CHAPTER 1
Introduction to AI-Driven Value Management

The rise of value management as an established business practice has been decades in the making. Yet it remains an evolving and dynamic field, as our businesses continue to modernize and digitize in surprising new directions.

As a result, long standing dogmas for selling products and services to business customers are being shattered in the process.

In this chapter, we'll briefly explore how value management came to be as a successful enterprise selling strategy and then more recently as a driver of marketing campaigns, customer loyalty programs, and partner ecosystems. We'll look at how more companies today are demanding better returns from their business investments, and we'll examine the tectonic shift in customer relationships that is forcing sellers in nearly every industry to deliver tangible and recurring business value to the buyers of their products.

Finally, we'll introduce the game-changer: Artificial intelligence (AI) in all its forms and its potential for bringing the selling power of value management to more parts of the business, at lower cost, and at unprecedented speed and scale. We conclude with this book's bold thesis: That combining the power of AI with a state-of-the-art value management approach can empower businesses to realize 8X revenue outcomes.

A Brief History of Value Management

In the late 1940s, managers at the emerging industrial powerhouse General Electric (GE) were searching for ways to maximize business value by optimizing how the company deployed its scarce raw materials and human resources.1 The methods developed by GE were refined into a formal methodology and the term value management was born. Since then, value management has steadily grown in popularity, becoming a highly useful sales technique for many companies seeking to sell complex, high-cost products and services to customers in a wide range of industries.

In recent decades, the practice has continued to attract followers among business-to-business (B2B) companies, especially in the high-technology arena. We've seen the emergence of this trend firsthand working for large systems implementers and high-tech companies since the late 1990s. Without realizing it, we found ourselves on the front lines of the tech wars, helping our clients take advantage of the latest value management methods. By then, information technology (IT) had risen to the status of strategic investment for more and more enterprises, and the chief information officer (CIO)—finally—had gained a seat at the executive table. With large sums at stake, corporate leaders began asking an obvious question: Exactly how much business value are we going to get from these massive technology investments?

To keep their customers happy and their sales surging, technology vendors were determined to answer that question. Many chose to hire teams of management consultants (sometimes referred to as business value consultants or business value engineers) to get a monetary handle on the business value of their products and thus gain access to executive buy-in. These teams specialized in translating technical specifications and product features into the language of business outcomes. The customer value assessments they produced—commonly called business cases—soon became an integral part of the sales cycle for the company's most valuable prospects. The consultants often worked closely with sales leadership and the executive team to help customers justify large-scale technology investments and support their customers' capital investment process.

By the 2010s, the practice of value management had become a widely accepted sales overlay, a team of specialists supporting the sales team. The success of these teams in closing the largest and most strategic deals started attracting new converts in product marketing and in customer success organizations. These business functions were interested in building value messaging “upstream,” where it could inform the customers early in their decision-making process, as well as “downstream,” where it could drive subscription renewals, broaden the customer life cycle, and boost revenue streams.

However, in recent years, many value management initiatives have hit a budget wall. Stubbornly high costs—especially the expense of using highly paid business value consultants—have been a barrier to growing value management programs or expanding into other functions. Fragmented organizational structures—and their associated politics—can also make it difficult to expand these initiatives to other functions, such as marketing, professional services, and customer success. What's more, although more companies have access to robust digital tools to streamline customer research and analyses, they are often deployed in piecemeal fashion and fall short of reaching the overall goals of these platforms. Taken together, these costs and inefficiencies can create structural impediments to scaling value management programs across the enterprise and enabling companies to capture the potential of end-to-end value management.

Then, seemingly overnight, the world changed. The arrival of generative artificial intelligence (GenAI) in the form of ChatGPT and a plethora of other AI technologies and apps sent shockwaves through the digital economy and sparked a full-fledged cultural phenomenon. There appears to be no limit to AI's potential to radically disrupt the way companies do business, from designing and manufacturing products exponentially faster to providing customer service that is uncannily human.

Companies seeking to build an enterprise value management program will be prime beneficiaries of the AI revolution, adding incredible speed, automation, and economies of scale to what are currently highly manual, error-prone, and costly processes. To introduce the opportunity of AI, let's look back at how far we've come with this technology.

The Evolution of AI

As early as the 1950s, rule-based or classical AI was developed for symbolic manipulation and logic to mimic human decision-making. The 1980s saw the advent of neural networks, which introduced the concept of learning from data by simulating the interconnectedness of neurons in the human brain. And since the early 2000s, with the availability of computing power at lower and lower costs, we've seen rapid strides in predictive AI or machine learning. This has enabled us to build algorithms that learn from data to make predictions or decisions without being explicitly programmed.

Though it has been around for more than a decade, GenAI burst into the limelight in 2023. GenAI can generate new content such as images, text, or music, often indistinguishable from human-created content. Another emerging area of interest is agentic AI, also called autonomous AI, which can act independently, making decisions and taking actions in complex environments without human intervention. The AI capability that can create scale for value management today is a combination of predictive AI and GenAI.

For now, GenAI is in its infancy. But we already can envision how and what this new technology can do to generate a new wave of opportunity for value management. As companies struggle to extend their value management programs to the whole enterprise, GenAI offers a practical solution that any B2B company, following a set of proven strategies and techniques, can master.

This book provides that blueprint. It begins with examples of currently successful value management programs and shows how GenAI solutions can take these programs further, empowering more parts of the business and unlocking significant new revenue opportunities. This book also draws a road map highlighting key phases of an ideal deployment that ensures successful business outcomes and minimizes common traps that we've seen delay or stall previous value management initiatives.

HOW GENERATIVE AI POWERS VALUE MANAGEMENT

Wikipedia defines GenAI as “artificial intelligence capable of generating text, images, videos, or other data using generative models, often in response to natural language prompts.” GenAI's role in OpenAI's blockbuster ChatGPT is just one example of how this technology is redefining society. It is now finding its role in a myriad of business applications, including marketing, sales, and more recently, value management programs. For example, if integrated well into a company's existing sales workflow, GenAI capabilities can help a B2B seller to:

• Automate email responses and actions
• Summarize documents, text, and videos
• Document question and answer (Q&A)
• Analyze and aggregate data
• Visualize data (e.g., charts, infographics)
• Convert text to speech
• Inform/provide Q&A for images and charts
• Convert text to images
• Convert text to collaborative workflows
• Convert text to code actions
• Analyze sentiment
• Translate communications
• Review and summarize documents

These AI capabilities and thousands more can be customized to match the unique workflows of different industries and integrated into existing sales operations. Its potential for automating and accelerating critical tasks can help reduce costs and enable organizations to successfully scale...