularly innovative example from the company’s famous M&M’s brand, where AI-driven experiences allow consumers to interact with the beloved M&M’s characters through generative AI platforms. Consumers can now engage with these characters in real-time, creating a new layer of personalized brand interaction that deepens customer engagement.
- Corporate Communications: AI is also transforming Mars’ internal operations. Bogner explains how the company uses AI to translate corporate communications into different languages, ensuring that messages are consistent across its global workforce. This helps Mars maintain alignment across its operations, regardless of geographic location or language barriers.
The Shift to Generative AI: Unlocking New Capabilities
Bogner points to the growing impact of generative AI technologies, particularly large language models (LLMs) like OpenAI’s ChatGPT, which Mars has integrated into various business functions. These models enable more natural, conversational interactions between users and machines, providing enhanced capabilities for tasks such as customer service, content generation, and decision support.
Mars is already using these generative AI models to improve corporate communications, marketing content creation, and internal knowledge management. For instance, Bogner highlights how the company is utilizing AI-powered chat interfaces to help employees and stakeholders quickly access key information from Mars' enterprise systems, such as ERP and CRM platforms. This reduces the time spent searching for data and increases operational efficiency across the organization.
One of the most exciting applications of generative AI, according to Bogner, is its potential to revolutionize how employees interact with complex, legacy systems. By front-ending these systems with natural language interfaces, such as ChatGPT, Mars is making it easier for employees to access and use critical data, thereby improving decision-making and streamlining workflows.
Ethical AI Governance: Safeguarding Trust and Accountability
A significant portion of Bogner’s presentation is dedicated to the ethical implications of AI. Mars has developed a comprehensive AI governance framework, which includes an AI Council and working groups responsible for overseeing the responsible deployment of AI technologies across the company. This governance structure ensures that AI initiatives at Mars adhere to strict ethical guidelines, including principles of fairness, transparency, and privacy protection.
“We’ve instituted an AI working group and an AI Council to ensure that every AI project aligns with our principles,” Bogner says. Mars evaluates each AI initiative based on a set of criteria that includes bias detection, accuracy, transparency, and the prevention of harm. Bogner emphasizes that maintaining human oversight, or what he calls the “human-in-the-loop” approach, is critical to ensuring that AI systems deliver accurate, reliable, and fair outcomes.
Mars’ responsible AI principles are particularly important as the company continues to expand its use of generative AI and machine learning models. By embedding ethical guidelines into every AI deployment, Mars is able to mitigate risks such as algorithmic bias, privacy violations, and unintended consequences. “We don’t just want AI to be effective; we want it to be responsible,” Bogner states.
Looking to the Future: The Evolution of AI in the Enterprise
Bogner concludes his presentation with a forward-looking view of AI’s potential within the enterprise. He believes that the future of AI lies in greater collaboration between humans and machines, with AI serving as an enabler of human creativity and problem-solving. He predicts that as AI systems become more advanced, particularly in their ability to interpret natural language and provide contextually relevant information, the need for human oversight will evolve.
“Human-in-the-loop will remain critical, but AI will continue to get better at understanding us,” Bogner says. He points to the growing importance of prompt engineering, a process where humans provide instructions to AI models to guide their outputs. However, he predicts that AI will soon become sophisticated enough to handle more complex tasks with minimal human input, creating new opportunities for businesses to scale their operations.
Bogner also highlights the rise of domain-specific AI models, known as Small Language Models (SLMs), which he believes will play a crucial role in the future of enterprise AI. Unlike general-purpose models, SLMs are tailored to specific business functions, such as supply chain management or marketing, making them more cost-effective and easier to manage. “SLMs will allow us to focus AI on specific business needs, delivering greater value with fewer resources,” he predicts.
A Strategic, Principle-Driven Path to AI Integration
Lee Bogner’s presentation at the 1ArtificialIntelligence conference provides a comprehensive roadmap for large organizations seeking to integrate AI in a way that aligns with their strategic goals and ethical responsibilities. Mars’ approach to AI serves as a model for how companies can successfully implement advanced technologies while maintaining a focus on human oversight, responsible innovation, and long-term value creation.
By following the POST methodology, aligning AI initiatives with core business objectives, and embedding ethical principles into every deployment, Mars is unlocking the full potential of AI while safeguarding the trust and accountability that have defined its operations for over a century. As AI continues to evolve, Bogner’s insights offer a clear guide for organizations navigating the complexities of AI adoption, ensuring that technology remains a tool for growth, responsibility, and human collaboration.
