Jak robić prospecting, research i ofertowanie wielokrotnie szybciej z AI – AI_Sales LIVE

Core thesis (what the video teaches)

AI-enabled prospecting/research/offering becomes materially faster and more effective only when you combine:

1. a sales method (playbook),
2. consistent company context, and
3. buyer/customer context—without leaking confidential customer data into public LLMs.

The presenters argue most firms chase more inquiries rather than improving conversion effectiveness from existing leads.

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Key frameworks / playbooks (explicitly referenced or operationalized)

3-context framework for good AI outputs in sales

To produce useful, sales-ready outputs, they emphasize combining:

• Method context: a consistent salesperson workflow (how AI should help inside a repeatable sales process)
• Company context: who the vendor is, how it delivers value, and how customers buy
• Market context: what’s happening in the prospect’s market/competitive landscape

Then they add a crucial missing piece:

• Customer data context: a prepared value proposition + buyer persona + purchasing process for the specific buyer
  • kept confidential

Buyer Persona / “Bayer Persona” model (their terminology)

• Not just “a person,” but a segment made of multiple internal roles in the buying committee.
• Deal cards auto-build a purchasing committee (e.g., estimator/PM/director roles) and map each role’s concerns.

Value proposition creation & iterative improvement

• Value propositions are refined via AI-guided questioning and experiments.
• Refinement shows up over multiple weeks:
  • Week 1: context design
  • Later weeks: iterative refinement

Hallucination control process

• Cross-check outputs across at least two models and/or by reading sources.
• They stress hallucination risk doesn’t remove the need to prepare for meetings—AI mainly reduces prep time.

Meeting/Call preparation playbook

Structure:

1. opening
2. discovery
3. problem & cost
4. why it happens
5. stakeholder/role context
6. positioning
7. objections & responses
8. next steps

AI-generated artifacts:

• deal/prospect cards
• pre-call briefs
• discovery question sets
• negotiation plans (including an example focused on salesperson negotiation consistency)

AI Sales workflow structure (operationalized via the “Seller” app)

• Assistants use consistent stages.
• Outputs are attached to a single deal card so later assistants can reuse the full history.

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Concrete examples / case studies mentioned

Example 1: Preparing an “InPost meeting” from minimal vs. full context

They demonstrate that a “lazy prompt” (tasking AI to prepare a meeting) still yields some useful facts, such as:

• ~1.4B parcels delivered
• strategic challenges: acquisition and ownership transformation (including mention of “Advent in 2026”)
• market tensions (e.g., “war with Allegro”)
• international expansion via acquisitions
• entry into e-commerce/marketplaces

With full method + company + market context + their internal methods, the model produces a much more tailored meeting scenario, including:

• persona/segment fit differences (large scale, public company, formal decision process)
• specific implications for Sellwise’s value

Example 2: Deal-card-driven prospecting + CRM-linked notes (InPost)

• The app (“Seller”) generates a deal card immediately when prospecting starts.
• It stores “meeting/research history” on the card so different assistants share the same context.
• It integrates with CRM for at least:
  • HubSpot
  • Pipedrive
• It generates role-specific outreach:
  • Sales Director message ≠ CEO message (different problems/goals)

Example 3: “Szympol” (wood/sawmills segment) with weak public data

They show AI can still build a prospecting plan even when:

• the company has no website / limited public info
• compliance/verification risks exist

Included red flags:

• lack of verifiable public signals
• decision-maker unknown → “check before contacting”
• inbound absence → reliance on traders/activity

Example 4: Mock conversation quality review (effectiveness focus)

A simulated discovery conversation was evaluated as weak by their “Seller” feedback, including issues like:

• no discovery questions asked
• generalized guesses instead of probing
• inconsistent next step vs customer signal
• missing details: priorities, must-have vs nice-to-have, budget, purchasing process, problem impact

They treat this as a system to generate actionable coaching feedback from meeting context.

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Key metrics / KPIs and numbers mentioned (business-relevant)

Sales efficiency / effectiveness claims (inputs, time, and business value)

• Preparation time reduction:
  • manually collecting similar meeting material: “2–3 hours”
  • with their system: “much less time” (not precisely quantified)
• Small business leakage estimate from chaos:
  • small service companies lose about 100k–300k PLN annually (range repeated)
  • sometimes ~200k–300k PLN per year plus marketing/expenses
• ROI-style problem sizing examples:
  • problems worth ~3M PLN/year
  • 8M PLN/year
  • and a large case of ~48M PLN/year
  • (used to justify pricing by tying solution value to quantified problem impact)

Subscription / pricing timeline (program)

• “AI Sales” described as being on pre-sale until March 4th (price increases after).
• Pre-sale price:
  • PLN 1,000 net cheaper per person
• Team discount condition:
  • at least 3 people for better pricing
• Access timing:
  • “Seller” access in training mentioned as starting only since April
  • (after the buyer persona/value proposition context is prepared earlier)
• Training duration/workload:
  • 6 weeks
  • 6–10 hours/week, asynchronously possible
• Refund policy:
  • after second week (if prospecting module/context isn’t right): full refund, no questions asked

Infrastructure / token usage (cost drivers)

• Token cost depends on usage frequency:
  • example test spend: ~10 dollars during a full day of testing
  • heuristic “normal use” budget: PLN 100–200
• Public model fallback costs:
  • “from $20/month” for GPT/Gemini/Claude-style usage (not tied to Seller)

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Actionable recommendations (how to apply what they teach)

• Do not start with “a prompt.” Start with the workflow

  • Define the method your salespeople follow.
  • Build company and market context aligned to that method.
  • Only then query AI.

• Use AI to create meeting-ready artifacts

  • Create a deal card containing:
    • target segment + roles (buying committee)
    • signals and hypotheses
    • research summaries with citations (where available)
  • Generate:
    • pre-call brief
    • contextual discovery questions
    • objections + responses
    • negotiation plan

• Guard against hallucinations

  • Cross-check across models or verify sources.
  • Treat AI output as input to preparation, not guaranteed truth.

• Handle confidential customer data via API-based systems

  • Don’t paste transcripts/emails/contract info into public chat tools.
  • Their stance: enterprises use API querying due to privacy/security policies.
  • They criticize “just uncheck training data” approaches as insufficient for enterprise compliance.

• Tie AI output to CRM and a single source of truth

  • Store assistant outputs and meeting history in one place (deal card + CRM).
  • This prevents repetition and context loss across team members and time.

• Make outreach role-specific

  • Sales Director vs CEO messaging should reflect different incentives and decision processes.

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“Seller” app: what it is and why it matters (operational strategy)

• Purpose: enable AI sales while keeping customer data confidential and maintaining full context continuity.
• Core capabilities:
  • value proposition, buyer personas, purchasing process
  • prospecting: generate deal cards on the spot
  • research: deeper client research before outreach
  • CRM: notes/updates to HubSpot and Pipedrive initially
  • meeting prep: pre-call briefs + discovery questions + objection handling
  • call/discovery feedback: evaluate conversations and extract coaching insights
• Pricing/access (as described):
  • “Seller” is free for participants of the AI Sales course for a year
  • Seller access/training access timelines depend on when persona/value proposition context is prepared

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Presenters / sources (mentioned at end of transcript)

• Szymon (main presenter; host/coach)
• Dominik (demonstration request)
• Daniel (asks about CRM integration)
• Karol (asks about usage/approach)
• Grzegorz (previous webinar reference)
• Bartek Puck (mentioned for a separate AI implementation event)
• Dawid (asks about token cost/usage)
• Simon (references appear across the discussion)
• Sellwise / AI Sales (organization brand; product “Seller” created by the team)

Mentioned entities: InPost, Allegro, FedEx, HubSpot, Pipedrive, OpenAI, Gemini, Claude, GPT Cloud/Chat, Brave Education, AI Managers.