Beyond "data-first": how AI can restore the human element in business processes

The last decade has been marked by the "data-first" revolution. Companies rushed to collect metrics, create dashboards, and let algorithms guide their decisions. But what if this approach had inadvertently sidelined something equally valuable: a deep understanding of business processes and human expertise?

The Unforeseen Consequences of "Data-First"

This paper reveals a trend that many organizations will recognize. The aggressive focus on data and metrics in recent years has led to two worrying developments:

• Process Knowledge Erosion: As companies became obsessed with measuring everything, they began to lose sight of the underlying business processes that generate those metrics. Teams focused on metrics rather than operational analysis. Knowledge of the process behind the data eroded.

• Professional Devaluation: Professionals began to feel disconnected from their own expertise. Instead of thinking critically about process improvement, they began to rely entirely on what the data told them, even when their professional intuition suggested otherwise.

At this financial services company, I observed that process improvement was no longer optional. No one truly understands or cares about processes anymore, and that's not just my opinion, it's the employees', not a consultant's. The actual flow of operations is blurred; everything revolves around data, dashboards, and, of course, better decision-making.

I'm not saying that the shift to data is a bad thing. However, the cost of moving to AI-driven automation can be high when it comes to transforming an organization. Automating the process is fine, but be prepared to find workarounds, deal with a lack of accountability, operational heterogeneity, not to mention parallel processes... What if AI became more accountable and respected the process?

The vision: suggest, facilitate, support

Rather than using AI to automate and eliminate human involvement, the framework proposes three complementary roles for artificial intelligence:

Suggest

AI can observe the flow of operations and ensure that people make the right decisions regarding the next steps in the process they are involved in. It augments people's capabilities, ensuring not only that they follow the optimal process in a given context, but also that they comply with regulations and the compliance framework in their actions.

AI analyzes several dimensions (risk profiles, customer lifecycles, transaction history, life events, and statistical performance of similar stocks) to identify opportunities. Instead of making decisions, it guides human experts toward relevant possibilities and directs them to the appropriate applications to seize those opportunities.

Facilitate

AI manages administrative tasks by pre-filling forms, preparing customized arguments for customer interviews, and simulating the impact of recommendations on customers' situations. This allows professionals to focus on higher-value relationships and activities, while giving them access to advanced features such as automated communications and document intelligence.

Support

AI provides advanced predictive data (particularly regarding regulatory compliance, such as KYC), tracks action pipelines, sends timely reminders, and ensures that regulatory steps are correctly followed, including scoring, verifications, and withdrawal deadlines.

The Cultural Advantage

This approach offers several compelling advantages over traditional automation:

• Preserves human autonomy: Rather than replacing human decision-making, AI becomes an agent that understands the business context and regulations, while leaving final responsibility to professionals who understand the dynamics of relationships and implicit situations.

• Manages complexity: AI helps coordinate parallel processes while maintaining overall consistency, which is crucial when multiple business workflows intersect.

• Provides intelligent safeguards: AI acts as real-time protection, alerting to risks of deviation or non-compliance without restricting initiative.

Most importantly, this approach doesn't threaten existing roles; it strengthens them. It is culturally acceptable because it enhances human expertise instead of replacing it.

Balancing Data and Processes

The framework proposes a hybrid data/process approach organized around four key areas:

• Enriching KYC (data-driven): using scoring, open data, and predictive analytics to improve customer due diligence processes.

• Streamlining the user experience (process-driven): structuring experiences around business processes, facilitating access to advanced features, and suggesting actions for advisors.

• Delivering advanced features (process-driven): notification centers, automated communications, and AI-powered document processing.

• Data aggregation (data-driven): displaying key metrics directly in customer interfaces.

It's not about choosing between data and processes, but about using each where it delivers the most added value. It's also about designing new applications that fully leverage AI and human-machine collaboration.

Broader Implications

While this example comes from the financial services sector, the principles apply to all industries.

I advised a luxury goods company in recent years, and I can tell you that not a single process was ever mapped into an Enterprise Automation (EA) repository. It contained capabilities, domains, data, and applications, but not a single process. We tried to populate the process repository, but it was hopeless. No one was taking ownership.

A process EA repository is invaluable for these transformations. I mean, it should always be the case, but I don't think that's said clearly enough. Companies that make the effort to maintain such a repository can envision a clear path forward on their automation journey.

Everywhere, organizations are striving to find ways to harness the power of AI without losing the human judgment and process expertise that drive sustainable success. The key idea is that AI doesn't necessarily have to be "pro-process" or "pro-data." Without a thoughtful business orientation, AI can perpetuate purely statistical approaches that neglect crucial context. But when properly integrated with human expertise, it can actually help restore the importance of business processes that years of data-driven thinking may have obscured.

The Way Forward

As we enter the AI ​​era, the companies that thrive will likely be those that reject the false choice between human expertise and artificial intelligence. Instead, they will find ways to use AI to make human professionals more effective at what they do best: understanding context, building relationships, and making nuanced decisions in complex situations.

The future is not about pitting humans against machines, but rather about collaborating with them to achieve goals neither could accomplish alone. This sometimes means using the most advanced technologies to restore the most fundamental business capabilities: deep process knowledge and expert human judgment.

This framework stems from the strategic work Gabriel Greenfield conducted as part of his mission to qualify the opportunities AI presents in customer-facing business processes. His approach offers a compelling alternative to the traditional narrative of automation.