A CEO’s AI Integration Playbook: Collaborating with Your CTO and CFO for Success

Your C-suite can’t adopt AI if you don’t adapt to AI.

Ninety-eight percent of CEOs believe in the immediate business advantages of artificial intelligence (AI) implementation. And they’re absolutely right. AI has the power to transform organizations for the better; but equally, a failed AI implementation runs the risk of harming your reputation and operations. And that’s the part everyone’s worried about. 

But more than half (54%) of organizations have already experienced cost savings and increased efficiency from using AI in IT, business, or network processes. An almost equal amount (53%) have seen improvements in IT or network performance because of AI, and just under half (48%) have already used AI to create a better customer experience. 

The benefits of implementing AI are real. But so are the risks—and they’re not always related to failing at AI implementation; more often than not, they’re related to failing to take action at all. It’s estimated that three-quarters (75%) of organizations could be put out of business through their own failure to scale AI. 

In this CEO’s playbook, we will guide you through a step-by-step gameplay of how you, the CEO, can best prepare to implement AI with a little help from your friends in the C-suite. 

Executive Summary

10 Questions CEOs Should Ask About Artificial Intelligence

• How can AI make our company more competitive?
• What will AI mean for our long-term goals?
• How can AI help us address challenges and opportunities in our industry?
• What are some of the best ways to use AI that align with our strategy?
• What kind of return can we expect from investing in AI in terms of saving costs and generating revenue?
• How should we budget for AI projects, and what should our spending priorities be?
• Do we have the right in-house talent for AI projects, or should we consider external partnerships?
• How do we ensure that our AI initiatives follow regulations and ethical guidelines?
• How can we handle data security and privacy in the age of AI?
• What technology do we need for successful AI implementation?

What CEOs Should Do Right Away

The first step in creating your AI implementation strategy (or refining and enhancing it) is to answer the 10 questions we’ve laid out under the “10 Questions CEOs Should Ask About Artificial Intelligence” heading. Although the questions might seem straightforward at first glance, when you try to answer them, you’ll realize they’re quite tricky to tackle.  

But you have to tackle things of this magnitude the same way you’d eat an elephant, “bite for bite.” And to take the first steps in answering these questions, there are a few things you, the CEO, should get sorted out and get clarity on right away. 

1. Start the Conversation in the C-suite 

Don’t blindside your C-suite with an AI implementation plan. Rather, test the waters by starting some preliminary discussions regarding how your organization can benefit from AI. 
 
You likely won’t catch your C-suite team by surprise, though. At this point, all corporate leaders, or all good ones, at least, have started to consider whether they need to pivot to AI-oriented business solutions. In fact, seven out of 10 (71%) C-suite leader say increasing the use of AI is a priority due to the competitive advantages it offers, and nearly two-thirds (62%) said they’re concerned their organization isn’t implementing AI fast enough. 

To start the conversation, start by defining AI’s role in achieving a competitive advantage in your company. For example, if you are an agribusiness CEO, you might sit down with your C-suite and share a case study showcasing how AI-powered precision agriculture has increased a competitor’s yield by 15% while reducing water and fertilizer usage by 20%. 
 

2. Get Ready to Play the Collaboration Game  

In the immortal words of the British pop bards, The Beatles, “I get by with a little help from my friends.” And so should you if you’re planning on successfully integrating AI in your company. What that means is you need to get ready for some serious cross-functional collaboration. 
 
To kickstart this collaboration, facilitate inclusive decision-making. Encourage your C-suite team to participate in discussions, provide comments, and even offer criticism on the points made during the “Start the Conversation” phase.  
 
From this point, create cross-functional AI teams within the C-suite to ensure that your AI strategy aligns with your overall business strategy. At a minimum, involve your CFO and CTO in this team, and ideally, your COO, CMO, and CHRO as well. For the purposes of this playbook, we will provide you with specific insights on how to approach your CFO and CTO. 

With these steps taken, establish robust communication channels to discuss AI and the progress made in your strategy and implementation. This can be as simple as a weekly meeting or as complex as utilizing a project management system. You’ll know what works best for your business. 
 

3. Become a Master of Governance and Compliance

In November 2022, when ChatGPT was first unleashed on the general public, generative AI was the equivalent of the wild west. There were few rules and even fewer rule enforcers. But that’s changing.  
 
By the end of 2022, 37 AI-related bills were passed into law. The US led the charge and passed nine AI-related laws that year. Spain also passed five and the Philippines passed four. It’s likely countries will just continue to ramp up AI regulations, especially with events like the writers’ strike in Hollywood still fresh in everyone’s minds. 
 
As the CEO, the compliance and ethical side of AI will have to be your concern, too. 

You need to start by educating yourself about relevant regulations, such as the GDPR, CCPA, and industry-specific guidelines. But the laws and regulations aren’t the only thing that you should consider—in the age of AI, ethics are more important than ever. You need to ask yourself how you’re going to use AI responsibly. 
 

4. Bring Your Plan to the Boardroom

Now, it’s time to involve the board. Only a quarter of organizations have AI on their board agenda. By taking this step, you’ll be getting ahead of 75% of your competitors.  
 
Securing your board’s full oversight and support for your AI initiatives is important. Overlooking this is a common CEO mistake.  
 
Nearly 14% of board directors don’t know if their organization is currently using AI or if they plan to use AI in future. This doesn’t reflect well. What’s more, 38% of board directors don’t have a thorough understanding of AI’s advantages and risks. Your mission is to change this. 
 
Start by providing your board with an overview of your preliminary AI implementation plan. You’ll need to emphasize the potential improvements AI can bring to your organization, while also addressing the risks.  
 
Your board members are likely to have concerns that you will need to attend to. Fifty-nine percent of board members are worried that AI is a high-security risk to their organizations. You’ll need to convince them otherwise by discussing how you’ll be mitigating security risks and complying with the relevant regulations and legislation.

Don’t overlook the board secretary’s role, either. They can ensure AI-related topics are included in the board’s agenda and structure it to allocate ample time and resources for AI discussions. They can also help you to prepare and distribute informative materials that offer an overview of your preliminary AI implementation plan and its objectives ahead of the next board meeting. 
 

How to Collaborate with Your CFO on AI Implementation 

1. Lay Out Your Financial Strategy

When you talk to your CFO about financial strategy, they’re likely going to bring up one big thing: profitability. Fair play to them, too. When we go through big transformations like getting into using AI, it can be easy to lose track of the real goal: profit.  
 
The first thing to mention is that AI has the potential to increase global corporate profits by $4.4 trillion USD every year. That’s not small change. 
 
Your CFO will likely be curious about the return on investment (ROI) the company can expect from investing in AI to do a thorough cost-benefit analysis, but to get to this point, you first need to determine profitability.
 
When evaluating AI projects’ profitability, keep in mind that the long-term return on investment rather than immediate cost savings is often more important for large digital transformation projects like these. Understand that some projects may have longer payback periods but could offer substantial value in the future.  
 
But determining profitability can be tricky in and of itself if you’re unsure what the real impact of AI will be on your business. Sure, you likely have a good idea of costs, perhaps even a solid number, but it’s harder to predict how much value AI will add to your business until it’s in use.  
 
To give your CFO the best possible shot at predicting profitability, you should consider launching small AI pilot projects. During these projects, you and your CFO can track the impact on profitability, customer satisfaction, or other relevant metrics. This will give you some insight on how much value AI can really add to your operations.  
 
After your pilot projects are done and you have a better idea of how and where AI can contribute to your company’s profitability, you should develop methods to measure and quantify the ROI for AI initiatives. To do this, sit down together and compare the costs associated with AI implementation with the expected financial gains (which you’ll now have a better idea of after your pilot projects).  
 
Once you’ve done all of the aforementioned and your CFO agrees that an investment in AI will lead to higher profitability, it’s time to decide on some Key Performance Indicators (KPIs) that your CFO can use to track your AI implementation’s financial success. These might include metrics like revenue growth, cost savings, and operational efficiency. 

2. Allocate a Budget and Work on Prioritization

When it comes to budget allocation, you need to ask, “How much should we be spending?” And there’s no one-size-fits-all answer to this question.  
 
Around 50% of top technology executives are currently allocating the largest portion of their budgets to AI. Google, in the past decade alone, has dedicated over $200 billion to AI investments. It’s important to note, however, that the average business operates on a different scale than industry giants like Google. On average, businesses invest between $6,000 and $300,000 USD in tailor-made AI solutions.  

Interestingly, we also see regional disparities in AI spending patterns. Companies in certain parts of Asia are outpacing their Western counterparts in AI investments. For instance, in Oceania, Eastern and Southeast Asia, 52% of companies are allocating $1 million USD or more annually for AI, surpassing the 47% in western Europe and the 38% in North America. 
 
These are big budgets. You may not need to spend $1 million USD to achieve your desired results, but it’s important to keep in mind that you need to budget for more than just the cost of training and the software. For example, if you’re training AI on your own data but find you don’t have enough of it (or it’s not high quality enough), you may need to invest in purchasing AI training datasets. That’s just one example of the unanticipated costs that can pop up. 
 
But, back to the question of “How much should we be spending?” The answer will lie in how much you can afford to spend and how much you anticipate profiting from or benefiting from this expenditure. It is, however, important to allocate resources and budget not only for your initial costs like purchasing software and perhaps even training datasets but also for ongoing maintenance, training, and improvements. 
 
You need to consider the long-term costs of AI projects as well, including the cost of talent retention and infrastructure scaling. You also need to consider iterative budgeting, which allows for adjustments to your budget and planned spending as technology evolves and as you gain more insights from ongoing AI projects. 
 

How to Collaborate with Your CTO on AI Implementation

1. Analyze Your Existing Technology Infrastructure and Plan for the Future 

It can be exciting to envision a future in which your company has embraced AI and is making strides as an innovator in your industry. But don’t become too fixated on the future. After all, it’s impossible to plan for the future without a proper inventory and overview of the present. 
 
You and your CTO need to sit down and evaluate your current IT infrastructure. You’ll need to consider data storage, processing power, network capabilities, and data quality. 
 
Through this evaluation, your goal should be to understand the strengths and weaknesses of your existing infrastructure. You’ll also need to determine the specific hardware and software requirements for your AI implementation plan. This should include considering the types of AI models, algorithms, and tools needed.  
 
Data management is, by far, the infrastructure challenge that most businesses grapple with, with 32% identifying it as their primary concern. But data quality and data management aren’t the only issues. Twenty-six percent of companies are concerned about their security infrastructure, 20% are worried about their compute performance, 13% are anxious about their infrastructure’s networking capabilities, and 8% are concerned about their infrastructure’s storage capabilities. 

During your evaluation with your CTO, you’ll likely discover elements of your infrastructure that need upgrading before you can fully immerse yourselves in AI implementation.  
 
Once all of the above is completed, it’s time to choose your vendors. You, your CTO, and the relevant teams or departments can collaboratively evaluate potential vendors for hardware and software solutions. When doing so, consider factors like reliability, scalability, support, cybersecurity, and cost. 
 

2. Decide on a Data Strategy and Data Governance Policy

Many organizations that want to harness AI want to train models on their own data. But it’s important to understand that AI is a “data hog.” It requires an almost insatiable amount of quality data to function well, and the quantity depends on the task, AI method, and desired performance.  
 
Traditional machine learning requires less data than deep learning. While 1000 samples per class is a minimum for simple machine learning, that number is often vastly insufficient for more complex AI and deep learning.
 
In general, the complexity of the task that you want your AI to complete should match the volume of training data. The “rule of 10” suggests having ten times more data samples than parameters, although even this isn’t a hard and fast rule.  
 
Your CTO and their teams should be able to provide you with more insights on the amount of training and testing data your AI model will need. If they are not able to do so, the vendor who supplies your AI software may be able to shed light on the issue. Barring this, you may need to consider approaching outside help. 
 
Data quality is also important when it comes to training your own AI model. It requires “clean” data. Clean data is free from errors, maintains uniform formats, and addresses class imbalances in classification tasks. You will also need to remove outliers and anomalies from your data to ensure data integrity. 
 
You and your CTO should discuss and decide on any additional data acquisition needs too, and through this discussion, you should aim to determine if partnerships, acquisitions, or data exchange agreements are necessary to enrich your data assets to get the most out of your AI. 
 
From there, you’ll need to work together to make data easily accessible to the relevant teams, AI developers, and vendors. For this, you’ll need to address data silos and facilitate integration across departments. Then, you’ll need to create a framework for ongoing data governance oversight. This framework should include regular data audits and reviews to ensure that data quality and accessibility standards are maintained.   
 
The two of you should also work closely to define data governance policies that align with the organization’s AI strategy and broader goals. These policies should cover data security, compliance, privacy, and usage. You may also need to rope in your legal and compliance teams to make your data governance policy watertight (and ensure it meets all the relevant legal requirements).    

Your C-Suite is the Biggest Determiner of Your AI Success 

As the CEO, you know that your efforts are important. But without the support and collaboration of your entire executive team, your efforts mean nought.  

That’s why it’s important to have C-level leaders who are adaptable, agile, and ready to embrace the future of business.  

Assessing the capabilities needed to enable your AI vision can be overwhelming, especially as you venture into uncharted territory. At Stanton Chase, we evaluate top executives daily and proficiently calibrate their AI capabilities against the marketplace. 

At Stanton Chase, we evaluate top executives daily and proficiently calibrate their AI capabilities against the marketplace.

We believe in building partnerships with forward-looking companies. We don’t just set our clients up for current success; we set them up for success for decades to come. If you need help with leadership assessment or executive search to take your AI strategy to the next level, our consultants are only an email away. Click here to reach out. 

About the Authors

Daniel Casteel is the Global Functional Leader for CEO Search and Succession and Managing Partner of Stanton Chase Nashville. He is a veteran global executive who leverages his extensive experience in building executive leadership teams across diverse industries and geographies for the benefit of his clients. He serves as a trusted adviser to senior management in a variety of industries within the Fortune 500, as well as family-owned enterprises, middle-market companies, and private equity firms. 

Daniel founded Stanton Chase Nashville in 2005 and has since established a top-caliber team in Georgia, Alabama, and Tennessee that brings great depth of industry expertise and business acumen to deliver outstanding leadership solutions for clients.

Dr. Oliver Ziehm is a Partner at Stanton Chase Düsseldorf with over 20 years of experience in consulting. He’s also the Global Sector Leader for Technology and Professional Services. Prior to joining Stanton Chase, he worked at Kienbaum, PriceWaterhouseCoopers, IBM and CSC, where he built a vast international network in IT and consulting. Dr. Ziehm is considered a trusted advisor in these industries and has a deep understanding of current and future trends.

Dr. Ziehm completed his studies in business administration at Cologne University, HEC Hautes Etudes Commerciales in Paris, and Wroclaw University of Economics in Poland. He has a Ph.D. in Business from Breslau University and is also a certified business coach.