GLOSSARY
AI agent
An AI agent can connect research and actions. Clear permissions, bounded assignments and measured outcomes determine whether it helps a sales team.
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AI, sales and negotiation clearly explained. A starting point for better questions, informed decisions and your next practical application.
GLOSSARY
An AI agent can connect research and actions. Clear permissions, bounded assignments and measured outcomes determine whether it helps a sales team.
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An algorithm follows defined steps. In sales pricing, repeatability matters alongside objectives, exceptions and the incentives created by the chosen rules.
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Anthropomorphic AI appears human. In sales, trust should depend on evidence, clear limits and verifiable performance rather than a familiar conversational tone.
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An API connects software and data. For B2B sales, clear ownership, appropriate access and reliable handling of failed or repeated requests determine its value.
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AI in B2B sales: task suitability, error consequences and review effort determine value. Learn how to assess an application against a realistic baseline.
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Automation can reduce sales workload. Assess the complete process, including exceptions, checking, rework and any new bottleneck created by higher output.
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A baseline makes AI value testable. Compare similar tasks, quality requirements and total effort, including correction, instead of timing only the first answer.
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A benchmark compares performance under defined conditions. Sales teams also need relevant tasks, critical-error analysis and tests using their own workflow.
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Bias can distort customer prioritisation. Examine data gaps, resource allocation and feedback loops before relying on a model to assess sales opportunities.
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A business case connects AI benefits with costs, alternatives and testable assumptions. Trace how local time savings could produce real economic value.
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A cache avoids repeated work but can return stale information. Sales applications need clear freshness rules, tenant boundaries and deletion controls.
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Cloud provides flexible computing resources. For B2B applications, demand, total cost, responsibilities and practical switching options determine suitability.
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A cluster distributes work across computers. Its value depends on application design, remaining bottlenecks and tested recovery, not simply the machine count.
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Context shapes how AI interprets a negotiation. Relevant facts, visible assumptions and information capable of changing a decision matter more than volume.
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Context engineering selects and updates the information AI receives. In sales, the relevant facts must be available at the decision point where they matter.
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A context window limits how much information a model can process. Long sales files require reliable handling of exceptions, versions and supporting passages.
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Data protection also applies to personal information in B2B sales. Establish purpose, recipients, necessary data and deletion paths before using AI analysis.
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Data residency concerns storage and processing locations. Customers need evidence across the data chain and separate checks of retention and data protection.
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Deterministic processes repeat the same result under identical conditions. AI workflows still need checks of inputs, rules and whether the result is correct.
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Digital transformation changes workflows and responsibility. In sales, value depends on better decisions and measurable improvements across the whole process.
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Embeddings help retrieve similar content. Sales teams need relevant matches linked to original text, current versions and the right access permissions.
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An evaluation tests AI on specific tasks and error consequences. Sales teams need quality, reasonable review effort and reliability in realistic conditions.
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Evidence establishes support for a claim. Sales research needs relevant sources, contrary findings and a clear separation of facts from interpretation.
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Explainability makes AI recommendations open to challenge. Useful explanations show relevant inputs, understandable reasons and the limits of the evidence.
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