Information has never been easier to collect. Yet many organizations still struggle to turn it into an edge, with conversations focused on storage and tools rather than on how effectively information supports real decisions under pressure.
“There’s a persistent misunderstanding of treating information as an asset to be stored rather than a capability to be deployed,” says Jacques Nack, CEO of JNN Group;, an AI-powered compliance and analytics firm. He works with regulated organizations to redesign information flows for compliance, risk, and governance, using AI-driven systems to reduce audit friction and accelerate decision-making at scale.
The true prize is not more data, but better decisions. “The real value is not in having the information. Having too much information can be a risk,” Nack says. “It’s having the right information at the right moment for the right decision.”
In his work across data privacy, cybersecurity, and AI-driven compliance, he has seen how this move from accumulation to activation changes everything from audit performance to product velocity. At the core of this approach are three structural shifts: designing information around the questions leaders need to answer; making knowledge genuinely findable before chasing completeness; and treating information as a living system that evolves through continuous feedback.
Treat information as a decision engine, not a warehouse
Most organizations talk about information management as if it were a filing problem. Nack’s view is closer to product design. Information should be shaped to answer the decisions a business must make, at the speed the business must make them.
When evidence, policies, and controls are structured for retrieval and reuse, audits become more predictable. “When the information is not ready to use to respond to an audit request, they scramble, it costs more money, they lose time, they lose cycle,” he says. The cost is momentum. Too many hands get pulled into urgent evidence hunts, and the organization’s focus fractures.
“The real advantage is when the information management is designed for the decision, not just for the data,” he says. He calls the metric speed to data, meaning how quickly an organization can move from a signal, question, or problem to understanding and action.
Where information programs break down
When a company invests in analytics platforms and still cannot answer basic questions quickly, the failure is rarely technical alone. Nack points to a specific three-way misalignment: tools that are implemented without being embedded into day-to-day workflows, cultures that revert to old habits under pressure, and leadership directives that declare the organization data-driven without changing how decisions are actually made.
“Technology without cultural adoption becomes expensive shelfware,” he says. “Culture without leadership always fades after the initial enthusiasm.” Leadership mandates without the right tooling and incentives can also trigger a different failure mode. “You create shadow systems.” People route around systems that slow them down, rebuilding workflows in spreadsheets, email threads, and one-off processes.
If there is one consistent weak link, Nack says, it is the middle layer: process design. Organizations buy platforms and hire top consultants, then proclaim they will be data-driven. “But nobody redesigns the workflow so that it actually molds to how the company works,” he says. The result is a system that technically exists but operationally fails because it hinders the job rather than helping it.
That breakdown has long-term consequences. Knowledge becomes trapped in tribal memory, and every new hire pays a tax in time and errors. “If you don’t scale that insight, then every new team member is starting from zero,” Nack says.
Build around questions, not categories
Nack’s first practical shift is to design for the question, not for the answer. Many information architectures are built around categories, with finance in one place, customers in another, and compliance somewhere else. “Decisions don’t respect those boundaries,” he says. Risk assessments, vendor approvals, and incident responses draw from multiple domains at once.
Designing for questions forces teams to start with the decisions that matter. What does a product leader need to approve a feature? What does a compliance officer need to demonstrate control effectiveness? What does a security team need to triage a signal into an incident or dismiss it as noise? When a system is shaped around those workflows, silos have to dissolve because the business question is cross-functional by nature.
Make it findable, then make it complete
The second shift is about pragmatism. Many organizations chase completeness first, amassing complex repositories. The problem is that no one can navigate them under pressure.
“You have these very sophisticated databases, but nobody really knows what’s in them,” Nack says. His priority is discoverability. “Make the information findable first.” A smaller, well-indexed knowledge base can outperform a sprawling system if it lets teams locate, validate, and reuse evidence quickly.
In AI-driven compliance and governance, that mindset shows up as reuse across frameworks. “One piece of evidence can satisfy multiple frameworks,” he says. A single, properly documented access-control record can support ISO requirements, SOC reporting, and anti-money laundering controls when it is structured and mapped well. Findability turns compliance from repeated manual effort into an extensible library.
Keep information alive with feedback loops
The third shift reframes information as dynamic. “Static information is decaying,” Nack says. Regulations evolve, business models change, and teams update workflows. If an organization pays to store data that is never revisited in decision-making, it is paying to preserve entropy.
His answer is to build feedback loops into information flows so that each decision updates the underlying system. That can include governance triggers when policies change, automated prompts to refresh evidence before it goes stale, and post-incident reviews that convert lessons into new controls or better metadata. The objective is a system that learns, so the next decision is faster and more accurate than the last.
AI will magnify both order and error
Nack is optimistic about AI’s capacity to bring order to messy information ecosystems, particularly through pattern recognition. “AI excels at pattern recognition in very large data sets,” he says.
But AI also raises a harder question around accountability. As data scatters across systems, it becomes harder to trace responsibility. “The answer cannot be it was the AI.” Leaders will need governance that makes decisions auditable, assigns ownership, and monitors drift. There is also the risk of automating yesterday’s mistakes. AI trained on historical compliance data can perpetuate errors and outdated practices.
“AI is like a teenager. You give them your American Express card and let them loose in the mall,” Nack says. The question is whether controls exist: limits, tracking, guardrails, and clear expectations. Without them, the technology will test boundaries.
Information management has always been about more than data hygiene. In Nack’s view, it is an operating advantage measured in speed, confidence, and resilience. Organizations that design information around decisions, make it findable, and keep it alive through feedback loops will be positioned to use AI as an accelerator rather than a liability.
Follow Jacques Nack on LinkedIn or visit his website for more insights.
