The Renaissance of Data Engagement: From Manual Endeavors to AI-led Orchestration

By George Davis, Founder and CEO of Frame AI

The cliche is true: data is the new oil, a valuable resource that can fuel innovation, drive decision-making, and improve operational efficiency. However, despite the vast amounts of data being generated and collected, there is a persistent problem: businesses have inadvertently created a bottleneck in deriving value from data. This bottleneck occurs because when capturing data in CRMs, CDPs, and data lakes, it is impossible to anticipate the diverse ways it will eventually need to be used. The result is dormant data that must be mobilized through cumbersome multi-team projects to design new data pipelines for each use case, such as lead scoring or compliance analysis.

These projects often face numerous challenges. They compete for priority within data teams, causing delays that result in outdated requirements. Once delivered, these pipelines can be difficult to iterate upon and improve, hampering the organization’s ability to adapt to changing business needs. In some cases, valuable data projects never see the light of day because stakeholders are unaware of relevant data sources elsewhere in the organization or cannot secure a spot in the limited project pipeline.

While artificial intelligence (AI) holds the promise of a solution, it is still being applied narrowly. At present, AI is being deployed in specific, peripheral use cases, such as enterprise search and search-based chatbots. Large language models (LLMs) are powerful but cannot yet comprehensively understand and reason quantitatively over massive datasets. AI assistants cannot surface data that users didn’t explicitly ask about, and they often fall short when integrating data into operational processes.

However, a new paradigm called AI Orchestration is emerging as a promising approach to bridge the gap and move closer to the vision of a true intelligence layer for data. In AI Orchestration, AI continuously processes incoming data to identify signals relevant to various organizational stakeholders – like an agent acting on that stakeholder’s behalf in every interaction. These signals serve as indexing points for data, allowing for efficient cross-channel analysis using traditional, large-scale machine-learning techniques. This analysis helps identify patterns of interest within the data.

One of the key advantages of AI Orchestration is its ability to proactively surface relevant information to business leaders without the need for them to initiate specific queries. It shifts a reactive approach, where stakeholders must request and wait for data insights, into a proactive one, where relevant insights are delivered in real-time. This transformation enables organizations to make more data-driven decisions and respond swiftly to changing conditions.

AI Orchestration facilitates the integration of data into operational processes. It doesn’t just aid in research; it actively participates in the execution of business strategies. For example, it can surface emerging market trends, customer preferences, or potential compliance issues and trigger automated responses or recommendations. This level of integration is a game-changer, enabling businesses to leverage data not as a static resource but as a dynamic force that shapes their daily operations.

By continuously processing incoming data, identifying relevant signals, and enabling cross-channel analysis, AI Orchestration offers a proactive and integrated approach to extracting value from data. Organizations can relieve the data bottleneck and empower their business stakeholders to make data-driven decisions in real time. While the road ahead may be long, AI Orchestration holds the potential to unlock the true value of data, making it a transformative force in the business world.

Got a Question? We’ve Got Answers.