AI Return on Investment Doesn’t Start with 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. Yet a Gartner survey reveals only 36% of CFOs express confidence in their ability to drive AI impact. This gap between knowing AI is important and having confidence in execution isn't a technology problem — it's a strategy problem.
Companies recognizing real returns moved past viewing AI as an experiment and into building the technology into their infrastructure.
Businesses 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, and it’s 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. - 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 companies to create messaging that aligns with customers' interests and sensibilities while maintaining brand identity and may contribute to better sales outcomes. - 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 support compliance monitoring activities with industry and government regulations. - Improves investment decision making.
Companies 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. - 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. 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.
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, though results vary.
Overall, the survey revealed notable reductions in costs and improvements in profitability across all business units surveyed during the year. 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.
- 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. This is one of the greatest contributors to meaningful business impact because it creates transformational value rather than just incremental efficiency gains. - 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. 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. - Set growth objectives alongside efficiency targets.
Organizations that pair efficiency goals with revenue growth and innovation targets capture the most value, as opposed to companies with cost reduction as their primary AI objective. When AI strategies target growth initiatives, like 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. - 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.
High-performing companies establish corporate AI governance early, creating policies for compliance, security and data quality before scaling. According to Gartner, mastering these fundamentals early enables organizations to move pilots into production twice as fast as those that don't.
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 can support making that transition possible.
