AI Skills for Sales
Practical sales training that helps salespeople use AI to improve research, preparation, communication and execution without outsourcing judgement to the technology. Start with the sales task, use AI where it adds value, verify the output and keep the salesperson responsible for what happens next.
By Iain Swanston, Founder, Klozers · Updated September 2026
AI can accelerate sales work. The salesperson remains accountable for relevance, accuracy, judgement and action.
AI skills for sales are the practical skills salespeople need to use AI to improve preparation, research, communication and execution without outsourcing sales judgement to the technology. The salesperson remains responsible for understanding the customer, checking the output and deciding what should happen next.
- Start with the sales task, not the AI tool.
- AI can accelerate research, preparation, drafting and organisation.
- Human judgement remains essential for relationships, discovery, stakeholder engagement and commercial decisions.
- AI output must be checked for accuracy, relevance and appropriate use of information.
- The most useful AI applications differ by sales role and sales process.
Where should AI fit into B2B selling?
AI is strongest where it can accelerate sales work that involves gathering, organising, interrogating or drafting information. Market analysis, ICP research, account research, identifying possible compelling events, meeting preparation and first-draft communication can all be candidates for AI support.
AI should support the salesperson's judgement, not replace it.
The boundary matters. In complex B2B sales, the salesperson still needs to establish credibility, develop relationships, obtain introductions, conduct discovery, understand stakeholder dynamics, interpret what the customer means and decide how to progress the sale.
A useful question is therefore not simply, “Where can we use AI?” It is, “Which parts of this sales process benefit from AI, and which parts still require human judgement and relationships?”
The banking-leader interview supplied for this refresh illustrates the distinction. Its sales observation was that AI can compress work such as market analysis, ICP work, compelling-event research and company reports, while access to a complex opportunity can still depend on a human introduction. We use that as contextual input, not as independent Klozers research.
Who is AI Skills for Sales for?
The programme is designed for B2B sales teams that want practical AI capability without turning sales training into software training.
SDRs and BDRs
Use AI to support market, ICP and account research, identify possible compelling events and prepare more relevant outreach.
Account Executives
Use AI for meeting preparation, opportunity organisation, stakeholder context and follow-up around active opportunities.
Account Managers
Use AI to support account research, stakeholder understanding and preparation for retention and growth conversations.
Sales Managers
Use AI to support preparation, analysis and coaching while retaining managerial responsibility for interpretation and feedback.
What the AI Skills for Sales training covers
1. Start with the sales problem
Identify the work to improve before selecting an AI approach. This prevents the course becoming a catalogue of prompts looking for a purpose.
2. Research markets, ICPs and accounts
Use AI to organise and interrogate available information for market research, ideal customer work and account preparation, while separating useful research from unsupported generated claims.
3. Identify possible compelling events
Use AI to surface and organise signals that may justify closer investigation. A generated suggestion is a research lead, not evidence that an event is real or commercially important.
4. Prepare prospecting and personalisation
Use AI to support outreach preparation while keeping the salesperson responsible for relevance, customer context and the final message.
5. Prepare for discovery and meetings
Use AI to organise background information and prepare areas to explore. During the conversation, listening, interpretation and follow-up remain sales skills.
6. Support opportunity and stakeholder work
Use AI to organise known information, identify gaps and prepare next actions. Generated output does not replace direct evidence from the customer.
7. Improve follow-up and communication
Draft and refine emails, summaries and follow-up using context supplied by the salesperson. Check accuracy, tone and commitments before sending.
8. Use AI for account growth
Support account research and preparation for retention, renewal and expansion conversations where those responsibilities sit with the role.
9. Practise and prepare
Use AI for role practice, scenario preparation and feedback where appropriate, while distinguishing generated feedback from verified customer information.
10. Verify, protect and decide
Check factual claims, challenge weak output and follow the client’s rules on what company or customer information may be entered into an AI system.
Build AI capability around the sales work that matters
We start with your sales process, your best practice and the work your people need to perform. AI is applied where it can support that work rather than imposing a generic tool-led workflow.
How is the training delivered?
AI Skills for Sales is private training delivered in person or live online. The programme is tailored to the client’s sales process, roles, approved AI environment and the work participants actually perform.
Best-practice-first design
What should participants be able to do differently?
Choose
Recognise which sales tasks are suitable for AI support rather than starting with a tool.
Verify
Check generated output for factual accuracy, relevance and missing customer context before acting.
Decide
Know when judgement, relationships and direct customer interaction need to take over.
AI use cases should follow the responsibilities of the role rather than a generic list of prompts.
Is AI Skills for Sales right for your team?
Designed for
- B2B sales teams that want practical, role-relevant AI capability.
- Organisations that want AI to support an existing sales process and customer approach.
- Sales leaders who want human verification and judgement built into AI-enabled workflows.
Not designed for
- Teams looking only for product feature training on a specific AI platform.
- Organisations expecting AI to replace relationship building, discovery or sales judgement.
- Teams without clarity on what information employees may use in AI systems.
What does AI Skills for Sales cost?
Price per person
Pricing approved for this refresh.
From £720
€880 · US $990 · AUS $1445
What moves the price
- Participant numbers.
- In-person or live online delivery.
- Depth of preparation and tailoring required.
- Travel requirements for in-person delivery.
Frequently asked questions
Is this a course about writing AI prompts?
No. Prompting is one practical component. The wider skill is knowing which sales task AI should support, providing useful commercial context, checking the output and deciding how to apply it.
Will AI replace the salesperson in this course?
No. AI supports selected sales work. Relationships, discovery, stakeholder engagement, judgement and commercial decisions remain human responsibilities.
Which sales roles is the course for?
The course can be tailored for SDRs and BDRs, Account Executives, Account Managers, Sales Managers and other B2B sales roles.
Do you train one specific AI product?
The programme is not built around one named product. Delivery can use the client’s approved AI environment where appropriate, while the underlying skills remain transferable.
Can the training be delivered online?
Yes. It can be delivered in person or live online.
Why should AI sales training start with the sales task?
Starting with the tool encourages people to search for somewhere to use it. Starting with the sales task creates a clearer test: what work needs to be done, what information is available, where is the friction, and can AI support that work without removing necessary judgement?
This is consistent with Klozers’ methodology-first approach. The client’s sales process and proven best practice come before a new technology workflow. AI should support the way the organisation needs to sell, not force selling around software features.
A salesperson can produce a technically good prompt for the wrong task. A polished output can still be commercially weak if the underlying ICP is wrong, customer context is missing or the next action makes no sense.
Participants therefore work from sales situations. They decide what they are trying to achieve, identify the inputs AI needs, inspect the result and decide whether it is useful enough to influence a real sales action.
Where can AI save time without taking over the sale?
Research and preparation are strong candidates because they often involve finding, comparing, structuring and summarising information. The banking-leader context supplied for this refresh described market analysis, ICP work, compelling events and company reports as examples of go-to-market work AI can compress.
The course uses that principle rather than adopting the interview as Klozers research. Participants learn how to support these tasks with AI and verify the result before it enters prospecting, a meeting or an opportunity plan.
Drafting is another support area. AI can provide a starting point for an email, meeting summary or follow-up. The salesperson still determines whether it reflects what the customer said, whether the tone is appropriate and whether commitments are correct.
This division is deliberate. The objective is to reduce preparation effort while protecting the parts of selling where context, judgement and human interaction materially affect the outcome.
Why do relationships and judgement still matter?
Complex B2B decisions involve people with different priorities, authority, influence and risk. Information can support preparation, but the salesperson still has to earn access, listen, interpret responses and decide how to engage the buying group.
The banking interview provides a useful example: its argument was that AI can perform significant go-to-market preparation but cannot itself secure the warm introduction needed to enter some complex opportunities. That is contextual input, not a universal research finding.
The same boundary applies during discovery. AI can help prepare possible questions, but it cannot determine in advance which answer matters most in the live conversation. The salesperson needs to listen and decide what to explore next.
AI sales capability therefore includes knowing when not to delegate. Good use is not measured by the number of tasks handed to AI. It is whether AI support improves the work without removing the human contribution the sale requires.
How should salespeople verify AI output?
Generated output should be treated as material to inspect, not evidence simply because it is written confidently. Participants should check factual claims against appropriate sources, confirm customer-specific information and identify assumptions in the output.
Verification also includes relevance. A fact about a prospect can be accurate but irrelevant to the reason for contacting them. Salespeople need to distinguish available information from information that genuinely improves preparation or a customer conversation.
Company and customer information creates another control point. The course does not assume every AI system is approved for every type of information. Participants should follow their organisation’s policies and the controls attached to the AI environment they are authorised to use.
The final control is the sales decision. Before sending, presenting or acting on generated material, the salesperson remains responsible for what it says and why it is being used.
How should AI use change by sales role?
An SDR or BDR typically spends more time creating new opportunities, so research, ICP work, account preparation, compelling-event investigation and outreach preparation are natural areas to examine. The goal is more relevant preparation, not simply more automated messages.
An Account Executive works further into active opportunities. Meeting preparation, organising known stakeholder information, identifying gaps, summarising discussions and preparing follow-up may therefore be more relevant. AI can help organise evidence, but it does not replace evidence from the customer.
Account Managers can apply the same principles to existing customers. Account research, stakeholder context and preparation for retention or growth conversations can be supported while the relationship itself remains human-led.
Sales Managers have another set of tasks. AI can support preparation, analysis and coaching exercises, but the manager remains responsible for interpreting performance, understanding the individual and deciding what feedback or coaching is appropriate.