Every organization—regardless of size, scale, geography, or even industry—must adopt an AI strategy or risk falling behind.

Organizations approaching the use and adoption of AI with purpose and precision, rather than simply reacting to FOMO (fear of missing out), will be better positioned to gain competitive advantages, while those who don’t will risk the real threat of irrelevance and obsolescence.

Why Every Organization Needs an Enterprise AI Strategy

AI adoption is rapidly increasing across industries, delivering clear, measurable benefits.

According to McKinsey, 50% of organizations have adopted AI in two or more business functions, with many seeing AI-related revenue gains across the board. Conversely, firms that lag in AI adoption are increasingly outpaced in operational efficiency, customer engagement, and data-driven decision-making.

A comprehensive AI strategy enables organizations to transform not only processes but entire business and service models. In financial services, AI is redefining client engagement and risk management; while in retail, it’s creating personalized customer experiences and predictive analytics.

As the AI-driven use cases proliferate, companies without a clear strategic approach to AI risk being outperformed by those who are (and will) leverage AI to meet both current demands and future challenges.

This is no longer about staying current; it’s about staying competitive.

Strategic, Not Emotional: An AI Strategy With Purpose

AI investments within organizations must be driven by purpose, not pressure.

The allure of AI has led many organizations to make impulsive investments without a clear focus, often resulting in fragmented projects, underwhelming results and overspending on compute costs. AI should serve the core mission of your organization to deliver both immediate and long-term value.

According to an Accenture report, companies with AI-led processes have experienced improvements in productivity and higher average revenue growth.

Before making any sizeable investment in AI, it’s critical to stick to the basics and consider: What are our core objectives? How will AI support our enterprise-wide goals?

This clarity is critical, especially in industries like financial services, healthcare and logistics, where precise and responsible decision-making is essential. Whether it’s automating workflows or enhancing customer experience, each AI initiative must be tied to tangible outcomes and measurable KPIs, such as cost savings, customer retention or productivity gains.

Two Key Lanes For Enterprise AI Strategy: Internal And External

A focused AI strategy generally falls into two lanes: internal applications to enhance productivity and external capabilities to improve enterprise service models and client outcomes.

Internal AI Applications

Internally, AI can streamline workflows, augment tasks and improve productivity across lines of business, departments and teams. Automation of repetitive processes such as document collaboration, compliance tracking and data analysis and reporting can enable employees to focus on high-value work.

According to Accenture, organizations that use AI to automate back-office functions can reduce operating costs by up to 30%. For example, financial services firms leverage AI-driven compliance tools that reduce the risk of human error and increase efficiency in regulatory reporting—a critical advantage in today’s heavily regulated markets.

External AI Capabilities

Externally, AI can elevate customer experiences and create added value for partners and clients. In client-centric industries, AI is already improving personalization, automating client service and enabling data-driven insights that support better decision-making. Accenture found that companies providing personalized experiences through AI-driven insights see customer conversion rates increase by 15%-25%.

Whether it’s a retailer offering personalized product recommendations, a healthcare provider using AI for faster diagnostics or a wealth management enterprise automating client onboarding and extracting critical data (such as expiration or maturity dates) from enterprise or client documents inside a Digital Vault , external AI applications and the use of private large language models (LLMs) can deepen relationships, enhance service models and deliver seamless client experiences that can position firms to build immediate trust at scale.

Budgeting For Your AI Strategy

Implementing an AI strategy requires clear budgetary planning and prioritization. AI initiatives often begin with smaller pilot projects that demonstrate ROI before scaling to larger applications and rollouts. Budgeting for AI involves accounting not only for immediate technology costs but also for the specialized talent and data infrastructure necessary for success.

According to Gartner, by 2025 we will start to see AI-focused talent and infrastructure spending outpace traditional technology investments.

When planned and executed thoughtfully, AI spending can result in exponential returns, positioning companies for potential long-term growth. However, leadership teams will need to budget for the ongoing maintenance, evaluation and scaling of AI initiatives to adapt to new challenges and opportunities, especially as AI continues to advance exponentially.

The Call To Act On AI: Strategic Decisions For Long-Term Success

I’ll leave you with this one. AI can level the playing field in a very positive way; it can also—and will—catapult organizations far beyond the competition when understanding and taking on the mindset that the sky (or rather the use case and application of AI) is the limit.

A comprehensive AI strategy, crafted with intention and driven by organizational goals, will set the foundation for resilience, relevance and growth in the years to come.

So, does your organization have an AI strategy?

This article was originally published by Daniel Kenny on Forbes.com.