Other Questions

It is a fair challenge. The Long-Term Strategy Conference 2026 (LTSP26) takes it seriously enough to address it directly, because the answer shapes everything else the conference argues.
The short answer is: in origin, no. In scale, speed, and invisibility, yes — and those three differences are what make Trendslop a categorically more difficult problem than the bad strategy advice that preceded it.

The pattern is not new
Senior strategy professionals have always encountered versions of this problem. The highly paid consulting firm that returns confident, polished recommendations that dissolve under scrutiny. The junior MBA graduate fluent in frameworks but without the judgment to know when to set them aside. The thought leader whose LinkedIn following grows in proportion to how consistently they say what their audience already believes. The board member who carries in a weekend read from HBR and presents its conclusions as strategic direction.
In every case the structure is the same: an outsider, working from incomplete and largely generic information about the organization, returns advice that sounds authoritative because it is drawn from the same pool of contemporary management thinking that everyone else is drawing from. The advice is not fabricated. It is real. It is simply not specific enough, not rigorous enough, and not honest enough about the organization’s actual situation to constitute genuine strategic insight.
Trendslop is that pattern. It is not new.

What AI changes about it
What large language models add to this pattern is not a new kind of failure. It is a new set of properties that make the familiar failure significantly harder to detect and significantly easier to act on before the detection happens.
The first property is speed. A consulting firm takes weeks to return its recommendations. A thought leader publishes monthly. An LLM returns in seconds — at a pace that compresses the gap between receiving advice and acting on it to almost nothing. The organizational immune response that bad advice used to trigger — the healthy skepticism that comes from having time to sit with a recommendation, compare it against internal knowledge, and notice what does not quite fit — has no time to activate.
The second property is volume. A consulting engagement produces one report. An LLM produces as many outputs as the organization is willing to generate, across as many strategic questions as it chooses to ask, simultaneously. The exposure to Trendslop is not a single event that can be evaluated and set aside. It is a continuous ambient condition.
The third property is persuasion. The Harvard Business Review study that coined the term Trendslop described large language models as power persuaders. They do not merely return answers. They return answers in a register — structured, balanced, articulate, confident — that is specifically calibrated to sound like the kind of advice a trusted senior advisor would give. The rhetorical quality of the output is entirely decoupled from the analytical quality of what it contains.
The fourth and most consequential property is invisibility. Bad advice from a consulting firm arrives with a source that can be interrogated, a methodology that can be challenged, and a set of humans who can be pressed when the recommendations do not hold up. Bad advice from an LLM arrives with none of those surfaces. It arrives as text. It sounds like thinking. And in the absence of a rigorous process baseline to compare it against, there is no obvious mechanism for detecting that it is Trendslop rather than insight.

Why this matters more in long-term strategy than anywhere else
Bad advice in short-term operational decisions hits reality quickly. The organization tries something, it does not work, and the course corrects within a quarter or two. The cost is real but bounded.
Bad advice in long-term strategy does not encounter that corrective mechanism for years. The Trendslop that enters a fifteen-year strategic plan in September 2026 may not reveal itself as structurally wrong until 2031 — by which point it has shaped hiring decisions, capital allocation, market positioning, and organizational capability in ways that are expensive and slow to reverse.
This is why LTSP26 treats Trendslop not as an interesting research finding but as an operational risk that is specific to long-term strategy work and that requires a specific kind of process response. The bad strategy advice organizations have always had was manageable, in part, because reality corrected it on a timeline that allowed adjustment. Trendslop embedded in long-term strategy does not offer that correction window.
That is what makes it different. And that is what makes process clarity, before AI application, the only reliable defense against it.

The Long-Term Strategy Conference 2026 (LTSP26) is organized around the EndPoint Method as its structural spine. That choice invites a reasonable question from any senior strategy professional who has worked with established frameworks: what does this offer that the approaches already in use do not?
The answer is not that the EndPoint Method replaces what works. It is that it addresses something most frameworks were not designed to address — and that AI has made the gap between what they address and what is now needed significantly more visible.

What most frameworks do well
The established strategy frameworks — whether Porter’s Five Forces, the Balanced Scorecard, Blue Ocean Strategy, or the various derivatives that consulting firms have developed and branded over the past three decades — are primarily analytical tools. They are designed to help organizations examine their competitive environment, identify strategic options, and structure the logic of a chosen direction. Applied well, they produce genuine insight. They have earned their place in serious strategy work.
What they were not designed to do is describe how strategy gets made as a human process — stage by stage, with attention to the sociology of each stage, the kind of thinking each stage requires, and the conditions under which each stage is vulnerable to failure. That is a different design intent, and it produces a different kind of framework.

What the EndPoint Method adds
The EndPoint Method is a six-stage framework for strategy creation that moves from current-state diagnosis through horizon selection, scenario generation, commitment and quantification, backcasting from the chosen endpoint, and execution planning. Its stages are not unfamiliar in isolation. Most strategy professionals will recognize each one as a legitimate component of serious strategy work.
What distinguishes the EndPoint Method is not the stages themselves but what it specifies about each one. Each stage is treated as a distinct sociological and cognitive environment — with its own dynamics, its own failure modes, and its own relationship to the stages before and after it. The framework identifies what kind of thinking each stage requires, what the team dynamics of that stage typically look like, and what a rigorous output from that stage actually consists of.
That level of stage-level specificity is what makes the EndPoint Method useful in the context of AI. Because it is only at that level of specificity that the questions AI-in-strategy requires can be answered: which stage benefits from AI intervention, which stage is harmed by it, and what a well-scoped persona for a given stage needs to be constrained by in order to produce something categorically better than a general LLM query.

Why this matters now specifically
Most strategy frameworks were developed before AI made the quality of the strategy process — as distinct from the quality of the strategy output — a consequential variable. When the bottleneck in strategy work was access to analytical tools, frameworks that provided those tools were the right response. When the bottleneck is the clarity of the process into which increasingly powerful AI tools are being applied, a different kind of framework becomes necessary.
The EndPoint Method does not replace the analytical frameworks most organizations already use. Several of its stages draw on exactly those frameworks as inputs. What it adds is the process layer that those frameworks assume but do not provide — and that AI, applied without that layer, consistently exposes as missing.

What this means for practitioners who already use established frameworks
If your organization uses an established strategy framework and uses it well, the EndPoint Method is not a disruption to that practice. It is a complement to it — one that clarifies where in the strategy creation process each framework belongs, what it can reliably contribute at that stage, and where AI can be applied alongside it without producing Trendslop.
The professionals who tend to find the EndPoint Method most immediately useful are not those who have no framework. They are those who have a framework they trust, have begun applying AI to the work it structures, and have sensed that something in the combination is producing results that are faster and more polished but not obviously more rigorous. That gap between speed and rigor is precisely what the EndPoint Method is designed to close.

This is the question that separates the Long-Term Strategy Conference 2026 (LTSP26) from most of the AI-in-strategy conversation currently in circulation. The market position — implicit in most AI adoption guidance and explicit in almost none of it — is that process clarity and AI application can develop in parallel. Use the tools, learn as you go, refine the process through iteration. It is a reasonable position for many organizational contexts. In long-term strategy work specifically, it is the wrong sequencing — and the reasons why are worth examining carefully.

The feedback loop problem
Parallel development works when feedback is fast. You apply a tool, observe what it produces, compare that output against what you needed, and adjust. The iteration cycle is short enough that errors surface before they compound.
Long-term strategy does not offer that cycle. The decisions made in a strategy process may not encounter meaningful reality feedback for three, five, or ten years. An organization that applies AI to a strategy process it does not yet clearly understand, and then waits for outcomes to signal whether the AI intervention was well-placed, is running an experiment whose results will not arrive until the cost of a wrong answer has already been paid. By the time Trendslop reveals itself in organizational performance, it has typically been embedded in three years of resource allocation, hiring decisions, and market positioning.

The amplification problem
AI does not introduce confusion into a strategy process. It amplifies whatever the process already contains. A process with genuine clarity about its stages, its bottlenecks, and its vulnerability to failure modes will produce better outputs when AI is applied to the right stages. A process without that clarity will produce faster, more eloquent, more confidently presented versions of whatever the process was already producing — including its errors, its avoidances, and its structural blind spots.
This is why the analogy that LTSP26 draws from manufacturing process improvement is precise rather than illustrative. The iron rule that governed automation decisions in serious manufacturing environments — understand before automating — was not a counsel of caution. It was a recognition that automation applied to a misunderstood process does not improve the process. It locks the misunderstanding in at speed and scale.
The same rule applies in strategy. AI applied to a foggy strategy process does not clear the fog. It accelerates it — with maximum eloquence.

The comparison problem
There is a third reason process clarity must precede AI application, and it is the most practical of the three. Without a clear, honest picture of what the strategy process currently produces at each stage — what good output looks like, what the range of genuine options is, what commitment to a direction requires — there is nothing rigorous enough to evaluate what the AI returns against.
In the absence of that baseline, AI output is assessed on the basis of whether it sounds convincing. And large language models, as the Harvard Business Review study that coined the term Trendslop demonstrated, are exceptionally good at sounding convincing. They are power persuaders. That is not a feature in a context where the ability to distinguish a real insight from a socially desirable one is the core professional capability at stake.

What alongside actually produces
Organizations that develop process clarity and AI application in parallel typically discover, eighteen months in, that the AI activity has been running ahead of the process understanding. The tools are in use. The outputs are being generated. But the basis for evaluating those outputs — the stage-level map, the bottleneck analysis, the baseline — has not kept pace. At that point, process clarity is no longer a prerequisite. It is a retrofit. And retrofits in strategy work are significantly more expensive than foundations.
LTSP26 exists to make the case for the foundation — before the retrofit becomes necessary.

The question sounds forward-looking. In practice it is diagnostic. The Long-Term Strategy Conference 2026 (LTSP26) approaches it not by describing an ideal future state but by identifying the conditions that must be present before AI can do anything in a strategy process that is reliably better than what the process was producing without it.
Those conditions are more demanding than most organizations expect. And most organizations are further from meeting them than their current AI activity suggests.

It is mapped at the stage level
An AI-ready strategy process is not one that has been described in five boxes or summarized in a planning calendar. It is one that has been articulated stage by stage — with enough specificity that each stage can be examined independently for what kind of thinking it requires, what the social dynamics of that stage look like, and what a good output from that stage actually consists of.
Without that level of articulation, there is no basis for deciding where AI belongs and where it does not. Every stage looks equally eligible for AI intervention, which means the intervention defaults to wherever AI is easiest to apply — which is rarely where the process most needs it.

It distinguishes between stages that benefit from AI and stages that are harmed by it
This is the condition most organizations have not yet examined. AI is not uniformly useful across a strategy creation process. Some stages benefit significantly from what a well-scoped AI instrument can provide — breadth of scanning, pattern recognition across large data sets, rapid generation of options that human teams then evaluate. Other stages are structurally vulnerable to AI intervention — particularly those where the quality of the output depends on tacit organizational knowledge, on the sociology of the team doing the thinking, or on the kind of commitment that only emerges from a process that the participants have genuinely worked through rather than reviewed.
Plugging AI into the wrong stage does not produce a neutral result. It produces something that looks like progress while the actual bottleneck remains untouched — which is precisely what LTSP26 calls “Deck-Chairing” (the stuff people did on the Titanic when they misread the assignment.)

It has a baseline rigorous enough to evaluate what AI returns
An AI-ready strategy process gives its practitioners something to compare AI output against. Not a gut feeling. Not a prior deck. A clear, honest picture of what the organization’s current situation actually is, what the range of genuine strategic options looks like, and what commitment to a chosen direction requires in terms of specificity and accountability.
Without that baseline, AI output cannot be evaluated. It can only be accepted or rejected on the basis of whether it sounds right — which is exactly the condition that makes Trendslop dangerous.

It uses personas rather than general LLM queries
A genuinely AI-ready strategy process does not ask large language models what the strategy should be. It deploys focused AI instruments — personas — that are scoped to specific subprocesses, constrained by the sociology and knowledge requirements of those subprocesses, and evaluated against the stage-level baseline the process has already established. The difference in output quality between a general LLM query and a well-designed persona applied to the right stage is not marginal. It is categorical.

What this means in practice
Very few organizations currently meet all of these conditions. That is not a criticism — the conditions have not been clearly articulated anywhere in the mainstream AI-in-strategy conversation until now. LTSP26 exists in part because the gap between where most organizations are and where an AI-ready process begins is wider than the market is currently acknowledging — and because closing that gap starts with understanding what it actually consists of.

This is the right question to ask before committing three half-days to any event. The Long-Term Strategy Conference 2026 (LTSP26) will not produce a new skill set in the conventional sense — no certification, no completed toolkit, no prompt library to hand to a team. What it produces is something that precedes all of that, and without which all of that tends to underdeliver.
Here is an honest account of what changes after attending.

You will be able to see Trendslop when it enters the room
Before LTSP26, most strategy professionals can sense that something is off when AI returns polished, confident, structurally identical advice regardless of the organizational context provided. After LTSP26, that sense has a name, a mechanism, and a diagnostic framework behind it. You will be able to identify Trendslop not just in what an LLM returns but in what a consultant presents, what a thought leader publishes, and what a board member carries in from something they read over the weekend. That recognition is not a minor upgrade. In long-term strategy work, where bad inputs compound over years rather than quarters, the ability to distinguish a real insight from a socially desirable one is among the most consequential capabilities a senior strategist can develop.

You will be able to locate the bottleneck in your strategy process
After LTSP26, you will have a clearer picture of which stages of your organization’s strategy creation process are genuinely vulnerable to AI failure modes — Trendslop and Deck-Chairing — and which stages are where AI intervention would produce the most significant improvement. That is a different and more useful question than the one most organizations are currently asking, which is simply where AI can be applied. Knowing where the bottleneck is changes the entire sequencing of any process improvement effort.

You will be able to evaluate AI persona design for strategy work
LTSP26 introduces working personas — focused AI instruments built for specific subprocesses within strategy creation. After attending, you will understand the design logic behind them well enough to evaluate whether a persona you encounter, commission, or build is appropriately scoped for the stage it is meant to serve. That is a capability that does not yet exist as a standard part of any strategy professional’s practice. It will increasingly matter as persona-based AI tools proliferate and the quality differences between them become consequential.

You will be able to make the process clarity argument inside your organization
This may be the most practically significant change of all. The argument that process clarity must precede AI application is not yet the consensus position in most boardrooms or leadership teams. The consensus position is that AI should be adopted broadly and optimized iteratively. After LTSP26, you will have the evidence base, the vocabulary, and the structured reasoning to make the counter-argument — not as a skeptic of AI, but as someone who understands precisely where and why the standard adoption approach breaks down in strategy work specifically.
That argument, made well at the right moment, is the difference between an organization that accelerates its fog and one that clears it.

Almost certainly yes. But the reason why depends on what “using AI in your strategy work” currently means in practice — and that is precisely the question the Long-Term Strategy Conference 2026 (LTSP26) is designed to help you examine with more precision than the phrase usually allows.

What most AI use in strategy actually looks like
When senior executives and strategy professionals describe using AI in their strategy work, they are typically describing one or more of the following: feeding strategic questions to a large language model and evaluating the responses, using AI to generate literature reviews or environmental scans, applying AI to presentation preparation and document drafting, or deploying AI tools to accelerate the research that precedes a retreat.
Each of these is a legitimate use of available technology. None of them is evidence that the strategy process itself has been improved by AI. In most cases they represent what LTSP26 calls “Deck-Chairing” — the application of AI to stages of the strategy process that were never the bottleneck. The output arrives faster and looks sharper. The quality of strategic thinking at the stages that actually determine organizational direction remains unchanged. (BTW, “Deck-Chairing” refers to the human tendency to shuffle chairs on a Titanic.)

The test worth applying
There is a straightforward diagnostic for whether your current AI use in strategy is substantive or symptomatic. Ask yourself three questions. First, can you identify precisely which stage of your strategy creation process each AI application belongs to? Second, do you have a clear view of why that stage specifically — rather than an adjacent one — benefits from AI intervention? Third, do you have a baseline rigorous enough to evaluate what the AI returns against something other than whether it sounds convincing?
If the answers to any of these are uncertain, the AI use is not yet grounded in the kind of process clarity that makes it reliably useful. It may still be producing value. But it is doing so despite the absence of that clarity, not because of it — and that distinction matters when the decisions being made carry five to ten year consequences.

What LTSP26 adds to existing AI practice
LTSP26 is not an introductory event. It does not spend time establishing that AI exists or that strategy matters. It is built for professionals who are already engaged with both and have begun to sense that the standard approaches are producing something less rigorous than the confidence of the outputs suggests.
If your current AI use in strategy is working well by every measure you currently apply to it, LTSP26 will give you better measures. If it is working well by some measures and producing something that feels like Trendslop by others, the conference will give you the vocabulary and the framework to tell the difference — and to act on that distinction in the stages of your process where it matters most.
The executives who will get the least from LTSP26 are those who arrive certain that their current approach is already optimized. The executives who will get the most are those who arrive having used AI in strategy work, sensed that something is still not quite right, and decided it is worth three half-days to find out what that something is.

The short answer is no. The longer answer is that the absence of a formal strategy process is not a disqualifier for the Long-Term Strategy Conference 2026 (LTSP26). In a meaningful sense, it is the condition the conference was built to address.

What most organizations actually have
When senior executives describe their organization’s strategy process, they typically describe one of two things. The first is a calendar — a sequence of retreats, reviews, and planning cycles that recurs annually or triennially and produces a document. The second is a deck — a set of slides that captures the conclusions of that calendar and circulates as the organization’s strategic plan.
Neither of these is a process in the sense that LTSP26 uses the term. A process, in this context, means a stage-by-stage understanding of what is actually happening when strategy gets made — what kind of thinking each stage requires, what the social dynamics of that stage look like, where judgment is being exercised, where it is being avoided, and which stages are structurally vulnerable to the failure modes that AI tends to introduce.
By that definition, very few organizations have a formal strategy process. What they have is a familiar rhythm and a recognizable output. The two are not the same thing.

Why this matters for AI
The reason this distinction is central to LTSP26 is that AI cannot improve what has not been clearly mapped. An organization that feeds its strategy questions to an LLM without that underlying clarity is not getting AI-assisted strategy. It is getting Trendslop — the same cluster of socially desirable, internet-average answers that every other organization is receiving — delivered with speed and confidence into a process that has no baseline to compare it against.
The absence of a formal process does not protect an organization from this problem. It accelerates it. There is nothing rigorous enough in the existing approach to resist or filter what the AI returns.

What LTSP26 offers in this situation
LTSP26 does not require attendees to arrive with a mapped process. It requires them to arrive with the willingness to examine honestly what their current approach to strategy creation actually consists of — and to engage with the argument that mapping it, even roughly, is the prerequisite for everything else.
The EndPoint Method, which provides the structural spine of the conference’s applied content, was developed specifically for organizations navigating this kind of process ambiguity. Its six stages offer a framework for developing that map without requiring that everything be resolved before the work begins.
If your organization does not yet have a formal strategy process, LTSP26 is not the wrong place to be. It may be exactly the right one.

The Long-Term Strategy Conference 2026 (LTSP26) runs September 15 to 17, 2026. The honest answer to this question depends less on the three days themselves and more on what those three days actually require of you.

What three days means in practice
LTSP26 is not a three-day full-immersion event. Each of the three days is built around a half-day block of programming. The sessions do not run concurrently in a way that forces difficult choices between tracks. The schedule is designed to accommodate senior executives who are managing live organizational responsibilities across the same period — which is to say, all of them.
The virtual format removes travel, accommodation, and the recovery time that follows both. What remains is a focused half-day commitment on each of three consecutive dates in September, with selected content available for replay where live attendance is not possible.
Furthermore, much of the content is available on demand (the personas for example) or at odd times to suit a global audience (e.g. at 12pm and 12am).

What the Visitor Pass allows
For executives whose calendars make even that commitment uncertain, the Visitor Pass is designed precisely for this situation. It provides access to a curated selection of sessions without requiring attendance across all three days. It is the appropriate starting point for a senior professional who wants to encounter the LTSP26 argument before deciding whether deeper engagement is warranted. The cost of that starting point is set at whatever you judge the initial exposure to be worth — including nothing.

What the Full Ticket requires
The Full Ticket is the appropriate choice for executives who have already decided that the AI-in-strategy question deserves their sustained attention and want access to the complete conference experience — all sessions, all persona interactions, all practitioner-led content. It asks for more time across the three days and returns proportionally more in terms of the depth and range of what the conference covers.
Furthermore, it grants a full year of convenient access to all content.

The real question underneath this one
Most senior executives who ask whether three days is realistic are not asking about calendar mechanics. They are asking whether three days on this particular topic, at this particular moment, is worth the opportunity cost of everything else those half-days could contain.
That is a question only the executive can answer. What LTSP26 can offer in response is this: the AI-in-strategy conversation is not going to resolve itself while you are busy with other things.
The organizations pulling ahead on this question are not doing so because they found a better tool. They are doing so because someone inside them made the time to understand the process problem underneath the tools conversation — and made that time before the pressure to act became the pressure to react.

Three half-days in September is a reasonable place to start.

The Long-Term Strategy Conference 2026 (LTSP26) is built around a single, contested argument: that process clarity must precede AI application in strategy work, and that most organizations are nowhere near the level of process clarity that argument requires. If that premise does not describe your situation, or does not interest you, the three days of September 15 to 17 are better spent elsewhere.
The following is an honest account of the conditions under which LTSP26 is unlikely to deliver what you are looking for.

You are looking for an AI tools comparison
LTSP26 does not evaluate, rank, or recommend AI platforms, prompt libraries, or subscription services. The conference takes no position on which LLM is superior for strategy work and has no sponsor relationships that would make such a position worth taking. If the primary question on your desk is which tool to adopt or how to consolidate your team’s AI subscriptions, this is not the event that answers it.

You are looking for a ready-to-implement framework
The conference will not produce a completed process redesign that can be carried directly into your next strategy retreat. What it produces is the clarity of understanding that makes a process redesign possible — which is a prerequisite, not a substitute. If your timeline requires a deployable solution within thirty days of attending, LTSP26 is the wrong sequencing. The right sequencing is LTSP26 first, implementation work after.

You are already certain about your position on AI in strategy
LTSP26 is built for professionals who are still holding the question honestly. If you have already concluded that AI is either transformative enough to lead your next strategy process without significant modification, or irrelevant enough to exclude from it entirely, the conference will spend three days contesting both conclusions. That is a poor use of your time if neither conclusion is one you are prepared to examine.

Your organization’s strategy process is not yours to influence
The content of LTSP26 is most useful to professionals who carry genuine influence over how strategy gets made in their organization or their clients’ organizations — not just over what the strategy concludes. If the process itself is fixed by mandate, locked by culture, or outside your remit to question, the awareness LTSP26 builds will have nowhere to land.

The timing is simply wrong
A major retreat is two weeks away. A board presentation is consuming everything. A restructuring is mid-flight. LTSP26 requires enough cognitive space to absorb an argument that runs counter to several things the market is currently saying loudly. If September is genuinely the wrong moment, the recordings of selected sessions will be available. The live experience, however, is where the argument lands hardest — and that window opens once.

If none of the above describes your situation, the registration page is the right next step.