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🎉 Good News! Marketing Cloud Next Trailhead is available again. Salesforce has already restored the Trailhead module that had been temporarily unavailable while fixes were being made. 🚀 I was just able to successfully create and install my second environment. To be safe, I went through all of the challenges again from start to finish, and I’m happy to report that everything completed successfully without any issues. ✅ Marketing Cloud Next Developer Edition creation ✅ Challenge completion Everything appears to be working correctly now. If you were waiting for the fixes or had previously encountered issues, now is a great time to give it another try! Happy learning! 📚 Trailhead: https://lnkd.in/gzu6Jmgs - Nobuyuki Watanabe 🔗 https://lnkd.in/gkeKyShH #Salesforce #Agentforce #MarketingCloudNext #MomentMarketer #MarketingChampion #MarketingChampions

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Hypothesis: AI is exposing something that was always true about consulting firms. And the firms that knew it all along are fine. Here’s the thing: a firm’s positioning and commercial success have always been a function of how well it knows which business issue it solves for which group of clients... ... and I mean not in theory, not in the deck - but in practice. In the room, in the day-to-day, in the “trenches” of a client’s strategy groups, hr teams, or IT operations. And the strongest firms have always differentiated themselves by - being very clear on the issue they solve - being opinionated about how the “solving” should be done - being both relentless and rigorous in their efforts to improve their craft (plus - this is another hypothesis of mine - having a tendency to be distant, if not skeptical, towards the tools and vendors involved in their issue resolutions ... you’re allowed to come at me in the comments for this one). The other firms, on the other hand, have always been, well, none of the above, or, should I say, not enough. And this has always been a commercial challenge... AI is just making it urgent. Because when AI compresses the value of “raw” capability (the task work, the outputs, the deliverables) then what’s left is the question clients were always asking underneath: do these people really see and do things we can’t see and do ourselves besides adding capacity? And the firms that don’t have strong answers here are headed for trouble, forced to do more for less, leveraging AI solutions to spin up a vicious circle of revenue and margin erosion. Their much-better-positioned, higher-performing counterparts, on the other hand - the firms that can answer the client question specifically and convincingly even before the first meeting - tell us they find AI even changes their competitive position for the better... because it helps to streamline their highly specific, highly repeatable offerings So, what are the not-yet-specific, capability-selling firms to do? Well, change, of course... because they have to - and because they can, too. In most cases, the required issue-resolution clarity is already there. Somewhere in the firm’s history, in its best work, in the problems it has solved repeatedly and well, is a very specific answer to that question. It just hasn’t been named yet. That’s the work. And it starts with the very conversations and trade-off discussions the firms in question have been sidestepping all this time... ... until AI came in to force the point. Hope this helps!

$500,000. That’s what MBB charges for a proper discovery engagement. Also known as “the good old days”. A Reddit thread caught my eye this week. Link in comments. “The hardest part of consulting isn’t the work itself. It’s everything before it even starts.” Scoping calls. Data discovery. Figuring out what the client actually has versus what they think they have. All of it unpaid. All of it necessary. Here’s the trap nobody talks about: The bigger the whale, the more discovery they expect. The more senior the work. The less it’s tracked anywhere in the business. Partners absorb it. Nobody logs the hours. It’s just considered part of winning the deal. But try to cut corners with ChatGPT and you end up looking worse. The brief is plausible. The data underneath is garbage. Consultants are caught in a squeeze. Do the work properly and eat the cost. Shortcut it and blow up your reputation. 89% of failed AI projects never did a formal data readiness assessment before committing resources. “Pre-engagement work IS the work, it’s just not labeled or priced that way.” The work that determines whether a project succeeds is the work nobody wants to pay for. And the consultants who figure out how to make discovery sustainable are the ones who’ll still be standing in three years.

Working with SaaS and AI founders on their GTM foundations, I’ve seen how hard it is to get GTM clarity. But turning that clarity into great assets is often a very painful and manual effort. Because once you’ve done the work to define your: → ICP → Positioning → Messaging You still need to turn those insights into: → a website → a sales deck → outbound messaging → LinkedIn content → ads And that usually means hours of work. Most of the challenges come down to 3 things: 1️⃣ Building a compelling storyline 2️⃣ Adapting the messaging to the medium (A sales deck is not a website. A website is not an ad. An ad is not a cold DM.) 3️⃣ Creating strong designs This is exactly where Pitch’s new AI Agent got me excited. Because it can take: → my sales deck template → your ICP doc → your positioning doc → your messaging framework → your website → your pricing page And turn that context into a customized first draft. The workflow is surprisingly simple: 1️⃣ Start with a proven sales deck template 2️⃣ Upload your GTM foundation 3️⃣ Add the prompt 4️⃣ Let Pitch Agent create the deck And I think this is where AI becomes genuinely useful for founders and GTM teams. I’ve put together the full workflow for free: → Sales deck template → Pitch Agent prompt → Positioning framework Grab for free: https://lnkd.in/edt4gTSY Have you been testing the new Pitch AI agent? | 20 comments on LinkedIn

𝐀𝐈 𝐢𝐬 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐜𝐨𝐧𝐬𝐮𝐥𝐭𝐢𝐧𝐠 𝐟𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐚𝐧 𝐦𝐚𝐧𝐲 𝐟𝐢𝐫𝐦𝐬 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝. For years, consulting pricing was built around time. More research hours. More analysis hours. More slide-building hours. More junior team capacity. But AI is now compressing a big part of that work. Research is faster. Market scanning is easier. Analysis cycles are shorter. Recommendations can be drafted quickly. Clients can see that fewer hours are needed. So the old question is starting to change. It is no longer only: “How much time did your team spend?” It is becoming: “What measurable outcome did your work create?” That shift pushes consulting firms toward outcome-based pricing models. Fees will increasingly be linked to client impact, cost savings, revenue improvement, operational efficiency, and agreed business results. This sounds attractive for clients, but it also creates a new challenge for consulting firms. They will need stronger measurement systems. Clearer contracts. Better attribution rules. More flexible revenue planning. And a serious rethink of partner compensation models. The firms that adapt early will not just sell advice. They will sell accountable transformation. AI will not remove the need for consulting. But it will remove the comfort of billing for work that cannot be clearly connected to value. The future of consulting belongs to firms that can prove outcomes, not just present recommendations. ♻️ Repost to help a team understand where they truly fit. ➕ Follow Prem for more | 132 comments on LinkedIn

AI is putting pressure on consulting’s billable hour... and my LinkedIn is full of repetitions of the fact. But ... what, exactly, can firms do? If you read the feed, then “value-based pricing” is the answer most people seem to point to - and they’re not wrong. But escaping the AI pressures doesn’t start with pricing... since pricing is an end state. There is an entire bunch of decisions firms have to make first, and they’re all critical to even getting to a point where you might consider some kind of value-derived pricing of your work. The seven steps Luk Smeyers and I outlined recently (I’ll add the chart again below) connect all these decisions to a journey, and since I already wrote about step 1 a few days back, I’ll spend the remainder of this post outlining step 2 - designing a firm’s proposition. The lever here: firms simply can’t price outcomes until they’ve defined which outcome they can deliver with high reliability... which is why vague, capability-focused propositions force time-and-materials almost by default: There’s simply nothing specific enough to price against. The moment a proposition is sharp, issue-led, outcome-focused, and specific to a distinct group of clients, however, “the value” a firm creates becomes much clearer indeed. Now, the practical question: How can you get specific? In our work, Luk and I try to find specificity in a firm’s existing successes... Here’s (roughly) how: 1️⃣ Level 1 - cluster past engagements by business issue category. Revenue growth, cost performance, risk and resilience, strategic transformation. Most firms can do this in a few hours. It tells you which outcome spaces the firm has really operated in profitably. 2️⃣ Level 2 - find the specific recurring challenge within the best of those engagements. Not “we help firms reduce cost.” But: “we repeatedly helped firms reduce cost in a specific situation, with a specific constraint, where a specific thing kept going wrong.” Inside every category, there’s a pattern. That’s where differentiation starts to emerge. 3️⃣ Level 3 - identify the specific contribution the firm made, repeatedly and successfully. What did this firm do - in those engagements, under those conditions - that others didn’t? That answer lives in the firm’s accumulated experience. It’s proprietary. It cannot be Googled. It cannot be replicated by a competitor without years of similar work. AI can replicate Level 1. It struggles with Level 2. And it usually cannot touch Level 3. Which means a firm that has mined its own history down to this level is very close to creating something “un-commoditisable” indeed - something specific enough to build an outcome-based promise around. Now, that’s the real work of Step 2. Not starting from a blank canvas. But starting from what the firm has already proven it can do ... and then getting specific enough to own it. And in 10 out of 10 cases I’ve personally seen, there’s always something. Hope this helps!

The consulting industry has a value definition problem. Most consulting firms still define what they deliver through broad capability coverage, long service lists, or practice overviews. This approach shaped the commercial logic of consultancies for decades. The broader the coverage, the easier it felt to win work. But in a competitive market, that model is under intense pressure. Buyers are tired of generic service lists and broad promises. They face specific, high-stakes problems and seek evidence that a consultancy has solved similar problems before. When a consultancy leads with services, it forces the buyer to do the heavy lifting. The buyer must translate those services into their own context and guess if there is a fit. This leads to longer sales cycles, persuasion-heavy pitches, and more custom work. Consultancies with the strongest performance do not lead with activities. They lead with the business issue, the outcome, and the proven path to get there. This shift fundamentally alters the commercial dialogue. The market is moving away from asking what services a consultancy provides, toward asking how confidently it can solve a specific business problem. Our latest article explores why winning consulting clients starts with defining the client problem, rather than presenting a service catalogue. https://lnkd.in/e-J27nyp]

I’ve been sharing advice on positioning for the last 4 years, and it’s been seen by millions of people. But you can really only get across so much in a LinkedIn post. For one, there’s a pretty strict character limit. Additionally, reading about deep positioning breakdowns in the context of a newsfeed is not the most conducive for deep learning. So we recently started a newsletter with much longer dives into these topics. It’s still in its infancy, so you have the chance to shop the content/format (we take the feedback super seriously, and read all the emails). Check it out if this is a topic of interest to you. I’ve been using Beehiiv and I may or may not have configured the welcome email correctly... | 32 comments on LinkedIn

Improving consulting firm performance starts with discovering the right direction Not inventing it ... Discovering it - preferably together. Here’s how this plays out in practice: Most firms that want to grow - or improve margins, or build AI resilience, or simply get out of the hamster wheel - start downstream: New messaging. More BD. A director hire. A refreshed website. And, don’t get me wrong... these things matter. But without a clear direction and strong commitment (David Maister used to call it a firm’s “deal”), there’s a high risk they get built “on sand.” To prevent this, and start your improvement efforts with a strong foundation, get a senior group together and discuss three questions: 1. Where does leadership want to take the firm in the next 3-5 years? Rapid growth? Stability and better margins? A long-term specialization? Some platform play or exit? The answer shapes everything that follows, so openness and honesty are required. 2. Will the firm do the same work, but better, or shift into something different? Luk and I often find this discussion isn’t as straightforward as you might expect - sometimes, even “staying where we are” requires a firm to change what the firm delivers (delivery-focused firms might need to do more strategy, and vice versa). 3. Which outcome space should the firm stay in - for now? Revenue growth, cost performance, risk and resilience, strategic returns (etc.). For most boutiques, picking one as a primary anchor is both feasible and recommended. For larger firms, what matters is that the spaces claimed are explicit, intentional, and coherent. (Perhaps worth noting: in many cases, simply having the outcome space discussion is valuable in itself. There’s no need to nail it perfectly or set anything in stone. But getting into the mode of outcome thinking - shifting from “what do we do?” to “what do we reliably help clients achieve?” - is a significant mindset shift.) Once these questions have been discussed openly, the next step is to ground the direction in reality rather than in vision. This means pulling a short list of recent engagements that fit the group’s answers to question number 2 from above, and where the firm created real value and delivered effectively. Reviewing them side by side. Looking for the pattern: recurring client situations, similar decision dynamics, familiar constraints... ... because that is the kind of raw material from which practical, sustainable strategies and propositions get built. Oh, a word of caution: the most common mistake here is what Luk and I call pseudo-reinvention - talking as if a major shift is underway while BD, delivery, and economics stay unchanged. It almost always ends in overpromising, internal confusion, and a proposition nobody believes. Real direction-setting is less dramatic. And far more useful. Hope this helps!

People overcomplicate positioning. There are really just three strategies... Strategy 1: Position in a mature category against existing vendors You place yourself in an established category and argue why you’re better than the competition. The upside: Customers already have budget for it, actively search for it, and don’t need educating. The risk: Mature categories have big, entrenched players. Without serious differentiation and resources, you’ll struggle to make the shortlist. Choose this if... • the category is large enough to meet your growth goals • you have the resources to compete head-to-head for the whole market • OR you’re willing to focus an underserved subsegment Strategy 2: Take an existing (but small) category to a new audience You position in a known (but likely nascent) category and sell it to people who haven’t heard of it yet. (E.g. Skrillex bringing Dubstep to the mainstream. DocuSign didn’t invent e-signature, but they focused on selling it to companies that used paper. Dropbox didn’t invent file sharing, but they popularized it.) The upside: • Less direct competition, more greenfield opportunity The risk: • You have to educate the market rather than waiting for inbound demand • You also inherit the category’s reputation (for good or bad) Choose this if... • you believe the category will grow and you can become a leader as it solidifies • you have the marketing patience to play the long game Strategy 3: Create your own category You refuse to fit into anyone else’s box and seek to build a new one. The upside: You get full control to define both the problem and solution framing. The risks: • you need to pick the right-sized problem (too broad and it’s unbelievable) • first movers rarely win (Airbnb beat Vrbo, Google beat AltaVista), and large incumbents may simply copy your blueprint once you’ve proven the market, as Microsoft did to Slack. Choose this if... • you genuinely believe any existing category label would hurt more than help • you’re prepared to invest years building awareness from scratch If you need help with your positioning, shoot me a DM and mention this post. | 60 comments on LinkedIn

Winning Consulting Clients: Start with Their Problem, Not with a Service List Welcome to the 151st edition of our newsletter. In this edition, the featured article explores the pitfalls of transitioning to a proposition-led consulting model and the steps we encourage consultancies to take to make it as seamless as possible. Enjoy reading, Luk & Florian Learn more in the featured article of our latest edition of ‘The Authority’ newsletter: https://lnkd.in/enPk-Kfy #consulting #consultancy #managementconsulting

How to supercharge your ICP with SPICED and nail ABM 💪 View [image 1] to see SPICED for ICP laid out. 𝗪𝗵𝘆 𝗱𝗲𝘃𝗲𝗹𝗼𝗽 𝗦𝗣𝗜𝗖𝗘𝗗 𝗳𝗼𝗿 𝗜𝗖𝗣? [Image 2] ICP 101 is creating a combination of: • Firmographics: company level data • Technographics: used technology stack Great for segmentation. But does it help you understand how your ICP buys and why? Nope. That’s where SPICED comes in: A framework that helps GTM teams diagnose customer needs and have more impactful conversations. Review [images 3, 4 and 5]. It’s developed by Winning by Design’s Jacco van der Kooij and detailed in his book Revenue Architecture: [𝗦]ituation: Leads to pain ⤵️ [𝗣]ain: Drives the necessity for change or impact ⤵️ [𝗜]mpact: Is needed to adhere to a critical event ⤵️ [𝗖]ritical [𝗘]vent: Determines decision making ⤵️ [𝗗]ecision: Is based on Decision Criteria & Process used in ME𝗗𝗗IC Practical example of how to apply SPICED to your ICP? Revisit [image 1] 𝗦𝘁𝗲𝗽𝘀/𝘁𝗶𝗽𝘀: • Interview 3+ customers and follow the SPICED framework learn about their buying journey. 𝗣𝗿𝗼 𝘁𝗶𝗽: plug a cheeky case study request at the end ;) • Interview Sales, CSM en C-level for the internal view of the ICP buying journey • Review existing case studies for additional content • Record everything. Aggregate the transcriptions and put them into AI to find common denominators. 𝗣𝗿𝗼 𝘁𝗶𝗽: use Koen Stam’s custom GTM GPT. Link pinned in comment. Ask another LLM like Claude challenge and optimize the outcomes. • Dig deep to find a critical event. Compelling is minimal, critical is powerful, mandatory is king. • Be meticulous with the [𝗗]ecision criteria & process. Master MEDDPICC to leverage it. • Brief ICP SPICED to sales to use in Discovery calls and check and refine your thesis [image 6]. HubSpot 𝗽𝗿𝗼 𝘁𝗶𝗽: use Playbooks to guide your reps through the right questions in the right order. Next optional steps 📌 review pinned posts for deep dive: • Develop SPICED for your buying committee [image 7] • Developed SPICED for your key buyer personas [image 8] • Develop customer stories based on SPICED [image 9] • Develop account plans based on SPICED (in HubSpot projects) [image 10 & 11] Aim for done, not for perfect. Iterate along the way. Include the entire GTM team. Come find me in the DM or the comments for the ABM worksheet and apply SPICED yourself. 🧡 Learn how to develop Revenue Architecture in HubSpot - watch webinar recording: https://lnkd.in/e-6kqWYm Good luck everyone and don’t hesitate to drop your questions in the comments 👏 #GTM #ABM #SPICED

𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲 𝗷𝘂𝘀𝘁 𝗮𝗻𝘀𝘄𝗲𝗿𝗲𝗱 𝘁𝗵𝗲 𝗯𝗶𝗴 𝗠𝗖 𝗡𝗲𝘅𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝘄𝗵𝗶𝗰𝗵 𝗔𝗠𝗣𝘀𝗰𝗿𝗶𝗽𝘁 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸 𝘁𝗵𝗲𝗿𝗲? The official developer docs now include a complete function index with Engagement and Next columns side by side. 41 out of 150 AMPscript functions are supported in MC Next today. If you are scoping a migration or just want a realistic picture of the effort, here is what the matrix tells us: 𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗡𝗲𝘅𝘁 𝘁𝗼𝗱𝗮𝘆: → All core math (Add, Subtract, Multiply, Divide, Mod) → Most string functions (Concat, Replace, SubString, Trim, Uppercase, Lowercase, Format...) → Key date and time operations (DateAdd, DateDiff, DateParse, FormatDate, Now, StringToDate) → Single-value DE reads (Lookup, Field, Row, RowCount, BuildRowsetFromJson) → Content blocks by ID, key, and name → Core utilities (Iif, IsNull, Empty, Output, Random, FormatCurrency, FormatNumber...) → RetrieveSalesforceObjects for Salesforce data access 𝗡𝗼𝘁 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝘆𝗲𝘁: → All DE write operations (InsertDE, UpdateDE, UpsertDE, DeleteDE and their CloudPages variants) → Multi-row DE queries (LookupRows, LookupOrderedRows, ExecuteFilter) → All HTTP functions (HttpGet, HttpPost, UrlEncode, RedirectTo) → All encryption (SHA1/256/512, MD5, EncryptSymmetric, DecryptSymmetric, JWT) → All Sites and CloudPages functions (QueryParameter, CloudPagesURL, RequestParameter...) → All SMS, Social, and Microsoft Dynamics CRM functions → All legacy API functions (InvokeCreate, InvokeRetrieve, InvokeUpdate...) Simple personalization with single-row lookups and string formatting? Probably a manageable migration. Complex logic relying on HTTP callouts, encryption, multi-row queries, CloudPages, or SMS? That needs proper re-architecture before moving to Next. This is the reference document that lets architects and developers have honest conversations about timeline and complexity with their stakeholders. 🔗 https://lnkd.in/dKMPyaYD 📝 𝗨𝗽𝗱𝗮𝘁𝗲 on three functions: they work in MC Next, but with behavioral differences worth knowing before you start migrating: 𝗙𝗼𝗿𝗺𝗮𝘁𝗗𝗮𝘁𝗲() 🔗 https://lnkd.in/d9sBVGg5 𝗟𝗼𝗼𝗸𝘂𝗽() 🔗 https://lnkd.in/degB8HrM 𝗦𝘁𝗿𝗶𝗻𝗴𝗧𝗼𝗗𝗮𝘁𝗲() 🔗 https://lnkd.in/dxVzr5mV Bottom line: “supported” does not always mean “compatible”. Test your date formatting logic and DE lookups carefully before assuming a lift-and-shift will work. #MarketingChampions 🏆#MomentMarketer #MarketingCloud #MCNext #AMPscript #SalesforceMarketingCloud #EmailMarketing #MarketingCloudNext | 14 comments on LinkedIn

“The use cases are endless” Founders use these words to raise money from VCs and get employees excited. Because endless use cases = huge TAM. But having lots of use cases for your product is a double-edged sword. Why? 👉 Because each use case requires a go-to-market program to actually get customers for your product. The more use cases you have… The more complex (and expensive) your sales and marketing programs will be. - - - Here’s the 5 types of Go-to-Market - based on number of use cases. 🟢 Vertical This is the most focused GTM strategy you could follow. This is route we take with FletchPMM. We’re out to help one specific group of people (early stage SaaS founders)… Do one thing (re-write their homepage with a clear positioning strategy). And because it’s so focused, we’re able to generate word of mouth referrals - Whenever a SaaS founder is considering re-doing the positioning on their website, we usually get a call. 🔵 Horizontal For horizontal → you’re getting very specific on a main use case and branching out into sub-sets of that use case. Take Calendly for example. The main use case: scheduling online meetings The sub-use cases they have to accommodate: → teachers scheduling parent-teacher conferences → sales people booking demos → recruiters booking interviews with candidates (Loom and Asana also fall into this bucket) 🟣 Vertical Solutions For vertical solution → you’re getting very specific on an industry segment and branching out into adjacent use cases. Take Procore Technologies for example: The main use case: managing construction projects The adjacent use case they have to accommodate: → Handling pre-construction tasks (designs, estimates, bids) → Financial planning → Managing an onsite workforce 🟠 Horizontal Suite This approach bundles multiple horizontal products into a single offering. Google and Microsoft do this very well. The key to this strategy is that you must have product-market fit with the individual products before you start bundling them up into a bigger offer. 🔴 Platform We define platforms as “raw material” products. You’d use them to build other things. But the use case of “building other things” makes these products difficult to market - Because the most compelling value propositions are nested in the more specific use cases. Take Airtable for example. It’s tempting to say, “You can build almost anything with it” But the most compelling value propositions are when you get specific: “Build a custom CRM in minutes” “Build an HR process” “Build a marketing campaign manager” - - - Remember, as you add use cases to your go-to-market programs, you’re introducing complexity and cost. And while you will have to add these use cases to become a large business… It’s almost always better to start with a vertical GTM strategy to dominate a segment. This early segment domination will then act as a wedge to broaden vertically or horizontally. | 16 comments on LinkedIn

Feeling the pressure in consulting right now? Try this... (a TVA process speed-run) Luk Smeyers and I are hearing it everywhere: The consulting market is in a reset. Pipelines are weakening for many firms; clients are getting even smarter; AI (obviously) eats into billable hour work... ... and we agree: In many sub-sectors, the pressure is rising. And in our experience, the firms feeling it the most are those still running the same playbook they’ve always run - promoting what they can do, rather than the specific problems they exist to resolve. This capability-led stance made sense forever... but the commoditization, internal complexity, and cost it invites is increasingly becoming a commodity. The way out? A deliberate shift into a different model: becoming more proposition-led, more prescriptive, more positioned to promise, own, and eventually price outcomes - rather than hours. Firms that make this shift build practices that attract better clients, deliver more consistently, and compound expertise over time... Now, onto the speedrun (easy to write up here, but expect this to take 12-18 months, minimum, and prepare for some difficult conversations throughout). The shift “under-pressure” firms should make before things become really difficult has seven steps: 1. SET A DIRECTION (months 1) Clarify where the firm has the strongest right to win. What work does it want more of - and where does past success give genuine credibility to pursue it? 2. DESIGN THE PROPOSITION (months 2-3) Build a sharp, issue-led value proposition: the specific problem, for a specific client, with a specific outcome. This is the foundation everything else rests on. 3. ESTABLISH GOVERNANCE (months 3-6) A proposition without discipline erodes fast. Build recurring reviews, tracking, and decision criteria that keep the firm honest about what it should - and shouldn’t - pursue. 4. CHANGE THE GO-TO-MARKET (months 4-9) Reposition how the firm shows up - messaging, content, BD approach - around the proposition rather than capability. This is where the market starts to notice. 5. REFINE THE SERVICE PORTFOLIO (months 6-12) Restructure offerings to reflect the proposition. Eliminate what doesn’t fit. Package what does into clear, repeatable programs with defined entry points. 6. BUILD IP AND THOUGHT LEADERSHIP (months 9-15) Develop the frameworks, published PoV, and content that make the proposition credible and visible at scale. This is what turns expertise into authority. 7. SCALE DELIVERY (months 12-18) Codify methods, enable the team, build the infrastructure for consistent, proposition-led delivery. This is where leverage and margin improvement kick in. Each step compounds the last. Together, they move a practice from constantly chasing work to consistently attracting it. Worth saving this if the pressure is real. Hope this helps!

Stay away from the AI Risk Zone in consulting. Or get commoditised. That might become one of the most important strategic priorities for consulting firms over the next few years. Many AI discussions in consulting still focus on tools, platforms, use cases, and productivity gains. But in our experience, the real AI question sits much deeper inside the consulting business model. Is the consulting firm still improvising its way through client work? Or has it designed a consulting proposition with a repeatable path to outcomes? That difference matters because AI resilience and repeatability are becoming tightly connected. The consulting firms in the strongest position are usually the firms with: ▪️ a designed consulting proposition for high-stakes business challenges ▪️ a repeatable path to reliable outcomes and impact ▪️ lower delivery variance, and reusable delivery assets ▪️ stronger IP creation ▪️ compounding expertise, tools, and data over time That is where AI starts creating leverage instead of speeding up a broken consulting model. We mapped this thinking into a simple matrix. 1️⃣ The AI Risk Zone The most dangerous position is what we call the AI Risk Zone: • low business stakes • low repeatability This is work that is inconsistent, difficult to defend, highly variable, and often dependent on ad hoc consulting efforts. Exactly the type of consulting work clients will increasingly question, reduce, automate, or replace. 2️⃣ High-Stakes Expert Dependency Another interesting quadrant is “Trusted Experts. Weak Compounding.” These consulting firms often do highly valuable strategic work, but the delivery still depends too much on individual experts rather than a well-designed proposition, a reusable methodology, and compounding IP. - Trusted expertise exists. - Weak compounding remains. That creates scaling pressure, delivery variance, and increasing dependency on senior talent. 3️⃣ The AI Resilient Zone The strongest quadrant is the AI Resilient Zone: - high business stakes - high repeatability This is where consulting firms build a defensible advantage: - Their consulting proposition is carefully designed - Their path to outcomes improves over time - Their IP compounds - Their delivery becomes more scalable - Their expertise becomes more reusable - And AI strengthens the consulting proposition instead of exposing the lack of one 👉 THE TAKEAWAY 👈 We have observed that AI does not reward random consulting work very well. AI rewards consulting firms that design what they want to be remembered for and compound their expertise around it. | 11 comments on LinkedIn

59 klanten vorig jaar. 8 daarvan deden 50% van onze omzet. En toch kregen ze vrijwel evenveel aandacht. Dezelfde hoeveelheid meetings, dezelfde opvolging. Dat klopt gewoon niet. Nu, we hadden die klanten al in tiers zitten hè. Tier 1, tier 2, tier 3. Netjes in een spreadsheet. Maar er was geen enkel systeem dat daar iets mee deed. Tier 1 klant? Soms wekelijks contact, soms een maand niks. Hing af van wie er toevallig tijd had. Dat is eigenlijk het probleem dat ik bij de meeste bedrijven zie. De tiering bestaat wel, maar het zit in een sheet of in iemand zijn hoofd. Niet in je CRM. En als het niet in je CRM zit, gebeurt het niet consistent. Dus we hebben dat bij onszelf gefixt. Drie tiers, elk met een vaste cadans. Tier 1: strategisch account. Wekelijks of tweewekelijks contact, maandelijkse value review, kwartaalmeeting met MT erbij. Tier 2: vaste cadans, maandelijks contact. Gestructureerd, efficiënt. Tier 3: minimale overhead. Lage complexiteit, basic SLA’s. De tier bepalen we op drie zaken: ICP-fit, maturity van de klant, en groeipotentieel. Niet alleen wat iemand nu oplevert, maar wat erin zit. En dan in HubSpot gezet. Met properties, automatische taken, dashboards die tonen welke tier 1 klant al twee weken geen contact heeft gehad. Het systeem wijst je erop in plaats van dat het afhangt van iemand die toevallig eraan denkt. Zonder dat zit je eigenlijk gewoon op een spreadsheet te staren die niemand opent. Ps. Aan klanten die meelezen; dit gaat niet over jullie, jullie zijn allemaal Tier 1 😉 🐵

People love to put things in categories. You can make your life way easier by doing it for them! If you refuse to pick a category, most people will put you in one that you don’t like without your consent. (Not saying category creation cannot work - it can, it has, and it will continue to... but more on that in another post.) But choosing a category is primarily a customer-centric activity. You are HELPING the customer know how to think about you. And more importantly, it helps them immediately see the differentiation that YOU WANT THEM TO SEE. By choosing the category, you’re defining the frame of reference by which you want your value to be judged. Are you expensive? Are you cheap? Are you complicated? Are you simple? Are you for pros? Are you for beginners? All these “differentiators” only have meaning when anchored to something the customer understands. If you shorten your time to clarity, you also shorten your time to a purchase. | 53 comments on LinkedIn