How Product Workflows Drive Strategic Decisions in 2026

Product organizations have been asking the same question for years: How do we turn customer intelligence into competitive advantage?

In our recent webinar, Ross Webb and Elena Levi unpacked new survey insights from product leaders and shared what’s actually changing as the product org moves from operational support to strategic influence.

As teams continue to struggle with strategy, the real tension now is proving impact in a world where product is becoming the organizational bottleneck.

Where Product Workflows Failed in 2025

Last year’s challenges centered on role clarity. This year’s data tells a different story.

Teams broadly understand what the different parts of a product organization—from product management and design to product operations and go-to-market—are supposed to do. What’s harder is demonstrating how that work moves the business forward. 66% of survey respondents said measuring success is now their biggest unresolved challenge, closely followed by managing product feedback loops. Role clarity, once the top concern, has dropped to third.

“Credibility comes from delivering results, and visibility comes from communicating those results. And you need both.”

— Ross Webb, Founder, Top Prods Community

That shift signals progress—but it also raises the bar. Product teams are expected to influence outcomes, not just enable process.

Feedback Loops Are the New Frontier

Across the discussion, one theme kept resurfacing: feedback loops are where product professionals either earn trust or lose momentum.

Collecting feedback isn’t the hard part anymore. The real work is turning scattered signals into insights teams can act on and close the loop on. More than half of product professionals surveyed said improving feedback loops is the problem they most want to solve next, underscoring just how central this capability has become. When feedback is fragmented across tools, teams default to gut instinct instead of evidence.

“When teams see that feedback is actually leading to action and that they’re being heard of, they’ll continue to provide it. If they don’t hear back, they’ll just stop collaborating.”

— Elena Levi, Director of Product, Payoneer

How to build effective product feedback loops:

1. Centralize collection: Choose one source of truth for customer voice. Top tools from the survey: Productboard for customer insights, JIRA for engineering alignment, Pendo or PostHog for product analytics.
2. Enforce data quality: Implement required fields in your feedback system. One respondent shared that this simple step "strengthened our data entry" significantly. As Ross emphasized: "Structure before automation. Required fields, enforcement, clean data first."
3. Close the loop: Connect feedback directly to roadmap decisions and communicate outcomes back to stakeholders. This builds the trust that keeps feedback flowing.

Ross compared this to sales ops with Salesforce: "It's their unquestioned source of truth. Product ops needs the same clarity. This is where customer voice lives. Full stop. Not 15 Excel sheets and definitely not 20 Miro boards you can create but never find again."

AI Won’t Fix Messy Foundations

AI is not the silver bullet we want it to be.

Most teams are using AI today for content generation and summaries. Far fewer trust it for prioritization or decision support. The reason is skepticism about the outputs.

Ross and Elena emphasized that AI amplifies whatever foundation you already have. Clean, structured feedback enables powerful synthesis and trend detection. Messy inputs lead to confident nonsense. Before automating workflows, teams need discipline around data quality and ownership.

That’s also where AI tools purpose-built for product workflows start to stand out. Productboard Spark, now in public beta, is designed to leverage your product and business context to support your real PM workflows, creating superior product briefs, competitive analyses, and feedback analyses. It builds your organization’s knowledge over time, not like generic AI tools where you send one-off prompts and it forgets everything after a session ends.

From Tactical Support to Strategic Influence

Perhaps the most important shift discussed was where product teams show up in the decision-making cycle.

High-performing teams aren’t waiting to be invited in after plans are set. They’re earning influence by owning feedback loops and surfacing insights early, and clearly communicating impact. Strategic influence comes from credibility built through delivery and visibility.

When product leaders can show how insights changed decisions, priorities, or outcomes, executive trust follows.

The Road Ahead

The wishlist for 2026 is ambitious, but attainable:

• Stronger, closed-loop feedback systems
• AI that augments decisions, not just documents them
• Metrics that prove product team impact without endless definition debates

If product is evolving from bottleneck to business partner, this webinar offers a clear look at what that transformation really requires.

Watch the full on-demand webinar and see how product goes from bottleneck to business partner.

Frequently Asked Questions: Product Workflows in 2026

How do you showcase the ROI on your AI-powered product workflow investments?

Focus on outcomes, not the transformation itself. Nobody wants to buy a transformation—they want results. Ross emphasized this point: "Who cares about AI transformation? People want to buy the results. You don't buy the nail, you buy the picture hanging on the wall."

Find ways to quantify impact in terms of time saved, money recovered, or processes streamlined. Elena shared Payoneer's approach: "We measure with outcome KPIs. For example, we track data completeness and velocity increases. Even if the numbers move slowly, you can show impact and prove the effort covers the cost of the tool."

Get stakeholders excited by showing them how it works in your specific context. Skip the generic Harvard Business Review statistics about AI failure rates and focus on real results in your niche.

What tools should I use to collect and organize customer feedback?

Start with whatever tool your team will actually use. As Ross put it: "The best camera is the one you've got on you." Don't spend $100,000 on enterprise software if a Google Form gets you the feedback you need.

For centralized customer insights, Productboard integrates with tools teams already use—Slack, Confluence, Salesforce. For product analytics, PostHog earned strong recommendations from both speakers for its ease of setup and connector ecosystem. JIRA remains essential for engineering alignment.

Elena added an important principle: "If you're collecting feedback from customers, use the tools and systems where they already are. Don't move customers to another system. It's better to have fewer capabilities and more responses."

For automation and workflow orchestration, learn tools like Zapier, Make.com, or n8n. Even investing 4-6 hours to understand these connectors will pay dividends in streamlined data flows.

How do you collect product feedback from internal teams?

Elena's answer was straightforward: "Surveys, anonymous feedback, and just talking to people. Shadow them, understand what frustrates them, see what they find useful—the same way we do with customers."

Ross pushed back on the temptation to scale feedback collection too quickly: "Sometimes the best feedback comes from the things that don't scale. You'll be in rooms where the smartest people are quiet and introverted. They won't tell you in a big meeting, but book a one-to-one and they'll share everything."

He shared a memorable example of persistence: Early in his career, he struggled to get time with a VP of Customer Success. He discovered she was friends with the head of implementation, started casually joining their smoke breaks, and eventually built a strong working relationship. "It's the things AI can't do—the really hard stuff like sitting down and understanding people—that gives you the best feedback."

Elena added: "The best feedback I get is turning to people in the elevator or kitchen and asking them questions out of the blue. When they don't know your role, you get really candid answers. That's golden feedback."

What AI tools are actually useful for product teams right now?

Current AI usage in product workflows breaks down as follows: 72% use it for content generation (meeting notes, PRDs, documentation), 52% for workflow automation, 48% for data analysis, and only 18% trust it for decision support or prioritization.

Elena highlighted "vibe coding" tools like Lovable, Bolt, and Replit: "Any tool that lets you be creative and solve problems visually. Instead of drawing on a whiteboard, take it into Lovable and let your team play with a working prototype. That makes a difference."

Ross currently relies on Claude Code and Cowork with as many MCP connectors as possible (Linear, Figma, Asana, etc.): "I run multiple agents in parallel. They update trackers, ping team members through Chrome extensions, and compress context. It's reduced my overhead by probably 60%, which frees me up to work with humans on the sticky problems."

For complex technical challenges, Ross recommends Grok Heavy with its multi-agent system: "It's expensive—$300/month—but when GPT and Claude can't solve something, Grok's four-agent approach often breaks through."

Both emphasized collaboration tools like Miro for distributed teams and reminded everyone not to overlook fundamentals: "One of our tools is Word," Elena laughed. "Writing documents is really important. Any LLM that helps with that—I've been using Copilot and it does a good job—is valuable. And Excel remains the most versatile tool in the world."

How do you enforce data quality without creating too much friction?

Elena's currently leading a modernization initiative at Payoneer that puts data quality front and center: "Clear and proper data is the basis for everything. With AI, 'garbage in, garbage out' becomes much more than wrong decisions—the system can go wild, and we don't want that."

Her team's approach involves proper event tracking, cataloging, and schema definitions: "You need to invest in doing your data correctly first. Then everything becomes simpler. Infrastructure done properly leverages everything, especially with AI."

For feedback systems specifically, implement required fields but keep them minimal and purposeful. One survey respondent noted that required fields in Productboard "strengthened our data entry" without slowing teams down. The key is balancing structure with speed—enforce the fields that truly matter for decision-making, not every possible data point.

Ready to transform your product workflows? Watch the full on-demand webinar to hear Ross Webb and Elena Levi's complete discussion, including detailed examples, live Q&A, and tactical frameworks you can implement immediately.