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Should You Still Invest in Google Ads as AI Changes Search?
It's a fair question. People are using ChatGPT or another LLM to research products. Google is answering more questions directly in search results. AI Overviews can summarize an answer without the person ever clicking a website. So, do businesses still need to invest in Google Ads? Yes. AI is absorbing the early research stage of the buyer journey, not the moment someone decides to act. Google Ads still capture that moment, the search that happens once a person already knows roughly what they want and is comparing specific options. That distinction, between researching and buying, is the whole answer. The rest is the “why” and the “how.” Researching Intent vs. Buying Intent: The Distinction That Matters Now Think about how you use search compared to a few years ago. I used to Google almost everything. Now, if I'm trying to understand a complicated topic, compare a few ideas, or figure out what I should even be asking, I'm probably starting with ChatGPT or Claude. I'm not alone. Some of the searches businesses used to rely on for traffic are shifting there too. Someone looking for a basic definition may never click through to a website. AI can answer that on its own. But researching and buying aren't the same thing. At some point, the person researching a problem has to find the company, product, or service they're actually going to use. Researching Intent The searcher is still learning, comparing categories, or figuring out what to even ask. Example: “what is account-based marketing.” Buying Intent The searcher already knows what they want and is comparing specific providers. Example: “ABM agency” or “best ABM agencies for SaaS.” Intent Type Example Search Where AI Shows Up Where Google Ads Shows Up Researching “what is account based marketing” Often fully answered by AI, no click needed Rarely converts, low commercial value Buying “best ABM agencies for SaaS” AI may shortlist options High-intent moment, Google Ads’ strongest zone AI can easily answer the first kind of search. It can't replace the second kind, because at that point the person is choosing between real options, and that's exactly where Google Ads have always been strongest. Google Ads Capture Demand. They Don't Create It. This is something I think gets lost in paid search conversations. Google Search Ads are really good at capturing existing demand. Someone has a problem. They know enough about that problem to search for a solution. Your ad shows up at that exact moment. But if nobody is searching for what you sell, adding more keywords and increasing the budget isn't going to magically create demand. I've seen businesses try to force Google Ads to work for products or services people don't know exist yet. The campaigns struggle, and eventually the conclusion is that “Google Ads don't work.” Sometimes that's not the problem. Sometimes the business is trying to capture demand that doesn't exist yet. That's where other channels matter. Paid social, video, content, events, and PR can introduce the problem or solution before someone ever opens Google. The job of Google Ads is to be there once that awareness turns into intent. Branded Search May Matter More, Not Less This is the part I keep coming back to. More research is moving into ChatGPT, AI Overviews, Gemini, etc. People may do less of their early research on traditional search engines. But what happens after the research? In my experience, this is often where branded search comes in. A buyer used to search ten different questions and visit ten different websites. Now they might ask an AI tool to help narrow down their options, then go to Google and search the names of the two or three companies they want to look at further. That makes a deliberate branded search strategy more important, not less, as AI reshapes how people research. Someone might already know your brand from Claude, a podcast, LinkedIn, or a recommendation. Their Google search isn't necessarily the start of their journey. It might be one of the last steps. I push back on one common claim: “We rank number one organically for our brand, so we don't need to run branded ads.” Maybe. But I want to look at the actual search results first. Are competitors bidding on your name? Are third-party review sites showing up? Is a large AI Overview, shopping block, or local pack pushing your organic listing down the page? The search results page isn't ten blue links anymore, and owning more of it can matter, especially when the person searching already knows who you are. SEO, GEO, and Paid Search Aren't Separate Islands Marketing teams love putting channels into boxes. SEO owns organic search. Paid media owns Google Ads. Someone else is suddenly responsible for GEO. What GEO Actually Means GEO, or generative engine optimization, is about making it easy for AI systems like ChatGPT, Gemini, and AI Overviews to understand and surface your brand in generated answers. That's not how people actually research, though. A buyer might discover a company in ChatGPT. Then they see the brand again on LinkedIn. They Google the company name, click a paid ad, and leave. Three days later they come back through an organic search. They finally convert through direct traffic. Which channel gets credit? Wrong question. The better question is whether the company was visible throughout the process. Google Ads don't directly make a company more likely to get cited by an LLM (ChatGPT, Claude, Gemini, etc) or show up in an AI-generated answer. But paid search still supports the bigger visibility picture. It helps capture demand created elsewhere. It gives you faster feedback on the words buyers actually use. And it shows you which problems and solutions drive action, not just traffic. That information should get shared across paid search, SEO, content, and GEO. The channels are different. The person researching doesn't care. When Google Ads Aren't the Answer I love Google Ads. I also don't think every business should run them. If there's little to no search volume for your product or service, search ads may not be the best place to start. Say your sales cycle is long and your conversion tracking stops at a form fill. You might end up optimizing for leads that never become customers. If your landing page doesn't clearly explain what you do, more traffic won't fix that. And if you can't connect marketing activity to actual business outcomes, spending more won't help. You'll just spend more without knowing what's working. Google Ads work best when there's existing intent to capture and the infrastructure to measure what happens after the click. Without those two things, the answer isn't always “spend more.” Sometimes the answer is to fix the foundation first. So, Should You Still Invest in Google Ads? Yes, but be more deliberate about why. Google doesn't own the entire research journey anymore. The value of Google Ads is that search still captures moments of intent. AI may help someone understand their problem and build a shortlist. But when that person searches for a solution, compares providers, or types a brand name into Google, there's a reason behind it, and that's the moment Google Ads can still be incredibly valuable. If you're not sure where your brand actually shows up right now, across paid, organic, and AI-generated answers, that's worth a real look before you touch the budget. See Where Your Brand Shows Up in Search Get a free look at your brand's visibility across paid search, organic results, and AI answers like ChatGPT and Google's AI Overviews, before you decide where to shift budget. Frequently Asked Questions Is Google Ads still worth it if people are using AI to research? Yes, for the buying stage. ChatGPT and AI Overviews are absorbing early, informational research queries, but Google Ads capture the later moment when someone already knows what they want and is comparing specific options, which is where paid search has always performed best.. What is GEO in marketing? GEO stands for generative engine optimization. It's the practice of structuring content so AI systems like ChatGPT, Gemini, and Google's AI Overviews can understand it and surface it in generated answers, similar in spirit to SEO but built around how AI systems extract and cite information rather than how they rank pages. What is the difference between AEO and SEO? SEO, or search engine optimization, focuses on ranking web pages in traditional search results like Google's blue links. AEO, or answer engine optimization, focuses on getting content selected as the direct answer in featured snippets, voice search, and AI-generated responses. SEO is about visibility on a results page, while AEO is about being the answer itself, and both rely on similarly clear, well-structured, and specific content. Should branded search campaigns be cut if a company already ranks first organically? Not automatically. Ranking first organically doesn't account for competitor bidding on your brand name, review sites appearing above your listing, or AI Overviews and shopping blocks pushing your organic result further down the page. Check the actual search results page before dropping branded ad spend. Do Google Ads help a brand get cited by AI tools like ChatGPT? Not directly. Google Ads don't influence whether an AI system cites or surfaces a brand in a generated answer. What paid search does provide is faster data on the exact language buyers use and which offers drive action, information that can inform SEO and GEO efforts even though the channels themselves work independently. What's the difference between researching intent and buying intent in search? Researching intent is a searcher still learning about a topic or category, for example “what is account based marketing.” Buying intent is a searcher who already knows what they want and is comparing specific providers, for example “best ABM agencies for SaaS.” AI tools increasingly answer researching-intent queries directly, while buying-intent queries remain where Google Ads perform best.
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7 B2B Sales Strategies to Grow That Your Team Can Implement This Week
Why the Basics Beat Every B2B Sales Tactic You’re Trying Most B2B business leaders aren’t struggling to grow sales because they lack ambition. It’s not even a lack of effort. Usually, success comes down to executing the fundamentals consistently and with a system. Not the flashy stuff but rather the boring, repeatable, human stuff that compounds over time. After working with over a thousand businesses and helping drive more than a billion dollars in top-line revenue growth, we’ve seen a consistent pattern. The best businesses are doing the basics better than everyone else, and they’ve built teams that do the same. What Are the Most Effective Strategies to Grow B2B Sales? To grow sales, you need to combine consistent fundamentals with intentional execution. This means structured onboarding, regular sales training, a referral-driven approach to existing clients, and a personal storytelling strategy. Businesses that implement even one or two of these systematically with clear targets and measurement see improvements in revenue and retention. Why Is Sales Growth So Hard for B2B Businesses? Sales growth is hard because in B2B many industries are commoditized and salespeople are skilled at interviewing without necessarily performing well. Growth levers like referrals, cross-sells, and client retention require intentional systems that many companies never formalize. The good news is that the fixes are straightforward, repeatable, and often don’t require a large budget. Here are seven places you can start: 1. Fix the Two Root Causes of Sales Turnover High turnover is a systems problem as much as it is a people problem. Two factors account for most sales attrition: a poor onboarding experience and insufficient training. A repeatable onboarding process reduces early churn and a formal sales training program produces measurable performance gains. Choose a training program that uses a data-driven, outcomes-based approach to measure both knowledge and application. 2. Know the Top Five Mistakes Every Salesperson Makes Even top producers repeat critical errors daily: talking too much and listening too little, failing to set clear meeting objectives, asking the wrong questions, not differentiating themselves from the competition, and failing to ask for the business. That last one is the most costly and common. Asking for the business shouldn’t only happen at the end of a sales cycle. It should happen at every stage of every conversation, whether that means asking for the next meeting, the next decision, or the next introduction. 3. Ask the One Question Prospects Never See Coming Try this in your next prospect meeting: “Tell me something about yourself that I wouldn’t know without talking to you directly.” It breaks the script, forces genuine reflection, and creates a memorable moment. The goal isn’t the answer itself. It’s the emotion the question produces: the prospect feels heard, engaged, and genuinely seen, which is the foundation of trust. This works because it sidesteps everything a prospect has already rehearsed. It requires them to think, not recite. And a prospect who thinks during your meeting is someone who remembers you afterward. 4. Structure Your Organization to Actually Scale To scale sales effectively, you need the right people in the right roles, documented processes, and technology that provides visibility. Hunters and account managers are fundamentally different profiles. Misaligning them is one of the most common and expensive mistakes in sales org design. Ask yourself: Are your hunters actually hunting, or are they managing accounts? Do you have a repeatable process your team follows consistently? Does your technology give you leading indicators, or just lagging reports? When people, process, and tools are aligned, forecasting becomes more reliable, onboarding becomes faster, and growth becomes repeatable. 5. Mine Your Existing Book of Business Your existing client base is your highest-leverage growth channel. Most referral-driven industries dramatically underuse it. Here’s how to activate it intentionally: Market mapping: Identify which clients don’t yet have products or services you offer, then create a plan to cross-sell or upsell. Warm introductions: Before a renewal meeting, pull two or three names from a client’s LinkedIn connections. Go in with a specific ask, not a vague one. Customer appreciation events: Publicly recognize your best clients. It solidifies relationships and shows prospects what it looks like to be in your corner. Measurable referral targets: Set a goal, whether that’s one referral per month or one per quarter. Track it and adjust based on what’s working. The clients you already have are doing business with you because they trust you. Make it easy and intentional for them to introduce you to people. 6. Write the Letter Your Clients Are Waiting For A personal letter from the owner or CEO to their client base is one of the most powerful and underused tools in B2B sales. This is not a company newsletter or a marketing blast but rather a direct message. Clients want to know what you’re learning, how your business is evolving, and that you’re investing in being better for them. What to include in a letter to clients: An insight or strategy you picked up at a recent industry event A challenge your business is working through and how you’re addressing it An investment you’re making in people, technology, or process A genuine ask for feedback or engagement It takes about an hour to write. The differentiation it creates in a commoditized market is outsized. People buy from people, and a letter like this is one of the most human things you can send. 7. Go Back for the Business You Lost Many clients who leave a business eventually regret that decision. It’s a significant recoverable opportunity that organizations often never pursue. Set up a re-engagement process quarterly or semi-annually and assign it to a newer team member who needs confidence-building activity. Former clients already know your firm. You don’t need to re-introduce yourself. Why wining back clients matters beyond revenue: They validate your business in a way no new client can Their story – why they left and why they came back – is your most compelling social proof They tend to be more loyal the second time around There is no more powerful proof point than a client who returned. Build a process to create more of those stories. What Growth-Minded Business Leaders Do Differently The businesses that grow consistently aren’t necessarily selling a more differentiated service. They’re executing on fundamentals that most leaders acknowledge but few formalize: They onboard with intention and train with consistency They ask questions that create emotional connection They structure their teams around strengths They mine their existing relationships before chasing new ones They tell a personal, emotional story in meetings, in letters, and online They treat lost clients as recoverable business, not closed chapters Pick one strategy from this list and implement it this week. Set a target, measure the result, and then build from there.
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The Skill 90% of Salespeople Get Wrong & How AI Sales Tools Can Help
What Is the Salesperson’s Weakest Skill? The weakest skill for most salespeople is handling objections. Brickwork’s research, drawing on assessments of more than 350,000 salespeople, finds that over 90% of reps are ineffective at uncovering and then resolving objections. Since objections arise in most selling situations, this is a big reason why so many salespeople consistently miss quota. I’d argue it’s the most consequential and neglected skill in B2B sales. The Numbers Are Hard to Ignore 64% of salespeople fail to ask for commitment. That alone is alarming. But consider the downstream effect – you’ll never hear a customer’s objection unless you ask for the business first. Which means the 90%+ failure rate on objection handling likely understates the full scope of the problem, because it only counts reps who got far enough to encounter one. Salespeople know it’s a problem, yet they still can’t execute. Why? Because they’ve been taught the wrong things. Why Traditional Sales Training Fails on Objections The Methods Haven’t Changed in Decades The techniques most sales training companies teach for handling objections haven’t meaningfully evolved. The Feel, Felt, Found method dates back generations: “I know how you feel. Other people felt that way until they found…”. Equally persistent is the myth that objections are buying signals. Major sales training companies continue to teach this. They’re not. An objection is a customer’s response to an unasked question. It’s a signal that something critical was missed earlier in the discovery process, not an invitation to celebrate. Training Offers Tips Instead of a Process Most objection-handling frameworks from well-known training vendors collapse into the same four vague steps: Listen: As if active listening isn’t something good reps are already doing throughout the entire conversation. Question: Sound advice, but with no direction on which questions to ask or how they connect to the buyer’s specific concerns. Respond: Frequently accompanied by psychological frameworks (cognitive dissonance, behavioral research) that offer no practical, repeatable guidance. Confirm: Verifying that the objection was addressed but failing to follow through with the actual next step of asking for a commitment. These steps function as a detour. Salespeople leave the sales process, wander around, and hope to find their way back. To handle objections well, they need to stay on the path toward gaining a sale without ever abandoning their methodology. Stalls and Objections Are Not the Same Thing One of the most damaging mistakes in conventional sales training is treating stalls and objections as interchangeable. They are not, and handling them the same way almost always fails. Examples of a stall include “Let me think it over,” “Send me a proposal,” and “Call me next week.” They don’t reflect a specific concern and instead mean: I’m not quite sold yet. Sell me some more. An objection, by contrast, always ties to one of the five buying decisions every customer makes: Salesperson, Company, Product/Service, Price, or Time-to-Buy. “Your price is too high” and “I prefer the competition’s solution” are examples of an objection. These require fundamentally different responses than a stall, yet nearly every training program lumps them together. What Actually Works: Objections as Unasked Questions The most effective reframe in objection handling is this: an objection is a customer’s response to an unasked question. When a customer pushes back after you ask for commitment, it’s almost always because a critical question went unasked during the discovery phase. The objection reveals a buying decision that wasn’t adequately addressed. The correct response is to return to that discovery phase, ask the best questions to surface what’s really driving the resistance, and then work back through to a commitment. This approach keeps salespeople inside their selling process. No detours. No psychological jargon. Just a disciplined, repeatable path. Distinguishing Stalls From Objections in Practice When you ask for commitment and receive a stall, don’t challenge it. Instead, use what Brickwork calls a universal stall breaker. This is an empathetic acknowledgment followed by a Trial Feature Benefit Reaction (TFBR) sequence that re-engages the customer without confrontation. If, after that, the customer surfaces a specific concern tied to one of the five buying decisions, now you have an objection and know how to address it. The sequence matters. Skipping it is how most objection-handling frameworks fall apart. The Knowledge Gap vs. the Application Gap There’s an important distinction buried in our assessment data: most salespeople actually know quite a bit about the five critical selling skills. The overall average knowledge score is 62 out of 100. But they only apply about 44% of that knowledge in real selling situations. This is an application problem that training content alone won’t fix. What fixes it is practice: deliberate, repetitive, corrective practice in realistic selling scenarios. The skills where the gap between knowledge and application is widest? Gaining commitment, sales call planning, and questioning – the exact skills at the heart of objection handling. How AI Is Closing the Objection-Handling Gap This is where the conversation shifts from diagnosis to solution and where AI is starting to make a difference. Traditional sales training happens roughly four times a year. A rep attends a session, receives a knowledge boost, and then returns to their territory. Within weeks, old habits return. The skills never become second nature because there simply wasn’t enough practice. Tandem by Action Selling addresses this directly with AI-driven coaching built on the proven 9 Acts sales methodology. Instead of waiting for the next quarterly training cycle, reps can practice objection handling as often as needed. And they do it in a private, judgment-free environment against AI buyer personas built from your actual ideal customer profile. The impact is measurable: Consistent practice between live training sessions ensures skills compound rather than fade. AI personas that mirror real prospects give reps exposure to the exact objections they’ll face in the field, not generic role-play scenarios. Immediate feedback on delivery, pacing, question quality, and objection resolution without waiting for a manager coaching session. Reporting that tracks progress against goals, giving sales leaders visibility into who is improving and where coaching is still needed. What gets measured gets learned. AI tools like Tandem make it practical to measure objection-handling skills continuously, not just at training checkpoints. The Path to Mastery Selling is a skill that can be taught, measured, and improved. But improvement requires the right foundation: a well-documented methodology, training grounded in valid learning principles, and manager reinforcement. Now, AI-powered practice closes the gap between what reps know and what they can execute under pressure. If your team is struggling to hit its quota and you’re not sure why, the data points to a place worth examining. 90% of salespeople can’t handle objections effectively. That’s a training problem — and in 2026, it’s a solvable one.
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AI in Sales: How to Separate the Helpful From the Hype
The AI Pressure Is Real. So Is the Risk of Getting It Wrong. Every sales leader I talk to these days is hearing the same thing: “What’s your AI strategy?” The pressure is real. If you haven’t figured out exactly how AI fits into your sales motion, it’s easy to feel like you’re already behind. But the noise around AI in sales is louder than the signal right now. New tools keep flooding the market, and most of them are making big promises. Before you make a decision – or worse, make the wrong decision – you need a clear framework for evaluating what’s actually worth your team’s time. What Should AI Sales Tools Actually Do for Your Team? AI sales tools fall into two broad categories: Productivity tools that save time and efficiency Skill-building tools that make your salespeople better at selling Both have genuine value, but they solve very different problems, and the right choice depends on what your team actually needs most. Most of the tools you'll encounter fall into the first bucket. They automate admin work, summarize calls, draft emails, and give reps back hours in their week that can be redirected toward selling. They also help with prospect research, surfacing what reps should know about a buyer before they ever get on a call, so they walk in prepared instead of winging it. These are valuable. But there’s a another category of tools designed to develop selling skills with the kind of deliberate practice that changes performance. That’s where the bigger long-term upside lives. 4 Baseline Questions to Ask About Any AI Sales Tools Before you get into the specifics of what a tool does, start with these questions: 1. Will my data stay secure? When your salespeople use an AI tool, your proprietary information (client data, playbooks, IP, etc.) shouldn’t be used to train someone else’s large language model. Make sure you understand how the tool handles data. 2. How difficult is implementation? People often underestimate the time and cost to get an AI sales tool up and running. Ask specifically: How long before the tool is usable? And will you need to pay a third party to configure it, or can your team stand it up independently? Slow, expensive implementations drain your return on investment (ROI) before you’ve seen a single benefit. 3. Will my reps actually use it? Adoption is everything. If your salespeople are already overwhelmed, adding a tool that requires extensive training before is a recipe for shelfware. The steeper the learning curve, the lower the adoption. Ask yourself honestly: if a rep opened this tool cold, would it be obvious what to do? 4. Can I see whether it’s working? As a sales leader, you need visibility. Can you tell whether your team is using the tool? Can you see what results they’re getting? If the tool is a black box from a management standpoint, you’ll have no way to evaluate your ROI or make changes when adoption stalls. Going Deeper: Evaluating AI Sales Tools for Skill Development If you’re specifically looking at AI sales tools designed to help your team get better at selling, not just work faster, three additional questions matter. These tools are built around practice: giving reps a low-stakes environment to rehearse their conversations, work out their talk tracks, and sharpen their skills without doing it in front of a live prospect. Think of it like a batting cage. Your reps take swing after swing without a prospect on the line. The problem every sales leader knows is that most salespeople don’t practice nearly enough. And when they do, it’s usually during an actual sales conversation. AI changes that equation, but only if you pick the right tool. 5. Does it reinforce your sales methodology? The AI tool should align with the specific sales process and methodology you’ve already invested in. If your team has been trained on a particular framework and the AI roleplay tool is giving feedback that pulls them in a different direction, you’re undermining the training investment you’ve already made. When evaluating these tools, ask: Can the feedback be configured or calibrated to match our sales methodology? The answer to that question will tell you whether the tool multiplies your existing investment or dilutes it. 6. Does the practice feel real? The value of AI roleplay is directly tied to how realistic the interaction feels. Does the AI sound like your ideal customer profile? Does it surface the kinds of objections your prospects actually raise? Does it use the industry language your buyers use? A practice interaction that sounds nothing like a real prospect conversation isn’t building the right muscle memory. Before you commit to a tool, ask for a demo that mirrors your specific buyer, and evaluate whether it would truly prepare your team for a real conversation. 7. Can it analyze real sales conversations too? The most powerful AI skill-building tools close the loop. This means a rep uses the AI to rehearse before a meeting, then uploads a recording of the actual Zoom or Teams call afterward to get feedback on how they really performed. This creates a continuous improvement cycle: practice before, analyze after, get better for next time. If an AI tool can only do one half of that loop, you're leaving a significant part of its potential value on the table. Cutting Through the AI Noise You don’t have to feel overwhelmed by the volume of AI tools hitting the market. The landscape is crowded, but the framework is simple: Start with your goal. Are you trying to make your team more efficient, or are you trying to make them better sellers? The answer shapes everything else. Ask the baseline four questions for any tool you evaluate: data security, implementation cost, ease of use, and leadership visibility. If you’re evaluating a skill-building tool, add the three additional criteria: methodology alignment, realism, and the ability to analyze real calls. The sales leaders who will get the most from AI aren’t the ones who move fastest. They’re the ones who move clearly by knowing what problem they’re solving and holding every tool to a consistent standard.
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The RevOps Approach to Defining Your ICP
Do You Know Your Ideal Customer Persona (ICP)? Most go-to-market (GTM) teams think they know their ideal customers. Ask them to write it down – with specificity, consistency, and data to back it up – and the conversation changes quickly. What seemed like a shared understanding turns out to be a collection of individual opinions loosely orbiting the same concept. And that gap misaligns sales and marketing efforts. Yes, marketing plays a role in defining your ideal customer profile (ICP), but it’s also a revenue operations (RevOps) function that touches every layer of your go-to-market strategy. Why Does Having the Right ICP Definition Matter? Your ICP defines the specific subset of your total addressable market (TAM) where your business wins fastest, your margins are strongest, and retention compounds over time. Where you win is the key phrase, not where you could theoretically sell. Not every company that fits a broad industry category - your ICP is the bullseye. It’s the accounts that close quickly, show value quickly, and expand reliably. Without a disciplined ICP definition, organizations start chasing anything with a budget. The consequences cascade across the entire revenue motion: Customer acquisition costs climb as sales and marketing resources spread across low-fit accounts Win rates drop because reps are qualifying on budget rather than fit Churn increases because customers who weren’t a great fit to begin with rarely become long-term success stories Forecasting breaks down because the pipeline is full of noise And AI doesn’t fix this problem, it only amplifies it. If your ICP is badly defined or constructed, AI-driven lead scoring, intent prediction, and outreach personalization will just help you find more of the wrong customers faster. The RevOps Framework for Your ICP ICP discipline lives inside the Readiness pillar of the RAISE framework – the operational model we use here at Brickwork. Readiness establishes clarity (before you try to optimize) and ensures your go-to-market model is grounded in data and not just instinct. ICP definition is one of the foundational RevOps levers inside Readiness, and it requires moving through four distinct steps. Step 1: Quantify Your Market Before you can define your ICP, you need to understand the market landscape you’re operating in. TAM (Total Addressable Market): The full universe of potential buyers; a directional planning metric (not a daily focus) SAM (Serviceable Available Market): The buyers you can realistically reach with your current GTM model. ICP (Ideal Customer Profile): The accounts within SAM where you win most efficiently and retain most effectively. Think of it as a target - TAM is the outer ring, your entire potential market. The middle rings are broader segments and adjacent use cases. The bullseye is your ICP, and the goal of ICP definition is to get precise about that center. This framing matters for RevOps because it ties directly to territory design, pipeline coverage modeling, and resource allocation. If your territories don’t balance TAM, SAM, and ICP against rep capacity, you’ll either overcrowd accounts with multiple reps or leave high-value ICP whitespace uncovered. Step 2: Formalize ICP Attributes Across 4 Dimensions Vague ICP definitions fail in practice because they can’t be operationalized. “Mid-market SaaS companies that need to improve sales efficiency” isn’t going to cut it. Effective ICP definition requires four specific types of attributes: Firmographics: The structural characteristics of target accounts, including industry, company size, revenue range, employee count, geography, and business model. These are your table-stakes filters. Technographics: The technology stack, existing integrations, and level of digital maturity. A company running a modern CRM and marketing automation platform will onboard and adopt your solution very differently than one managing pipeline in spreadsheets. Behavioral and intent signals: Buying signals, content consumption patterns, website engagement, event attendance, and search intent data. These factors are important for prioritization, separating companies that fit your ICP in theory from ones that are actively buying Value-based traits: The specific pain points your solution solves, the use case fit, expected profitability, and likelihood to retain. This is often the hardest dimension to nail down, but it’s where the real signal lives. What problems do your best customers have in common? What outcomes do they achieve that your average customers don't? The goal is to combine all four dimensions into a scoring model that can be built directly into your CRM and marketing automation systems. If your ICP only lives in a deck or a one-pager, it isn’t operationalized, it’s just documented. Step 3: Build ICP Into Your Systems and Processes ICP definition becomes ICP discipline only when it’s embedded in how your revenue team operates day-to-day. In practice, this means: CRM scoring and qualification criteria. Your ICP attributes should be reflected in lead scoring models, account scoring, and stage qualification requirements. If a prospect doesn’t meet ICP thresholds, that should be visible in the opportunity record, not discovered after a rep has spent three months on the deal. Territory and segmentation design. Territory planning should be built around ICP density and not just geography. Use CRM and enrichment data tools like ZoomInfo, Apollo, or Clay to identify ICP-fit accounts within each territory. Then ensure your coverage model reflects the actual distribution of your ideal buyers. Marketing and demand generation targeting. Your ICP should define which audiences your campaigns target, which content assets you build, and how you prioritize leads. Campaigns pointed at non-ICP audiences generate volume without velocity. Hiring and enablement alignment. Sales reps and customer success managers need to understand the specific value drivers, pain points, and buying behaviors of those accounts. AI-powered role-play training that simulates buyer personas is one of the best ways to close the gap between a documented ICP and consistent rep execution. Step 4: Validate and Refine ICP Against Closed-Won Data Your ICP should be a living model, continuously validated against real data. The richest source of ICP signal is your closed-won customer base. Analyze your best customers and look for the patterns that cut across your four attribute dimensions. Which industries? Which company sizes? Which technology stacks? Which pain points? Equally important: run the same analysis on your churned customers and lost deals. What are the attributes of accounts that seemed like a fit but weren’t? ICP definition is as much about identifying disqualifiers as qualifiers. Run this analysis quarterly. Your market shifts. Your product evolves. Your win patterns change. An ICP that was accurate 18 months ago may be drifting away from your actual best-fit buyers without anyone noticing until the churn data tells the story. Where AI Fits Into Your ICP Definition AI accelerates ICP precision, but only if your foundation is solid. Once your ICP attributes are established and your CRM data is structured, AI tools can: Continuously refine ICP scoring by analyzing closed-won patterns across your customer base Surface intent and fit signals to automatically prioritize ICP-aligned accounts showing active buying behavior Flag ICP drift when new deals begin falling outside established parameters, an early warning system for GTM misalignment Enable ICP-based training by creating AI buyer personas that think and respond like your ideal customers, so reps practice the real conversations they’ll have in the field AI can help you find more of your best customers, but only if you’ve defined what best means first. Because when your ICP is precise and embedded in your systems, the downstream effects compound across every part of your revenue engine. Pipeline quality improves. Win rates climb. Customer acquisition costs drop. Retention strengthens. Forecasts become more reliable. And AI tools, which are increasingly central to how high-performing GTM teams operate, become genuinely predictive rather than just automated.
Your Reporting Isn't Broken. Your Data Is
What Is Sales and Marketing Alignment? (And Why Most B2B Teams Get It Wrong) Sales and marketing misalignment is one of the most expensive and preventable B2B problems. When leads fall through the cracks, and unqualified opportunities inflate your pipeline, the reason is almost always structural and Revenue operations (RevOps) is the proven solution. True alignment happens when both teams operate from the same playbook: a shared definition of your ideal customer, a common language for funnel stages, agreed-upon handoff criteria, and metrics that hold each function accountable to pipeline outcomes, not just activity. With genuine alignment, marketing doesn't hand off leads and walk away. Sales doesn't treat marketing as a vendor that produces pitch decks. Instead, sales and marketing co-own revenue outcomes through a shared operational system. Why Most Sales and Marketing Alignment Efforts Fail Most companies attempt alignment with weekly syncs, a shared Slack channel, or a joint QBR. These help, but they don't solve the real problem: sales and marketing are measuring different things, using different definitions, and looking at different dashboards. Marketing reports marketing-qualified leads (MQLs). Sales doesn't trust them. Sales reports pipeline. Marketing can't see how campaigns contributed. Leadership sees two conflicting stories and no clear path forward. The maturity signal of a truly aligned go-to-market (GTM) organization is that each team knows exactly who they're targeting, how they engage, and what success looks like. That bar is higher than most teams realize, and it's not sustainable without a RevOps strategy. The RevOps Foundation: The RAISE Framework The five elements of Brickwork's RAISE framework provide the structural backbone that makes alignment both possible, durable, and empowering for your teams with AI. Readiness You can't align around a plan you haven't clearly defined. Readiness means establishing a validated go-to-market model, a disciplined ideal customer profile (ICP), and a plan of record (POR) — the single source of truth that connects board-level targets to executional math. Alignment This is where you formally structure sales and marketing coordination. Synchronize goals, share funnel definitions, align compensation, and establish a unified operating rhythm. That's alignment — operationalized. Intelligence Intelligence transforms raw data into decisions. Key metrics: pipeline coverage ratios, MQL-to-SQL conversion rates, marketing-sourced pipeline contribution, and forecast accuracy. When both teams see the same numbers from a single source of truth, the debate shifts from "whose data is right?" to "what do we do next?" Systems and Enablement Systems and Enablement close the loop by ensuring your CRM, marketing automation, and customer success platforms are integrated and governed — and that reps and marketers have the playbooks and training to execute consistently. How to Build Aligned Funnel Definitions The lead-to-revenue handoff is where most misalignment lives. A well-functioning RevOps operation explicitly defines every stage: Lead → MQL → SQL → Opportunity → Closed → Renewal Each handoff must have documented criteria, SLA timelines, and clear ownership — measured and governed in the CRM, not enforced through goodwill. Mature organizations benchmark their MQL-to-SQL conversion rate at 15–25%. Align on Your ICP Before You Align on Anything Else ICP alignment means marketing campaigns are built around the same firmographic, technographic, and behavioral criteria that sales uses to qualify prospects. Brickwork benchmarks ICP fit % at ≥ 80% for mature marketing organizations. The Metrics That Drive Real Revenue Accountability Hold marketing accountable for pipeline and revenue — not just MQL volume. Core KPIs for mature revenue organizations: Pipeline Contribution %: Marketing-sourced pipeline as a share of total pipeline (benchmark: 35–60%) MQL → SQL Conversion Rate: Qualified leads accepted by sales (benchmark: 15–25%) Pipeline ROI: Pipeline created ÷ marketing spend (benchmark: 5–8×) ICP Fit %: Share of leads meeting ICP criteria (benchmark: ≥ 80%) Customer Acquisition Cost (CAC) by Channel: Spend ÷ new customers, tracked for trend improvement Key maturity benchmarks for sales teams: ≥ 3× pipeline coverage per segment A formal deal review cadence with clear inspection criteria Win/loss reporting shared back to marketing Win/loss analysis is one of the most underused alignment tools in B2B organizations. Sharing root-cause analysis from lost deals with marketing closes a feedback loop that improves targeting, messaging, and campaign strategy. The Operating Rhythm That Sustains Revenue Alignment Weekly forecast calls between the CRO and sales ops to validate pipeline health Biweekly deal reviews for deeper looks at strategic opportunities with cross-functional input Quarterly pipeline reviews to ensure CRM data integrity and remove stale deals Quarterly win/loss/slipped analysis for insights shared with marketing, product, and GTM strategy Monthly commission review board meetings to align sales, finance, and operations on comp governance With marketing in win/loss reviews and sales in pipeline attribution discussions, alignment stops being aspirational and becomes structural. Where to Start: 3 Diagnostic Questions Start here to identify your structural alignment gap: Do sales and marketing agree on the ICP by firmographic, technographic, and behavioral criteria? Are your funnel handoffs documented, measured, and governed in your CRM with defined SLAs at every stage? Can marketing prove its pipeline contribution with multi-touch attribution — and does sales trust those numbers? If you can't answer yes to all three, you have a structural alignment gap that needs to be fixed.
Your Data Is Accurate. So Why Can't You Use It?
"Clean data" and "usable data" aren't the same thing. Most organizations know they have a data quality problem. The typical response: launch a cleansing initiative to remove duplicates, fix formatting, standardize naming conventions. And then, six months later, the same problems come back. Here's the uncomfortable truth: in most cases, the data isn't wrong, it's just unusable. Records may contain accurate information but lack structural consistency, relational integrity, or contextual completeness. When data can't support segmentation, automation, forecasting, or benchmarking, it stays dormant. It looks fine. It just can't do anything. The Difference Between Clean and Usable Clean data answers: "Is this field correct?" Usable data answers: "Can this record support a decision without manual interpretation?" A company name can be spelled perfectly and still be disconnected from its parent entity. A revenue number can be accurate and still be misclassified. A complete contact record can still lack the attributes needed for segmentation. Accuracy alone doesn't create decision-readiness. Usability does. Four Conditions That Determine Operational Usability 1. Contextual Completeness Does the record include all attributes needed for analysis, not just the minimum required fields? A customer missing industry classification may be accurate but analytically useless. 2. Dimensional Consistency Are lifecycle stages, revenue categories, and status definitions standardized across systems? If two departments define "active" differently, both can be correct while the dataset remains fragmented. 3. Relational Integrity Are entities properly linked? Accounts to hierarchies, assets to owners, transactions to entities. Without this, aggregation distorts reality. 4. Structural Sustainability Is the data protected against regression? One-time cleansing fails unless validation rules, ownership, and intake discipline are embedded into daily operations. Why Cleansing Projects Always Regress Data is dynamic. Every day it's created, modified, and integrated across workflows. Without intake controls and validation standards, entropy always wins. Editing records isn't the same as designing structure. Data activation is an engineering discipline, not a cleanup project. AI Is Only as Useful as the Data Beneath It Here's where this gets urgent: AI-powered features (next best action, churn prediction, deal scoring, and automated segmentation) all depend on data that is structured, complete, and relationally sound. Bad data doesn't just produce bad reports. It produces confident-looking AI recommendations that are dead wrong. The organizations unlocking real value from AI aren't the ones with the most data. They're the ones whose data is built to be used. The Question Has Changed It's no longer "Is our data clean?" It's "Is our data built to drive outcomes?" If your team has been through a cleansing initiative that didn't stick, the issue almost certainly wasn't effort. It was the absence of structural sustainability. Cleaning without designing is temporary by definition. Where to Go From Here Start by asking these three questions about your most critical data sets: Are all the attributes needed for segmentation, scoring, and forecasting consistently populated and not just the required fields? Do your key definitions of "active customer," "qualified lead," and "revenue" mean the same thing across every system and every team? Are your records properly linked across systems, or does each platform maintain its own isolated view of the same entity? If the answer to any of these is no, you don't have a reporting problem. You have a usability problem, and no amount of dashboard redesign will fix it. Brickwork helps revenue organizations build data foundations that are engineered for use, not just accuracy. If your data looks right, but still can't drive decisions, that's exactly the gap we're built to close. → Find out where your data foundation actually stands — take Brickwork's Revenue Operations Data Maturity Assessment
You Don't Need MDM. You Need Your Systems to Talk to Each Other.
Most data fragmentation problems aren't a centralization problem. They're a resolution problem. Most organizations know they have a data quality problem. The typical response: launch a cleansing initiative: remove duplicates, fix formatting, standardize naming conventions. As organizations grow, data spreads across systems. The CRM holds customer data. The ERP holds financials. Every platform uses its own IDs and naming conventions. External feeds add even more inconsistency on top. This isn't failure — it's a byproduct of growth and specialization. The mistake is what happens next. When inconsistencies become visible, the default reaction is to pursue enterprise Master Data Management (MDM) — a centralized "single source of truth" that governs everything. For large, highly regulated enterprises, that can make sense. For most mid-market and growth-stage organizations? It creates more complexity than the original problem. What You Actually Need: Entity Resolution The real question isn't "How do we centralize our data?" It's "Which records across our systems refer to the same real-world entity?" That's a resolution problem, and it can be solved through structured orchestration across three layers: 1. Clustering Group potential duplicates using deterministic (exact match) and probabilistic (fuzzy) logic based on name, address, identifiers, or metadata. This narrows matches without forcing consolidation. 2. Matching Apply confidence scoring and survivorship logic to determine when records should link or merge. This harmonizes relationships while respecting each system's native identifiers. 3. Master Layer Design Create a lightweight canonical reference that maps relationships across systems without replacing them. This layer coordinates intelligence rather than overwriting operational truth. Why Orchestration Scales Better Than Consolidation A pragmatic orchestration model respects what each system was built to do: CRM continues to manage pipelines ERP maintains financial integrity Operational platforms sustain domain-specific workflows The canonical layer resolves identity and relationship ambiguity across all of them, without demanding structural centralization. The result: Faster implementation Lower operational disruption Reduced governance overhead Easier integration of acquired systems Orchestration over consolidation. Coordination creates clarity. Not enforced uniformity. A Note on the Data Foundation Making systems talk doesn't mean skipping the hard work underneath. Orchestration still requires governed pipelines, validated data, and a reliable canonical layer. It just doesn't force all of that into a single monolithic system. The foundation is still there. It's simply distributed intelligently across the systems that already own each domain, rather than rebuilt from scratch in one place. That distinction matters, because it's what makes this approach faster to deploy, easier to maintain, and far less disruptive to the teams who depend on those systems every day. AI-Powered Pipelines — and a New Way to Interact With Your Data AI doesn't just accelerate pipeline execution; it enables self-healing workflows, intelligent quality checks, and anomaly detection that would have required a full data engineering team to manage manually. Pipelines that once broke silently now flag issues, adapt, and recover automatically. But the most exciting shift is how teams interact with their data once it's orchestrated. When your systems are intelligently connected, your team can query across all of them in plain language: no SQL, no waiting on a data team, no building a new report from scratch. Just ask the question and get the answer from your live data. Questions like: "Which accounts are showing early churn signals this quarter?" "Show me pipeline coverage by region compared to last year." "Which product lines are underperforming against forecast?" Your data. Your questions. Answered in seconds. You Don't Need to Rebuild Everything The most common objection to fixing data fragmentation is the assumption that it requires ripping out existing systems and starting over. It doesn't. What it requires is a layer that resolves identity across your systems, coordinates intelligence between them, and delivers that intelligence to the people who need it, in a format they can actually use. That's orchestration. And for most mid-market and growth-stage organizations, it's a faster, cheaper, and far less disruptive path to a coherent view of their data than MDM ever will be. Three Questions to Assess Where You Stand When you acquire a new company or add a business unit, how long does it take before their data appears in your consolidated reporting, and is that timeline acceptable? Do your teams spend meaningful time each week reconciling data across systems that should already agree? If a senior leader asked a cross-system question about your business right now, could anyone answer it in under an hour without building a custom report? If any of those answers are uncomfortable, the issue isn't your systems. It's the absence of orchestration between them. Brickwork helps revenue organizations connect their data ecosystems intelligently, without the cost, complexity, or disruption of enterprise MDM. If your systems are fragmented and your team is spending time reconciling instead of deciding, that's the gap we're built to close. → Find out where your organization stands — Take Brickwork's Revenue Operations Data Maturity Assessment
How to Find Gaps With a Sales Skills Assessment
Why You Need a Sales Skills Assessment When did you last assess your sales team’s strengths and weaknesses? Not whether they hit quota. Not whether they have pipeline. Whether they have and can apply the specific skills needed to be successful. For most revenue leaders, the honest answer is not recently, if ever. They need a way to uncover gaps before they show up in business results. What Is a Sales Skills Assessment? A sales skills assessment is a structured evaluation of a salesperson’s competencies across the full range of behaviors that drive revenue, from attitude and mindset to tactical selling skills to how consistently they execute. It replaces gut feel and anecdotal manager feedback with a clear, scored picture of where each rep stands and what needs to change. When building or scaling a revenue team, it answers a question that pipeline data can’t: Do we have the right people doing the right things the right way? And where can our existing team grow? What Pipeline Data Alone Won’t Tell You Most GTM leaders rely on outcomes such as closed revenue, win rates, average deal size, and pipeline coverage to evaluate their teams. These are lagging indicators. By the time a skill gap surfaces in your metrics, it has already cost you deals. The more dangerous problem is self-perception. Salespeople tend to rate themselves higher than they perform, especially in areas like relationship management and strategic selling, where the definition of good is fuzzy. Without an objective framework, managers are coaching to perception, not reality. And hiring decisions get made on interviews and intuition rather than evidence. A structured assessment changes that. It creates a baseline that makes every coaching conversation, hiring decision, and team-design choice easier to make and to defend. The 4 Dimensions of a Sales Assessment Our people and training assessment at Brickwork evaluates reps across four dimensions, each scored on a 1–5 scale, where 5 represents mastery of the skill or competency. Together, they give GTM leaders a full picture not just of what reps know but of whether they show up and execute. 1. Attitude This is the foundation. No amount of skill development helps a rep who isn’t coachable, doesn’t believe in what they’re selling, or folds under pressure. Attitude competencies include: Coachable: Receptive to critical feedback and willing to adjust behavior based on it All In/Adheres to Company Values: Genuinely believes in the company’s mission, not just going through the motions Passionate: Displays real enthusiasm for the product and inspires that in buyers Resilient: Stays composed through rejection and adversity; bounces back without a performance hit Competitive: Has a genuine desire to win, not just to participate Customer Loyalty/Customer Service: Acts with the client’s interest in mind, not just their own number 2. Skills The core selling competencies. This is where most pipeline gaps originate: Pre-Call Preparation: Does the rep arrive with a plan or improvise? Client Development: Are they expanding existing accounts or staying comfortable with the initial contact? Communication: Can they deliver a clear, compelling message and champion the opportunity internally? Relationship Development: Are they building trust and retention, or just staying visible? Client Strategy: Do they understand what the client is trying to achieve at a business level and position accordingly? 3. Activity Skill without consistent execution is just potential. This dimension looks at whether reps are doing enough of the right things repeatedly. Prospecting: Are they actively hunting new business through multiple methods? Proposal Activity: Do they generate enough proposals to sustain pipeline? Sales Calls: Are they in front of enough buyers often enough? Follow-Up: Do they stay disciplined about re-engaging? Networking: Are they building a referral and lead ecosystem beyond the CRM? 4. Knowledge Even the most motivated rep can’t win without the right context: Product: Deep understanding of what they’re selling and how it delivers value Market/Industry: Fluency in the buyer’s world, trends, and pressures Competition: Understanding who the customer buys from, who you’re up against, and how to sell on value instead of price The Micro-Topics That Make Coaching Actionable Broad dimension scores tell you where to look. Micro-topics provide hyper-specific areas, challenges, or best practices your team needs guidance on. Brickwork’s assessments pull out these topics from the results, so you see exactly what to fix and how to make measurable improvements. Here are some micro-topics that surface most often as gaps: Active Listening When a rep isn’t truly listening, they miss buying signals, misread objections, and pitch solutions to problems the customer never confirmed. Active listening is the foundation for rapport, trust, and every conversation that follows. Asking the Best Questions Reps who don’t ask the right open-ended questions to surface symptoms rather than root causes and propose solutions that don’t stick. This micro-topic builds the questioning framework that helps buyers articulate what they care about most. Sales Cadence and Contact Management Most reps know they should follow up; they just don’t have a structured system for doing it consistently. A strong cadence balances calls, voicemails, social touches, and email in a sequence that keeps opportunities moving instead of going cold. Elevator Pitches and Value Propositions When reps can’t quickly articulate why a prospect should care, they lose the room before the conversation starts. This module addresses both the elevator pitch and the value proposition, with practice on real buyer personas. Presentations: The Do’s and Don’ts Most reps walk through slides rather than lead a conversation. Coaching includes live delivery practice against a real company deck, scored on confidence, opening and closing statements, and overall impact. Social Selling/LinkedIn LinkedIn is a pre-call planning tool as much as a prospecting channel, and reps who use it strategically show up to client conversations with more context and credibility. This gap compounds, as reps can steadily lose ground to competitors who show up consistently on social media. Objection Handling Reps need to uncover what’s actually behind the objection before attempting to address it. A rep who hears “we don’t have budget” and pivots immediately to pricing has misread the situation. Coaching focuses on curiosity-first responses that surface the real blocker. From Assessment to GTM Decision-Making The real value of a skills assessment isn’t the scores but rather what the scores enable. For GTM leaders, that means: Smarter hiring. When you know what mastery looks like across your best performers, you can interview against a trusted standard. Assessment data from your existing team defines the profile you’re hiring for. Faster onboarding. New reps have known gaps before they start. An assessment in the first 30 days surfaces those gaps early, so you’re not waiting 90 days to realize someone needs help with prospecting. Targeted development. Group coaching raises the floor. Individual coaching raises the ceiling. Assessment data tells you exactly what each rep needs so that development is specific, and your managers’ time goes where it will have the most impact. Honest team design conversations. Sometimes an assessment confirms that a role, a territory, or a coverage model needs to change, not just the rep in it. That’s a harder conversation, but it’s the right one. The Bottom Line Missed pipeline targets don’t start at the end of the quarter. They start weeks or months earlier in calls that weren’t planned, questions that weren’t asked, or follow-ups that never happened. A sales skills assessment gives GTM leaders the visibility to get ahead of that by knowing specifically, measurably, and early enough to do something about it.