AI Appreciation Day: The Real-World Evolution of AI in Business Strategy

AI in the enterprise is no longer a futuristic concept; it’s a critical part of how companies operate, compete and grow. Over the past few years, what was once met with hype or hesitation has become an essential driver of a successful business strategy. From personalizing customer experiences to guiding decisions across marketing, analytics and customer service, AI is helping organizations get more from their data and deliver more to their customers.

As we mark AI Appreciation Day, it’s clear we’ve entered a new era – one where responsible, grounded and business-aligned AI is no longer optional. The real challenge isn’t whether to use AI, but how to use it well.

Smarter Data Starts with AI

Enterprises are awash in data, much of it fragmented across systems, silos and teams. A recent survey found that data professionals spend nearly half their time preparing data before it can be used, a staggering tax on innovation.

AI is becoming a force multiplier in the customer data space. From automating identity resolution to generating real-time segments to making activation decisions, AI is helping teams accelerate time-to-value and focus more on strategy than data wrangling. Tools like ChatGPT, Claude and Perplexity have opened new possibilities, but the most effective applications of AI still come down to solving practical problems: eliminating manual workflows, reducing lag between insight and action and building smarter, privacy-safe customer experiences.

At the core of it all is a simple truth: AI doesn’t fix bad data. If your data is siloed, incomplete or out of date, even the most advanced models will fall short. That’s why building reliable, accessible data assets is step zero for any enterprise AI effort.

What Responsible AI Looks Like in Practice

With power comes responsibility. As AI takes on a more central role in business workflows, its design and governance matter more than ever.

Responsible AI is about more than fairness, explainability and privacy; it’s about ensuring AI tools are usable, auditable and aligned with real-world constraints. Trust is earned when teams can inspect model behavior, provide feedback and adapt systems to evolving needs. Tools built on AI must support versioning, change tracking and transparency by default.

Yet even as adoption surges, 72% of executives say their organizations have integrated AI across most initiatives—fewer than one in three say they’re ready to manage the associated risks. Responsible AI demands shared frameworks, cross-functional collaboration and a deep understanding of both model limitations and organizational readiness.

Privacy is another non-negotiable, requiring a technical foundation where a persistent, stable customer identity is securely managed. It’s entirely possible to design AI that delivers personalized experiences without compromising customer trust, but any such effort must begin with the prerequisite of a unified customer identity foundation to enforce consent and governance at scale.

Personalization that Performs

Few use cases showcase AI’s potential more clearly than personalization. Whether it’s an email campaign, in-app experience or customer service interaction, modern consumers expect brands to know who they are and what they want, all without being invasive.

AI helps brands meet those personalization expectations at scale. But effective personalization still depends on one thing: high-quality data. That means resolving customer identities across devices, modeling behaviors as they happen and ensuring data is clean, complete, current and accessible.

According to McKinsey, brands that embrace data-driven personalization can boost revenue by 5–15% and improve marketing ROI by up to 30%. But to get there, enterprises are increasingly using AI not just for analytics, but for preparing the data itself—automating modeling, decision-making and delivery across business systems.

We see this every day. Brands are using AI to improve match rates, predict attributes like lifetime value and activate customer data across campaigns, channels and lifecycle stages, without writing custom code or maintaining brittle data pipelines. That kind of infrastructure unlocks both scale and speed.

What’s Next: The Future of AI in Enterprise Strategy

Over the next 12–24 months, AI will shift from bolt-on tools to being deeply embedded in enterprise infrastructure. To stay competitive, enterprises will need systems that are not just AI-compatible but AI-first.

Here’s what that looks like:

• Data Readiness at Scale
  Static warehouses will give way to data stores that provide AI the rich context necessary to continuously refine, augment and activate customer data in real time. This agility lets teams deliver insights faster, with less engineering overhead.
• Use-Case-Specific Modeling
  Instead of building one master customer model, enterprises will use AI to adapt customer context to each individual workflow, whether it’s marketing segmentation, optimizing real-time journeys or executive reporting.
• Composable AI Tooling
  Modular, interoperable AI components will let teams build, test and iterate quickly, starting small and realizing incremental value. This will encourage experimentation and tighten the loop between product, data and business teams.
• Rise of Enterprise AI Agents
  AI copilots will go beyond answering questions for customers. They’ll take action on a customer’s behalf, using the customer’s profile with a brand as a starting point. The brands with the most accurate customer data will benefit disproportionately from this.
• Accessible AI for Everyone
  Thanks to generative interfaces and low-code tooling, AI will no longer be limited to data scientists. Business users will be able to explore trends, generate content and take action without needing a PhD or a ticket in the queue.

Aligning AI with Strategy, Not Just Technology

Ultimately, the question isn’t whether AI is powerful—it’s how you align your strategy to best take advantage of it.

The most successful organizations will be those that invest not only in AI capabilities but in the underlying data infrastructure, governance and culture to make it work. That means building for transparency, prioritizing data quality and giving every team the tools to move fast and responsibly.

We’ve seen how AI can unlock value when it’s grounded in clean customer data that is designed for usability and embedded across functions. As we look ahead, it’s clear that AI isn’t just about models or code—it’s about people, partnerships and purpose.

The road ahead is full of possibilities, and that’s something worth appreciating.