TL;DR Conversational marketing built its entire premise on buyers visiting your website to learn. That world is gone. Buyers now research in ChatGPT, form opinions in Perplexity, and arrive at your website, if they arrive at all, already 70%+ through their decision.
This guide maps the full evolution from chat-based engagement to signal-based agentic AI, shows what to keep and what to discard, and gives revenue teams a practical framework for building the new system inside HubSpot using the ARISE® methodology.
The Shift Nobody Planned For
Between 2018 and 2023, conversational marketing was the answer to every B2B pipeline problem. Chatbots. Drift playbooks. Personalised website experiences. Intent-triggered pop-ups that offered demos before visitors clicked away. The logic was sound: meet buyers where they were, on your website, and reduce friction before they disqualified themselves.
Then the results plateaued. Then they declined. Then, for most B2B SaaS teams, the channel stopped working almost entirely.
This wasn't gradual. According to Paul Sullivan, founder of Arise GTM and author of Go-To-Market Uncovered (Wiley, 2025): "Since 2022, the way B2B revenue teams engage the market has changed significantly. The erosion of cold calling and cold email, the deep focus on customer experience and retention, and the rapid rise of agentic AI have seen things both improve and decline. Because of all this, conversational marketing suffered. The first reason is that people visit websites less and less and look for immediate answers more and more."
The platforms shifted. LinkedIn became the primary battleground for B2B communications. Reddit and similar communities became the research sites buyers trusted, replacing G2 and review aggregators.
Answer engines, ChatGPT, Perplexity and Google AI Overview became the first port of call for product evaluation, not vendor websites.
And the website chat widget, designed for a buyer at the start of their research journey, found itself addressing a buyer who had already formed their view before clicking the link.
This is not a story about a channel dying. It's a story about buyer behaviour evolving faster than most GTM stacks could follow. The teams that recognised this early have rebuilt their engagement layer around signals, autonomy, and agentic execution. The ones still optimising their Drift playbooks are running hard in the wrong direction.
What Conversational Marketing Was — And Why It Actually Worked
Before writing it off, it's worth being honest about why conversational marketing worked so well for so long. Understanding that explains exactly where the gap is now.
The core premise was elegantly simple: buyers doing website research had questions that weren't answered by static content, and a live or semi-live chat experience could answer them in real time, qualify intent, and move the buyer toward a demo or meeting while their interest was highest.
At its best, conversational marketing reduced time-to-meeting, increased form conversion rates, and gave revenue teams a way to engage accounts that would have otherwise bounced without leaving any contact data.
The tools that built the category, Drift, Intercom and HubSpot Conversations, were genuinely innovative. They brought CRM data into the chat experience, enabled personalisation based on visitor context, and started the industry conversation about removing friction from the buyer journey. None of that was wrong.
The problem was the premise it depended on: a buyer visiting your website because they didn't know the answer yet. That premise held through 2022. By 2024, it had fundamentally broken.
Three Shifts That Made the Old Model Obsolete
Shift 1 — Buyers moved off your website before you could talk to them
Answer engines changed where buyers research. A CMO evaluating a RevOps consultancy in 2026 doesn't start with a Google search that lands them on a vendor site.
They ask ChatGPT or Perplexity, get a synthesised answer that draws from multiple sources, form an initial shortlist, and then visit websites specifically to validate decisions they've already started making.
By the time they hit your chat widget, they're not in discovery mode — they're in confirmation mode.
The teams winning in this environment aren't optimising chat. They're building SEO and GEO strategies that make Arise GTM the answer those tools cite. The battle has moved upstream.
Shift 2 — The signal changed from conversation to behaviour
Conversational marketing optimised for a single signal: what the buyer typed into a chat box. That's a thin, unreliable signal compared to what's now available.
A buyer's product usage data, email engagement patterns, pricing page visits, CRM stage, LinkedIn activity, and intent data from Bombora or 6sense collectively tell a far richer story than any chatbot conversation.
Paul puts it directly: "B2B SaaS teams I work with have dropped conversational intelligence completely. The focus has shifted to immediate answers from Claude, ChatGPT, and Perplexity from website chatbots. Nobody wants semantic answers anymore; they want concise answers to problems immediately, and 2020s-style chatbots are not that."
Shift 3 — The execution layer automated the conversation away
The third shift is architectural. Conversational marketing required a chat interaction to initiate the next step in the buyer journey. Agentic AI eliminates that dependency entirely.
An AI BDR doesn't wait for a buyer to start a conversation; it reads intent signals, identifies the right moment, and initiates personalised outreach autonomously across the channels where that buyer actually operates. The conversation happens where the buyer is, not where the vendor's chat widget is deployed.
As Paul observes: "Now we have AI BDRs and SDRs and AI call centres that can get the job done, and the best teams are facilitating this." The execution gap that conversational marketing tried to fill with website chat has been closed by autonomous agents working simultaneously across email, LinkedIn, and voice at scale, without waiting for a website visit to trigger them.
What Agentic AI for Revenue Teams Actually Means
At this point in most articles on this topic, the author reaches for a sci-fi analogy and the reader's eyes glaze over. Let's be specific instead.
Agentic AI for revenue teams refers to autonomous AI systems that can execute multi-step GTM workflows, from identifying in-market accounts and personalising outreach, to updating CRM records, triggering sequences, and routing qualified conversations to the right human, without requiring manual intervention at each step.
The architecture has three layers.
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The perception layer reads signals: intent data from third-party platforms, product usage events from your application, CRM stage changes, email engagement patterns, LinkedIn activity, and website behaviour.
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The decision layer determines what to do: which account to prioritise, which message to send, which sequence to trigger, whether to escalate to a human rep.
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The execution layer acts: sending the email, updating the HubSpot record, booking the meeting, posting the Slack alert, generating the pipeline report.
What makes this different from traditional marketing automation is the decision layer. A HubSpot workflow follows a fixed if-this-then-that logic. An agentic system reasons about context, handles exceptions dynamically, adapts based on outcomes, and executes across multiple systems simultaneously.
The difference, as Paul has written in the agentic GTM operating model, is like comparing turn-by-turn GPS directions that don't adapt when roads are closed to a navigation system that monitors traffic in real time and reroutes around accidents before they cost you an hour.
Leevr Arise GTM's agentic platform, the ARISE® methodology, and ARISE OS, digitised into a live system, implements this architecture natively in HubSpot.
The RevOps Agent handles 70+ HubSpot functions covering lead routing, data hygiene, lifecycle management, and CRM operations.
The BI and Insight Agent connects to Databox via MCP for automated reporting and real-time anomaly detection.
The GTM Strategy Agent is trained on the ARISE® methodology and MEDDIC/MEDDPICC frameworks and connects directly to your HubSpot data to analyse deals and evaluate GTM motion.
Together they function as a digital GTM team that operates continuously, not one that clocks out at 6 pm or takes three weeks to produce a pipeline report.
What to Keep, What to Cut, and What to Rebuild
This is where most migration plans go wrong. Teams either try to save everything (adding agentic capabilities on top of a broken conversational layer) or burn everything down (discarding genuine value that took years to build). Neither works.
Paul is specific about what deserves to survive: "Rather than discarding conversational marketing strategies, the most valuable components to retain include empathetic qualification, proactive conversational triggers, omnichannel context, and sentiment-based routing."
Keep and integrate these:
Contextual routing and sentiment detection. The ability to read frustration, confusion, or high-intent signals mid-conversation is genuinely valuable, not just as a chat mechanic but as an agentic input.
When an agent detects that a prospect has visited your pricing page three times in a week, read three case studies, and opened every email sequence, that sentiment-equivalent signal should trigger a different action than a cold account with one blog visit. The principle survives; the channel it ran through doesn't.
Proactive behavioural triggers. The idea of initiating engagement based on buyer actions, time on pricing page, returning visits, trial feature limits hit, is exactly what agentic AI does best, but without requiring the buyer to be on your website when the trigger fires.
An AI BDR for HubSpot can initiate the right conversation on LinkedIn or via email the moment the signal appears in the data, not when a buyer happens to be on your site.
Omnichannel session continuity. The principle of a unified customer profile that persists across channels is more important in an agentic system than it was in a conversational one.
Agents need to inherit the full conversation and engagement history to make contextually intelligent decisions. If your conversational marketing stack built that profile, the data is valuable; the interface it sat behind isn't.
Cut these immediately:
Decision-tree chatbot logic. Rigid if/then button-click flows built for 2018 buyer behaviour have no place in a system designed for 2026 buyers. They signal to buyers that they're talking to a machine, and not a clever one.
Generic timed drip campaigns. The scheduled email sequence disconnected from real buyer behaviour is what gives "automation" its bad reputation. Agentic systems trigger communications based on dynamic intent, not on the fact that it's been 72 hours since the last email.
Website-dependent initiation. The entire model of waiting for a buyer to arrive at your website before engagement begins is the architecture to replace. Signal-based systems engage buyers where they are, when their intent is highest, without requiring a website visit as the trigger.
The Migration Path: Three Stages Using the ARISE® Framework
Moving from a conversational marketing stack to an agentic revenue system isn't a single project; it's a staged architectural evolution. The ARISE® methodology, developed by Paul Sullivan and published in Go-To-Market Uncovered (Wiley, 2025), maps precisely to this transition.
Paul explains the staged progression: "The ARISE framework guides teams from simple conversational models to agentic systems by systematically embedding autonomy, reasoning, and tool execution into the architecture. Rather than relying on humans for every prompt, teams use this approach to enable AI to ingest real-time signals, decompose goals, and execute multi-step workflows autonomously."
Assess — Audit before you build
The transition starts with brutal honesty about your current state. For most B2B SaaS teams, this means a HubSpot portal audit that identifies data quality issues, broken workflows, duplicate records, and unmapped fields. This step cannot be skipped.
As Paul notes in his guidance on agentic AI implementation, the single biggest mistake is automating broken processes on top of dirty data. An agentic system running on a contaminated CRM doesn't just underperform; it actively damages relationships, sending outreach to existing clients as if they were cold prospects or offering discounts to accounts in active upsell conversations.
Map your existing conversational marketing touchpoints at this stage. Which are still generating any meaningful engagement? Which has flatlined? Which represent data assets, unified customer profiles, behavioural history, qualification data, worth carrying into the new system?
Research and Ideate — Design the signal architecture
Before any agentic workflow can execute intelligently, it needs reliable signals to act on. This stage defines the perception layer: what data flows into the system, from where, and how it's structured.
For HubSpot-based teams, this means ensuring product usage data flows into custom objects, intent data from third-party platforms enriches contact and company records, and scoring models are built on behavioural signals rather than demographic fit alone.
This is also where you define the specific workflows the first agents will own. The principle here matters: start narrow. A single high-volume, repetitive workflow, inbound lead qualification, trial user activation, post-demo follow-up sequencing, deployed as an agentic process and measured against your previous human-or-bot version, gives you proof of concept and organisational confidence.
The ARISE OS pre-builds the 12 most common revenue workflows so teams don't have to design from scratch.
Strategise, and Execute — Deploy, govern, compound
Deployment without governance is how agentic AI gets a bad reputation inside organisations. Paul is direct about this: "Treat the agent like an inexperienced junior hire — review its actions, evaluate outputs, and refine its prompts daily until it is thoroughly trained on your ICP and playbook."
The governance cadence in practice means daily reviews in the first month, weekly reviews in months two and three, and monthly strategic reviews once the system has demonstrated consistent output quality.
Leevr, Arise GTM's proprietary agentic platform, handles the continuous monitoring layer, surfacing performance gaps, identifying optimisation opportunities, and flagging anomalies in real time rather than waiting for a monthly reporting cycle.
This is the compounding mechanism that makes agentic systems improve over time rather than degrade as market conditions shift.
The Metrics That Replace What You Were Measuring
This shift isn't just architectural; it changes what leadership should measure. And some of the metrics most marketing and revenue teams currently report to the board become genuinely misleading in an agentic system.
Paul is specific about what to retire: "CMOs and CROs must abandon static, channel-specific KPIs in favour of system-level metrics that evaluate AI agent efficiency, autonomous pipeline generation, and the overall velocity of AI-driven customer journeys."
The metrics that lose relevance fastest are click-through rates and traffic volume (AI systems optimise for intent-driven engagement, not clicks), cost per lead (obsolete when agents autonomously qualify and nurture accounts), and email open rates (increasingly unreliable with Apple Mail Privacy Protection and AI inbox filtering making them disconnected from actual business value).
What replaces them:
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agent-influenced revenue — the total pipeline generated or influenced by autonomous AI agents;
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autonomous resolution rate — the percentage of sales queries or customer inquiries successfully resolved without human intervention;
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customer lifetime value to AI cost (CLV:AIC) — the financial return from customers acquired and retained relative to the compute and operational costs of the agents; and
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pipeline velocity measured as the number of AI-engaged opportunities multiplied by average deal size and win rate, divided by average sales cycle length.
The shift in measurement reflects a shift in what revenue leadership is actually doing. Paul captures it directly: "Agentic AI shifts marketing and revenue leadership from measuring tactical outputs to orchestrating autonomous value creation."
What the Next-Generation Revenue Team Looks Like
The organisational implications of this shift are as significant as the technical ones. The question isn't just what tools to deploy, it's what kind of team deploys and governs them.
Paul's view on this is unambiguous: "CMOs must shift from being campaign managers to architects of transformation. Anyone resting on their laurels and watching those below them is open to be replaced, why wouldn't a savvy CEO want the upcoming, relevant person to take the organisation into the AI future?"
The specific capabilities worth building or hiring for right now, in priority order:
Agentic AI governance. Not general AI literacy, specific capability in designing governance frameworks that ensure brand compliance, manage hallucination risk, and protect data privacy in autonomous systems.
This is the capability gap most B2B SaaS marketing teams have and don't know it. AI Marketing Operations managers who design intelligent systems rather than prompt chatbots are the hires of 2026.
Revenue attribution and commercial acumen. With CFO scrutiny on marketing budgets intensifying, CMOs need to speak in payback periods, pipeline quality, and CAC:LTV ratios,, not engagement metrics. Marketing Data Scientists and Revenue Marketing Leads who can translate fragmented data into board-level narratives are the analytical backbone the agentic model requires.
GEO specialisation. As Paul notes, "traditional SEO is evolving, building a strong presence within LLM training data and generative search engines is the new frontier." GEO specialists who understand how to structure your brand's digital footprint for AI-driven discovery aren't a nice-to-have in 2026.
They're the difference between being cited and being invisible when buyers ask an answer engine which agency to use.
T-shaped generalist marketers. The era of isolated channel specialists is ending. The integrated "growth pod" model, which cross-functional teams can shift direction rapidly based on real-time data, requires marketers who are broad across the funnel and deep in one or two areas.
As Paul puts it, they need to act like product managers: horizontal experts with a niche specialism, continuously relevant rather than periodically useful.
What This Looks Like in Practice: The ARISE® Agentic Revenue System
The abstract becomes concrete when you see the full system operating. Arise GTM's Leevr is the clearest working example of what the transition from conversational marketing to agentic revenue execution produces in practice.
The system replaces the website chat layer with five coordinated agents.
The RevOps Agent handles the high-volume operational work that previously required either manual effort or rigid, automation-led routing, data hygiene, lifecycle handoffs, CRM record management and continuous operation, flagging exceptions for human review.
The BI and Insight Agent, connected to Databox via MCP, generates pipeline reports, detects anomalies, and surfaces revenue intelligence without a RevOps manager spending Monday morning building dashboards.
The GTM Strategy Agent, trained on the ARISE® methodology and MEDDIC/MEDDPICC qualification frameworks, analyses your actual deal data and evaluates the effectiveness of your current GTM motion, functioning as the strategic advisor that most early-stage B2B SaaS teams can't yet afford to hire full-time.
The MCP (Model Context Protocol) layer is what makes this practically achievable rather than theoretically interesting. MCP is the secure orchestration layer that lets agents connect to your tools, HubSpot, Customer.io, Databox and SEMrush, with appropriate permissions and without exposing raw credentials.
It's the difference between agents that look impressive in a demo and those that work reliably in a live, constantly evolving revenue stack.
Deployments typically begin with the GTM Blueprint, a scoped diagnostic that maps your current state and identifies the three to five highest-ROI agent deployments for your specific situation, and go live within five to seven days.
The first agents typically address the workflows that consume the most RevOps time but have the least strategic value: lead routing accuracy, CRM data hygiene, and post-event follow-up sequencing.
What Happens Next: Six Signals Worth Watching
The collapse of the website as the primary buyer touchpoint will accelerate. As GEO and AEO strategies mature and more buyers get answers from AI tools before visiting vendor sites, the website becomes a confirmation layer rather than a discovery layer. The teams that've already shifted their acquisition strategy upstream into answer engines will compound that advantage.
LinkedIn will become more structured and more automated simultaneously. The platform's dual role as the primary B2B communication channel and the primary target for AI-generated outreach creates tension that will force LinkedIn to implement more sophisticated filtering. The teams with genuine thought leadership and authentic engagement will separate further from the noise.
MCP adoption will mainstream in the B2B GTM stack. As Paul noted in his RevOps leader's guide to MCP servers: for building a revenue agent system you'll operate for two or more years, MCP is the only connectivity standard that makes sense.
HubSpot, Databox, and Customer.io all have official MCP servers. The teams building on this infrastructure now will inherit a maintained, vendor-supported foundation. The teams using custom integrations will spend 2027 maintaining them.
The AI BDR category will consolidate around a small number of dominant platforms. Right now, there are dozens of vendors claiming AI SDR capabilities. Most are wrappers on top of standard outbound automation with a generative AI layer on the message. The real AI BDR platforms, those with genuine agentic reasoning, multi-system integration, and intent-based triggering, will prove their value at scale, and the noise will fade.
Agentic competitive intelligence will become a standard RevOps function. The quarterly competitive review, by the time it reaches a sales rep, is already stale. Always-on competitive intelligence, agents that continuously monitor competitor pricing, messaging shifts, product announcements, and hiring patterns, and surface deal-specific alerts in real time, replaces a process that currently costs revenue teams between 15 and 20% of competitive win rates on deals where they're operating with outdated information.
The organisations that govern well will compound the fastest. The teams deploying agentic AI without governance frameworks are accumulating risk. The teams that build oversight cadences, human-in-the-loop checkpoints, and evaluation frameworks are building systems that improve over time. In two years, the gap between governed and ungoverned agentic deployments will be visible in revenue data.
Build the New System. Don't Patch the Old One.
The common mistake we see in 2026 is B2B SaaS teams adding agentic AI tools on top of a conversational marketing infrastructure that was designed for different buyer behaviour. The chat widget stays. The chatbot playbooks stay. A new AI tool gets bolted on to "improve" the experience. Nothing meaningful changes because the underlying architectural premise is still waiting for buyers to arrive at a website.
The shift this article describes isn't an upgrade. It's a rebuild. The buyer journey starts somewhere else now. The engagement layer needs to operate where the buyer actually is. The measurement model needs to reflect autonomous value creation rather than tactical channel outputs. And the team running it needs to be architected for governance, attribution, and continuous optimisation rather than campaign management.
It's time to rise, not react. If you're ready to build the agentic revenue system your 2026 GTM motion requires, start with the GTM Leak Diagnostic to identify exactly where your current model is losing pipeline. Or speak to Paul directly about what an AROS deployment looks like for your specific stage, stack, and ICP.
About the Author
Paul Sullivan is the founder of Arise GTM and author of Go-To-Market Uncovered (Wiley, 2025) — the B2B go-to-market playbook for SaaS and fintech companies. A HubSpot Platinum Partner since 2016, Paul has over 17 years of hands-on GTM experience spanning B2B SaaS, fintech, and professional services across the UK, US, and Europe.
He created the ARISE® go-to-market methodology, developed the ARISE OS revenue architecture, and built Leevr, the agentic AI platform that automates GTM execution for B2B revenue teams inside HubSpot. Paul's work has been featured in Entrepreneur.com and he is a recognised voice in B2B GTM strategy, RevOps transformation, and the practical application of agentic AI for revenue teams. He is based in London, UK.
Connect with Paul on LinkedIn or follow Arise GTM's weekly GTM intelligence at arisegtm.com.
FAQs
What is agentic AI for B2B revenue teams?
Agentic AI for revenue teams refers to autonomous AI systems that execute multi-step GTM workflows, identifying in-market accounts, personalising outreach, updating CRM records, triggering sequences, and routing qualified conversations to human reps, without manual intervention at each step.
Unlike traditional automation, which follows fixed rules, agentic systems reason about context, handle exceptions, and operate continuously across multiple systems simultaneously. Arise GTM's AROS deploys this architecture natively inside HubSpot, with pre-built agents covering RevOps operations, BI reporting, GTM strategy, and lifecycle execution.
What is the difference between conversational marketing and agentic AI for sales?
Conversational marketing initiated buyer engagement through website chat, waiting for visitors to arrive before the conversation could begin. Agentic AI for sales initiates engagement based on behavioural signals, intent data, product usage, CRM stage changes and email patterns, regardless of whether a buyer has visited your website.
The channel moves from a website chat widget to wherever the buyer actually operates: LinkedIn, email, or voice. The trigger moves from a website visit to a detected intent signal. The logic moves from a decision-tree script to contextual reasoning. Critically, the buyer no longer needs to come to you first.
How does agentic AI integrate with HubSpot?
Arise GTM deploys agentic AI natively inside HubSpot using the Model Context Protocol (MCP), a secure orchestration layer that lets AI agents connect to HubSpot contacts, companies, deals, workflows, and reporting with appropriate permissions.
This means agents can read CRM data, update records, trigger workflows, route leads, and generate reports inside HubSpot without requiring a separate platform or custom integration maintenance. The AROS system includes a RevOps Agent with 70+ HubSpot functions and a BI Agent connected to Databox via MCP for automated pipeline intelligence.
What is AROS and how does it work?
AROS is Arise GTM's AI GTM Operating System — a multi-agent revenue system deployed inside HubSpot and connected to your GTM stack via MCP. It includes five coordinated agents: a RevOps Agent for operational CRM work, a BI and Insight Agent for reporting and anomaly detection, a GTM Strategy Agent trained on the ARISE® methodology for deal analysis and GTM evaluation, a Customer.io Lifecycle Agent for retention and expansion automation, and an AI BDR for outbound prospecting. All agents are governed by the ARISE® framework and monitored continuously by Leevr, Arise GTM's proprietary agentic optimisation platform. Typical deployments begin with the GTM Blueprint diagnostic and go live within five to seven days.
When should a B2B SaaS company transition from conversational marketing to agentic AI?
The signals that indicate the transition is overdue: your website chat volumes have declined significantly over the past 12 months; your chatbot qualification rates are below 5%; your buyers are arriving at your website with a shortlist already formed; your RevOps team spends more than 30% of their time on operational CRM tasks rather than strategic work; or your competitive intelligence process operates on a quarterly rather than continuous basis. Any two of these being true means the conversational marketing model is no longer the primary constraint worth optimising, and an agentic architecture will unlock more pipeline per pound of investment.
What does it cost to deploy agentic AI for revenue with Arise GTM?
Arise GTM's AROS AI GTM Operating System is a monthly subscription starting from £5,000 per month for the Starter tier, which includes three pre-built agents. All engagements begin with a GTM Blueprint — a one-time diagnostic scoped to your specific situation — priced at £12,500. Growth-stage and enterprise deployments are custom-scoped based on the number of agents, systems connected, and operational complexity. The ROI case for most mid-market B2B SaaS companies above £3M ARR is compelling — the Agentic AI ROI Calculator models the expected return against your current team costs and pipeline metrics.
What should CMOs hire for to stay ahead of this shift?
Paul Sullivan's framework for the CMO capability roadmap through 2026 and 2027 prioritises five areas: agentic AI governance specialists who design oversight frameworks for autonomous systems; marketing data scientists who translate revenue attribution into board-level narratives; GEO specialists who build your brand's presence inside LLM training data and generative search engines; T-shaped generalist marketers who operate as horizontal experts with a deep niche specialism; and creative directors who build authentic brand narratives that AI cannot replicate at scale. As Paul puts it directly: anyone resting on their current expertise without continuously evolving toward relevance in an AI-native GTM environment is open to be replaced by someone who already operates there.
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