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December 12, 2023

Enterprises Have Already Made Deep Investments in Data Infrastructure; With AI, They’ll Finally See a Return

George Davis


The rise of generative AI has captured the imagination of tech enthusiasts and enterprise leaders alike. The promise–and risk–of AI is of a world transformed at a pace never seen before. Enterprises, both fledgling and established, are keenly pondering the implications AI brings to their strategic roadmaps. But with so much of the conversation revolving around the far-reaching impacts of AI, businesses risk missing the most immediate applications today.

As it turns out, the clearest near-term opportunity for AI lies in driving more value from the investments that businesses have already made.

Ten Years of Digital Transformation

Over the last decade, most enterprises worked hard to build digital castles around a resource that was newly recognized as valuable: customer data. The interactions each business has with its market are its most unique asset – and for large enterprises, one that is truly irreplaceable. Implementing CRM systems, building vast data warehouses, integrating customer data platforms (CDPs) and adopting sophisticated analytics tools was a way of demonstrating an understanding of this value. The vision of data-driven marketing and customer experience (CX) operations was simple – collect extensive data, store it securely and then use it to tailor engagements that maximize value for customers and the business.


Many businesses are still figuring out that last step. The sheer volume of data, coupled with its complexity, continues to pose significant challenges. Traditional methodologies used to mine this data are often stymied by time constraints and resource limitations. The paradox is clear; while data is abundant, actionable insights from it are (often) scarce. Worse, analyzing data takes time, but most insights age poorly. Many initiatives, despite their promise, are shelved or underutilized because of the inherent inefficiencies of manual data management.

As a result, these platform investments (CRMs, CDPs, data lakes, etc.) drive ongoing costs, but have remained stagnant at the starting line, waiting for tools and projects that transform data assets into business outcomes. Often, businesses are surprised by the effort and time required from ML and data engineering teams to go that last mile, turning warehouse data into lead scoring, CX insights or marketing segments. Shouldn’t big initial investments in data infrastructure make it easier to find these applications?

AI provides this long-awaited transformation, offering breakthrough insights at a relatively marginal cost, enabling businesses to transition from manual and passive data interpretation to proactive orchestration and analytics.

From Automation to Orchestration

Much of the product landscape and discussion around AI focuses on automation–tools that ideally replace repetitive labor, at the risk of disrupting jobs or degrading experiences. Certain products, such as enterprise search and writing assistants, can be deemed augmentation: they help workers perform their job better. But enterprises should reach farther, towards orchestration: AI that actively brings data to where it’s needed, improving the way teams cohere and collaborate, allowing data to demonstrate its value with less time and effort.


The beauty of orchestration lies in its proactive, multiplicative nature. Enterprises, equipped with AI-driven insights, can now anticipate market shifts, preempt challenges and assist all members of the team in ways that reflect their individual responsibilities. With effective orchestration, each team’s operations generate data that helps the next team make even better decisions. Support aides marketing, marketing aides product, and product usage drives sales, completing virtuous circles around the enterprise.

This shift to orchestration means that enterprises can pivot from being purely responsive to strategic and anticipatory. With AI sifting through the data, patterns emerge that were previously invisible to the human eye, enabling businesses to forecast trends, anticipate challenges and tailor strategies for success in advance. Teams no longer need to wade through convoluted processes or rely on one-off dashboards for analytics projects; AI’s orchestration capabilities enable them to swiftly and efficiently derive actionable insights, ensuring that business decisions are data-informed, proactive and timely. AI not only democratizes data access within enterprises but also ensures that this data actively informs and refines strategy, making businesses nimble and forward-thinking.

Instant Impact, Today

While the long-term possibilities of AI are undeniably fascinating, its immediate applications bear significant potential. Enterprises today stand at an inflection point. The vast data resources and tools they’ve accumulated over years can now be activated to drive tangible business outcomes.

The businesses that outperform at this inflection point will be the ones who avoid the distractions of both distant AI frontierism, and of overly narrow short-term automation at the edge of their business.  They will instead look at the ways AI can immediately begin to unlock value from the sunk costs of their data infrastructure by drastically enhancing the ways their teams collaborate.

About the author:George Davis is the founder and CEO of Frame AI. George received his PhD from Carnegie Mellon University and has over 20 years of experience in applied AI technology and sciences. 

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