C-Level Conversations - From Panic to Plan: Building an AI Strategy That Works for Your Business

For this article, rather than some stock art at the opening.  I thought I would say hello and thank you for your time and interest in what I have to share. I really do appreciate it. Yes – that is me!  No tricks, no gimmicks.  Well, maybe a hint of the iPhone’s magic wand.

For those who don't know me well, I wear two hats—one as a fractional senior executive with a leading enterprise solutions company and the other as a consultant and advisor, rolling up my shirt sleeves and working directly with trailblazing clients who challenge the limits of innovation to create marketplace impact.

As part of my day, I have the privilege and opportunity to constantly learn and engage with business leaders and teams, guiding them in understanding the evolving business landscape, fostering value-driven innovation, and strategizing solutions to address their most pressing business needs and reach their goals. 

It would not be a stretch to say AI is at the top of many leaders’ minds today. Let’s dive into a recent conversation with a CEO from a mid-sized company contemplating the best way to kick-start his company’s AI journey. Of course, I can’t disclose the specific client, brand names, or services as that would violate professional confidentiality. Still, the presentation of this discussion with some adjustments and liberties may be helpful to others who need an AI strategy and don’t know where to begin.

I’ve known and worked with this CEO successfully for years. The company has built an impressive business—offering products and services that serve a critical and leading role in their unique and now data-intensive industry.  

The company’s leadership team is strong and among the best in their field.  Much of their leadership has grown up with the company.  They have great systems and business processes.  Their financial performance is strong, with year-over-year growth exceeding industry benchmarks and healthy margins, supported by a solid balance sheet and robust positive cash flow.   By all measures, the company and CEO are market segment leaders and executing at a high level.

The CEO had asked me to meet to discuss a pressing issue and wanted me to share my thoughts and insight, explaining this conversation wasn’t about a new product launch, market expansion, scaling an operational area, selecting enterprise tools, fixing a struggling project, or a competitor making waves. This time, the CEO was feeling pressure—from his Board, major investors, employees, and himself to develop and execute an AI strategy.  I asked a few questions to understand more, but was asked just to come in and we could talk more.  As always, I prepared for the meeting by completing some research to see what their industry, adjacent industries, competitors, and customers were doing or not doing related to AI.

A few days later, we met at the CEO’s office.

“Hi Greg, you know us, and I need your perspective.  We need an AI strategy,” he said, pausing and, for effect, continued. “…and we need it now. Where should we start.”

Like many savvy executives, the CEO understood the importance and influence AI was starting to play in the marketplace, and his technology teams had some early-stage projects underway. Still, he wasn’t entirely sure what it meant for his company. AI was everywhere around him—investor calls, competitor announcements, industry keynotes, and now even a few applications in his office suite – although he shared, he had no idea how to use them effectively.

AI was just another tech trend to the CEO, along with some marketing buzz.  The CEO was uncertain if there was a tangible way to drive significant company and customer value from AI. He wasn’t sure at this point AI was a worthwhile investment for his company and “didn’t want to do AI just for the sake of it as a rubber stamp and marketing fluff that offered no real customer value.” He also “did not want to put his company at risk of falling behind the market.” However, he felt they already may be.  Or worse yet, he did not want to “pour money and resources into a big black hole.”  The CEO also had limited confidence in his team’s current experience, expertise, and knowledge of AI, “how could they if we haven’t made significant strides forward.”

As mentioned, this wasn’t our first strategic conversation, and I knew the CEO’s company, products, services, and people well. Over the years, they built an impressive business and became a rising mid-market powerhouse. Still, they also tended to chase technology fads without always aligning investments with real business value. They had completed several large “me too” projects without much bottom-line impact—investments made simply because others were doing them. When you dominate an industry, are growing rapidly, and have solid margins, you can get away with a few R&D flyers here or there that don’t fully pay off.  The board and investors will give you a pass.  This time, the CEO said, “feels different,” and “wanted to, no, had to get it right.”

After listening to the CEO’s concerns and thoughts, I inquired about their current AI efforts.  “We recognize AI as a transformative force and a key priority for our future growth. As we evaluate opportunities to integrate AI across our operations, we focus on leveraging automation, predictive analytics, and enhanced customer engagement to drive efficiency and create value. We are committed to a thoughtful, responsible approach—balancing innovation with governance—to ensure AI strengthens our market position and supports long-term, sustainable growth.”  The CEO then named a few pilot programs and their status.

After the CEO finished, I remarked that it sounded like a paragraph from a shareholders’ meeting or annual report but provided no real substance on their current strategy. 

I asked directly if they had any form of an integrated strategy or plan beyond the pet projects and who was tasked with driving the current efforts at an executive level. 

After some additional discussion and posturing, the company had no unified AI strategy, no one on an executive level was driving within their scope of responsibility, and possessed limited internal practical knowledge. He also shared that there is some organizational posturing and in-fighting between the business, customer areas, and technology, with risk and compliance recently starting to weigh as to who should own.  A  few years ago, they faced a similar challenge related to data and information. I guided them on the strategic value of creating a Chief Data Officer (CDO) role, setting clear objectives for the CDO grounded in a purpose-driven strategy with measurable goals and objectives. I helped them recognize information and its processes as a competitive, renewable asset they could capitalize on and monetize - and they did.

This conversation had some similar overtones but was different in many ways.  The CEO was seriously concerned and sensed the entire business landscape was about to change rapidly. The CEO was looking for structure and where to start and asked directly, “Where should we focus our resources, and what are others doing?”

How to Classify AI - The Four Buckets

As they were at the starting line of their AI journey, rather than diving into use cases, market and competition information, organizational structure, and technical details as a starting point, I wanted to provide him with a practical framework for thinking about AI.  The appointment of a business leader and organizational structure could come later. 

I find that most companies approach AI reactively—either looking at what competitors are doing or testing out and experimenting with tools in isolation and fitting the business to the solution. Rarely do they step back and develop a comprehensive strategy built on business goals and objectives.  In many ways, organizations’ AI strategies, or lack thereof, remind me of the “build it, and they will come” or “put my head in the sand and pretend it won’t impact me” mentality of the Dot Com days. We all know how that worked out for many companies.

I began by explaining that I classify AI at most companies into four distinct categories, all essential to any strategy.

Figure 1 – Four Buckets (Categories) of Organizational AI

Bucket One - Embedded AI - The AI You Already Have

This category refers to the AI capabilities already integrated into an organization’s existing technology stack—whether in ERP, CRM, security tools, desktop applications, or cloud infrastructure. Many enterprise software providers, including IBM, Microsoft, Google, Amazon, Meta, Workday, SAP, Oracle, Salesforce, Adobe, ServiceNow, and Check Point Security, embed AI into their platforms to enhance business operations. Look at the marketing of almost any solution in a business or technical landscape, and there is usually some form of AI.  Yet, most companies fail to recognize or fully utilize these built-in AI features.

Too often, organizations overlook what they already have and instead focus on acquiring new AI tools without first leveraging the AI embedded within their existing systems.

I pointed out several examples from their business and technology landscape.

• AI-enhanced CRM analytics and intelligent agents could improve sales forecasting and automate customer service.

• AI-powered ERP automation could streamline operations, optimize cash flow, and create seamless employee and customer experiences.

• AI-driven security tools could improve threat detection by identifying anomalies in user behavior.

• AI features in their office productivity suite—such as automated email prioritization, document summarization, automated meeting transcripts and action items, and predictive text. Besides, employees outside of a few IT employees, including the CEO, did not utilize these features.

This lack of awareness is common.

Awareness and Adoption

Despite having access to AI within purchased products and services, most companies struggle to take advantage of it due to:

• Lack of awareness – Employees and leadership don’t realize these AI capabilities exist in their current systems.

• Insufficient training – Teams aren’t equipped to use AI-driven functionalities effectively.

• Resistance to change – Many employees stick to traditional workflows instead of adopting AI-enhanced processes.

• Staying current on purchased services and products – These are changing fast.

Many companies already own powerful AI-driven capabilities but don’t stay up on the vendor’s roadmap or continuously train their teams to use the latest features effectively.

How Companies Can Maximize Embedded AI

1. Conduct an AI Audit – Identify AI functionalities already available within enterprise software.

2. Increase Awareness – Continuously educate employees about embedded AI features and their benefits.

3. Enable Usage – Provide hands-on training and ensure AI tools are integrated into workflows.

4. Measure Impact – Track AI-driven improvements in efficiency, decision-making, and automation.

As an essential first step, organizations should assess how they are (or aren’t) leveraging the AI already embedded in their systems. Many companies are sitting on untapped AI potential that could provide immediate business value—often at no additional cost. The key is not just having AI and knowing how to use it.

I emphasized this point to the CEO by asking, “You’re already paying for AI in many places, but do you know about it, and are you using it to get value out of every dollar you spend on these solutions – I bet your competition is or will?”

Bucket Two - Informal & Shadow AI - a.k.a. Bring Your Own AI (BYO AI)

This is the Wild West of AI—the tools employees already use that the company may not officially support, regulate, or even know about.

Informal AI adoption is happening organically, often through ChatGPT, Perplexity, Gemini, Google AI Studio, AI-driven, and the endless parade of AI-based research and productivity applications that help employees create content, presentations, and reports, analyze and summarize data and information, write reports, automate task, and more. With the rise of remote and hybrid work environments and powerful mobile phones with fast connectivity, the use of informal & shadow AI is accelerating as employees seek ways to work more efficiently.

I emphasized this point to the CEO.

“I guarantee people in your company are using AI. And if you don’t have a policy around it, you have a risk management problem. If the AI they use is beneficial, do you want that siloed—or brought out into the open so the entire organization can learn from it and flourish?”

The Double-Edged Sword of Informal AI

Informal AI use isn’t inherently bad. It often unlocks significant productivity gains and provides a glimpse into the future of work. Employees who adopt AI organically are usually creative thinkers and problem solvers, finding ways to improve efficiency, automate repetitive tasks, and enhance decision-making. However, without governance, BYO AI can introduce significant risks:

• Security vulnerabilities – Employees may unknowingly share sensitive or proprietary information with external AI platforms.

• Compliance concerns – AI-generated content could violate industry regulations, data privacy laws, or company policies.

• Intellectual property risks – Employees may use AI-generated content that unintentionally infringes on copyrights or licenses.

• Misinformation risks – AI tools sometimes generate inaccurate or misleading content, leading to potential errors in reports, analysis, or decision-making.

How Companies Can Approach Informal AI

Instead of shutting down AI use outright, the first step is understanding how it’s being used. This means engaging employees in a collaborative conversation:

1. Encourage Transparency – Ask employees what AI tools they use, how they help, and what problems they solve.

2. Make It Fun – Approach this with curiosity rather than immediate restriction. Ask:

   1. What AI tools do you use?

   2. How did you discover them?

   3. What value do they provide in your day-to-day work?

3. Assess Risk vs. Reward – Some informal AI tools might be valuable enough to formalize and incorporate them into company-approved solutions. In contrast, others pose risks that require clear guidelines or restrictions.

4. Develop a Policy, Not a Ban – Create a structured AI usage policy that:

   1. Identifies approved vs. restricted AI tools.

   2. Defines what data can and cannot be shared with AI systems.

   3. Encourages experimentation within a safe framework rather than stifling innovation.

   4. Rewards new ideas and recommendations.

Informal AI use is inevitable—the real question is whether an organization chooses to ignore, resist, penalize, or harness it. The best approach is to acknowledge, learn, nurture, and guide it rather than letting it operate in the shadows.

Remember, a company’s early AI adopters are often its most innovative and overlooked employees, closest to the work and the customer—understanding how they use AI can unlock untapped efficiencies, reduce risk, and create a culture of responsible AI adoption.

Bucket Three - Incremental or Feature-driven AI Enhancements - AI-Driven Product & Service Improvements

This is where AI starts delivering clear, direct, measurable business value to your customers and the value of your business by enhancing existing processes, products, and services.

Rather than pursuing disruptive, large-scale AI initiatives, this category focuses on practical, business-driven improvements that optimize workflows, reduce inefficiencies, and enhance customer experiences. These enhancements are not about reinventing the business but making existing products, services, and processes more intelligent, faster, and effective. 

Most companies see tangible, measurable benefits from these AI enhancements. These incremental improvements often align well with business objectives. They can frequently be tracked through key performance indicators (KPIs), SLA reductions, and standard business metrics from Accounting 101, making them a measurable, low-risk opportunity to introduce AI internally and externally. They are not unlike the projects on your strategic roadmap today across your company.

I summarized this concept to the CEO, "Think of this as AI that improves your existing processes, products, and services. Not revolutionary, but evolutionary.  These opportunities can be found by looking across your existing value chain.”

For those unfamiliar with a value chain, it is the end-to-end sequence of activities and processes a company performs to create, deliver, and capture value, transforming inputs into products or services that generate competitive advantage and customer satisfaction. It encompasses everything from the initial design and sourcing of materials to the final delivery to the customer, with each step adding value to the product or service and contributing to its overall market worth. Essentially, it is the entire lifecycle of a product or service. [1]

To provide a jumpstart, I offered some initial AI ideas to consider.  We discussed them in specifics related to their company, products, services, and processes.  Unfortunately, I can’t share those details. 

• More intelligent search functionality – AI-powered search to deliver more relevant, contextual results, improving user experience and helping customers and employees find and provide information and answers faster and independently.

• Self-service AI agents – Virtual assistants reduce hold and response times by providing brand-consistent product and service support, answering customer inquiries, offering account information, and freeing human agents for more complex issues.

• AI-enhanced sales process – Sales coaching, prospect identification, personalized messaging, qualification and lead scoring to drive targeted activities, and sales automation to make the sales cycle more effective.

• Advanced AI and ML data analysis – Enhance the speed and accuracy of data processing across all business areas, providing faster insights and recommendations for decision-making.

• Automated regulatory reporting – Streamline compliance tracking and regulatory submissions, reducing manual effort and minimizing errors.

• Personalized marketing and recommendations – Look at customer segmentation in new ways and then provide personalized content and recommendations to increase engagement and conversion rates. 

• Content creation – Create, post, and distribute new marketing content automatically or rework and update existing.

• Enhanced workflow automation – AI automates manual, repetitive tasks, enabling employees to focus on higher-value work.

• Real-time translation services – AI enables instant multilingual communication, improving accessibility for global customers.  This may open up a world of non-customers that can now be reached.

• Document processing – Speed up contract and document analysis, invoice processing, and document data extraction and classification and reduce administrative overhead.

• Fraud detection and risk management – Continuously monitor transactions and system activity to detect anomalies and patterns to prevent fraud.

• Quality control – Enhance manufacturing and service quality monitoring by identifying real-time issues, defects, or inefficiencies.

The key takeaway for organizations is that AI adoption does not have to be an all-or-nothing proposition. Strategic AI enhancements, with a positive business case behind them, can drive measurable ROI and reasonable payback periods without requiring massive overhauls or disruptive changes. By starting with incremental improvements, companies can build internal confidence, gain stakeholder buy-in, and establish a strong foundation for more advanced AI-driven initiatives in the future.

This approach is especially effective when companies leverage and learn from the insights gained from the first two AI categories. I restated to reinforce the ideas.

1. Embedded AI (The AI You Already Have) provides a foundation of the existing capabilities that others are doing that the company can learn from in a tangible way – a training ground.

2. Informal AI (Bring Your Own AI - BYO AI) to reveal grassroots innovation happening organically within the workforce and company today.

These categories act as a catalyst for identifying high-impact AI opportunities that align with business goals and help put your company in the AI mindset.

Bucket Four - Net-New AI Products & Services - AI as a Business Model

This is the big leap—where AI is no longer just a feature enhancement or a tool for boosting efficiency but becomes the foundation of an entirely new product, service, or business model. This bucket represents a fundamental shift. One where AI enables companies to decouple, rethink, or create a new value chain by creating something new and better rather than merely improving what already exists.

At its core, this approach is about leveraging AI to expand beyond traditional business models and create new market opportunities, customers, and revenue streams.

Companies that pursue this strategy are not just using AI to enhance current operations. They are reimagining industries and redefining what’s possible. However, this often represents a company's highest-risk, most capital-intensive AI investment. Unlike investing in AI-enabled enterprise products and services, embedding AI into existing products, services, and processes by making incremental feature improvements, launching an AI-first business requires financial resources, specialized AI talent, and a long-term vision—often in uncharted territory.

I asked the CEO a critical question.

“What part of your business is most at risk from AI, and why or why not?”

This is where most companies fail to think big enough or outside of today. Too often, AI is viewed only as a means to improve efficiency and optimize existing operations. But AI’s most significant potential isn’t in refining the status quo—it’s in breaking market barriers, creating new demand and new markets, and reaching new customers in ways that weren’t possible before. Companies that fail to consider this broader perspective risk being disrupted by AI-native competitors or substitutes who don’t share the same industry assumptions or constraints.

Many leaders make the mistake of assuming that their market will remain untouched. They believe that they are insulated from AI-driven change because they currently dominate their industry. However, history has repeatedly shown that market dominance is rarely permanent—especially when new trend-based innovation changes and competition rules emerge. The companies that thrive in the AI era will recognize AI’s potential to reshape industries and proactively build business models that align with the future.

Key Considerations for AI-Driven Business Innovation

To kickstart the conversation, I asked a few questions and discussed a few points for the CEO to consider. Tailoring them to the CEO’s company’s unique products, services, and operational processes. While I can’t disclose the specifics of our discussion, the following examples should offer a solid general framework. 

• Industry pain points – What is the dumbest thing your industry does or has not fixed?

• New customer segments – Are there non-customers who face similar challenges to your existing customers but aren’t looking for traditional solutions? AI might enable an entirely new way to serve them.

• Adjacent industry opportunities – Could AI lower the barriers to entry in a neighboring market, allowing you to serve new industries with minimal friction?

• AI-powered revenue models – Does AI allow for a shift from traditional product or subscription sales to AI-driven subscriptions, automation, or insights-as-a-service? Many companies that once sold one-time products now offer AI-powered ongoing services that provide continuous value.

• Be willing to cannibalize your existing business – Could you use AI to create new products and services, even if it disrupts your existing offerings? Many companies hesitate to do this—but failing to adapt doesn’t prevent disruption. It often just delays it.

You may notice a theme that these items closely align with the principles and thinking of Value Innovation. A proven strategy that can help businesses achieve differentiation and low cost simultaneously, breaking the trade-off between value and cost to create uncontested market space. [2]

The Critical Role of AI Readiness and Momentum

We explored exciting possibilities for AI-driven products and services, and it is nice to brainstorm about the future. I also stressed that companies should not skip the “fast track” and “quick win” foundational steps of AI adoption we discussed in buckets 1, 2, and 3.

Too often, organizations jump straight into ambitious AI business models without ensuring they have the right mindset, people, processes, funding, and organizational support following a Value Innovation process.

While all four AI categories can be pursued simultaneously, the companies that successfully launch AI-driven business models have already built a strong foundation through Embedded AI (The AI You Already Have), Informal AI (Bring Your Own AI), and Incremental AI Enhancements. These provide essential insights, experience, and internal readiness that can increase the likelihood of success in more significant AI initiatives and create a culture open to change and possibility.

I cautioned the CEO that a net-new AI business model may differ significantly from the company’s current structure, culture, and leadership approach. Leading this kind of transformation requires visionary leadership, strategic planning, and the willingness to rethink core business assumptions. It also demands organizational agility and fortitude, which many established companies struggle with. Unlike startups, which can build AI-first business models from the ground up, traditional and established enterprises must navigate legacy systems, entrenched processes, and cultural resistance to change.

This type of transformation can be incredibly challenging for currently successful companies. It isn’t easy to justify disrupting a business already performing well with a strong leadership team, in-demand products and services, and a loyal customer base. However, the assumption that market dominance will continue indefinitely is one of the most dangerous blind spots in business.

The Danger of Complacency – A Lesson from Business History

I reminded the CEO that business history is filled with cautionary tales of once-dominant companies that failed to anticipate or respond to disruptive technologies. Companies like Kodak, Blockbuster, Nokia, and BlackBerry were all industry leaders—until new technologies reshaped their markets and rendered their business models obsolete. Each of these companies had the resources, expertise, and brand power to adapt, yet they failed to recognize the speed and scale of disruption.

The market risk of AI-driven disruption is already reshaping industries today. Companies that fail to act now may not have the luxury of catching up later.

The most significant impact of AI will not come from small efficiency gains or incremental improvements. It will come from companies willing to rethink their value chain, redefine their industry, and create entirely new AI-driven business models. Organizations embracing AI innovation now will lead their industries in the AI age—while those that hesitate may struggle to stay relevant.

I cautioned the CEO against assuming that today's results guarantee future success and AI is tomorrow’s problem. As the Grateful Dead song Uncle John’s Band warns, “When life looks like easy street, there is danger at your door.”  In the song "Scarlet Begonias," they also share, "Once in a while, you get shown the light, in the strangest of places if you look at it right." I reiterated that following the Four Bucket approach to AI, an actionable strategy might be closer than you think.

...OK—the Dead lyrics were one of the adjustments and liberties I spoke about at the beginning of the article.

First Steps to an AI Strategy

At this point in our conversation, I could see the wheels turning. We have covered quite a bit of ground in a short period.

We discussed the four bucketed categories in more detail, expanding on the why, what, how, and who as a first step on their journey. 

To summarize again, these were:

Figure 2. Four Focus Areas for Organizational AI

The Path Forward

Too often, companies deploy AI just to say they have it. They introduce AI-powered chatbots that customers ignore, implement AI analytics that no one trusts, or launch AI initiatives that amount to little more than marketing fluff. If AI isn’t aligned with clear business objectives, customer needs, or operational efficiencies, it quickly becomes an expensive distraction rather than a competitive advantage.

The pressure to “jump on the AI bandwagon” is real. Still, the real challenge isn’t just about AI itself—it’s about ensuring a business remains competitive in a rapidly evolving landscape.

By the end of our conversation, the CEO had a structured and clear first step forward that could be discussed with their team. What began as a sense of urgency and uncertainty transformed into an opportunity to craft a thoughtful, business-aligned AI strategy—driven by value rather than fear. AI was no longer just a checkbox to keep up with competitors; it was now a strategic enabler capable of driving innovation, efficiency, and growth.

For any CEO or business leader facing the same challenge, my advice is this: AI isn’t a race—it’s a roadmap. Success won’t necessarily go to those who move first but to those who move with intention—aligning AI with business goals, empowering their teams, and making deliberate, value-driven decisions that lead to sustainable competitive advantage.

The organizations that thrive in the AI era will approach AI with thoughtfulness and purpose—leveraging existing capabilities, exploring new opportunities with strategic intent, and fostering a culture that embraces change with agility and confidence. AI will continue to evolve, as will expectations surrounding it. The challenge isn’t simply adopting AI—it’s ensuring that AI adoption makes sense for the business, its people, and its customers.

The future won’t be shaped by AI alone but by how leaders choose to use it. Those who approach AI with clarity, purpose, and adaptability won’t just survive—they’ll lead. 

Best of luck on your AI journey,

Greg

References and Citations

1. Teixeira, T. S. (2019). Unlocking the customer value chain: How decoupling drives consumer disruption. Crown Currency.

2. Kim, W. C., & Mauborgne, R. (2017). Blue ocean shift: Beyond competing – proven steps to inspire confidence and seize new growth.  Hachette Books.

Additional Tools Utilized and Resources

An actual human – Greg Valyou, wrote this article, and the ideas are mine.  In addition to calling on my experiences, knowledge, and research, the following AI tools were utilized to augment that process.

1. Napkin AI. (2025). Figure 1. – Figure 1 – Four Buckets (Categories) of Organizational AI. Napkin AI. Retrieved and created from https://app.napkin.ai.

2. Napkin AI. (2025). Figure 2. – Four Focus Areas for Organizational AI. Napkin AI. Retrieved and created from https://app.napkin.ai.

3. Grammarly. https://www.grammarly.com. Accessed February 1-15, 2025.

   1. Spelling, grammar, sentence structure, and plagiarism checks.

4. ChatGPT (4o). https://chatgpt.com. Apple Store Application. Accessed February 1-15, 2025.

   1. During draft creation, reviewed limited suggested options for adjusting some paragraphs for clarity.

Images and Media

Cover image, Greg Valyou Headshot, Copyrighted Image, All Rights Reserved, October 5, 2024