AI Return on Investment Begins with Strategy, Not Technology

Artificial intelligence ranks among the top investment priorities for 39% of U.S. CEOs, according to The Conference Board's 2026 C-Suite Outlook report.1 Yet a Gartner survey reveals only 36% of CFOs express confidence in their ability to drive AI impact.2 This gap between knowing AI is important and having confidence in execution isn't a technology problem — it's a strategy problem.

Success in corporate AI strategy isn't about chasing every new tool. Companies recognizing real returns moved past viewing AI as an experiment to building the technology into their infrastructure. The CEOs champion the strategy, which includes redesigning workflows and establishing data readiness before scaling deployment.

Corporations are using artificial intelligence to unlock efficiency and innovation.

Diverse industries use artificial intelligence, particularly generative AI, including healthcare, financial and professional services, manufacturing, and information technology, for a variety of operations.

Seventy-one percent of corporations use gen AI tools in at least one business function and nearly half use it for three or more functions.3 Artificial intelligence is transforming corporate operations in meaningful ways.

• Automates repetitive tasks for improved productivity.
  Generative AI tools complete repetitive tasks like researching, outlining and editing content, code generation and data visualization much faster than humans. Integrating gen AI into employee workflows frees up time for higher-value work. According to Deloitte, 66% of businesses have achieved productivity and efficiency gains with AI.4
  
  
• Personalizes and scales customer experiences.
  Artificial intelligence can inherently understand nuanced customer traits and make product recommendations to create personalized experiences. Gen AI is applicable to every aspect of the marketing funnel, from content creation and website development to customer targeting and sales process refinement. This enables corporations to create messaging that aligns with customers' interests and sensibilities while maintaining brand identity and boosting sales. Deloitte's research reveals that 38% of organizations have enhanced customer relationships, thanks to AI.5
  
  
• Accelerates innovation.
  Changes in consumer demand often outpace research and development processes. Gen AI tools quickly collect and analyze market data, delivering timely recommendations. Prototype development and product launches accelerate with AI-enabled insights.
  
  
• Transforms business models.
  Gen AI's analytical capabilities elicit insights to target new customer segments, refine value propositions, adjust pricing strategies and cost structures, and identify new revenue streams. In addition, AI helps organizations develop and manage company records and policies, as well as track compliance with industry and government regulations.
  
  
• Improves investment decision making.
  Corporations can use gen AI models to make informed, data-driven investment decisions. For example, when considering an investment, corporate leaders can use gen AI to quickly analyze its performance with detailed visualizations. These tools can also forecast investment performance across varying economic conditions. Data from Deloitte indicates 53% of organizations have experienced enhanced insights and decision-making due to AI.6

Gen AI drives increased profitability and growth.

Through increased productivity and in-depth analysis, generative AI models are modernizing organizations’ approach to growth and profitability. Corporations can adapt swiftly to dynamic market demands and changing global economies for competitive advantage. 

Where are organizations experiencing the highest revenue impact with gen AI tools? Respondents to a McKinsey survey said supply chain management and service operations are the areas in which they’re seeing the greatest changes, with considerable increases in the same year. In the first half of 2024, only 5% of respondents cited revenue impacts of greater than 10% to supply chain and inventory management. But in the second half, that percentage rose to 19%. Similarly, service operations revenue impact increased 15% (to 18%).7

Operational improvements also yielded impressive cost reductions. Twenty-nine percent of respondents in software engineering reported lowering costs by 10% to 20% or more during the second half of the year. Knowledge management followed, with 27% citing the same levels of reduced spending. Although results varied among business units, most survey participants noted cost reductions.8

Overall, the survey revealed notable reductions in costs and improvements in profitability across all business units surveyed during the year. Data from Deloitte reinforces this trend, indicating 84% of those investing in AI and gen AI are gaining ROI.9 Aligning AI investments with corporate finance strategy is key to making those returns more predictable.

The companies seeing real AI returns share common strategies.

While AI can be game-changing, companies often get stuck in the pilot phase of implementation. Those that move beyond experimentation and capture real value tend to share a thoughtful, strategic approach to AI. Objectives should include emphasizing integration, leadership commitment, balanced objectives and solid data foundations.

1. Redesign workflows around AI, not vice versa.
   A common mistake in AI implementation is layering it atop existing processes when, in fact, this limits what AI can do. Instead, AI adoption presents the opportunity to rethink business models and restructure workflows that include AI.
   
   High performers, which McKinsey defines as organizations attributing EBIT impact of 5% or more to AI use, are nearly three times as likely as other companies to have fundamentally redesigned workflows in AI deployment.10 This is one of the greatest contributors to meaningful business impact because it creates transformational value rather than just incremental efficiency gains.
   
   
2. Make AI an executive-level priority.
   Recent research shows that nearly three-quarters of CEOs serve as their organization's main decision maker on AI — and that's a good thing.11 When the CEO personally owns the AI strategy, a company is more likely to outperform those delegating it to IT because it ensures the transformation gets the resources and alignment needed. In this approach, the CEO sets the vision, allocates the budget and holds leadership accountable for adoption. Consider that in high-performing corporations, teams were three times more likely to strongly agree that senior leaders were committed to the success and ownership of AI initiatives.12
   
   
3. Set growth objectives alongside efficiency targets.
   Organizations that pair efficiency goals with revenue growth and innovation targets, unlike companies with cost reduction as their primary AI objective, capture the most value. When AI strategies target growth initiatives, such as entering untapped markets, developing new products or improving customer lifetime value, the technology drives revenue, not just cost savings.
   
   Companies should track both cost savings and revenue impact from the outset of deployment. These metrics help organizations prioritize innovation and long-term growth, rather than simply defaulting to cost-cutting measures.
   
   
4. Invest in data readiness before scaling.
   Poor data quality and inadequate infrastructure limit both data analytics and artificial intelligence capabilities and are common reasons why corporate AI projects fail. In fact, some organizations underestimate the data and controls that successful implementation requires — top factors contributing to AI implementation failure.13
   
   High-performing corporations take a different approach. They establish corporate AI governance early, creating policies for compliance, security and data quality before scaling. This groundwork supports both current applications and emerging technologies like agentic AI, so companies can adapt quickly to future needs across the enterprise. According to Gartner, mastering these fundamentals early enables organizations to move pilots into production twice as fast as those that don't.14

AI agents are transforming the industry.

If you’re ready for the next level of generative artificial intelligence, AI agents are it. These tools independently execute multi-step tasks, make decisions and act without constant human oversight. Unlike traditional AI assistants that respond to prompts, AI agents operate with defined goals and determine the best path to achieve them.

Corporations are open to the many possibilities of agentic AI. Sixty-two percent of organizations in a recent study are at least experimenting with AI agents.15

AI agents are already showing up in healthcare, manufacturing, technology and other industries, and delivering results. For example, electronics manufacturer Foxconn and BCG developed an AI agent ecosystem that automates 80% of decision-making processes, generating roughly $800 million in value. In information technology, a partnership between Advanced Micro Devices and Synopsys doubled developer productivity with agentic AI.16

As organizations continue to adopt and incorporate advanced AI technologies into daily operations, industry leaders are closely watching the rise of autonomous systems. Experts predict AI agents will be the new normal of enterprise applications by 2029.17

For companies that build the right infrastructure, AI delivers.

The primary challenge for leaders is the uncertainty regarding how to optimize return on AI investments. Research, however, points to a clear path. In a 2026 PwC survey of global CEOs, 12% reported cost savings and revenue gains. What sets these executives apart is that they established strong AI foundations, including responsible AI frameworks, for enterprise-wide integration.18

Agentic AI raises the stakes even higher. Without strong, proactive governance and data readiness, deploying autonomous agents safely will be nearly impossible. Companies that secure these foundations now, including protocols for risks like generative AI scams, will be positioned to realize value as capabilities expand. Simply put, organizations that deploy AI agents effectively outpace those that don't — and readiness determines who can move first.

Scale your AI infrastructure for enterprise growth.

Building AI infrastructure requires both strategic vision and capital allocation. For companies ready to move from experimentation to enterprise-wide deployment, the right financial partner makes that transition possible.

Synovus will be with you at every step. Whether you're evaluating AI infrastructure needs, navigating governance frameworks or assessing the fiscal impact of emerging tools like agentic AI, we'll collaborate with you to understand both the opportunities and the capital required.

For more information, complete a short form and a Synovus Treasury & Payment Solutions Consultant will contact you with more details. You can also stop by one of our local branches.