How Managers Can Benefit from Multi-Modal AI Business Companions

by Daniel J. Finkenstadt, Vijay Govindarajan, Andre Martin, and Tojin Thomas Eapen

How Managers Can Benefit from Multi-Modal AI Business Companions

Image Credit | Héctor J. Rivas

A guide for crafting engaging, impactful AIBCs to amplify your ideas.

Business books have long suffered from limitations that reduce their effectiveness in driving real-world application. Consider a CEO who has just finished reading a new business strategy book. Although the book provided rich examples and thought-provoking models that stimulated her thinking about potential market moves, she struggles to translate these high-level insights into concrete actions for her daily decision-making. The gap between theory and practice leaves her frustrated, unable to fully leverage the book’s wisdom to tackle her company’s specific challenges.

This scenario is all too common. Business professionals often find it difficult to bridge the divide between the general principles outlined in books and the unique, complex realities of their own organizations. Without tailored guidance on how to adapt and apply strategic concepts to their specific contexts, they may fail to realize the full value of the insights they have gained.

As an author of a business strategy book, you face a related challenge. While you hope your book resonates with business leaders and helps companies succeed, it is impossible to address every scenario your readers might encounter. Personalized, one-on-one advice could greatly enhance the impact of your ideas, but the inability to scale this level of support means many readers will be left adrift, reducing the overall effectiveness and reach of your work.

Imagine, however, equipping your book with an AI-powered companion - a conversational agent trained on the specific context and teachings of your book, available to readers 24/7. This AI Business Companion (AIBC) would be capable of interpreting readers’ questions, understanding their unique business challenges, and offering tailored, actionable advice based on your book’s frameworks and strategies. By providing this critical bridge between theory and application, AIBCs can greatly amplify the impact of your ideas, enabling readers to successfully leverage your insights to drive real change in their organizations.

The rise of Generative AI has now made such AIBCs a reality. There is immense global interest, among authors and readers alike, in the transformative potential of books that can converse with and guide readers. However, practical knowledge on how to develop and implement these innovative tools remains limited.

In our business research, consulting, and solution design work, we have found AIBCs to be highly effective for conveying complex ideas and strategies. This article draws upon our experience to showcase the potential of this innovative approach. By providing guidance to both authors and business leaders on creating impactful AIBCs and sharing best practices we have developed, we demonstrate how these conversational agents can dynamically engage readers and help them better understand and apply business concepts to their unique contexts. The AIBCs we have created for our own projects have extended the reach and impact of our work. We believe this powerful approach has immense potential, and hope this article inspires authors and managers alike to harness AIBCs to maximize the value their business research and ideas can provide.

Conversing with Publications Using GenAI

Generative AI (GenAI) has become a ubiquitous topic of discussion since OpenAI launched ChatGPT in November of 2022. ChatGPT was the most-read topic on Wikipedia for 2023 with nearly 50 million page views.1 This technology opened the world of large language models and generative AI to every person with a computer and an internet connection. The coding barrier has been broken. Since its launch, we have seen the proliferation of various generative AI models covering every genre of creativity, audio, video, imagery, and text, in every conceivable use case. Then, a year after the launch of ChatGPT, OpenAI launched “GPTs”. GPT is OpenAI’s label for its no-code customizable tool that allows users to build their own version of a blend of generative AI text, imagery, and data models based on simple prompts.2 These models can be tailored for use. They can be developed to assist users in planning travel, building case studies, conducting scenario planning, etc.3 Coincidentally, they can also be designed to serve as a guide for interpreting and learning from publications.

Artificial intelligence has been used in various aspects of publications for some time, from customizing content and conducting fact-checking to generating artwork and figures. However, its potential to revolutionize how business leaders and managers engage with and learn from publications remains largely untapped. By equipping their publications with custom AI conversational agents, authors can engage readers who might otherwise be intimidated by the source material, require additional clarification, or struggle with concept application. This not only helps disseminate the author’s ideas to a broader audience but also facilitates deeper, more meaningful learning experiences.

The benefits extend to both readers and authors. For readers, conversational AI enhances accessibility, breaking down complex concepts into digestible, contextualized guidance. It allows them to tap into the publication’s insights on-demand, as they encounter real-world challenges in their work. For authors, AI apprentices offer a scalable way to magnify their impact, providing personalized support to readers that would be impossible through traditional means. By leveraging the vast knowledge base of language models, these AI agents can also help authors explore new contexts and applications of their ideas, expanding the scope and relevance of their work. Moreover, as new information emerges and business realities evolve, conversational AI allows for the dynamic updating and reuse of a publication’s core insights, ensuring that the wisdom remains fresh, relevant, and applicable for readers navigating an ever-changing business landscape.

The launch of multimodal generative AI like GPT-4o has further transformed the potential of these AI assistants. Users can now engage in audible conversations with the AI agent, simulating a lifelike interaction akin to consulting with an expert colleague or mentor. This voice-based interface enhances the immediacy and relatability of the experience, making it even easier for business professionals to seek guidance and clarification in real-time. Simultaneously, the AI maintains a textual record of the exchange, allowing users to revisit key insights and advice post-hoc. This combination of spoken interaction and written documentation creates a uniquely powerful and flexible learning tool, empowering business leaders and managers to more fully integrate the wisdom of publications into their daily practice.

Leveraging AI Assistants for Business Advantage and Monetization

AI assistants powered by leading business publications offer a unique opportunity to enhance accessibility, user engagement, and learning outcomes. By providing personalized guidance, context-specific examples, and multi-lingual support, these assistants make complex concepts more digestible and applicable for a global audience.

For authors and publishers, AI assistants represent a scalable solution to drive deeper understanding and application of a publication’s insights. This enhanced engagement can lead to increased readership, customer loyalty, and new revenue streams through subscription-based access, value-added services, or licensing opportunities.

Monetization strategies may include:

  1. Tiered access: Offer basic Q&A functionality for free while providing advanced features like personalized coaching, in-depth analysis, or industry-specific applications for a premium.

  2. Licensed integration: Allow businesses to integrate the AI assistant into their internal training and decision-making processes for a fee.

  3. Sponsored content: Partner with companies to create sponsored content or use cases showcasing how the AI assistant can solve specific business challenges.

  4. Affiliate marketing: Recommend relevant products or services within the AI assistant’s responses, earning a commission on resulting sales.

For business users, AI assistants offer a powerful tool to stay current, relevant, and adaptable in the face of evolving challenges. By providing on-demand access to the latest insights, trends, and best practices, these assistants empower professionals to make informed decisions, develop innovative strategies, and drive growth in their organizations. For authors and publishers, the continuous updates to the AI assistants ensure that their content remains valuable and sought-after, positioning them as thought leaders and trusted advisors in their respective fields.

A Ten-Step Approach to Building AI Business Strategy Companions

When designing a custom AIBC for a specific business strategy publication, several critical design elements must be considered to ensure the tool effectively guides users in applying the publication’s insights to their unique business challenges. This section outlines a structured 10-step approach to building such interactive assistants.

  1. Align with Business Objectives: Tailor the AI model to align with the core business strategies and frameworks outlined in the publication. Ensure the assistant is designed to help users navigate complex business challenges and identify opportunities for growth and innovation.

  2. Curate Relevant Training Data: Choose training data that accurately reflects the publication’s key principles, case studies, and actionable insights. Ensure the data set is diverse and representative of various business contexts to maximize the assistant’s relevance and applicability.

  3. Prioritize Ethical Business Practices: Embed ethical considerations into the AI’s decision-making processes, ensuring it guides users towards strategies that prioritize integrity, sustainability, and social responsibility. Implement safeguards to prevent the AI from recommending actions that could harm stakeholders or compromise ethical standards.

  4. Customize for Business Domains: Customize the AI to specialize in specific business domains covered in the publication, such as marketing, finance, or operations. This enhances the assistant’s ability to provide targeted, domain-specific guidance and insights.

  5. Ensure Strategic Coherence: Focus on maintaining the coherence and logical consistency of the AI’s recommendations. Ensure the assistant can guide users through complex decision-making processes, helping them evaluate trade-offs and prioritize actions that align with their overall business strategy.

  6. Design for Executive Engagement: Create an intuitive and engaging user interface that caters to the needs and preferences of business leaders and executives. Ensure the assistant can communicate insights and recommendations in a clear, concise, and persuasive manner.

  7. Maintain Transparency and Accountability: Be transparent about the AI’s role as a strategic advisor and the limitations of its recommendations. Encourage users to critically evaluate the AI’s suggestions and make informed decisions based on their own judgment and expertise.

  8. Foster Continuous Learning: Continuously update the AI model to incorporate new business trends, case studies, and user feedback. Engage subject matter experts to validate the assistant’s recommendations and ensure its ongoing relevance and effectiveness in driving business outcomes.

  9. Mitigate Business Risks: Implement measures to minimize the risks of the AI recommending strategies that could expose businesses to legal, financial, or reputational harm. Regularly audit the assistant’s outputs to identify and address any potential risks or unintended consequences.

  10. Measure and Optimize Business Impact: Track and analyze the real-world business impact of the AI assistant’s recommendations. Use this feedback to refine the AI model and optimize its ability to guide users towards strategies that drive measurable improvements in key performance indicators. Regularly assess and communicate the value the assistant delivers to the business to justify ongoing investment and support.

An Example of AI-assisted Publication Conversations

We also built an exemplar business publication AIBC using the information found in Vijay Govindarajan’s HBR Top 50 article, “The CEO’s Role in Business Model Reinvention.” Using OpenAI GPTs, we built the “3-Box Solution Sage” Using this GPT we can explore the article’s concept as seen below starting with this prompt:

Can you summarize the 3-box scenario in the Hasbro case?

3-Box Solution Sage output

The Three-Box Solution is a strategy framework for managing an organization's present and future growth, proposed by Vijay Govindarajan and Chris Trimble. It involves balancing three key tasks:

  1. Box 1 - Manage the Present: This box focuses on the core business and emphasizes performance excellence in current operations. It's about making the existing business model as efficient and effective as possible.
  2. Box 2 - Selectively Forget the Past: This involves letting go of past practices and old success models that are no longer effective or relevant. It's about challenging and overcoming legacy processes and mindsets that can hinder future growth.
  3. Box 3 - Create the Future: This is about innovating and developing new business models, strategies, and growth avenues. It requires investing in research and development, exploring new markets, and being open to revolutionary ideas.

Examples from the article:

  • Infosys Technologies Limited: A prominent example in the article is Infosys Technologies. They managed their present (Box 1) by excelling in their core service of custom software development. For Box 2, they engaged in selective forgetting by adapting to new market demands and shifting from just software services to providing end-to-end IT solutions, which involved rethinking their business model. For Box 3, creating the future, they invested in new ventures like Infosys Consulting, which marked a departure from their traditional business into management consulting.
  • IBM’s BlueGene Project: IBM’s BlueGene project illustrates Box 3. IBM hypothesized that future supercomputers would rely on networks of ordinary chips rather than a single super-fast chip. This required a significant departure from existing computing paradigms and involved running disciplined experiments to test new ideas, embodying the essence of Box 3.
  • The High Jump Analogy: The article uses the evolution of the high jump technique in athletics as a metaphor. The transition from the "scissors" jump to styles like the "western roll," "straddle," and finally the "Fosbury Flop" mirrors the process of moving from Box 1 (perfecting the existing technique) through Box 2 (abandoning traditional methods) to Box 3 (creating innovative approaches for better performance).

These examples illustrate how organizations can navigate the challenges of current operations while strategically positioning themselves for future success by balancing the demands of these three boxes.

And we can also use the GPT to apply general concepts from this top 50 article to our own targeted business situation as shown below.

We prompted the GPT to provide enriched assessments of the 3-box solution to our hypothetical firm that provides clean drinking water to global markets and provide the output in table form.

3-Box Solution Sage output

Additional Considerations for AquaGlobal

  1. Sustainability: As an organization providing an essential resource like water, AquaGlobal needs to integrate sustainability into every aspect of its operations, from sourcing to distribution.
  2. Community Engagement: Engaging with local communities and understanding their specific water needs can lead to more effective and culturally sensitive solutions.
  3. Regulatory Compliance: Staying abreast of global and local environmental regulations and ensuring compliance is crucial for long-term operations.
  4. Technology Integration: Leveraging technology for better water management, prediction of water needs, and efficient distribution systems will be key in future growth.
  5. Global Trends: Understanding global trends in climate change, water scarcity, and environmental policy will help AquaGlobal stay ahead in creating future strategies.

Other potential use cases

The possibilities are nearly limitless for multi-modal AIBCs. We offer a few business use cases below that we have already successfully tested or are planning to test soon:


Business authors have long struggled to provide specific, tailor-made advice to managers at scale, but the advent of custom AI models is set to revolutionize the industry. In the near future, having an AI companion for each publication will not be a novelty but a consumer expectation and industry standard. These AI assistants will transform how businesses leverage the insights from leading publications, enabling them to access personalized guidance, explore new applications, and stay ahead in an ever-evolving landscape.

For authors and publishers, conversations with publications will change how they enhance accessibility, learning, and reusability in their products. By embracing this technology, they can expand their reach, deepen reader engagement, and unlock new monetization opportunities. We are on the bleeding edge of this concept today, but it is likely to be foundational in the very near future. In fact, we have a GPT built for this article already to help you consider its uses for your own publication.

Link: GPT Business Book Advisor





Daniel J. Finkenstadt
Daniel J. Finkenstadt Dr. Daniel J. Finkenstadt is an Assistant Professor of Enterprise Sourcing at the Naval Postgraduate School, Monterey, CA. He earned his PhD in Marketing from UNC Chapel Hill. Dr. Finkenstadt teaches courses and publishes papers related to innovation, defense acquisition, supply chain and category management.
Vijay Govindarajan
Vijay Govindarajan Vijay Govindarajan is the Coxe Distinguished Professor at Dartmouth’s Tuck School of Business and Faculty Partner at the Silicon Valley incubator Mach 49. He is the author of The Three Box Solution. Govindarajan is one of the world’s leading experts on strategy and innovation and a two-time winner of the prestigious McKinsey Award for the best article published in the Harvard Business Review.
Andre Martin
Andre Martin Dr. Andre Martin, Assistant Professor of Marketing at Notre Dame's Mendoza College of Business, specializes in marketing strategy and privacy. His expertise extends to artificial intelligence, machine learning, and natural language processing, focusing on innovative text analysis applications.
Tojin Thomas Eapen
Tojin Thomas Eapen Tojin Thomas Eapen is Assistant Professor in the Marketing Department at the Robert J. Trulaske, Sr. College of Business, University of Missouri - Columbia. His work is in idea management, digital usability, sustainable design, and innovation.