Happy Sunday, Everyone!

I hope you’re taking some well-deserved time to step away from the inbox, close the ad account, recharge and enjoy the last rays of summer before the NFL season (Go Birds), Q4 planning and (yes, really) 2026 arrive.

This summer has been one of the busiest in recent memory for me – travel, events, new business, client meetings, hiring…it’s felt like a non-stop, perpetual whirlwind. To bring some order to my chaotic calendar, one of the things I’ve started to do is bring my note-taker (shoutout to Otter.ai) to every meeting AND create a process to surface insights from those meetings.

As I’ve been going through those notes, one thing stood out: the number of times the following question has occurred in a meeting: “What’s our AI strategy?”

According to my (well, Otter’s) notes, it’s come up at least 16 times in the last 3 months - and that’s just virtual meetings.

It’s more than a question; it’s THE question that is front-and-center for every CMO, CEO, investor, owner, operator and/or marketing leader with whom I’ve had a conversation.

The problem is that it’s the wrong question.

Let’s take a step back: for the past ~2.5 years, the pace of AI evolution in the marketing sector has been both unrelenting and overwhelming. It feels like there’s something new - a model, an application, a use case, a capability, a tool, a workflow - that’s the next big thing, every single week. The sheer volume of unsolicited messages I get from people building thinly-veiled GPT wrappers promising staggering improvements is insane. We've finally reached an inflection point where the noise has become so deafening that executives across-the-board are compelled to respond.

Just in the last few months, I’ve watched this scramble play out seven ways to Sunday: leadership teams approving AI “pilots” just to be seen as “doing AI.” Multiple 8/9 figure brands that have junior employees with individual GPT licenses spinning up AI content…with no strategy, POV or structure. And agencies are certainly not immune – just last week, I was passed an agency deck that had “AI” in it 19 (!!!!) times – and the worst part was that at least half of them made no sense as a use case/workflow, let alone something that will materially improve a brand’s P&L.

We’re in the golden age of AI theater.

Everyone wants to be seen “doing” AI (less they fall behind), which has (rather predictably) led to organizations doing crazy stuff – from random announcements/company-wide emails, to assembling “AI teams” to licensing a dozen AI tools for who-knows-what. And we’re now at the point where all that theater has bubbled up to the leadership table, and the people that sit around it are asking: “what’s our AI strategy?” Since we’ve all been too busy doing AI theater, that question keeps getting passed down until someone provides an answer that sounds both smart and plausible.

But here’s the truth: there are very few smart answers to incoherent questions.

AI is a capability. It’s a productivity enhancer. It’s a force multiplier. It’s not something that sits off in a little silo attached to your organization; it’s something with the potential to help every constituent part of your organization perform at a higher level.

You don’t need an “AI strategy” any more than you need an “electricity strategy” or an “Excel strategy” – what you need is a business strategy. AI only matters insofar as it improves your ability to execute that business strategy more effectively and/or efficiently.

That’s what this week’s issue is about.

Let’s start with the framing. Most companies are asking the wrong question (“what’s our AI strategy”) – so let’s reframe it: AI is a general-purpose technology that can either (1) reduce costs, (2) improve choices or (3) enable entirely new capabilities. That’s it. Every meaningful use case ladders up to one of those three.

That changes the question to: “How do we create or compound economic advantage using AI?”

When you approach it that way, AI initiatives start to look a lot more like capital allocation decisions and not pet projects designed to garner applause on business Tinder (also known as LinkedIn). The distinction may sound small, but it matters a great deal – because if you’re not making investment/allocation decisions with the P&L in mind, you’re just conducting an exceedingly expensive science fair project.

With the proper framing in place, the next question is: how do we make AI investments that create/compound those economic advantages? Simple: treat them like a portfolio:

1. Efficiency Bets (Low Risk, Fast Payback): They’re low-risk, capped-upside bets that reduce or eliminate the busywork/tedious tasks that comprise 10% - 30% of your team’s time. Examples include: workflow automations, mundane task automations, simple customer support/success processes, research augmentation, meeting/deliverable summarization tools. At their core, each of these improves organizational efficiency, reduces knowledge loss and/or improves the quality/quantity of the service you provide. None of them are going to transform your organization – but they’ll quickly fade into the background, quietly becoming central to your organization in much the same way that fast WiFi or cloud storage are critical.
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2. Effectiveness Bets (Medium Risk, High Impact): Every team has a set of things they *wish* they had time to accomplish. Effectiveness bets are - at their core - the application of AI to the things on that list. These things focus on improving decision quality, customer experience and/or capability performance/execution. Think: improving your inventory/performance forecasting, creative ideation/iteration, developing real-time customer insights, post-click optimization, etc. Nothing here is going to replace your marketing team, but done well, it can make that team exponentially more effective and efficient.
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3. Expansion Bets (High Risk, Asymmetric Upside): Finally, there’s the expansion bets. This is where new categories, new AI-native features and breakthrough products live. I would include anything from agentic solutions to fully-automated chatbots to automated ad managers here – these are the things we often hear hyped on LinkedIn, but (candidly) are rarely ready for prime time. If you’re going to make bets here, it is absolutely essential to have both controls and continue/kill criteria in place.

This framework mirrors portfolio theory in finance: core holdings, growth plays, and speculative bets. If this all sounds familiar after the last two issues, it should. The principles of compounding, risk-adjusted return and capital reallocation apply here, too.

The Big Takeaway: AI Theater Happens When You Confuse Tools With Outcomes

Flashy tools with no connection to operating leverage? That’s theater. It’s a show that distracts you (and your team) from what really matters.

Instead of a fancy product demo, your goal should be to craft a compelling narrative that connects what you’re doing to how your organization makes money. It should be straightforward, credible and measurable. For example:

“Our primary focus with AI is improving our operating leverage. We’ve identified 14 routine tasks that together account for 21% of our team’s time, and we’re rolling out automated workflows for each. We expect this to reduce the time spent on those tasks by 80%, with little-to-no impact on the quality of deliverables/outcomes. Our secondary focus is reinvesting the time saved above, alongside AI, into three core areas of opportunity: (1) improving our post-click experience; (2) creating real-time audience insights and (3) improving creative efficiency. Based on conservative forecasts, we believe this will allow us to reduce our customer acquisition costs by 13% in Q4 and 17% in Q1 2026. Finally, we’re also beginning several agentic pilots that could transform the shopper experience. These are small, strategic bets that could have a transformative impact on the business if they hit.

That narrative is infinitely more credible than 99% of what I hear. That’s what we should all be working toward. Bridging this chasm requires us to re-focus what we’re doing around what actually matters to their organization.

The reality is that most marketers aren’t lacking in intent - they want to use AI more. The gap I see - overwhelmingly - is infrastructure. They don’t have the tools, systems or support to go from “I have this GPT license and a way-too-long to-do list” to “Here’s how I can automate these 19 tasks so I can get more done.”

Nothing that follows in this section is sexy. It’s not going to garner the “oohs” and “ahhs” from the LinkedIn crowd. What it will do is improve productivity and expand margins, while allowing your team to spend more time on higher-leverage activities.

AI tools are accelerants: integrate them in a great process, and you’ll reap exponentially higher returns; apply them to a broken system, and all they’ll do is multiply the chaos.

Let’s get to it:

1. Get Your Internal Data “AI-Ready”

Data is the raw material that powers any AI initiative. As the old saying goes, “garbage in, garbage out.” Most companies still don’t know what data they have, who owns it or whether it can be trusted. These organizations are stuck in the 2010s view of data-as-a-warehouse, vs today’s data-as-an-operating-system.

This is not a fun shift to make. The first step is taking an inventory of what data you have. This should include everything: internal docs. User guides. Technical specs/docs. Processes. Notion boards. Templates. Checklists. Content. All of it.

Odds are, you have reams of data you haven’t used in years (if not decades) – some of which might be quite valuable, and a great deal of which might no longer be relevant. If you just set an AI loose on all of it, the probability is quite high that it’s going to incorporate some of the wrong stuff in the output. Documenting everything allows you to exclude the stuff that’s no longer relevant, which (in turn) improves the quality of the output. Less garbage in = more good stuff out.

This doesn’t stop there. Once you have the list, the next step is to build taxonomies and ontologies, standardize naming conventions, tag entities and map relationships (this process and this process are both part of this service, etc.). The goal of this work is to help the system understand the relationships between all of the content/documents you’ve cataloged – so the AI is able to grasp that Service 1 includes X, Y & Z, and is positioned in this way to this audience and that way to that audience.

If this sounds boring and tedious, it’s because it is. But this is the boring, tedious work that compounds into a massive advantage over time.

The second benefit of this is provenance. When you can prove where your data came from, how it is secured and who touched it, you don’t simply minimize risk - you create confidence and build trust. That, in turn, helps you win more business.

2. Brand Taste - Clarify Your Brand, Voice & Tone

AI has effectively reduced the cost of creation to near-zero. Anyone with a GPT license and some free time can create a near-unlimited number of image, video and/or text assets. The impact of that is that volume is no longer the differentiator; that honor now belongs to taste.

Most brands have not codified their brand, voice or point of view. That’s a mistake. Without guidelines, the creative outputs (whether text, images, audio or video) are AI slop. Generic, boring, undifferentiated sludge.

The smart move is to develop a “brand guide” that includes three core components:

• The Non-Negotiable Rules - these are the “must follow” elements for your brand - the things you will NEVER compromise. Your brand promise, your desired emotional connection with your audience, your brand values.
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• The Guidelines - these are your preferences, but they’re not written in stone. This might be the types of images you feature, the kinds of comments/reviews you amplify, the benefits you highlight.
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• Examples/Illustrations - AI models tend to respond quite favorably to “do this / not that” structures - so illustrate each of the above.

The scarce resource is not content. It is attention. The question isn’t whether you can produce at scale - anyone with a GPT license can. The question is whether people will stop, read and care – and that’s a question of taste.

3. Workflow Re-architecture

I’ve worked with hundreds of organizations, from solo ventures to billion-dollar companies. Across every one, there are 20% of the tasks that take 80% of the time. The exact nature of those tasks vary, but the rule always holds.

I firmly believe the biggest gains from AI won’t come from shaving a few minutes off a task. That kind of incremental efficiency might garner some praise at a weekly standup, but it doesn’t change how your business runs.

The real unlock is rethinking the task entirely. Not “how can we do this faster?” but, “Why do we do it this way in the first place?”

Start by mapping your top 10 recurring workflows based on volume × value. Go through your daily to-do list / hours log (if you have one) and identify the routine tasks that take the most time (and if you have more junior people doing the task, ask them…then actually listen). Once identified, rebuild those workflows with AI integrated into the system, not just sprinkled on top.

Your goal should not be to automate people away, but rather to design processes where:

• Manual tasks are fully automated (think: copy-and-pasting data from X to Y)
• Handoffs (i.e. marketing drafts copy, design lays it out, dev implements) are eliminated
• Repetitive tasks (like tagging, summarizing or formatting) are fully automated
• Human judgment is reserved for edge cases, escalation, or strategic input

This is the difference between bolting AI onto a broken process and using it to reduce complexity. When done right, you’re not just speeding things up. You’re reducing the total number of steps, decisions and dependencies – that’s what frees up your people to do more of the work that matters.

4. Real Governance

Let’s start with this disclaimer: governance isn’t sexy. In the move-fast-and-break-things AI world, talk of governance is usually about as well-received as a Chris Rock skit in church. But….no one wants to be the reason your chatbot hallucinated a product claim or your “personalized” emails accidentally included sensitive customer data.

That results in most companies defaulting to one of two extremes when it comes to AI governance:

• Wild West: Everyone’s using generative tools in their workflows, but no one knows what data is exposed, what goes into outputs, how data is being used or what risks are accumulating behind the scenes.
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• Fortress Mode: Legal locks everything down. Teams are forbidden from using AI tools for even basic, low-risk tasks. Innovation & experimentation die. The company ends up watching competitors ship, learn and iterate while they’re still drafting their usage policy.

Both approaches fail. The first opens you up to real risk + potential legal exposure. The second guarantees you fall behind. The alternative - and what high-functioning, forward-looking companies are doing - is a happy medium. That tends to look like a lightweight governance layer that enables innovation while still managing risk.

• Define “safe to try” vs. “needs review.” Make the criteria explicit. If a use case uses non-sensitive inputs, generates draft copy and isn’t customer-facing, use it! If it references regulatory claims, pricing data or customer data, send it for review (red light). For items that fall in between those extremes, assign a yellow light, where someone more senior has to weigh in before deciding on a course of action.
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• Document decisions AND procedures – one of the easiest lifts for any organization is to have a living Notion (or similar) where decisions, procedures and prompts are stored. This allows everyone in the company to have full transparency into what’s being done, all while saving a ton of time (since you won’t have to draft internal docs and your people won’t have to try to recreate a prompt that already works).
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• Make compliance a feature, not a blocker. The best guardrails work like lane assist: they help people move faster because they know where the edges are. I know that compliance has gotten a bad rap over the years (and most of it is deserved), but there’s also massive value in ensuring that everything you create/put out/publish doesn’t pose an existential risk to your company.

Done right, governance isn’t red tape. It’s what allows teams to test, learn and create with confidence. We’re all operating in the digital equivalent of the wild west. Hallucinations, copyright exposure and regulatory missteps happen every day - what is needed is a way to minimize the risk while maintaining the upside, which is exactly what this provides.

5. Talent & Training

This may be obvious, but paying for a bunch of AI tools doesn't magically make your people more effective, competent or productive.

Training your people on how to use those AI tools does.

Your goal should be to make two fundamental changes: (1) shift from individual instances to scalable use cases and (2) move from hiring for technical excellence to judgement.

Let’s start with (1). Just the other day, I was talking with one of our digital team members who was “vibe coding” a JavaScript setup for landing pages. He built conditional redirects off a radio button selection, sending users to different thank-you pages based on their response to a form question. The idea was to provide both a tailored experience AND better analytics data. It’s a clever use case. The problem was that it was built for a single client, when we have at least 5 that would benefit from it being implemented. We solved that by adding this to our library of effective prompts, then sharing it with the entire team – so other account teams who could use it were able to access and implement it in minutes. And as each of those client teams implemented the workflow, they uncovered several weird edge-cases, which we then documented and used to further refine it.

There’s nothing magical about this – it’s just training. The difference is that this training transforms one person’s clever experiment into a scalable capability the entire agency can use, and that all of our clients can benefit from.

That’s the difference between tinkering and leverage. Individual hacks live and die inside one client account or team member’s head. Scalable use cases are documented, shared, refined and eventually become part of the “operating system”. When you build that kind of library, every new engagement/project starts a step ahead of the last one.

This brings me to the second shift (and this is one that’s going to have HR furious): stop hiring purely for technical excellence. Tools are closing that gap every day. What you cannot automate is good judgment. Knowing when an output is good enough. Spotting when an edge case will break something important. Choosing which ideas to run with and which ones to put back in the box.

AI makes technical execution cheaper. That, in turn, pushes the premium to synthesis, taste & judgement. The teams that hire people with those skills will quickly find themselves at a massive advantage, because their people will be better equipped to manage increasingly-more-sophisticated technical tools.

6. Measure The Right Thing

Let’s get clear about one thing: the goal of any/all of this is NOT “AI adoption” - it’s better business economics. That’s the whole reason we’re doing this, so we need a measurement/accountability system that aligns with that goal. I’d recommend you focus on three core areas:

• Operating Leverage: are our investments in AI tools allowing us to realize real cost and/or time savings on our operations? Are we able to automate/expedite routine tasks, thereby freeing up our team to do higher leverage work? Are these tools reducing the time required to produce key deliverables (i.e. monthly reports)?
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• Effectiveness: Doing something faster or cheaper is only good IF the quality of the output is as good as (or, ideally, better than) what was produced before. If you save 3 hours by automating a client report (good!) but the quality of the automated report is so poor/disjointed that your team now needs to spend 4 hours explaining to the client/stakeholder the content and what it means, you haven’t actually accomplished a damn thing. The same holds true for AI-generated landers or creatives: they should be as effective, if not more effective, than what you had before. The goal must be not only to improve efficiency, but to improve effectiveness.
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• Visibility: This is the one most people forget. How often is your brand showing up in AI answers? How much traffic is coming from assistants like ChatGPT or Perplexity? Is the brand visible in the new discovery surfaces like AI overviews or AI mode, where customers are starting their journey? This isn’t just about working smarter inside the walls; it’s about making sure the brand isn’t invisible as these tools re-write how people find things.

At the end of the day, the only thing that matters is this: is your organization more profitable, more effective and more discoverable than you are today? That’s the thing we’re all trying to figure out how to do, and the best way to accomplish it is by having a scorecard that maps to it.

6 Tactical Moves You Can Execute Now

If the preceding section is theoretical, this one is exceedingly practical. The reality is that AI is here to stay, and there are plenty of ways you can integrate it into your day-to-day right now.

Here are a few things that we are doing and what I think marketers should be doing yesterday:

1. Optimize for AI Overviews + Assistants

Search isn’t dead. It’s evolving.

​SparkToro’s latest research shows that while just 20% of Americans use AI tools more than 10 times a month, that usage is enough to make a dent, particularly among high-intent, early-adopter audiences. But here’s the kicker: traditional search traffic hasn’t meaningfully declined. Which means Ai tools are NOT a channel replacement; they’re a visibility expansion opportunity (something I’ve said for years - AI will make people search more, not less):

The takeaway: it’s not “either/or.” It’s “yes/and.” AI Overviews are carving out top-of-funnel real estate in addition to classic rankings. And, for now, the competition for that space is light… which makes this a rare opportunity.

Fortunately (and despite all the AI bro comments to the contrary), appearing in AI Overviews is not a super-secret science; it’s just good, old-fashioned SEO. If you want to appear in more AI Overviews, here’s the quick playbook:

• Identify which searches are impacted by AI overviews (yes, you will have to do some searching!). For the ones that are, note which sites are included (yes, you’ll have to do some clicking).
• Document the commonalities + the weaknesses in the AI Overview – if an answer has wrong/outdated/partially correct/incomplete information, that’s an opportunity!
• Once you’ve identified the opportunities, build pages designed for conversational intent: “Best X for Y in Z context”
• Create layered content: 1-sentence takeaways, 1-paragraph summaries, and full deep dives, all on the same page
• Ensure your content actually communicates real, legitimate, unique value – if you’re just saying the same things as everyone else, there’s little reason for any engine to include your content in their outputs.
• Structure pages cleanly: H1–H3s, FAQs, comparison tables, glossary sections
• Add schema (FAQ/Product/How-To)
• Fix page speed + crawl issues
• Publish content with real bios, citations and external credibility signals

Remember, you’re writing for people, not keywords or language models. If you create content that adds value to your audience and is easily intelligible to the LLM, you’ll quickly find yourself included.

2. Treat AI Assistants as Channels

Most CMOs I’ve spoken to are still treating referrals from AI tools (Perplexity, Claude, ChatGPT) as a curiosity, not a true channel. That’s a massive mistake. We’ve had several clients that have gone from <1.0% of traffic from ChatGPT to >5% in the last 90 days. That may not sound huge (and, even at 5%, it’s the 8th-largest source of traffic), but the rate of increase is shocking.

This is the SEO goldrush all over again. The numbers are/will be small at first, until they’re too overwhelming to ignore. The solution is to begin tracking referrals from ChatGPT, Perplexity, Claude, et al now. Spend time understanding the differences in session types/quality between AI referrals and standard search (this is a great use case for Heatmap.com or Microsoft Clarity).

Once you have clarity on how much you’re appearing now, the next step is to increase your visibility. Publish explainers and glossaries in crawlable, indexable formats. Contribute to public knowledge graphs where appropriate.

When clients ask if this is worth the effort, my answer has been: “If your target audience is asking questions (doesn’t matter if it’s on Reddit or to ChatGPT), you want your content to be the answer.”

That starts by treating these assistants like the channels they are.

3. Own the Post-Click Experience

So much of the conversation around AI overviews + their impact on search right now is on visibility (who is showing up, how much traffic is it generating, etc.) – but none of that matters if your post-click experience doesn’t convert. The reality is that AIOs tend to drive longer, more conversational queries (something we’ve known for quite some time) – which means that expectations of your post-click experience are higher.

A generic “contact us” page isn’t going to cut it for a user who just typed “best CRM for AdTech B2B SaaS companies in US” – that user expects the lander to match the intent of their query.

The reality is that most brands are still playing catch-up in this department.

Align your landing experiences with the shape and structure of long-form, conversational queries (filter your Google Search Console for queries with >8 words). These queries aren’t transactional in the traditional sense; they’re layered with context and constraints.

That means building modular, targeted landing pages that speak to real-world situations: “best platform for X use case,” “solutions under Y budget,” “how this compares to Z.” Your goal for each of these pages should not just be to sell the service/product - it should be to help your visitor make a smart, confident decision.

This approach creates space for compounding returns. Why? Because once you find a variant that lifts conversion or AOV, whether it’s a headline, a section order, a pricing frame or an objection-handling block that improvement holds across every future visitor who fits that same profile.

The process for this is simple (and can be easily augmented with AI):

• Build modular, intent-matched landers that speak to specific use cases, constraints, and objections
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• Run A/B tests that focus on downstream metrics like CVR, AOV, and lead quality
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• Roll learnings back into your core templates so each new page gets better by default

Then repeat. Iterate. Stack improvements.

This is how post-click becomes a flywheel. Not just a place to recover wasted traffic but a surface where smarter decisions and better experiences multiply over time.

4. Ask AI About Your Brand, Then Fix The Gaps

Have you asked ChatGPT what it thinks about your company? Or Perplexity to describe your product/service? Or Gemini to tell you what sets your business apart from your competitors? If not, you should. Chances are, the answers are incomplete, outdated, or just plain wrong.

That’s a brand risk hiding in plain sight.

The defensive move is to seed the internet with clarifying content. Publish canonical explainers. Create source-of-truth pages that models can pull from. For regulated industries, sign and date them. Give the models something better to reference.

If you don’t, someone else will.

There’s an offensive play here, too. Most of your competitors haven’t done any of this, which means their AI footprint is thin, stale or non-existent. Your goal should be to treat every unanswered question about your competitors as an opportunity to frame the conversation in your favor. If an assistant generates a side-by-side comparison, the company with fresh explainers and recently updated buyer’s guides will look credible, while the one with a two-year-old press release will look asleep.

You should absolutely defend your own presence. And you should fill the gaps your competitors leave. Any information vacuum will be filled; the question is whether that’s by you or by someone else.

5. Increase Content Velocity Without Going Generic

AI can (and will) generate endless creative. Without structure, judgment and taste, most of it will be AI slop. If you want to avoid publishing reams of generic garbage, the key is to revise your workflow:

Step #1: Use AI to help uncover patterns in how your audience is searching for information/solutions (customer insights)

Step #2: Based on that, have AI draft content that is likely to appeal to your audience as they’re searching for that information

Step #3: Feed all of the content generated through the brand guardrails (discussed above) AND a taste filter

Step #4: Have a real person with good judgment review, edit and polish everything that comes out, so everything that gets published is both valuable and relevant.

The fastest way to scale this is by building a library of proven content structures. Formats like:

• Myth vs. Fact
• Buyer’s Guides
• Teardowns
• What to Expect
• Comparison Tables
• Checklists

These formats work because they speak to how people actually evaluate solutions. They break down complexity. They frame decisions. They let you communicate authoritatively without being overly sales-y. Once built, each of these content types can be reused and adapted across product lines, personas, or industries, without reinventing the wheel each time.

This is how you scale velocity without sacrificing quality. AI does the heavy lifting. Your team applies the judgment. Together, that creates content that’s fast, on-brand, and grounded in real user needs.

6. Start a Regular Portfolio Review

One of the benefits of portfolio-style management is that it provides a structure for you to scale, maintain or deprecate individual bets without undermining the initiative as a whole. As you make this transition, one of the best things you can do is to schedule time to review each set of bets you’re making (efficiency, effectiveness, expansion) impartially – what’s working? What’s not? Which processes does it make sense to invest in automating? Which procedures should we leave alone?

It’s easy to automate something once, then forget about it – even when that automation ceases to pay dividends or spawns unintended consequences. The inevitable result of that approach is tech debt, frustration and patchwork automations/workflows, in which a new automation is created to fix the deficiencies of an existing automation.

The superior approach long term is to treat every automation or AI-driven workflow like a living asset, not a one-off project. That means revisiting it regularly, assessing whether it’s still delivering value and making an unbiased, clear-headed decision whether it should be scaled, refined, or retired.

The reality is that - like any portfolio - some assets will compound indefinitely. A reporting automation that saves five hours a week for each of a dozen teams worth doubling down on. Others will hit diminishing returns. Maybe the workflow only works under certain conditions, or the maintenance overhead has grown larger than the time it saves. At that point, it’s better to deprecate it than keep propping it up.

What you’re building isn’t a collection of hacks. It’s a portfolio. And like any portfolio, your job is to maximize returns and minimize drag. That’s why regular (monthly/quarterly) reviews matter - they keep you honest. They make sure you aren’t using new workflows/tools as band-aids on top of broken ones.

All that leads to the real advantage: over time, you’ll grow a portfolio of high-performing, margin-expanding automations, workflows and tools while your competition winds up with spaghetti workflows and staggering tech debt.

Your AI Strategy Should Read Like an Operating Plan

A good AI strategy isn’t a deck or a demo. It’s not a list of tools, a set of GPT licenses or a pilot program. It’s an operating plan that explains three things with ruthless clarity:

Where the leverage is. How you’re going after it. How you’ll know if it’s working.

That plan should read like a portfolio, with efficiency bets that quietly reduce friction, effectiveness bets that improve outcomes and expansion bets that explore what might create new revenue streams. Some will be successes, and some will be failures. The discipline is in reallocating resources (capital, time, focus) and attention regularly, so the portfolio as a whole keeps getting stronger.

Everything else is theater.

So, as the noise ramps up this fall, skip the shiny objects. Focus on workflows, leverage, distribution and the cadence of review and reallocation. The winners won’t be the ones with the most pilots or the flashiest announcements; they’ll be the ones who quietly went about the boring work of using AI to build a better, more efficient, more effective business.

And that’s the paradox. In a space obsessed with trends, sexiness & hype, the real advantage belongs to the people/brands disciplined enough to ignore it.

And when the inevitable question comes back — “What’s our AI strategy?” — you’ll have an answer that isn’t just coherent, but credible. One that moves the business forward, not just the conversation.

Cheers,

Sam

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