Configure, Price, Quote (CPQ) software is part of a larger ecosystem that helps your business connect your front of house sales with engineering and delivery functions. Rather than a one-off project, your CPQ implementation is a core part of digital transformation that impacts sales, engineering, operations, and ultimately, your customers.

Many implementation challenges come from how well organizations prepare their tech stack, teams, and processes. And the process is not one-size fits all, but there are best practices to position your business for success. These eight steps will help you build the right foundation, so you can go beyond go-live and deliver lasting value across your entire business.

1. Define and align on vision

The first question of implementation isn’t technical. It’s strategic.

“Why are you implementing CPQ?”

Define the business outcomes you want to achieve: faster time-to-market, improved customer satisfaction, a better e-commerce experience.

Because CPQ is a cornerstone of the quote-to-order process, it’s also a critical driver of digital transformation. That’s why it requires CEO sponsorship and a regular place on your leadership agenda to ensure the implementation continues to align with overall strategy.

2. Translate vision into tangible success metrics

A bold vision is inspiring, but to guide a project it must be measurable. Break it down into clear success criteria: efficiency improvements, reduced time-to-delivery, margin protection, or customer satisfaction gains. What are you limited by with your current solution, and what long-term needs do you need to address? Use that to help guide what your team should improve and measure.

Use frameworks your teams already know, such as KPIs, OKRs, or something similar so that you don’t have to reinvent the way that your teams track success.

3. Map target processes

Copying the past only leads to the same outcomes that your current solution provides, which are limiting. Simply digitizing legacy processes means you miss the opportunity to rethink how you work. If the system isn’t intuitive, you risk underwhelming adoption and poor ROI.

Instead, map your target processes. Where are today’s bottlenecks? Which steps are unnecessarily manual? What would a seamless end-to-end sales journey look like? A new CPQ solution is your chance to design for the future.

Start by mapping the user journey. Define which systems should lead at each step, clarify handover points, and establish a master data strategy so product, pricing, and customer information flows consistently. At the same time, challenge the status quo:

• Do all existing systems still need to be in place?
• Can you eliminate redundant process steps?
• Where can you reduce manual handovers?

4. Prepare systems and data

Where do you start to create a master data strategy? Clean, structured data is the backbone of any CPQ implementation. Focus on these three areas first:

1. Organization and governance: Document how your company sells today, emphasizing approvals, workflows, and governance across all business units and regions. Consolidate it into one clear picture instead of scattered notes or tribal knowledge.
2. Product data structure: Identify gaps or “white spots” where documentation is weak. Transition product data into a consistent format, clean it up, and use the opportunity to simplify your portfolio and reduce complexity.
3. Pricing data and strategy: Review regional variations and outdated models. Clarify how pricing should work in the future, and align stakeholders on a common strategy before building it into CPQ.

Getting these three areas right not only speeds up implementation but also ensures your CPQ system reflects how your business actually operates.

5. Choose a project approach that fits

There’s no universal “best” methodology. What matters is what works for your organization. Agile methods can be useful, but not everything in CPQ can be done in small, flexible steps. For example, product modeling and pricing usually need clear, fixed targets.

Two rules of thumb:

• Don’t over-plan upfront. Different vendors solve problems in different ways, so leave room for flexibility.
• Avoid the “big bang.” Pilot with a product line, market, or region to learn before scaling.

6. Build and train the right team

Your people are the driving force behind your CPQ implementation. Define a focused core team with clear responsibilities and make CPQ a business priority rather than a side project.

Training should begin early. Teams need to understand both the system and the project methodology. Just as importantly, involve long-term maintainers from the start. CPQ lives well beyond go-live; those who will manage it later need to be part of the build today.

7. Set reasonable budgets

If you can’t explain why money is being spent, it will be hard to defend when priorities shift.

Start by building a business case that flows directly from your vision. Then, group budgets by themes or project phases rather than individual requirements. This makes it easier to manage spend and connect budget to overall value without getting lost in the line-item details.

Finally, make budgets transparent. Share not just the numbers but the reasoning behind them. When stakeholders see how budgets connect to value, it’s easier to set priorities and keep the project moving forward.

8. Make change management a critical part of leadership’s agenda

Perhaps the most underestimated factor in readiness is change management. CPQ reshapes how sales, engineering, and operations collaborate, as well as how customers experience your company.

Engage end users early, especially sales. Align the program with your company culture and pace of change. Give executive sponsors a visible role in leading the transformation and use your roadmap as a communication tool to show progress and reinforce the bigger picture.

Set the stage for success with Tacton CPQ

A CPQ project’s success is decided long before go-live. With a clear vision and a strong focus on structured change, you create the foundation for lasting value.

At Tacton, we know CPQ is part of a larger transformation in how you sell, engage buyers, and connect the front office with operations. Our Buyer Engagement Platform is built to guide manufacturers through this journey, combining industry expertise with proven implementation practices for a smoother, more fruitful rollout.

Ready to start your CPQ journey the right way?

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An enterprise Configure, Price, Quote (CPQ) software implementation can take months to years and millions of dollars depending on the level of your product complexity. With CPQ ROI being a key measurement of implementation success, it’s vital to expedite that time-to-value as much as possible. AI in CPQ, but especially AI-assisted CPQ implementation in the form of AI product modeling assistants, is changing the game by increasing adaptability and reducing time, costs, and energy for both initial implementation and future product launches.  

Learn how businesses can accelerate time-to-value and cut CPQ implementation in half with the help of AI. 

AI-assisted CPQ implementation time has long-term benefits 

Time spent modeling is time not selling. When implementation is slow, teams spend weeks or months setting up product rules, features, and combinations, often manually. This delays the time when sales teams can actually use CPQ to quote and close deals.  

But beyond time-to-value, automating product modeling with the help of AI has hidden benefits for long-term go-to-market agility and customer experience.  

• Easier collaboration: Lower learning curves let more teams (even non-experts) shape and refine product models.

• Faster product launches: Quoting processes are ready to adapt when your product lineup changes.

• Continuous optimization: Faster iterations make it easier to refine configuration logic and adapt to market needs.

• Stronger customer focus: With less effort tied up in implementation, more focus shifts to sales strategy and customer experience.

How AI helps implementation teams streamline modeling

Traditionally, one of the most time-intensive parts of CPQ implementation has been building product models—defining rules, features, constraints, and dependencies that govern how products can be configured and quoted.

By applying AI and machine learning internally, CPQ vendors can:

• Interpret existing product documentation (from spreadsheets, PLM exports, or written instructions) and structure it into a usable model faster, reducing the need for manual rework.
• Generate functional base models that serve as a starting point for solution consultants, cutting weeks from setup without sacrificing accuracy.
• Automate repetitive tasks like building combination tables or cascading attributes across configurations, helping experts focus on refinement rather than data entry.
• Lower complexity for collaboration by providing dynamic, visual ways for cross-functional teams to review and validate models earlier in the process.
• The result isn’t that customers “plug in” their data directly—it’s that vendors can deliver implementations faster, with fewer bottlenecks, thanks to these behind-the-scenes AI efficiencies.
• Push the model directly into CPQ: Once the model is complete (or even partially complete), teams can export it directly into the CPQ. They can also choose to continue refining within the AI environment or switch seamlessly to the CPQ platform to test and deploy in real quoting scenarios.

How much time can AI save in CPQ implementation? 

Early applications of AI in product modeling suggest that it can significantly reduce implementation timelines. For example, what used to take a month to model in a traditional process may be shortened to about a week when supported by AI-enabled efficiencies. 

For more complex product models, we estimate that AI has the potential to cut implementation time for solution consultants as much as 50% for more complex models, depending on the quality of existing product data and the complexity of configuration rules. 

These figures aren’t exact benchmarks but rather directional estimates based on current testing and early use cases. The key takeaway is that AI can meaningfully reduce the manual effort involved, enabling faster time-to-value and easier iteration during implementation. 

Increasing CPQ ROI with AI-assisted product modeling 

Speed to value directly impacts your bottom line. When implementation is delayed, so is your ability to quote accurately, respond to customer needs, and generate revenue. Every month spent on internal configuration work is a month lost on customer engagement and growth. 

With AI-assisted CPQ implementation, you eliminate weeks or months of manual setup and drastically reduce reliance on scarce technical resources. This lets you: 

• Accelerate quoting cycles and close deals faster 

• Reduce costly errors by starting with a data-driven, validated product model 

• Enable faster feedback loops, improving pricing, bundling, and product strategy over time 

• Free up your team to focus on revenue-driving activities instead of backend maintenance 

Crucially, faster implementation helps shift the perception of CPQ from a major IT project to a strategic tool that you can use to go to market faster and more strategically. 

Go to market faster with Tacton 

At Tacton, we’re focused on reducing the complexity of CPQ implementation so you can go live faster, adapt quicker, and engage buyers more effectively. 

If you’re exploring CPQ, talk to us about how we’re making implementations more seamless and predictable, so your teams can focus on selling. 

When manufacturers roll out CPQ, the initial focus often centers on launch. Configure the system, train the team, and track early usage. But what happens after go-live matters just as much as the launch itself. Adoption levels shift over time, sales teams develop workarounds, and product portfolios grow more complex.  

CPQ introduces new ways of working, which means it requires active leadership, reinforcement, and measurement. The organizations that maximize their investment embed analytics into their change management strategy and create a cycle of continuous enablement that keeps adoption high and ROI growing. 

Moving from metrics to organizational change 

Adoption metrics are a useful starting point. Quote completion rates, error frequency, time-to-quote, and feature usage reveal how the system is performing. However, measurement alone doesn’t drive improvement. For CPQ to remain effective, those insights must be tied to change management activities: coaching, training, process adjustments, and leadership communication. 

How to achieve continuous enablement  

What is continuous enablement?  

It’s using a structured feedback loop to ensure that CPQ continues to evolve alongside the business. This continuous enablement operates as a practical extension of change management principles: 

• Keep a pulse on behavior. Beyond login counts, look at how different roles and regions are really using CPQ. Are they relying on it for every deal or still defaulting to spreadsheets for certain products? 

• Spot resistance early. Underused features, repeated errors, or shadow processes are signals that something isn’t working. Addressing them quickly prevents small frustrations from becoming cultural barriers. 

• Enable with intent. Use adoption insights to focus training, simplify steps, or provide job aids that meet users where they are. Targeted support builds confidence faster than generic enablement. 

• Close the loop. Re-measure after interventions. Did the extra coaching reduce errors? Did a workflow adjustment improve quote completion? Feeding results back into the system makes enablement evidence-based instead of reactive. 

This cycle takes adoption from a one-time measurement into a continuous enablement practice that reduces resistance and strengthens user confidence.  

CPQ change management strategy in practice 

Change management becomes most effective when it’s grounded in real adoption data. Analytics provides the visibility leaders need to shape ongoing support and improvement across several dimensions. 

• Training – Usage data highlights where to invest in tailored enablement. New hires may need foundational sessions, while experienced users benefit from advanced coaching. Regional or role-specific training can address unique adoption gaps. 

• Communications – Adoption insights fuel storytelling. Sharing success metrics and reinforcing best practices keeps momentum high and helps users see the tangible value of CPQ. 

• Governance – Embedding analytics reviews into quarterly business updates or steering committee meetings ensures adoption stays on the leadership agenda and aligned with broader business goals. 

• Resistance management – Analytics reveal signs of resistance, such as users bypassing workflows or reverting to spreadsheets. Spotting these early allows managers to intervene with coaching, sponsorship, or process adjustments before disengagement spreads. 

• Usability and process design – Patterns like frequent quote abandonment or repeated errors often point to usability issues, not user reluctance. Feeding these insights into CPQ improvements reduces friction and builds user confidence. 

The key is that analytics makes change management evidence-based instead of reactive. Rather than guessing where adoption is failing, leaders can use clear signals to guide training, communication, and process improvements that keep CPQ adoption strong. 

Why continuous enablement matters 

When CPQ usage is reinforced through ongoing change management, the impact extends beyond system performance. Organizations see: 

• Faster quoting cycles as users grow confident with workflows.

• Fewer errors and less rework thanks to reinforced training and process clarity.

• Higher margins as compliance improves and manual workarounds decline.

• Improved morale as sales teams experience CPQ as a sales partner, not a barrier.

Continuous enablement ensures CPQ remains aligned with business strategy long after the initial implementation. 

How Tacton supports lasting efficiency and enablement 

At Tacton, we believe CPQ adoption should be managed like any other business transformation. That’s why our approach combines proven implementation practices, ongoing services, and analytics capabilities to ensure lasting success. 

If you want to strengthen your change management strategy and increase adoption with a data-driven approach, Tacton can help. Learn more about Tacton CPQ or schedule time with us.  

Quoting has long been treated as a back-office process to streamline for speed and accuracy. But when quoting and configuration live in a digital platform, they create a foundation for sales intelligence that reveals how customers buy, how markets shift, and where there’s growth potential. 

According to the Tacton 2025 State of Manufacturing Report, based on insights from global manufacturers, this opportunity is largely underutilized. Forty-three percent of manufacturers still rely on manual configure, price, quote (CPQ) processes, mostly spreadsheets. At the same time, nearly half of spreadsheet users say they are “satisfied” with their processes, suggesting that many organizations underestimate the strategic value of digitizing sales data, especially within CPQ.  

Data maturity in manufacturing sales: Sales intelligence vs. sales reporting

A majority of manufacturers today are still in manual reporting mode, with 55% reporting manually and only 37% using analytics embedded in their CRM, CPQ, or specific system. They track traditional CPQ metrics, such as quote turnaround times, error rates, discount frequency, that measure efficiency but stop short of strategy. 

Sales intelligence represents the next stage of data maturity. It leverages the data generated by every configuration and quote as a living dataset where sales teams work to: 

• Uncover customer preferences and price sensitivity across regions and segments. 

• Reveal demand shifts that indicate where markets are growing or contracting. 

• Show which deals and configurations deliver sustainable margins versus which erode profitability. 

The key difference isn’t in producing more dashboards. It’s in simplifying data reporting and intelligence, so sales and quoting data are embedded in systems.  

Quoting data becomes a shared intelligence base when it’s captured consistently in the same source of truth. Sales, finance, and product teams no longer rely on anecdotal knowledge or siloed reports.  

The risks of poor data maturity and disconnected sales intelligence

The risks of not using CPQ as a sales intelligence tool compound over time. 

First, knowledge gets locked away. With 30% of manufacturers expecting significant retirement in their sales and engineering workforce within the next five years, failing to digitize quoting and configuration data risks losing critical context about how products are sold. A centralized, digitized system, such as a CPQ platform, ensures that expertise isn’t confined to a few individuals. It becomes accessible to every relevant stakeholder. 

Second, errors are normalized and signal weak data maturity. Even with rapid quoting cycles, errors persist. In fact, 58% of manufacturers that have streamlined their quoting still report frequent quote quality issues. If a company can’t trust its quotes, it can’t trust the underlying data. Analytics built on bad or inconsistent data will mislead decision-making, and quoting speed without intelligence creates noise and takes away the opportunity to learn from sales and product performance.  

Finally, strategic blind spots persist when data is layered in too many systems. Without consistent reporting embedded in the platform where sales works, such as CPQ, manufacturers miss signals about customer behavior and competitive dynamics. Deals are treated as isolated events rather than as data points that, when connected, could shape smarter account strategies or reveal emerging trends. 

Three key requirements for building a sales intelligence foundation 

To evolve from transactional quoting to strategic sales intelligence, manufacturers must first simplify their data landscape.  

• Consistency over complexity: The key to maturity is capturing data reliably in one place, not adding more tools. Integrating and embedding data into a central platform ensures inputs are standardized and usable. 

• Cross-functional accessibility: Intelligence grows when shared. Sales, finance, and product leaders need aligned visibility into how quoting patterns affect margins, growth, and competitiveness. 

• Learning loops: Every quote is a learning opportunity. Over time, trends in pricing, product mix, and deal outcomes can guide strategy—if those patterns are captured and fed back into the system. 

Develop a data-driven sales engine with Tacton

The State of Manufacturing Report shows that while quoting remains a weak link, a major opportunity lies in advancing data maturity. By treating CPQ as a sales intelligence system rather than a transactional tool, manufacturers can unify data, simplify layers of technology and data, and gain the insights needed to compete strategically.  

Learn how to build a data-driven sales engine with Tacton, the leading CPQ buyer engagement platform for manufacturers. See how CPQ can become a central layer for your smart factory to help you improve quoting, product profitability, lead times, and so much more.  

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