The CEO’s Information Problem

The 30% Tax on Every Decision

Here’s a number that should concern every leader: knowledge workers spend 2.5 hours per day—roughly 30% of their workday—just searching for information.

Not analyzing information. Not making decisions based on information. Not creating value from information. Just finding it.

That’s according to IDC research, and the numbers get worse when you dig deeper. Forrester found that workers navigate an average of 367 different software applications to do their jobs. APQC calculated that only 30 hours of a typical 40-hour week are actually productive—the rest is lost to information problems, communication overhead, and administrative friction.

Add it up: your organization pays an enormous information tax on every decision. Before anyone can act, they first have to hunt.

This is the real problem AI should solve. Not “how can machines do human work?” but “how can humans get better information faster?”

Most AI strategies focus on automation—eliminating tasks. That’s why 95% of them fail. The organizations getting real value from AI have flipped the question. They’re not asking “what can AI do?” They’re asking “what decisions would we make better with better information?”

That’s the shift this chapter will help you make.

The Information Gap

Every decision-maker faces the same fundamental challenge: the information you need to make a good decision exists, but getting it takes too long or costs too much effort.

Think about the last important decision you made at work. You probably had some information—but not all of it. You could have gotten more—but the deadline was approaching. You made the best call you could with what you had.

Now multiply that by every decision, every day, across your entire organization.

The result is a cascade effect: - Incomplete information leads to suboptimal decisions - Suboptimal decisions create problems - Problems require firefighting - Firefighting consumes time that could gather better information - Which leads to more incomplete information - And worse decisions

This isn’t a technology problem. It’s been true since the first organization was formed. But the scale has changed dramatically. The volume of available information has exploded. The velocity at which things change has accelerated. The variety of sources and formats has multiplied.

Consider what a typical knowledge worker deals with today: - Email volume has increased 4x since 2015 - The average company uses 130+ SaaS applications - Meeting time has increased 10% year-over-year - Slack/Teams message volume doubles every 18 months

Your brain hasn’t evolved to match. Neither have your organizational processes.

The traditional solutions—hire more people, work longer hours, use better search tools—don’t scale. You can’t outrun exponential information growth with linear effort increases.

This is where AI actually creates value—not by replacing human judgment, but by closing the gap between the information you need and the information you have.

Automation vs. Decision Support

The automation mindset asks: “How can AI do this task for me?”

This seems logical. Machines can work faster than humans, 24/7, without getting tired. If AI can do a task, shouldn’t we let it?

Here’s the problem: the automation mindset focuses on replacement. It seeks full autonomy. It measures success by tasks eliminated. And it fails 95% of the time because it ignores the messy reality of how work actually gets done.

Rachel from Chapter 1 had the automation mindset. “AI will handle customer inquiries.” She ended up with a chatbot that confidently gave wrong coverage information because no one was asking what decisions the chatbot was making on the company’s behalf.

The decision support mindset asks a different question: “How can AI help me decide better?”

This mindset focuses on augmentation, not replacement. It seeks better information, not full autonomy. It measures success by decision quality, not tasks eliminated. And it works with the intern model—because AI becomes a research assistant, not an autonomous actor.

The difference is subtle but profound:

Automation Mindset	Decision Support Mindset
“AI handles customer emails”	“AI summarizes customer sentiment so I can prioritize responses”
“AI writes our reports”	“AI drafts analysis so I can focus on recommendations”
“AI approves expenses”	“AI flags anomalies so I can investigate exceptions”
“AI schedules meetings”	“AI identifies conflicts so I can make tradeoffs”

In every case, the human stays in the loop. AI provides information; humans make decisions. This is why the decision support approach succeeds where automation fails—it respects the reality that consequential decisions need human judgment, even when AI can help.

The Three Information Problems AI Solves

AI is particularly good at solving three specific information problems. Understanding these helps you identify where AI can actually help—and where it can’t.

Problem 1: Volume

You have too much information to process manually.

Every customer service team faces this: hundreds of feedback messages, support tickets, survey responses. The information exists—customers are telling you exactly what’s wrong—but there’s too much to read. So you sample. You guess. You miss patterns.

The volume problem shows up everywhere: - Legal teams with thousands of contracts to review - Sales teams with hundreds of prospect interactions to track - Product teams with endless user feedback to synthesize - Executives with reports they never have time to read fully

AI solution: summarization, synthesis, and pattern detection. Instead of reading 500 survey responses, you get a summary of the top 5 themes. Instead of scanning every support ticket, you get alerts when a new issue pattern emerges. Instead of skimming reports, you get the key takeaways with the ability to drill down.

This isn’t automation. You’re still making decisions about what to prioritize, how to respond, what to fix. AI just made those decisions better-informed.

Problem 2: Velocity

Information moves faster than you can keep up.

Markets shift. Competitors launch products. Customer preferences evolve. By the time you’ve gathered enough information to make a confident decision, the situation has changed.

The velocity problem is particularly acute in: - Competitive markets where first-mover advantage matters - Customer service where response time affects satisfaction - Risk management where early warning prevents larger problems - Hiring where good candidates disappear within days

The old approach—quarterly reports, monthly reviews, weekly syncs—can’t keep pace with daily changes.

AI solution: monitoring, alerting, and trend identification. AI can track changes across more sources than any human team, surfacing what’s relevant without requiring you to constantly scan. You set the parameters for what matters; AI watches for it continuously.

Again, this isn’t automation. You’re still deciding what matters and how to respond. AI just shortened the time between “something changed” and “you know about it.”

Problem 3: Variety

Information is scattered across formats and sources.

Some critical information lives in spreadsheets. Some in email threads. Some in meeting notes, slide decks, CRM systems, Slack channels, recorded calls, shared documents. To make a good decision, you need to synthesize across all of it—but just finding where everything lives is exhausting.

This is the “I know we talked about this somewhere” problem. The information exists. Someone captured it. But it’s in a format or location that makes it effectively invisible when you need it.

Traditional approaches fail here because they require standardized formats. “Just put everything in the CRM” or “just document everything in Confluence” sounds good but never happens completely. Reality is messy.

AI solution: integration, extraction, and normalization. AI can pull relevant information from multiple sources and present it in a consistent format, reducing the hunt. A question like “what did the customer say about pricing concerns?” can search across emails, call transcripts, support tickets, and meeting notes simultaneously.

The theme across all three problems: AI doesn’t make decisions for you. It gets you the information you need to make decisions yourself, faster than you could get it manually.

How This Applies to Your Role

The information problem manifests differently depending on your position. Here’s what decision support looks like at different levels:

For Department Heads

The decisions you make daily: Where should my team focus? Which escalations are real problems versus noise? How should I allocate limited resources?

The information gap: You get dozens of inputs—emails, reports, complaints, requests—but you can’t process all of them deeply. You end up making prioritization decisions based on whoever is loudest or most recent, not necessarily what’s most important.

AI decision support opportunities: - Pattern detection across support tickets or customer feedback - Sentiment tracking to identify emerging issues before they escalate - Exception flagging so you can focus on anomalies, not routine cases - Summarization of lengthy reports or email threads

The key question: What would you prioritize differently if you could actually see all the signals?

Example in action: A customer success director I worked with was spending 2 hours daily reading through support tickets to identify escalation patterns. She implemented an AI summary that categorized tickets by severity, product area, and sentiment—delivered to her inbox each morning. Same information, 10 minutes instead of 2 hours. The time saved went into actually solving the problems she identified.

For Individual Contributors

The decisions you make daily: How should I spend my time? What should I work on first? When is something important enough to escalate?

The information gap: You’re often missing context. You don’t know what happened in the meeting you weren’t invited to, what the customer said in a different channel, or what priorities shifted since your last sync.

AI decision support opportunities: - Research synthesis when you need to get up to speed quickly - Meeting and conversation summaries to fill context gaps - Status aggregation across multiple systems or channels - Competitive or market intelligence gathering

The key question: What context do you wish you had before making your next decision?

Example in action: A product manager I know starts every sprint planning meeting with an AI-generated summary of what customers said about related features in the past month—pulled from support tickets, sales calls, and NPS comments. Before, she’d go into planning with incomplete context, then discover misaligned priorities mid-sprint. Now she has the full picture before decisions get made.

For Small Company CEOs

The decisions you make daily: Where should I focus limited resources? Which opportunities are real versus distractions? When do I need to intervene personally?

The information gap: You’re the decision bottleneck with the least time to research. Your inbox is overwhelming. Your direct reports each present partial pictures. You make consequential decisions with whatever information is at hand—often incomplete.

AI decision support opportunities: - Daily briefings that synthesize overnight developments across the business - Competitive intelligence monitoring without manual research - Meeting preparation that pulls relevant context from multiple sources - Decision frameworks that surface relevant precedents and data

The key question: What information would you gather if you had unlimited time—and how can you get 80% of that value in minutes?

Example in action: A founder running a 40-person company used to start each day reading through Slack channels, emails, and project updates—45 minutes before he could make his first decision. He implemented a morning briefing workflow that synthesized the previous day’s key developments, flagged items requiring his attention, and highlighted decisions pending his input. Same awareness, 10 minutes instead of 45.

For Senior Leaders

The decisions you make daily: How should we position strategically? Which signals indicate emerging opportunities or threats? What message needs to reach the organization?

The information gap: You have access to more information than anyone else—but it’s fragmented across reports, meetings, and conversations. Synthesizing it into coherent strategic understanding takes enormous time. Often the most important signals are buried in operational noise.

AI decision support opportunities: - Cross-functional synthesis of what’s happening across the organization - Strategic intelligence that connects external trends to internal capabilities - Communication amplification that ensures consistent messaging at scale - Board and executive preparation that surfaces what matters from voluminous data

The key question: What patterns would you see if you could observe your entire organization simultaneously?

Example in action: A VP of Operations received weekly reports from six regional directors. Reading and synthesizing them took half a day. She implemented an AI workflow that extracted key themes, flagged anomalies, and identified cross-regional patterns. The same synthesis now takes an hour—and catches patterns she used to miss.

The Questions That Guide Implementation

Before any AI implementation, answer these four questions:

1. What decision will this help me make better?

If you can’t name a specific decision, you’re not ready. “Being more productive” isn’t a decision. “Deciding which customer issues to escalate” is a decision. Get specific.

2. What information gap is preventing that decision today?

Identify what’s actually missing. Is it volume (too much to read)? Velocity (changes too fast)? Variety (scattered across sources)? The gap determines the solution.

3. How will I verify the information AI provides?

Remember the intern model. What’s your review process? How will you spot-check for accuracy? AI can give you faster information, but unverified information can be worse than no information.

4. What happens if the information is wrong?

What’s the consequence of acting on bad information? This determines your review level (from Chapter 2) and whether this is even an appropriate use of AI.

If you can answer these four questions clearly, you have a decision support opportunity worth pursuing. If you can’t, step back and think harder—or pick a different problem.

These questions might feel obvious. But most failed AI implementations skipped them entirely. They started with “AI is exciting” and ended with “the pilot didn’t produce ROI.” The questions create the discipline that leads to success.

Write them down. Post them where you’ll see them. Ask them before every AI conversation.

Common Objections

“Some decisions need to be fast, not well-informed.”

Speed and information aren’t opposites. AI can deliver information in seconds that would take hours to gather manually. The question isn’t “do I have time for good information?”—it’s “how can I get good information faster?”

“My gut is usually right. I don’t need data.”

Your gut is pattern recognition from past experience—which is valuable. But past patterns may not apply to new situations. AI can augment your gut with patterns from current data, helping you recognize when the situation is familiar versus genuinely new.

“My role is about relationships, not information.”

Good relationships depend on being well-informed. Knowing your customer’s history, preferences, and recent interactions makes every conversation more valuable. Information isn’t opposed to relationships—it enables them.

“We don’t have the data for this.”

You might have more than you think. Before assuming you lack data, ask: “If I had an intern who could read every email, every support ticket, every survey response—what would I ask them to find?” That question often reveals data you’re already collecting but not using.

“Isn’t this just business intelligence by another name?”

There’s overlap, but key differences. Traditional BI works with structured data in defined formats. AI-powered decision support can handle unstructured information—emails, meeting notes, customer conversations—that never made it into your dashboards. It’s not replacing BI; it’s extending what information you can actually use.

Your Monday Morning Action Item

This week, identify your information gaps.

List your three most important recurring decisions—choices you make weekly or daily that significantly impact your work or your team.

For each decision, answer: - What information do you wish you had before making this decision? - Where does that information exist (or could it exist)? - How long does it take to get it today? - What happens when you decide without it?

That’s your decision support opportunity map. The biggest gaps—where important decisions suffer from slow or missing information—are where AI can actually help.

Don’t try to solve all three at once. Pick the one where the gap is clearest and the stakes are highest. That’s your starting point.

The organizations succeeding with AI aren’t the ones with the most sophisticated technology. They’re the ones who understand their information problems clearly enough to know where AI fits—and where human judgment remains essential.

What Comes Next

You now have the mental model for AI (the intern), the operating principles (Chapter 2’s four principles), and the strategic frame (decision support, not automation).

The next part of this book—Part 2, “The Audit”—will show you how to systematically map your decision landscape, score opportunities, and choose where to start. You’ll move from understanding the information problem to solving it.

But that mapping starts now, with your Monday morning action item. The exercise below isn’t just homework—it’s the input for everything that comes next.

The intern model tells you how to work with AI. This chapter tells you why AI matters for decisions. Part 2 will tell you where to start.