Conversation Automation Solutions For B2B

Forrester's 2022 evaluation of this market was called Conversation Automation Solutions. Its 2024 evaluation was called Conversation Automation Solutions For B2B.

Two words, and they mark a split. The 2022 field mixed customer service vendors with revenue vendors: Ada, Amelia, Conversica, Drift, DRUID, Genesys, Intercom, Kasisto, Qualified, and Rasa. Roughly half of those companies built products for support deflection and half built products for pipeline.

Those are not the same business. A support bot succeeds when a customer resolves their issue without a human. A revenue bot succeeds when a prospect books a meeting with one. Deflection and conversion are opposite objectives, and a single category containing both was always going to divide.

By 2025 the B2B evaluation includes 6sense, a revenue marketing platform. The category is now demand generation infrastructure, and it should be read that way.

What the software does

Conversation automation in B2B handles two-way dialogue with prospects and customers across chat, email, and messaging, using AI to sustain the exchange without a human in the loop for each turn.

Forrester's characterisation of the technology emphasises bidirectional communication across synchronous and asynchronous channels, supporting the full customer lifecycle rather than a single interaction type.

The lifecycle scope matters. These tools are sold for inbound qualification, outbound prospecting, nurture of leads that went quiet, event follow-up, renewal and expansion outreach, and reactivation of closed-lost opportunities. That is a wider set of jobs than the chat widget origin suggests, and it is where the current value sits.

From the widget to the inbox

The most consequential change in this category is where the conversation starts.

The first generation was inbound and site-based. A visitor lands on a pricing page, a widget opens, a bot qualifies them and books a meeting. The value proposition was capturing intent that was already present, and it worked because the alternative was a form and a two-day wait.

The current generation is outbound and email-based. 6sense's assessment in the Q4 2025 evaluation positions it for buying-group-oriented organisations whose primary goal is automating outbound conversations to increase meetings booked, with an AI email product shipping eight out-of-the-box conversation flows covering prospect outreach, data enrichment, and revenue enablement across opportunity types.

Conversica's Leader citation in the Q1 2024 edition describes revenue digital assistants retaining context across email, web, and SMS to progress conversations toward conversion goals spanning new logos, retention, cross-sell, and upsell. Reference customers valued multiregion and multilanguage deployment and reported improved sales and marketing alignment.

That is a different product from a chat widget. It initiates rather than responds, it runs over days or weeks rather than minutes, and it operates against a list rather than against traffic.

Anyone who watched the site-chat vendors get absorbed into larger revenue platforms will recognise why. Inbound chat is a feature. Sustained outbound conversation across a buying group is a system.

Inside the evaluations

The Forrester Wave: Conversation Automation Solutions For B2B, Q1 2024 scored nine providers against thirty three criteria, addressed to B2B revenue marketing professionals.

Conversica placed as a Leader with the top score in the strategy category, taking the highest possible score in twenty three of thirty three criteria including conversation personalisation, vision, and innovation.

The Forrester Wave: Conversation Automation Solutions For B2B, Q4 2025, published on 1 October 2025, expanded to twelve providers. 6sense features in that evaluation, with Forrester noting it was early to conversational AI through its 2022 acquisition of Saleswhale and has built on predictive AI expertise in revenue marketing.

Nine providers in 2024, twelve in 2025. That expansion runs against the consolidation pattern visible in most categories in this series, and it reflects revenue platforms adding conversational capability rather than new specialists appearing.

Forrester's demand data supports the expansion. Sixty four percent of B2B organisations planned to increase investment in conversation automation over the following twelve months, per its 2024 marketing survey.

The buying group problem this is aimed at

Forrester's own analysis of what generative AI changed here is worth reading carefully, because it is more specific than the usual claims.

The strategic value, in its framing, is the ability to sustain complex multipart, multiparticipant dialogue exchanges and retain context as a deal progresses and the conversation evolves. Contextual conversation, it argues, orchestrates interactions relevant to persona, journey stage, history, preference, and role in the decision process.

Role in the decision process is the phrase doing the work. B2B purchases are made by groups, not individuals, and the members of that group need different things at different moments. The economic buyer wants business case. The technical evaluator wants architecture. The end user wants to know whether it will make their week worse. Procurement wants terms.

A conventional nurture sequence treats each contact as an independent recipient on a track. A conversation automation system that understands buying groups treats them as participants in one deal, which means the technical evaluator's question about integration should inform what the economic buyer receives next.

That is the actual promise of the category, and it is more than personalisation tokens. It is also the part Forrester flags as unrealised, noting that most B2B organisations have yet to unlock the full potential of the generative capabilities these vendors introduced.

The gap is usually not the tool. It is that the underlying account and contact data does not identify buying group membership, so the system has nothing to orchestrate against.

The part worth being honest about

Automated outbound conversation at scale is, described plainly, AI-generated email sent to people who did not ask for it, at volumes a human team could not produce.

Three consequences follow, and none of them appear in a Wave.

Deliverability is the first and most immediate. Mailbox providers have tightened bulk sender requirements substantially, and reputation is domain-level. A system generating high volumes of outbound to cold lists with low engagement degrades the sending reputation that your entire go-to-market depends on, including the transactional and customer email that has nothing to do with the campaign. That damage is slow to appear and slow to repair.

Regulation is the second, and it varies enormously by geography. Under GDPR, unsolicited commercial email to individuals in the EU generally requires a lawful basis, and legitimate interest is a defensible one that has to actually be assessed rather than assumed. Several member states apply stricter national rules on electronic marketing. A platform that makes it trivial to send at volume also makes it trivial to send at volume into jurisdictions where the basis is weak, and the accountability sits with the sender rather than the vendor.

Brand is the third and the hardest to measure. A prospect who realises they have spent four exchanges with software, and who discovers this only when the illusion breaks, has learned something about how the company treats people. That cost does not appear in a meetings-booked metric.

None of this argues against the category. It argues for deciding where the boundary sits before the tool makes the volume decision for you, because the economics push in one direction and the constraints are all in the other.

The disclosure question nobody has settled

Related, and worth thinking about now rather than after it becomes a rule.

The persona name on the outbound assistant is an established convention in this market. Recipients reply to a plausible human name, sometimes for several exchanges, before a meeting is handed to a real person.

The EU AI Act includes transparency obligations for AI systems interacting with people, and the direction of regulatory travel across several jurisdictions is toward disclosure. Whether current B2B conversational outbound falls inside those obligations depends on interpretation and implementation detail that is still settling.

The commercial argument for not disclosing is that response rates fall. That argument is honest about its own logic, which is that the performance depends on the recipient not knowing.

A defensible position is available: disclose, and compete on whether the conversation is actually useful. Some organisations already do this and report acceptable results. Whether that becomes a differentiator or a requirement is not yet clear, but building a programme whose economics collapse under disclosure is a bet on regulation not arriving.

What it changes operationally

For a revenue operations function, this category creates a specific and underappreciated problem: attribution and hygiene.

An AI assistant that emails a contact, gets a reply, qualifies them, and books a meeting has generated activity across the CRM that looks like human activity unless it is deliberately marked otherwise. Reply rates, meeting source, and touch counts all become ambiguous. Sales productivity metrics that assume a human sent the email stop meaning what they meant.

There is a second effect on lead scoring. If engagement with automated conversation feeds a scoring model that was calibrated on human-generated engagement, the model drifts. Responding to a bot and responding to a rep are different signals about buying intent, and treating them identically inflates scores in a way that surfaces later as poor meeting quality.

The organisations that handle this well tag automated interactions explicitly at the point of creation and keep them separable in reporting. The ones that do not spend a year wondering why pipeline quality diverged from pipeline volume.

Where the category is going

The direction is agentic, which in this context means less about generating a better email and more about the system deciding what to do next without being told.

The current products run conversation flows, which are structured paths with generative language inside them. 6sense's eight out-of-the-box flows are exactly that: predefined objectives with flexible execution. The agentic version selects the objective, decides which member of a buying group to engage, chooses the channel, and adapts as the deal moves.

That is a meaningfully larger grant of authority, and it lands in a function where the cost of getting it wrong is a damaged relationship with a named account rather than a bad support interaction.

Which brings the category back to the split it started with. Support conversation automation could be evaluated on deflection rate because a resolved ticket is unambiguous. Revenue conversation automation resists that, because a meeting booked is not the outcome anyone actually wants. The outcome is a deal, and everything between the first automated email and the closed opportunity is attributable to a dozen things.

That measurement problem is the real constraint on this category, and no vendor solves it for you.

Forrester does not endorse any vendor named here, and tier placement should not be read as a recommendation to buy.