Content Creation And Optimization Solutions

This is a planned Forrester category. No Wave or Landscape has published under this name. Forrester has, however, published substantial research on B2B content operations, AI-driven content workflows, and the shift from search optimisation to answer engine optimisation, which is what this article draws on.

Optimisation used to mean making content perform better for the person reading it and the search engine ranking it. Both audiences were known, and the practices for serving each were well understood.

That has come apart in a specific and measurable way. Forrester reports that B2B organic traffic has declined between ten and forty percent over the past year, and that buyers using AI to research are roughly one tenth as likely to click through to a website.

A category being assembled now under the heading content creation and optimisation is therefore being assembled around a changed question. Not how to produce more content faster, and not how to rank it, but what content is worth creating when the primary consumer may be a machine that summarises it without sending anyone to you.

What the category will need to cover

Reading Forrester's B2B content research, the scope divides into four areas that current tooling handles unevenly.

Planning and strategy, meaning deciding what to create against buyer needs and buying group roles rather than against a content calendar.

Creation and production, including generative assistance, brand and tone consistency, and the review and approval workflow that most organisations identify as their bottleneck.

Optimisation, which now spans traditional search, answer engines, and increasingly agent consumption, and which no longer has a settled definition.

And measurement, which Forrester's analysts identify as the hard problem the market is moving toward rather than away from.

Forrester's own survey data on where the pain sits is worth carrying into any evaluation. Just over half of marketers cite inefficient content creation and review processes, along with sales and marketing misalignment, as their biggest content operations challenges.

Note that neither of those is a creation-speed problem. One is a workflow problem and the other is an organisational alignment problem, and generative tooling addresses the first partially and the second not at all.

The prediction that runs against the market

Forrester's 2026 outlook for this space contains an observation that any buyer should hold onto, because it contradicts every vendor roadmap in the category: content technology vendors will roll back some of their AI agent plans.

That is an unusual thing for an analyst firm to predict in a market where agentic capability is the primary differentiator being marketed.

The reasoning is not stated in what is publicly available, but the mechanism is easy enough to infer from how content work actually functions. Agentic content production requires the system to make judgement calls about claims, positioning, compliance, and brand voice, at a volume that exceeds review capacity. When something goes wrong, it goes wrong publicly and with the organisation's name attached.

The practical consequence for anyone evaluating in this category now is to weight demonstrated capability over roadmap. A roadmap commitment to autonomous content agents is, on Forrester's own forecast, among the more likely things to be quietly deprioritised.

What optimisation means when the reader is a model

The shift from search optimisation to answer engine optimisation is the substantive change this category has to absorb, and it inverts several established practices.

Search optimisation was a competition for position. Rank higher, get the click, and the click was the outcome being optimised. That produced a set of tactics around keywords, link acquisition, and content length that everyone in the field knows.

Answer engine optimisation is a competition for citation. The outcome is being referenced in a generated answer, which may or may not produce a visit. Forrester's framing is that visibility now comes from investing in original evidence that demonstrates expertise, builds credibility, and contributes something new to the conversation.

Three practical differences follow, and they matter more than the vocabulary.

Structure serves extraction rather than crawling. Content that answers a specific question directly, in a self-contained passage, is easier for a model to lift and attribute than the same information distributed across a long narrative with the answer implied.

Originality becomes functional rather than aspirational. A model synthesising from many sources has no reason to cite the fourth article restating a consensus. It has a reason to cite the source of an original figure, a proprietary dataset, or a distinct position. Content that exists to cover a keyword has lost its economic basis entirely.

And restraint reads as credibility. Verifiable claims, stated limits, and specific numbers survive summarisation better than superlatives, which get dropped or flattened.

None of that is exotic. It is closer to how trade publishing and analyst research have always worked than to how content marketing has worked for fifteen years, which is an uncomfortable observation for teams whose volume targets were set under the old model.

Two thirds of content from outside the content team

Forrester's prediction that by the end of 2026 traditional content teams will no longer create two thirds of content in B2B organisations is the operational fact that will shape this category most.

Generative tools have put content production into the hands of anyone who needs it. A product manager writes a one-pager. A sales engineer produces a customer-specific deck. A regional team localises a campaign without asking. None of it passes through the function that owns brand, messaging, or claims.

Forrester's guidance is that leaders must equip employees with decision frameworks, training, and guardrails or risk diluting the brand and degrading customer experience.

That converts into a specific requirement for the tooling. If most content is produced outside the content team, the platform's job is less about production capability and more about making the right thing easy for people who are not content professionals: templates that carry the messaging architecture, approved claims that can be inserted rather than invented, and review that catches the material with real risk without queueing everything.

Which is a governance product wearing a creation product's label, and it is a reasonable prediction that this is where the eventual evaluation criteria will concentrate.

Lisa Gately's framing of the leadership shift captures the same thing from the management side: the job moves from approving output to designing the system that determines how work gets done, who owns judgement, and how trust is protected.

From assets to prompts

The other structural prediction in Forrester's research is that the manual processes used to create, personalise, and stockpile complete content assets become obsolete.

The reasoning is that real-time personalisation requires modular content and creative elements that people and machines can assemble on the fly, combining structured and unstructured data, delivery attributes, program rules, audience context, and signals generated as content is consumed. Jessie Johnson's prediction is that B2B marketers will shift from static content toward writing and sharing prompts that generate personalised experiences at the moment of need.

If that holds, it changes what a content library is. Today it is a repository of finished assets, most of which are never used, organised by campaign and format. The version described here is a set of components, claims, evidence, and rules from which an experience is assembled per interaction.

Two things follow that are worth thinking through before the vendors get to define them.

The first is that modular content requires a discipline most organisations do not have. Components must be written to be recombined, which means no dependency on surrounding context, consistent voice at the fragment level, and metadata describing what each piece asserts and for whom it is appropriate. That is closer to technical documentation practice than to marketing writing.

The second is that prompt-generated experiences move the review problem. If the asset is assembled at request time, nobody approved the specific thing the buyer saw. Approval has to shift to the components and the rules, which is a different governance model and one that regulated industries will find harder to accept than the vendors currently acknowledge.

The hard part moves to proof

The most useful sentence in Forrester's current thinking on this area concerns where the difficulty is heading. The next frontier is measuring content value by linking visibility, engagement, and brand presence in AI-generated results to business outcomes, and the hard part shifts from creation and production to proof.

That is a bigger change than it sounds, because the measurement infrastructure of the last decade assumed a click.

A buyer who reads a summary of your position inside a generated answer, forms a view, and later arrives via a branded search has been influenced by content that will never appear in an attribution model. Multiply that across a buying group and the gap between influence and measurable engagement widens until the reporting stops describing what is happening.

Which produces an awkward interim state for anyone running demand generation right now. Traffic is falling for reasons that are not performance failures. Content is working in a channel that cannot be instrumented the way the previous one could. And the metrics that budget conversations run on still assume the old mechanics.

The organisations handling this reasonably are measuring what they can, meaning presence and citation in generated answers for the queries that matter to them, and treating it as a leading indicator rather than pretending it converts cleanly to pipeline. The ones handling it badly are reporting the traffic decline as a content performance problem and responding by producing more.

What the evaluation will have to decide

When Forrester scores this category, three choices will reveal what it thinks the market is.

Whether the criteria weight production or governance. A criteria set built around generation speed, format coverage, and creative quality describes a production tool. One built around brand controls, claim libraries, approval routing, and distributed enablement describes a governance layer for a workforce that is now mostly untrained content creators.

How it handles answer engine optimisation, which currently has no agreed methodology, no reliable measurement, and vendors claiming capabilities that are difficult to verify. Any criterion here will be contested.

And whether measurement is scored at all. Forrester's analysts have identified proof as the emerging hard problem. If the evaluation reflects that, it will be the first content technology assessment to treat attribution in an answer-engine world as a product capability rather than as someone else's problem.

Until then, the practical position is the one Forrester's own framing implies. The constraint has stopped being production capacity. Deciding what is worth creating, keeping it credible when most of it is produced outside the content function, and proving it did anything are the three problems, and no current tool solves more than one of them well.

Forrester does not endorse any vendor named here, and nothing in this article should be read as a recommendation to buy.