5 ways AI saves your sales team 10 hours a week on proposals

Your sales pipeline is full of potential deals, the proverbial phone is ringing off the hook, and your sales team is growing to match. But every sales process has its weak points that can undo all of your team’s hard work. In some organizations, proposals are that weak point. Your sales team spends so much time drafting proposals that they’re losing out on potential deals to competitors who get proposals out the door almost instantly. You’re trying to close gaps and accelerate the process but it feels like something’s missing. Something all your competitors are using.

AI.

But you don’t want to just go to any AI tool, hoping it’ll fix the issues with your proposals. You want to pick the right tool and implement it in the right way.

Here’s why (and how).

Why Sales Teams Are Drowning in Proposal Work

In organizations that scale quickly, your sales team is a victim of its own success. If you’ve never put time into automating or otherwise streamlining your proposal creation process, then the more deals reach the proposal stage, the more swamped your teams get.

The Hidden Cost of Manual Proposal Creation

The cost of creating proposals manually goes a bit beyond just forcing your sales reps to block time in their calendar for some deep work:

• Selling time: According to Salesforce’s State of Sales Report, sales reps spend only 28% of their time actually selling. With meetings, data entry, and other administrative tasks eating into selling time, even an hour spent drafting a proposal can be an expensive hour.
• Low standardization and errors: When sales reps need to both produce more proposals and send them out more quickly, they’re bound to make mistakes. That can range from typos to entire sections being sent blank. Forget about proposals being consistent between reps.
• Bottlenecks: A manual proposal creation process in a pipeline that’s otherwise automated quickly becomes a bottleneck. Pretty soon, deals start backing up, and your sales team’s ability to close deals slows to a crawl.
• Lost deals: No matter how long your sales process is on average, there are moments when speed is essential. When it takes hours to send out a proposal, you risk losing out to competitors who get one out faster.

How Proposal Bottlenecks Kill Deals

Any stage that stalls deals can undo all the optimization and improvements you’ve made to the rest of your pipeline. The longer it takes for a prospect to receive a proposal after the activity that created interest, the less likely they are to convert. In fact, a prospect is 25.9% more likely to convert when you send a proposal within 24 hours rather than within three to four days.

A single proposal might not take days to draft, but the more deals move through your pipeline, the more deals accumulate at the slowest point in your process: the proposal stage.

So how can AI fix this?

Way #1: Instant Template Selection and Customization

AI-powered sales proposal automation doesn’t just draft content for you. It gives you a range of options to start from. When was the last time you drafted, used, and documented a template for a sales proposal? Not only can creating a library of sales proposal templates be time-consuming, but it’s hard to strike the balance between how thorough a template should be and how easy it is to customize to a client’s specific needs.

AI can generate proposals from scratch, remember which proposals work, and help you refine templates to match exactly what a deal needs. The best part? An AI tool trained on your sales process can learn from every deal—whether won or lost—to keep improving things.

How AI Matches Proposals to Client Needs

Think of every need a client has expressed as a data point. The problem? Those data points are typically spread out over multiple calls, emails, and even records in your CRM. Gathering all that information when you sit down to actually draft a proposal can stretch the time needed for that draft unnecessarily.

An AI tool can take all that data and turn it into insights, which are then used to draft the right proposal template for each prospect. With the right prompt—and the right data—a sales rep can draft a proposal completely tailored to a prospect’s needs in minutes, rather than hours.

Real Example: Better Proposals in Half the Time

Open supports private and public organizations in their IT & Digital transformation, helping them stay competitive. Because they work with public entities, securing contracts requires precise proposals that follow a strict framework with specific requirements and SLAs. Producing these proposals manually involved painstaking manual effort and a lengthy approval process.

The team at Open turned to cobl to produce proposals more quickly without sacrificing on precision and customizability. The result? A 50% reduction in response time for RFPs (Requests for Proposal). Engineers could focus on contextualizing AI-generated proposals instead of starting from scratch every single time. That meant more time saved for everyone, more pipeline, and faster turnarounds on proposals.

Check out the full story here.

Way #2: Automated Content Generation from CRM Data

Your CRM is the command center for sales operations and a repository of everything from contact information for prospects to terms for past deals. That data can occasionally come in handy for your sales reps, but for an AI tool, it’s a goldmine. It’s a massive amount of training data that can show an AI agent what works, what doesn’t, and inform everything it does from writing proposals to suggesting next steps for complex deals. It saves your team a ton of time they’d lose manually cross-referencing all that data.

Eliminating Copy-Paste Work

Sales reps often have to go looking for everything from contact information to terms and objections when drafting proposals. Not only can that involve hours searching through records, but there’s always the chance of an error being made when data is copied and pasted over.

When you use AI to generate proposals, you forego that entire process. Because it has instant access to the data in your CRM, there’s no more copying and pasting. Templates can automatically be modified to suit different prospect needs and contact information is always accurate without your reps needing to lift a finger.

Smart Data Integration Across Systems

AI tools don’t just pull from the data in your CRM. Some of them can even access information in other tools. Sure, your CRM is a strong repository of data, but does it have absolutely everything your sales team needs? Contact information might be buried in a Google Contact, while a report affecting pricing might be in a spreadsheet somewhere. AI tools can intelligently get the data they need across platforms, as well as recommending sales reps check on data in tools they can’t access, just in case.

Way #3: Intelligent Pricing and Product Configuration

Even a simple pricing structure can quickly get complicated when you have to adjust it to a prospect’s specific needs. A sales rep might spend as much as an hour crawling through pricing documentation—or, worse, have to ask a pricing expert—before they can add the right information to their proposal. AI tools generate proposals based on that data automatically, meaning no one has to go searching for it. And the more documentation you have about exceptions, special offers, and the like, the more accurate each proposal is the first time.

Dynamic Pricing Tables That Update Automatically

AI can generate a dynamic pricing table that gets updated automatically based on the data in your CRM or any manual adjustments a sales rep makes. This doesn’t just allow experienced reps to save time on the proposals they draft, it helps junior reps onboard more quickly as they learn how your pricing structure works as they work.

Eliminating the Majority of Quote Errors

Making an error on a quote that leads to a lost deal can be one of the worst feelings for a sales rep. AI can do the hard lifting of finding all the relevant information for drafting a quote, allowing your sales team to focus on validating its output instead of researching and drafting manually—saving precious time while increasing overall quote accuracy.

Way #4: Accelerated Review and Approval Workflows

Sometimes, your organization is growing faster than your sales team, creating bottlenecks in your pipeline. When proposals are created—and approved—manually, every step from drafting that proposal to sending it off to a prospect can be its own bottleneck. If only a few people are responsible for approving proposals before they’re sent off, they can quickly pile up and either cause a slowdown in your whole sales process or result in cut corners.

AI tools don't just draft proposals; they can also streamline your review and approval process. They don't—and shouldn't—completely replace that process, but they can make the difference between manually checking every box (e.g., where data was sourced, which terms were used) and reviewing a final proposal before it's sent out.

Parallel Approvals vs. Sequential Bottlenecks

Most approval workflows are sequential. A sales rep sends a proposal to their team lead. The lead verifies what they can, legal has the final say on finer terms of the deal, and other stakeholders might have their turn for deals that require a bit more work to close (e.g., special development work for a software product). All one after the other.

In a parallel process, each section of a proposal is sent off to the department that needs to approve it, meaning multiple approvals can happen simultaneously. AI tools pre-emptively mark sections of a proposal that need approval, allowing reps and leaders alike to leverage parallel approvals for faster resolution.

Stakeholder Collaboration Made Simple

AI copilots and similar tools can guide both reps and stakeholders through an approval process for even the most complex products. They can answer questions based on the data available in your CRM and other tools, as well as suggesting next steps when stakeholders need to secure additional approval from other departments.

Way #5: Proposal Analytics and Continuous Improvement

Proposals are inherently disposable. A proposal for a lost deal is rarely looked at again, while for won deals they’re quickly supplanted by contracts. But forgetting about proposals after they’re sent off creates one big problem; you can’t improve on them.

Your sales reps have too many responsibilities to save and pore over every proposal before writing the next one, and your leaders are busy fixing other problems. But for an AI tool, every proposal is a data point that can lead it ever closer to drafting proposals that win (almost) every deal. A tool like cobl, for example, stores documents, past proposals, and client needs, using all that data to create stronger, more specific proposals over time.

Learning from What Wins (and What Doesn't)

Most AI tools that can generate sales proposals are based on large language models, which use massive amounts of data to understand how people write, what they expect from a certain task, and how that task can be completed more effectively. That means the more proposals they see, the better they get at drafting them—especially when they also have access to the outcome of each proposal.

Optimizing Your Proposal Strategy with Data

Collecting every proposal your team has ever sent and turning them into data you can actually analyze is a massive undertaking, often out of reach for sales teams in organizations trying to grow quickly. But that’s not the case for AI. The right AI tool can crunch that data in a fraction of the time it would take for a human team, turning even hundreds of examples of proposals into trends and actionable insights. You might find takeaways like “shorter proposals lead to more closed deals” or “proposal template A outperformed template B.”

This is the kind of process that would take days, if not weeks to implement successfully. AI can do it in hours, if not minutes.

Calculating Your Team's ROI: The 10-Hour Promise

So now that you know how AI can save your team time on proposals, let’s get a sense of how much time you’ll actually save—as well as some other benefits.

ROI Calculator

To calculate the ROI you’ll get from using AI to generate proposals, let’s first identify the variables involved:

• Time spent per proposal
• Number of proposals you send each week
• Number of sales reps
• Hourly rate per sales rep
• Time spent creating a proposal with AI
• Monthly cost for an AI tool

To calculate the cost of drafting and sending proposals manually, we’d use the following formulas:

1. Time spent per proposal x Number of proposals sent each week = Total proposal hours
2. (Total proposal hours x Number of employees) x Hourly rate per rep = Total cost of sending proposals each week
3. Multiply that number by four for the monthly cost.

After calculating that number, we’d calculate the cost of sending proposals with AI, with some oversight from human employees:

1. [(Time spent creating a proposal with AI x Number of proposals) x Hourly rate per sales rep] = Weekly total cost of sending proposals
2. (Weekly total cost of sending proposals x 4) + Monthly cost for an AI tool = Monthly cost for sending proposals with AI

Now let’s start with an example of a small sales team at a rapidly-growing software company. Their team is aggressively trying to grow business, but often getting bottlenecked in their proposal process. Because the process isn’t clearly-defined yet, each rep loses a ton of time drafting individual proposals.

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Here’s how we calculate the cost of having reps manually draft and send invoices each week:

1. 2 x 10 = 20
2. (20 x 4) x 25 = 2,000

So this team spends $2,000 a week sending proposals, or $8,000 a month. Let’s say they found an AI tool that can create proposals in a way that only requires 15 minutes of time (i.e., 0.25 hours) per proposal from a single employee and costs $20 a month:

1. [(0.25 x 10) x 25] = 62.5
2. (62.5 x 4) + 20 = 82.5

Now to calculate the ROI of using this tool, we divide the cost of generating proposals with AI ($82.5) and the “profit” made from the time saved ($2,000-$82.5 = $1,917.50) and multiply the result by 100:

(82.5 / 1,917.50) x 100 = 4.3, or 430% ROI

The larger your team, the more time they’ll save and the greater the ROI.

Beyond Time Savings: Quality and Win Rate Improvements

Time and money aren’t the only savings you’ll get from using AI for generating proposals. With a defined, AI-powered proposal process, you’ll also:

• Increase overall proposal quality
• Improve win rates
• Accelerate pipeline velocity
• Clear up bottlenecks
• Free up time for innovating on your process

Getting Started with Sales Proposal Automation

What to Look for in AI Proposal Software

AI tools have spread at a remarkable speed, meaning finding the right one isn’t as simple as plugging a task and “AI” in a Google search and calling it a day. When comparing solutions, look for the following must-have features:

• A proven reduction in time spent: Many AI tools promise they’ll save you time generating proposals. Few actually have customer stories or other proof points to back it up.
• Consistent, customizable output: Generic chatbots like ChatGPT aren’t as customizable as dedicated tools, since each output is essentially created from scratch. Prioritize purpose-built solutions like cobl.
• Integration with existing systems: This can mean everything from being able to access the data in your CRM to exporting proposals in formats you actually use, like Word or PowerPoint.
• Full control: AI should only automate parts of your proposal process, with you keeping ultimate control along the way. You should be able to pause generation, customize outputs, and send out proposals yourself.
• Style extraction: AI proposal software should use examples of previous proposals to automatically apply branding, colors, writing style, and other elements to proposals it creates for you.
• Advanced personalization: AI-generated proposals shouldn’t all look and read the same. The right tool will allow you to customize a proposal’s length, format, and more without writing a dozen prompts.

Don’t hesitate to test a tool before you actually implement it, since even a small pilot project can tell you whether it’ll actually suit your needs.

Implementation Timeline and Best Practices

An AI tool like cobl can generate client-specific, brand-accurate sales proposals in minutes. A single user—or even a whole team—can implement cobl in five minutes, with their first proposal being ready in 10 minutes. That makes it a quick, easy way to start generating proposals and see the impact on your team’s time.

But if you're implementing AI tools organization-wide, here's a rough idea of what you can expect:

• Week 1: Initial planning and learning
  
  • This includes finding the right tool, testing its output on a few different proposals, and training your teams on using it.
• Week 2: Testing and progressive rollout
  
  • Before rolling out an AI tool to every single proposal, you’ll want to find smaller projects you can test it in. Maybe you’ll have just a few reps using the tool, for example, comparing their output with the rest of the team.
• Week 3: Team-wide implementation
  
  • Train your entire sales team on your AI tool of choice and let them find new ways to use productively.
• Week 4: Review and full implementation
  
  • After using your new AI tool for a month, gather relevant stakeholders and review the tool’s output. Does the ROI match your expectations? Have there been any problems to iron out?
  • Once this review is complete, you can choose to fully implement the AI tool or try a competitor.