Actively Vision Roadmap

Klaviyo Sales Intelligence Platform

Vision & H1 2026 Roadmap

Prepared for: Actively AI Team
Date: December 2025
Contact: Jay Chiruvolu, PM - Amplify Sales

1. The Vision: What We're Building

Mission

Build AI-native Sales Intelligence Platform delivering 70% efficiency improvement across Klaviyo's 600-person sales organization. By 2027, stateful agents handle administrative work, reps focus on relationships and strategy, and every account has complete context from first touch through renewal.

Morning at Klaviyo Sales: 2027

It's 8:00 AM on a Tuesday in December 2027. Sarah, a Mid-Market BDR, opens her laptop—not to Salesforce, but to the Intelligence Platform. Overnight, agents have been working. They've monitored her 180 workable accounts (out of 500 total in her territory), detected three new intent spikes, researched two companies that got funding, and identified five accounts where champions changed roles. Her morning brief is ready.

The dashboard shows today's intelligent prioritization: 8 accounts flagged as "Act Today" with combined scores above 90 (ICP fit + intent + timing). Each account has a complete intelligence package synthesized from systems she used to check manually: Salesforce, 6sense, LinkedIn, Clearbit, SimilarWeb, BuiltWith, Gong call history, and news. What used to take 30 minutes of tab-switching per account now takes 3 minutes to review because the agent has already aggregated, analyzed, and synthesized everything—including information from call transcripts & other sources she previously would have never seen.

The first priority is Acme Corp. The intelligence brief shows: fashion retailer, $25M revenue, Shopify Plus (perfect fit), Mailchimp contract expires in 4 months, new CMO hired 2 months ago (likely reviewing stack), LinkedIn activity shows frustration with attribution, 6sense detected three searches for "marketing attribution" this week, pricing page viewed three times. The agent has identified the CMO (Sarah Chen), verified her mobile number through Clay waterfall enrichment, and drafted a personalized email referencing her LinkedIn post about attribution challenges—with the same completed for 10 other potential contacts. Sarah reads the brief, makes a small edit to the email (adds a case study reference), and approves. The agent sends it. Three minutes total.

By 5pm, Sarah has worked through 15 accounts and booked 6 meetings. In the old world, she'd have booked 1-2 meetings after researching 8-10 accounts. The difference is that research happens continuously by agents who never sleep, synthesize signals humans can't manually connect, and present her with ready-to-execute plans rather than raw data to interpret.

The Intelligence Platform: Core Capabilities

1. Intelligent Daily Prioritization

Every morning, the system answers "What should I do today?" The priority queue is ranked by machine learning combining ICP fit, intent signals, and timing factors. It refreshes continuously—if an account shows intent spike at 2 PM, it moves to the top immediately.

Role-specific priorities:

• BDRs: Accounts by conversion probability (fit + intent + timing + workability)
• AEs: Deals by health + urgency + value (what needs attention now)
• CG AEs: Customers by expansion readiness + churn risk
• Managers: Team priorities with root cause analysis and coaching recommendations

Cross-system synthesis example: BeverageCo jumped to top priority at 10 AM when the agent detected someone forwarded your teammate's email to a VP, the VP opened it three times, and LinkedIn confirmed the VP as the decision-maker. Action: "Call within 2 hours while engaged."

2. Complete Account Context (3-Minute Research)

Click any account and get instant comprehensive intelligence:

• Company Profile: Industry, revenue, employees, growth trajectory, funding status (6 sources aggregated)
• Tech Stack: E-commerce platform, current providers, contract expirations, pain points (BuiltWith + integrations)
• Intent & Timing: Intent score with specific search terms, website behavior, LinkedIn activity, budget cycle timing, contract renewal windows (6sense + website + LinkedIn + news)
• Buying Committee: Decision-maker hierarchy, influence patterns, past engagement, relationship strength scores, recommended entry point (LinkedIn + org chart + Gong history)
• Past Engagement: Every call with transcript links and key moments, email exchanges with sentiment, demos with feedback, objections and how addressed (complete history with citations)
• Similar Customer Playbook: Comparable wins with key factors, common objections, typical deal structure, success metrics to reference (pattern matching)

Why now synthesis example: Global Retail Corp shows moderate intent (78/100) but high value ($85-120K). The agent connects: Q1 budget planning season, fragmented attribution across acquired brands (LinkedIn CEO post), expensive incumbent contract, GDPR hiring signal. Recommended strategy: Lead with multi-brand attribution demo, reference similar customer case study, target VP Marketing.

3. AI-Drafted Content (Review, Not Create)

Agents draft everything. You review and approve:

• Outreach: Emails and call talk tracks personalized to account intelligence, learned from your edits over time, includes relevant case studies
• Meeting materials: Prep briefs 30 min before calls, post-call follow-ups with ROI calculators, demo scripts customized to their use case
• Proposals: Pricing based on usage profile and similar customers, terms aligned to preferences, ROI projections using their stated metrics

Time transformation: What used to take 15-20 minutes of drafting per item now takes 2 minutes of review. You focus on strategy—does this positioning resonate? Should we multi-thread to CFO?—while agents handle tactical execution.

4. Automatic Administrative Work

You never touch Salesforce directly. Agents handle all updates:

• After every call: MEDDPICC fields extracted with confidence scores and citations, stage progression updated, next steps logged, follow-up tasks created
• After every email: Response tracked, sentiment analyzed, engagement level updated
• After every meeting: Attendance tracked, no-shows flagged, rescheduling coordinated automatically
• Activity logging: Perfect history without manual entry. CSM receiving handoff sees complete story without AE repeating everything.

The result: Your CRM time goes from 60 minutes per day to zero. You review what agents updated (5 minutes spot-check) but don't do any data entry.

5. Real-Time Intelligence During Calls

You're on discovery call with RetailCo. At 8:32, Lisa mentions they're evaluating Braze. Instantly, the agent:

• Surfaces Braze battlecard with positioning angles
• Shows you what to say: "Braze is powerful but complex—most mid-market teams find setup takes 3-4 months vs our 30 days"
• Notes this for MEDDPICC Competition field

At 12:05, someone new joins the call. Instantly, the agent:

• Identifies: John Smith, CFO
• Flags: Not on original invite (escalation signal—they're taking this seriously)
• Provides: CFO profile (cost-conscious, mentioned ROI three times in past emails)
• Suggests: Adjust approach to emphasize cost savings

Post-call, you do nothing. The agent already extracted all MEDDPICC fields, updated Salesforce, drafted follow-up email with ROI calculator, scheduled demo, and generated next best actions.

Your time: 45-minute call + 5-minute review = 50 minutes total (vs 45-minute call + 60 minutes of admin today).

6. Proactive Risk and Opportunity Detection

Agents monitor continuously, catching signals before they become problems:

• Champion changes: Detect engagement drops (Jamie hasn't opened last 3 emails, canceled meetings) + track role changes (promoted on LinkedIn) → Identify replacement (Sarah Kim, new Marketing Director), draft transition emails, recommend immediate outreach.
• Expansion timing: Track stated goals ("expand to SMS eventually" from 6 months ago) + monitor readiness (list growth, budget cycle) + similar customer patterns (85% attach at 6-9 months) → Draft expansion proposal.
• Churn risk: Health signals (send volume down 30%, support tickets up 3x) + relationship signals (champion not responding) + undelivered commitments → Coordinate intervention with specialists.

The impact: Nothing falls through cracks. Agents catch and respond to signals humans miss or discover too late.

7. Persistent Memory & Context Retention

Territory intelligence survives handoffs: Jenna (SMB BDR) moves into an AE role. Her territory knowledge—warm accounts, follow-up timing, champion relationships, pain points—currently lives in her head. With the Intelligence Platform, that knowledge persists. The new BDR sees every conversation with key moments highlighted, follow-up requests auto-surfaced ("RetailCo said check back December 2025" → appears in December queue), and relationship strength built over months.

Insights surfaced, not buried: Today, important information lives in 500+ Salesforce activities per account. With persistent memory, agents continuously extract insights. When Bill (ENTR AE) opens a churned account to re-engage, he sees the actual context: deliverability issues for 3 months, support couldn't resolve, attribution setup never completed, champion left frustrated. Recommended approach: Lead with "we've improved deliverability," acknowledge past issues.

Knowledge capture from humans: Territory insights in rep brains and spreadsheets get captured automatically—tier 1 target lists become Intelligence Platform priorities, "this contact is the real decision-maker" becomes buying committee intelligence, seasonal timing patterns become optimization rules.

The transformation: Context becomes organizational asset, not personal knowledge. Both humans and AI agents benefit from accumulated intelligence that doesn't disappear when someone changes roles.

Manager Intelligence: Strategic, Not Administrative

Emma (Manager) reviews team dashboard:

Pipeline health with root cause: Mark at 2.9x coverage (below 3.5x target). Agent identified root cause: Connect rate 22% vs team 35%. Diagnosed WHY: Mobile number coverage 60% (vs Sarah 85%, Lisa 82%). Recommended fix: Enable Clay waterfall for Mark's accounts. Auto-generated 1:1 agenda ready.

Performance pattern detection: Lisa crushing it at 40% self-sourced pipeline (vs team 20%). Agent analyzed her approach: Uses auto-sourcing alerts, acts within 24 hours. Recommendation: Replicate Lisa's playbook across team. Action: Agent drafted team meeting agenda featuring Lisa.

Deal risk flagging: GlobalCorp at risk—champion lost (detected via cross-system signals). Agent provided Mark's multi-threading plan. Emma reviews, approves. Follow-up queued.

What Emma doesn't do: Manually review 80 team deals, dig through Salesforce for context, guess at coaching needs. What Emma does: Strategic leadership, data-informed coaching, pattern replication. 30-45 minute review vs 2-3 hour admin burden today.

2. Required Capabilities

BDR Capabilities: Pipeline Generation (Year 1 Beachhead)

Question 1: What Accounts Should I Go After Today?

• Account Prioritization: ML-ranked daily list combining ICP fit, intent signals, and timing factors. Refreshes continuously—intent spike at 2 PM moves account to top immediately. Replaces 45-60 minute morning routine with 5-minute review of intelligent priorities.

• ICP Fit Scoring: 0-100 score based on firmographics (revenue, employees, industry), technographics (e-commerce platform, tech stack sophistication), and behavioral signals (website engagement, content downloads). Enables systematic multi-factor scoring vs manual experience-based judgment.

• Intent Signal Aggregation: Synthesizes 6sense, website behavior, LinkedIn activity, news, and funding into unified intent score with specific triggers. Surfaces "why now" by connecting multiple weak signals into strong composite (contract timing + new CMO + attribution frustration = ready to buy).

• Timing Trigger Detection: Automatically surfaces contract expirations, budget cycles, stakeholder changes, funding events. Solves discovery gap where critical timing data exists but isn't surfaced.

• Territory Validation: Instant answer to "Can I work this account?" Checks customer status, deduplication, parent-child relationships. Automates the manual categorization BDRs currently do to identify the 70% unusable territory (customers not marked, duplicates, B2B non-fits).

• Inbound Lead Processing: Automatic qualification and routing of inbound leads based on fit and intent. Eliminates manual triage time.

Question 2: Who Are the Right People at Those Accounts?

• Contact Prioritization: Within-account ranking of 30-40 LinkedIn contacts by decision-making authority, past engagement, and champion potential. Reduces 10-15 min manual analysis to automated ranking. Uses pattern recognition early-career BDRs lack.

• Buying Committee Analysis: Decision-maker hierarchy, influence patterns, multi-threading strategy. Enables systematic deep account penetration—239 prospects per account vs current 2-3 contacted.

• Contact Intelligence: Auto-populate contacts from LinkedIn, enrich with verified emails/mobiles, maintain currency as people change roles. Surfaces past engagement history without navigating to Activity tab and scrolling through 500+ activities.

Question 3: What Should I Say to Them?

• Account Research Automation: Pre-generated briefs synthesizing company profile, tech stack, pain points, timing triggers, competitive context, similar customer playbooks. Aggregates what currently takes 10-15 minutes across 5+ sources. Delivers 3-minute research vs 30-minute manual.

• AI-Drafted Outreach: Personalized emails and call talk tracks that learn individual rep voice from edits over time. Addresses current quality gap—team agent produces "better than I could write" but requires 3-5 iterations. Target: strong first draft with minimal edits (20-25 min → 5 min).

• Automated Knowledge Capture: Territory insights, contact notes, messaging strategies captured automatically as workflow happens—not in rep's head or scattered Notes app. Enables territory intelligence to survive handoffs.

AE Capabilities: Deal Progression

• MEDDPICC Auto-Extraction: Extracts all 8 MEDDPICC fields from Gong discovery/demo call transcripts with confidence scores and citations. Auto-updates Salesforce. Eliminates significant CRM update time.

• Deal Health Monitoring: Real-time assessment from engagement trends (email opens, calendar patterns, call sentiment). 7-14 day early warning before deals stall vs reactive discovery at forecast calls.

• Win Probability: Continuous prediction (±15 points accuracy) pattern-matching historical deals. Enables accurate forecasting and risk detection.

• Automatic CRM Updates: Stage progression, next steps, activity logging after every interaction—agent writes to Salesforce. CRM time 60 min/day → 0.

• Meeting Coordination: Scheduling, invites, optimal timing, attendance tracking, no-show flagging, automatic rescheduling.

• Follow-Up Automation: Post-call materials (ROI calculators, case studies) sent automatically with context-aware content. Never miss a follow-up.

• Proposal Generation: Pricing based on usage profile and similar customers, terms aligned to preferences, ROI projections using their metrics. 2 hours → 15 min review.

Manager Capabilities: Team Intelligence

• Manager Dashboard: Team performance with root cause analysis (Mark's 22% connect rate vs team 35%—WHY? Mobile coverage 60%). Coaching recommendations data-driven and specific per rep. Replaces poor Salesforce/Gong reports and 2-3 hour manual review with 30-45 minute strategic session.

• Performance Analytics: Identify what top performers do differently (one rep 40% self-sourced pipeline vs team 20%—how?). Extract patterns, replicate playbooks across team. Addresses current inability to coach with data.

• Coaching Recommendations: Skill gap identification, training needs, best practice suggestions based on call analysis and outcome patterns.

CG AE Capabilities: Post-Sale Intelligence

• Expansion Propensity: Readiness signals from product usage patterns + similar customer timelines. Flags opportunities 2-4 weeks early.

• Churn Risk Prediction: 60-90 day early warning with root cause (declining usage + support tickets + engagement drop). Intervention at 60% recovery rate vs 20% at 30 days.

Platform Capabilities: Foundation for All Personas

• Customer 360 Platform: Unified data layer with canonical IDs (via CMR), linking all customer data across Salesforce, Gong, 6sense, Zendesk, product usage. Solves information silos—enables cross-system synthesis that humans can't do manually. Foundation for all intelligence capabilities.

• Data Quality & Monitoring: Continuous validation, accuracy tracking, deduplication. Achieves 98-99% accuracy requirement. Single source of truth per data point.

• Command Centers: Role-specific interfaces (BDR, AE, Manager, CG AE) consolidating 15+ systems into one daily workspace. Morning brief, complete account context, approval interface.

• Notifications & Alerts: Critical signals (intent spikes, champion changes, competitor mentions, deal stalls) delivered in Slack where reps work. Daily digests (morning priorities, evening summary).

• AI Assistant: Natural language queries for ad-hoc analysis. Context-aware, knows what you're working on. Addresses reporting vacuum—conversational queries vs 700 individual reports.

• Continuous Learning: Email response pattern learning, call timing optimization, deal progression playbooks, ICP model evolution. Platform gets smarter with every interaction—network effects create competitive moat.

2. Discovery Validation

3-Week Discovery (Nov-Dec 2025): 11 stakeholder interviews + 4 shadow sessions across systems teams, sales leadership, BDR directors, frontline reps (Enterprise BDR, SMB BDRs, ENTR AE).

Core Insight: Pipeline generation consumes 80% of rep time and is THE constraint on efficiency—not closing skills, not deal management, but finding and engaging the right prospects.

From interviews: "Pipeline is the biggest issue. It's actually kind of impossible to hit quota for most reps because there's not enough demand."

The Three-Question Framework

Every BDR/AE asks three questions daily (validated across shadow sessions + BDR directors):

1. What accounts should I go after today? (Account prioritization)
2. Who are the right people at those accounts? (Contact identification)
3. What should I say to them? (Outreach personalization)

Current state: 45-60 min prioritization, 10-15 min contact selection per account, 20-25 min email drafting (with AI—still requires 3-5 iterations)

Problems identified:

• No systematic account scoring (manual synthesis across 6sense, Salesforce, BuiltWith, Clearbit, ZoomInfo)
• 70% of territory unusable (customers not marked, duplicates, B2B non-fits)
• Timing intelligence buried (contract renewals exist but not surfaced)
• Contact prioritization dependent on individual reps' mental models (early-career BDRs struggle)
• Deep account penetration not systematic (2-3 contacts reached vs 239 prospects identified)
• AI email quality gap (team custom GPT produces "better than I could write" but requires a few iterations for Klaviyo context & personalization)

3. H1 2026 Roadmap

Q1 Projects (Jan-Mar 2026)

Q1 Total: 22 dev weeks

Q2 Projects (Apr-Jun 2026)

Q2 Total: 32 dev weeks

H1 Total: 54 dev weeks (~3 engineers average)

H1 Target: 52k hours saved by 4/30/2026 (33% of 2026 goal)

H2 2026 Preview

H2 Target: 156k cumulative hours saved by 12/31/2026

4. Where Actively Fits

POC Phase (Q1 2026)

Actively Provides:

• Account prioritization engine (nightly scoring)
• Account research briefs (9 intelligence categories with citations)
• Decisioning strategy (account summary + strategy + contact strategy)
• Contact prioritization with reasoning
• Pre-drafted outreach (emails + talk tracks)
• Gong Engage integration (auto-populate sequences)
• Agent Inbox UX (Slack delivery)
• Assistant API (on-demand queries)
• Chrome extension (ambient LinkedIn context)
• Timing signal persistence (follow-ups tracked in state)
• Feedback loops (opt-out with reason, draft selection)

Klaviyo Builds:

• Custom Command Center dashboard (Vercel, validates extensibility)
• Messaging training (seed from top 2 BDRs + enablement)
• Territory management view (optional)

Success = 2x prospects/week per BDR

Scale Phase (Q2 2026)

Actively Expands To:

• All BDRs (200-300 across SMB, Mid-Market, Enterprise)
• Intelligence Layer operational at scale
• Smart Fields for Klaviyo-specific scoring (ICP fit, propensity models)
• Glean Integration: Actively agents query Glean for Klaviyo knowledge (battlecards, playbooks, case studies)
• Manager dashboard data (team performance analytics)
• AE foundation (MEDDPICC extraction patterns, Gong integration)

Critical Q2 Capability - Glean Integration:

• Combines: Account-specific intelligence (Actively) + "How we sell" knowledge (Glean)
• Solves: Generic AI email gap
• Impact: Email iterations 3-5 → 1-2

Expansion Phase (H2 2026 / 2027)

Actively Foundation Enables:

• AE deal progression (MEDDPICC, deal health, win probability, CRM automation, proposals)
• Manager team intelligence (performance analytics, coaching recommendations)
• CG AE post-sale capabilities (expansion propensity, churn risk - when product data integrated)
• Continuous learning (email patterns, call timing, playbook extraction)
• AI Assistant (natural language queries across all use cases)

Architecture: Actively provides agent infrastructure + state management + core intelligence → Klaviyo builds differentiated capabilities on top (Klaviyo-specific scoring, custom interfaces, domain logic)

Success Metrics

2026: 156k hours saved (52k H1, 104k additional H2 from compounding + new capabilities)

2027: Full lifecycle intelligence operational across 600 reps, all personas using platform daily

POC → Scale → Expand: Each phase validates next, Actively foundation enables progressive capability delivery