Why Do Tools for Product Managers Suck?

January 26, 2026 10 min read

Fifteen years of pitches for "the next big PM tool" have convinced me most of them are set up to fail. They start sleek and opinionated, then either bloat into a database nobody loves or get abandoned for spreadsheets and docs. The reason is simple: they optimize for the how and ignore the why.

The structural trap

No two product orgs run the same process. Even within one company, a growth squad and a platform team will move differently. A rigid tool tries to encode a single workflow and inevitably becomes either:

• Too opinionated to fit real work.
• So flexible it devolves into a spreadsheet with UI chrome.

So teams stuff the tool with tickets and roadmaps, then do the real thinking in meetings, docs, Slack, and their heads. The tool becomes a ledger, not a decision system.

How tools got here

Software made project management easy to codify: columns, statuses, checklists, burndowns. That is the how. The why—user needs, bets, tradeoffs, tone, risk posture—lived in scattered places:

• Sales calls and Gong recordings.
• Research notes and Miro boards.
• Strategy memos and pitch decks.
• Slack threads and email chains.
• Tribal knowledge from people who have seen this movie before.

PM tools kept trying to pull the how into a single view. They never pulled the why. Without the why, the tool cannot guide judgment. It just tracks tasks.

What the role actually is

If you boil product work down, it looks like this: 1. Seek out and synthesize information. 2. Refine an idea and decide what matters. 3. Execute with a team. 4. Learn if it worked; adjust or kill.

Step 2 is the core. It lives on context and judgment. Most tools barely touch it. They log what you decided, not how you got there or what constraints should guide the next decision.

Why "workflow-first" fails PMs

• Process mismatch: tools enforce one way to manage work. Teams spend hours shoehorning their reality into templates.
• Context loss: the tool does not hold the customer nuance, quality bars, or tradeoffs that shape good calls. Tickets become placeholders.
• Decision amnesia: why something was chosen gets buried in comments. Six months later, nobody remembers the rationale.
• Misaligned incentives: teams optimize for moving tickets, not shipping outcomes with the right quality and tone.
• Second systems: since the tool does not hold the why, teams build parallel systems in docs, Notion, or slide decks. Duplication and drift follow.

The universal layer PM tools missed

Across industries and stages, the specific processes differ, but the decision patterns rhyme:

• Who is the user and buyer?
• What job are we solving and in what order?
• What tone fits the audience?
• What quality bar applies for this release?
• What constraints (performance, compliance, privacy) are fixed?
• What do we measure to know it worked?
• What did we try before and why did we keep or kill it?

These are decisions, not tasks. They are reusable. They should travel with work and guide agents and humans. Traditional tools left them in scattered notes.

Why this matters more with agents

AI coding agents amplify the gap. They will build whatever is described. If the why and the constraints are missing, they build generic solutions fast. Rework explodes. PM tools that only store tickets cannot feed agents the decisions they need. The result is 10x execution on half-baked direction.

What a PM tool would need to avoid sucking

• A decision register: explicit, versioned choices about ICP, tone, dependencies, performance budgets, rollout rules, security posture, pricing principles.
• Structured briefs: one-page artifacts per initiative and task with user, goal, success, constraints, linked decisions, and rationale.
• Context ingestion: ability to pull from calls, docs, code, support, analytics to propose new decisions or highlight conflicts.
• Agent-readable outputs: context in formats agents can consume automatically. No copy-paste walls of text.
• Drift detection: alerts when work (agent or human) violates decisions or when decisions conflict.
• Feedback loops: edits and outcomes feed back into the decision set. The why evolves, not just the task list.
• Lightweight rituals: daily decision hygiene, weekly direction checks, monthly strategy pulses. The tool should support them without ceremony.

This is product management as decision infrastructure, not task plumbing.

A few examples of the gap

Tone and audience: A legal tech product needs sober copy. The PM tool tracks tasks. The why behind tone lives in a brand doc nobody reads. An agent ships playful modals. Support volume spikes.

Dependencies and patterns: The team standardizes on Jest and React Query. The PM tool has no field for decisions. Agents introduce Vitest and Axios. The build fragments.

Performance and reliability: The API must meet P95 latency of 300ms. The PM tool holds stories about features, not budgets. Agents add features that blow the budget. SRE finds out in prod.

Rollout rules: Enterprise features must be behind flags with audit logging. The tool tracks "Build feature X" and "Add flag." It does not enforce the rule. Agents skip logging. Compliance risk appears later.

All of these are decisions that should sit next to the work. They rarely do.

Why now

Three converging shifts make a decision-first approach feasible:

• AI can read and synthesize across sources. The cost of pulling context together dropped.
• Agents need structured input. A decision register gives them rails.
• Teams move faster. The penalty for missing context is higher because rework comes faster too.

Ten years ago, building a decision-aware PM tool would have meant armies of humans tagging data. Today, models can propose decisions, spot conflicts, and surface what matters. The human keeps control of judgment.

What this looks like in practice

• You finish a customer call. Instead of a wall of notes, you record three decisions: "Enterprise tone only," "Billing emails must include audit trail," "Activation is the north star this month." The tool updates the register. Agents consume it automatically.
• A new feature brief links to decisions: tone, logging, performance, dependencies, rollout rules. The agent drafts code within those constraints. Review focuses on edge cases, not framework choice.
• An agent suggests adding a new HTTP client. The tool flags a decision violation. You approve or reject. The register updates if you approve.
• Post-ship, you note that users were confused by copy. Tone rules update. Future features inherit the fix.

The PM tool stops being a graveyard of tickets. It becomes the system of record for decisions and context.

How to evaluate PM tools now

• Do they store decisions as first-class objects?
• Can they ingest context from calls, docs, code, and support?
• Can agents consume the data without manual copy-paste?
• Do they help you spot drift from standards?
• Do they keep rationale accessible months later?
• Do they reduce meeting load by making context available by default?

If the answer is no, you are buying another workflow tracker.

A short playbook if you are stuck with legacy tools

• Create a decision register outside the tool. Keep it in JSON or plain text. Link to it from tickets.
• Add a brief template to your current system. One page: user, goal, success, constraints, decisions, rationale.
• Script context export to agents. Do not rely on humans to paste.
• Track drift manually: note when work violates decisions; fix the source.
• Run a weekly direction check and publish decisions. Treat the tool as a delivery channel, not the source of truth.

It is a stopgap, but it reduces rework while you wait for better tools.

Anti-patterns in PM tooling

• Template overload: dozens of fields nobody fills.
• Process rigidity: forcing one roadmap format on every team.
• Everything-in-one-place claims that ignore how humans actually talk and decide.
• AI features that summarize tickets but never update decisions or constraints.
• Data hoarding: locking context in a tool without open outputs for agents and other systems.

A different bar

The bar for a PM tool should be: does it improve product judgment and reduce rework? Task completeness does not matter if the wrong thing shipped or the right thing shipped without the right tone, performance, or compliance. Decision infrastructure is how you raise that bar.

Signals a decision-first approach is working

• Rework drops because context arrives before build.
• Agents stop introducing random dependencies and patterns.
• PMs spend less time re-explaining rationale.
• Drift events decline; decisions get updated instead of ignored.
• Stakeholders learn the why from the tool, not just the what.
• Meetings shrink because context is already shared.

Signals you are stuck in old patterns

• Tickets describe tasks with no linked decisions.
• Agents and engineers copy-paste walls of text into prompts.
• Tone and compliance issues recur in every release.
• The "why" for a feature lives in someone's head or a deck from last quarter.
• Your tool usage is mostly status updates and burndowns.

How Brief fits

We built Brief around decisions and context because the old tooling patterns could not keep up with agent speed. It ingests calls, docs, code, tickets. It proposes decisions, lets you accept or reject, and feeds those to agents. It flags drift. It keeps rationale attached. It does not tell you how to run standups. It gives you the why on tap.

Coding agents, remote engineers, design partners, and execs all benefit because the same decision set guides their work. That is the difference between another PM surface and a strategic partner.

Stage-specific needs (and how tools miss)

Early stage: decisions change weekly. You need fast capture, not heavy process. Legacy tools demand hierarchies and projects before you have product-market fit. A decision-first system adapts as you learn.

Growth stage: you need consistency and speed. Tools that cannot enforce or surface decisions let teams drift: new dependencies, mixed tone, conflicting metrics.

Enterprise: compliance, audit, and change management matter. Traditional PM tools track approvals but rarely expose the underlying rationale or constraints to agents. A decision register with auditability serves both speed and governance.

A story of why workflow-only fails

A mid-market SaaS team standardized on Jest, React Query, and strict tone rules for regulated buyers. Their PM tool tracked epics and tickets. None of those standards lived there. Engineers prompted agents with whatever was in the ticket description. Within two sprints, the codebase had Vitest in one feature, Axios in another, playful copy in an enterprise flow, and missing audit logs on a billing change. QA caught some issues. Others escaped. Rework ballooned.

They added a simple decision register and linked it in every task. Agents consumed it automatically. Dependency creep stopped. Tone issues dropped. Audit logging became default. The tool did not change; the context did. That is what a PM tool should have done natively.

How to roll this mindset into your team

Week 1: Identify the five to ten decisions that matter most right now (tone, ICP, dependencies, performance, security, rollout rules). Write them down. Share with everyone.

Week 2: Add a one-page brief template to your current system. Require it for new initiatives. Link decisions.

Week 3: Automate passing decisions and briefs to agents. Stop trusting manual copy-paste.

Week 4: Start logging drift and rework causes. Summarize weekly.

Week 5: Prune and update decisions. Retire stale ones. Highlight new ones to the team.

Week 6: Evaluate whether your current PM tool helps or hinders this flow. If it hides decisions, work around it or replace it.

The future bar for PM tools

In a world where agents and small teams ship features in hours, tools that only track work are table stakes. The bar moves to:

• How quickly can we surface the right context to every actor (human or agent)?
• How reliably can we keep decisions current and visible?
• How fast can we spot and correct drift?
• How little ceremony can we get away with while keeping alignment?

Meet that bar and PM tools stop sucking. They become infrastructure for product judgment.