The AI industry is undergoing a significant shift: from reactive AI (predictive insights) to proactive AI (agents that act on a company’s behalf). Just as people require more information to handle complex tasks, AI requires expanded access to high-quality data to broaden its capabilities. An agent acting autonomously needs far more context than a model simply predicting the next word.

SymphonyAI CEO Sanjay Dhawan put it succinctly last month: “A general-purpose agent can look genuinely impressive under controlled conditions. It starts to struggle, however, in enterprise operations, which have constraints, interdependencies, and judgment calls that are obvious to anyone who has worked the floor and invisible to a model that has never been taught to see them.”

Furthermore, because AI agents often take multiple steps to complete a task, they need relevant context at every stage to operate accurately and efficiently. This evolution raises the stakes for data quality: any AI transformation can only be as strong as the connectivity and trustworthiness of its underlying data.

No company has picture-perfect data

First-party data is cleaner than it used to be, but it’s never perfect. Sparsity, volume, and freshness all matter, but the most common issue is fragmentation. The main challenge centers on integrating structured data (such as attributes and behavior) together with unstructured knowledge (documents and prior context) so that AI can act on it.

Even the average person inherently knows that the underlying data matters. According to a recent survey from my company, Prosper Insights & Analytics , the top three concerns among US adults regarding recent AI developments are: AI needs human oversight (40.4%), AI can provide wrong information like hallucinations (40.3%), and AI needs more disclosure/transparency on the data it uses.

Because AI is a horizontal technology, the data problem applies across every industry. Take marketing as an example. Many marketing teams still operate with data silos, including separate CRM, web analytics, and event collection streams. Achieving autonomous workflows for effective AI reasoning requires unified, clean data streams. Setting up these integrations while ensuring accuracy presents a major technical hurdle, but once you establish clean, unified data streams, you can adopt causal AI.

Causal AI demands a continuous feedback loop

Causal AI focuses on why things happen by mapping true cause-and-effect, rather than relying purely on statistical correlations. It uses structural causal models and graphs to test “what-if” scenarios and counterfactuals, making its suggestions highly transparent and less prone to bias or errors when conditions change in the real world.

Because most systems run on transactional or predictive signals, they can detail probability but not causality. Attributing what drove an outcome requires a feedback loop (act, observe, and feed it back). With AI, this feedback loop can be much tighter, and smarter.

Until very recently, this feedback loop was difficult to achieve because the technology to capture all the relevant information was not available. In marketing, for example, all the institutional knowledge (what works, for which audiences, and how each creative performs) lived strictly in people’s heads. It could never compound. Put another way, every campaign had to start from scratch.

“Because marketing is so visible and so consumer-facing, everyone has an opinion. Building consensus on the optimal campaign strategy requires a lot of time invested in chasing approvals, reconciling stakeholder input, and rebuilding assets,” Auxia co-founder and CEO Sandeep Menon told me. “When agents handle execution, the role of the marketing team shifts to setting objectives, shaping strategy, defining guardrails, and reviewing output. This empowers a new breed of marketer for the AI-era, the 10x marketer, to focus more on creative thinking and strategy than execution, allowing the learnings to compound.”

Compounding intelligence requires capturing context

Learnings compound when you shift from capturing records to capturing context. You have to ingest not just a company’s existing sources of truth, but also everything that it took to get there. If you only look at the outcome, you won’t learn much. If you collect what was debated, what was decided, how the decision was made, what the result was, and the why to all of the above, you’re on the path to see a return from your AI investment.

“What once required five people and multiple weeks can now be done by one supermarketer working with a team of AI agents,” Menon added. “Supermarketers are using agents to surface insights on months of campaign performance in minutes, suggest new creative approaches based on the gaps, and autonomously surface the right experience to each user at scale. Three years ago, this would take an army of people several months to get out the door.”

his is possible because teams can now provide their agents with all the context and guardrails needed to execute. This means the agents have access to the same company preferences, expertise, brand guidelines, and institutional knowledge that would have previously been split across five to 15 different members of the team. In short, context is king.

“Without context—a clear understanding of the specific relationships and rules within an organization’s data—AI agents cannot operate accurately and are far more likely to hallucinate, introduce bias, and produce unreliable results,” Rita Sallam, Distinguished VP Analyst at Gartner, said at an event in May. “Organizations that fail to adopt comprehensive context structures—supported by a robust data layer—will perpetuate data inefficiencies and face heightened financial costs, as well as legal and reputational damage.”

Whether your company is practicing law or creating the next big spot in Times Square, the goal remains the same: distribute that intelligence across the entire enterprise, instead of letting it walk out the door when employees leave the company. Agents can help you do just that, as long as you provide them with the right foundation.

Disclosure: The consumer sentiment study referenced above was conducted by my company, Prosper Insights & Analytics . This is the same dataset used by the National Retail Federation, and available from Amazon Web Services, Databricks, and the London Stock Exchange Group for economic benchmarking.