AI Sales and Solutions Engineering: A 12-Week Cybersecurity Course

What this cybersecurity course is

AI Sales and Solutions Engineering is a 12-week premium course for B2B sales engineers, account executives, and customer success engineers selling AI tooling, AI platforms, and AI services. The curriculum sequences twelve weekly modules across the AI sales lifecycle: AI buyer landscape, discovery for AI products, technical pre-sales, selling AI security, selling AI to engineering leaders, selling AI to compliance and risk, pricing AI products, AI customer success, AI evaluation in customer environments, procurement and legal for AI, renewal and expansion, and a capstone in which the learner authors a complete AI account plan. Every module pairs primary-source vendor documentation with a working artifact. Cybersecurity is woven through the entire curriculum because AI buyers in 2026 expect the seller to handle the security conversation as competently as the technical conversation. Authored by Julian Calvo, Ed.D., M.S. Applied AI specializing in Cybersecurity at Northeastern, with the Cybersecurity Sales Mastery course as the structural reference.

The course follows the AI sales lifecycle from buyer identification through expansion rather than the chapter order of a generalist sales book. Week 1 grounds the seller in who buys AI and what triggers the purchase. Weeks 2 through 6 walk discovery, technical pre-sales, and the three buyer audiences (security, engineering, compliance and risk). Weeks 7 through 11 walk pricing, customer success, evaluation, procurement, and renewal. Week 12 integrates the work into a complete AI account plan. Pedagogically the design draws on Kolb's experiential learning cycle (1984), peer-reviewed B2B sales research (Cuevas, Sharma, Plouffe), and the original Principled Seller Framework that anchors Cybersecurity Sales Mastery. Evidence quality is opinionated: every claim about an AI vendor pricing model is anchored to the official pricing page, and every claim about a buyer audience is anchored to peer-reviewed research or to publicly available IAPP, ISACA, or NIST documentation.

What you will learn

• Map AI buyers into the four primary segments and identify the named purchase triggers per segment
• Run AI-specific discovery that produces a named use case, named buying committee, named technical owner, named regulatory context
• Scope AI proofs-of-concept and design evaluation frameworks the customer's technical owner can execute alone
• Handle the cybersecurity conversation with the CISO at the working artifact altitude (SOC 2, ISO 27001, ISO/IEC 42001, model cards)
• Sell AI to engineering leaders by handling the build-versus-buy conversation directly
• Sell AI to compliance and risk audiences by carrying the right artifacts for each named role
• Walk AI pricing patterns and build a defensible token economics model
• Run AI customer success: onboarding milestones, value realization signals, quarterly business reviews
• Negotiate AI contracts: data use, model training, output ownership, indemnification, audit, foundation model dependency
• Drive renewal and expansion: usage-based pricing mechanics, named upsell triggers, named risks
• Author a complete AI account plan including discovery, technical evaluation, ROI model, and three-year expansion roadmap

12-week curriculum

1. Week 01 · 6h · 5 topics

   The AI Buyer Landscape

   Who buys AI in 2026, what they buy, what triggers the purchase, and how AI buying differs from traditional B2B SaaS buying. This module sets the buyer map the rest of the course returns to.

   Learning objectives and topics

   Learning objectives.

   • Name the four primary AI buyer segments and their distinct buying motions
   • Identify the named purchase triggers that move AI deals from interest to budget
   • Distinguish AI buying from traditional B2B SaaS buying across procurement, evaluation, and contract dimensions
   • Map the cybersecurity buyer overlap with the AI buyer landscape
   • Author a one-page buyer landscape memo for the seller's named territory

   Topics.

   • Four primary AI buyer segments
   • Named purchase triggers in AI deals
   • How AI buying differs from traditional B2B SaaS buying
   • Cybersecurity buyer overlap with the AI buyer landscape
   • Authoring the buyer landscape memo

   Assessment: 5 questions · 360 minutes total

2. Week 02 · 6h · 5 topics

   Discovery for AI Products

   Qualifying questions specific to AI buyers. The module walks the AI-specific discovery questions that traditional B2B SaaS discovery does not cover and shows how to run a discovery call that produces an actionable opportunity.

   Learning objectives and topics

   Learning objectives.

   • Run an AI-specific discovery call that produces named use case, named buying committee, named trigger, named criteria
   • Ask the AI-specific qualifying questions traditional B2B SaaS discovery does not cover
   • Identify the named technical owner who will run evaluation in the customer environment
   • Identify the named regulatory context the customer operates in
   • Author a one-page discovery memo that the seller's manager can read in five minutes

   Topics.

   • The AI-specific discovery questions
   • Identifying the technical owner who will run evaluation
   • Identifying the regulatory context
   • Running the discovery call
   • Authoring the discovery memo

   Assessment: 5 questions · 360 minutes total

3. Week 03 · 6h · 5 topics

   Technical Pre-Sales for AI Products

   Proof-of-concept design and the evaluation framework that gives the buyer confidence the product works. The module walks how to scope an AI POC, design an evaluation set with the customer, and run a pilot that produces a written recommendation.

   Learning objectives and topics

   Learning objectives.

   • Scope an AI proof-of-concept that the customer can run inside one quarter
   • Design an evaluation framework with the customer's technical owner
   • Pick the named success criteria and the named decision rule
   • Run the POC review meeting that produces a written recommendation
   • Author a one-page POC scoping document

   Topics.

   • Scoping an AI proof-of-concept
   • Designing the evaluation framework
   • Picking the named success criterion and decision rule
   • Running the POC review meeting
   • Authoring the POC scoping document

   Assessment: 5 questions · 360 minutes total

4. Week 04 · 6h · 5 topics

   Selling AI to Security-Conscious Buyers

   The CISO is in every enterprise AI buying committee in 2026. This module walks the security conversation: which questions the CISO will ask, which artifacts win the conversation, and how to position AI products to security-conscious buyers without overpromising.

   Learning objectives and topics

   Learning objectives.

   • Anticipate the named questions the CISO will ask about an AI product
   • Position AI security artifacts (SOC 2, ISO 27001, ISO/IEC 42001, model and data documentation) at the right altitude
   • Run a security review meeting that produces a written security finding the buyer's risk team can act on
   • Identify the cybersecurity threats specific to AI products (prompt injection, data exfiltration, supply chain) and how to discuss them honestly
   • Author a one-page CISO conversation playbook for an AI product

   Topics.

   • The CISO's named questions about an AI product
   • Positioning AI security artifacts at the right altitude
   • Running the security review meeting
   • Discussing AI-specific threats honestly
   • Authoring the CISO conversation playbook

   Assessment: 5 questions · 360 minutes total

5. Week 05 · 6h · 5 topics

   Selling AI to Engineering Leaders

   VP of Engineering, CTO, and Head of AI conversations. The module walks the technical buyer audiences who own the engineering decision and what each one cares about.

   Learning objectives and topics

   Learning objectives.

   • Distinguish the VP of Engineering, CTO, and Head of AI buyer profiles
   • Position the AI product against the build-versus-buy decision the engineering leader is making
   • Run the technical-fit conversation that produces a credible architecture diagram
   • Identify the developer experience signals engineering leaders weigh
   • Author a one-page engineering-leader conversation playbook

   Topics.

   • VP of Engineering, CTO, and Head of AI: three audiences
   • Build versus buy: the decision engineering leaders are making
   • Running the technical-fit conversation
   • Developer experience signals engineering leaders weigh
   • Authoring the engineering-leader conversation playbook

   Assessment: 5 questions · 360 minutes total

6. Week 06 · 6h · 5 topics

   Selling AI to Compliance and Risk

   Compliance, risk, audit, and privacy professionals are in every regulated AI buying committee. The module walks the IAPP and ISACA audiences and the artifacts that win the conversation.

   Learning objectives and topics

   Learning objectives.

   • Distinguish the General Counsel, Chief Compliance Officer, Chief Privacy Officer, and Chief Risk Officer audiences
   • Position AI governance artifacts (impact assessments, model cards, training data documentation) for compliance audiences
   • Identify the named regulatory questions IAPP-credentialed and ISACA-credentialed buyers will ask
   • Run the compliance review conversation that produces a written compliance opinion
   • Author a one-page compliance and risk conversation playbook

   Topics.

   • Four compliance and risk audiences
   • AI governance artifacts for compliance audiences
   • Regulatory questions IAPP and ISACA buyers ask
   • Running the compliance review conversation
   • Authoring the compliance and risk conversation playbook

   Assessment: 5 questions · 360 minutes total

7. Week 07 · 6h · 5 topics

   Pricing AI Products

   Token economics, value capture, and pricing-framework selection. The module walks the AI pricing patterns vendors use in 2026, the framework selection logic, and how to handle the pricing conversation with the buyer.

   Learning objectives and topics

   Learning objectives.

   • Walk the AI pricing patterns: usage-based, seat-based, outcome-based, hybrid
   • Walk token economics from API cost to gross margin
   • Pick the right pricing framework for the seller's product and the named buyer
   • Run the pricing conversation with the buyer's procurement team
   • Author a one-page pricing strategy for a hypothetical AI product

   Topics.

   • AI pricing patterns: usage, seat, outcome, hybrid
   • Token economics from API cost to gross margin
   • Picking the right pricing framework
   • Running the pricing conversation with procurement
   • Authoring the pricing strategy

   Assessment: 5 questions · 360 minutes total

8. Week 08 · 6h · 5 topics

   AI Customer Success

   Post-sale technical relationship management. The module walks the customer success engineer's job for AI products: onboarding, value realization, expansion triggers, and the handoff between the sales team and the customer success team.

   Learning objectives and topics

   Learning objectives.

   • Walk the AI customer success lifecycle from contract signature to renewal
   • Identify the named onboarding milestones that predict renewal
   • Identify the named value realization signals that predict expansion
   • Run the quarterly business review for an AI customer
   • Author a one-page customer success plan for a hypothetical AI customer

   Topics.

   • The AI customer success lifecycle
   • Onboarding milestones that predict renewal
   • Value realization signals that predict expansion
   • Running the quarterly business review
   • Authoring the customer success plan

   Assessment: 5 questions · 360 minutes total

9. Week 09 · 6h · 5 topics

   AI Evaluation in the Customer Environment

   Pilots, benchmarks, and success criteria in the customer's actual environment. The module walks how to design and run a pilot that produces a credible verdict and how to handle the difficult cases (mixed results, novel use case, comparison against an internal baseline).

   Learning objectives and topics

   Learning objectives.

   • Design a pilot that the customer's technical owner can execute without seller hand-holding
   • Pick benchmarks that map to the customer's named criteria rather than to vendor-friendly leaderboards
   • Handle mixed-result pilots without losing the deal
   • Compare the seller's product against an internal baseline credibly
   • Author a one-page pilot design document for a hypothetical opportunity

   Topics.

   • Designing a pilot the technical owner can execute alone
   • Picking benchmarks that map to customer criteria
   • Handling mixed-result pilots
   • Comparing against an internal baseline
   • Authoring the pilot design document

   Assessment: 5 questions · 360 minutes total

10. Week 10 · 6h · 5 topics

    AI Procurement and Legal

    AI-specific contract terms, data handling, and intellectual property. The module walks the procurement frameworks (TPSA, Common Assessment Framework) the buyer uses, the AI-specific clauses the seller has to handle, and the negotiating posture that closes the deal.

    Learning objectives and topics

    Learning objectives.

    • Walk the procurement frameworks: Technology Procurement Security Assessment (TPSA), Common Assessment Framework, internal frameworks
    • Walk the AI-specific contractual clauses: data use, model training, output ownership, indemnification, audit
    • Identify the named negotiating bands the seller operates in
    • Run the contract redline conversation with the buyer's procurement and legal teams
    • Author a one-page contractual posture document for a hypothetical AI deal

    Topics.

    • Procurement frameworks: TPSA, CAF, internal
    • AI-specific contract clauses
    • Named negotiating bands
    • Running the contract redline conversation
    • Authoring the contractual posture document

    Assessment: 5 questions · 360 minutes total

11. Week 11 · 6h · 5 topics

    Renewal and Expansion

    Usage-based pricing dynamics, upsell triggers, and renewal mechanics. The module walks how AI renewal and expansion differ from traditional B2B SaaS renewal, and how to drive net dollar retention above 130 percent on AI accounts.

    Learning objectives and topics

    Learning objectives.

    • Walk the renewal lifecycle for AI accounts (90 to 120 days before contract end)
    • Identify the named upsell triggers (usage growth, new use case, organizational change)
    • Handle usage-based pricing renewal mechanics (true-up, true-down, new commit)
    • Manage the named risks at renewal (foundation model term change, internal build option, competitive replacement)
    • Author a one-page renewal and expansion plan for a hypothetical AI account

    Topics.

    • AI renewal lifecycle
    • Upsell triggers in AI accounts
    • Usage-based pricing renewal mechanics
    • Renewal risks specific to AI accounts
    • Authoring the renewal and expansion plan

    Assessment: 5 questions · 360 minutes total

12. Week 12 · 6h · 5 topics

    Capstone: A Complete AI Account Plan

    Synthesize the eleven prior weekly artifacts into a complete AI account plan for a hypothetical strategic account. The capstone covers discovery, technical evaluation, ROI model, and the named expansion roadmap.

    Learning objectives and topics

    Learning objectives.

    • Integrate the eleven weekly artifacts into a single AI account plan document
    • Author the named ROI model for the account, defended with token economics math
    • Author the named three-year expansion roadmap with named milestones
    • Document the named risks and named mitigations across the account lifecycle
    • Submit the capstone for self-review against the published rubric

    Topics.

    • Capstone scoping: a hypothetical strategic account
    • Discovery, technical evaluation, and pilot integration
    • ROI model defended with token economics
    • Three-year expansion roadmap
    • Capstone submission and self-review

    Assessment: 5 questions · 360 minutes total

Capstone

Author a complete AI account plan that the seller's manager would treat as the working file for a strategic account

The capstone integrates the eleven prior weekly artifacts (buyer landscape memo, discovery memo, POC scoping document, CISO conversation playbook, engineering-leader playbook, compliance and risk playbook, pricing strategy, customer success plan, pilot design document, contractual posture document, renewal and expansion plan) into a single 30 to 50 page AI account plan. The plan covers the named buyer, the named ROI model with token economics math, and the named three-year expansion roadmap. The capstone is graded against three named failure modes: discovery gap, ROI gap, roadmap gap. A passing capstone earns the DecipherU AI Sales and Solutions Engineering certificate of completion.

Who it is for

• B2B sales engineers and solutions architects selling AI tooling, AI platforms, or AI services
• Account executives who own AI-product or AI-feature quotas at AI-native companies
• Account executives at traditional B2B SaaS companies who now sell AI features into existing accounts
• Customer success engineers managing post-sale technical relationships for AI products
• Sales leaders building or scaling AI go-to-market teams
• Cybersecurity sellers expanding into AI security tooling and platforms
• Channel managers building AI partner programs

Who it is not for

• Total beginners with no B2B sales, sales engineering, or customer success experience. Build foundational sales skills first.
• Sales professionals selling cybersecurity vendor products who are not also selling AI. Cybersecurity Sales Mastery is the right course for that audience.
• AI engineers wanting to learn the technical side of AI. The course is sales practice, not engineering. Engineers should look at AI Security Engineering.
• Buyers seeking proprietary AI vendor sales playbooks. The course is methodology and primary-source material, not vendor-specific scripts.

Prerequisites

• At least 2 years of B2B sales, sales engineering, or customer success experience
• Working familiarity with discovery, technical pre-sales, and procurement workflows
• Basic LLM literacy as a user (prompting, API exposure, structured output)
• Comfort reading API documentation and pricing pages
• Willingness to commit roughly 70 hours of focused study and artifact production across 12 weeks

What you get

• 70 hours of original cybersecurity AI sales curriculum across 12 weekly modules
• Eleven portfolio-grade artifacts produced across the 12 weeks (buyer landscape memo, discovery memo, POC scoping document, CISO conversation playbook, engineering-leader playbook, compliance and risk playbook, pricing strategy, customer success plan, pilot design document, contractual posture document, renewal and expansion plan)
• Certificate of completion issued for learners who finish all 12 weekly assessments and submit a capstone that scores at least 4 of 5 across the three named failure modes. The certificate is a digital credential with a verifiable URL listing the curriculum and the assessment outcomes.
• Lifetime access to course updates as AI vendor pricing pages, procurement frameworks, and the EU AI Act enforcement landscape evolve
• DecipherU community access (Defender tier and above) for peer review of the capstone account plan and post-course Q&A

Author

Frequently asked questions

Who is this cybersecurity AI sales course for?

B2B sales engineers, solutions architects, account executives, customer success engineers, and sales leaders selling AI tooling, AI platforms, or AI services. Cybersecurity sellers expanding into AI security tooling. Account executives at AI-native companies and at traditional B2B SaaS companies shipping AI features. The course assumes 2-plus years of B2B sales, sales engineering, or customer success experience.

What primary sources does the course cite?

Actual AI vendor pricing pages (Anthropic, OpenAI, Google AI Platform), NIST AI Risk Management Framework, NIST Generative AI Profile (AI 600-1), the EU AI Act consolidated text, IAPP AIGP body of knowledge, ISACA AAIA body of knowledge, Cloud Security Alliance materials for procurement, and peer-reviewed B2B sales research (Cuevas, Sharma, Plouffe). Pricing claims are anchored to the official pricing pages and rechecked quarterly.

How long does the AI sales course take to complete?

Roughly 70 hours of focused study and artifact production across 12 weekly modules. Most learners complete it in 12 to 18 weeks at 5 to 7 hours per week. Self-paced. The capstone is a complete AI account plan the learner can show to a hiring panel for a senior AI sales engineering role or use as the working file for a real strategic account.

How does the course handle the cybersecurity conversation in AI sales?

Week 4 is dedicated to selling AI to security-conscious buyers and walks the named CISO questions, the named security artifacts (SOC 2, ISO 27001, ISO/IEC 42001, model cards, pen test reports, bug bounty), and the named AI-specific threats (prompt injection, data exfiltration, supply chain). Cybersecurity is also woven through the procurement, customer success, and capstone work because the CISO is in every enterprise AI buying committee in 2026.

How is this different from Cybersecurity Sales Mastery?

Cybersecurity Sales Mastery (the existing $497 22-module course) covers selling cybersecurity vendor products to enterprise buyers under The Principled Seller Framework. AI Sales and Solutions Engineering (this course, $497, 12 weeks) covers selling AI tooling, AI platforms, and AI services. The structures are similar (premium length, capstone, original instruction grounded in primary sources) but the audiences are distinct. Cybersecurity sellers expanding into AI security tooling will benefit from both.

What is the capstone deliverable for the AI sales course?

A complete 30 to 50 page AI account plan for a hypothetical strategic account. The plan integrates the eleven prior weekly artifacts (buyer landscape memo, discovery memo, POC scoping, CISO playbook, engineering-leader playbook, compliance playbook, pricing strategy, customer success plan, pilot design document, contractual posture, renewal plan) and adds a defensible ROI model with token economics math and a named three-year expansion roadmap. Graded against discovery gap, ROI gap, and roadmap gap failure modes.

Companion course

Selling cybersecurity vendor products too? Cybersecurity Sales Mastery is the companion course.

AI Sales and Solutions Engineering covers selling AI tooling, AI platforms, and AI services. Cybersecurity Sales Mastery covers selling cybersecurity vendor products under The Principled Seller Framework. Both are $497 one-time. Cybersecurity sellers expanding into AI security tooling benefit from both.

Related cybersecurity content

• Cybersecurity for AI convergence area overview
• AI Security Engineering ($597, 12 weeks): the engineering counterpart for the technical AI security audience
• AI Governance and Risk ($397, 8 weeks): the governance counterpart for the GRC audience
• All Cybersecurity for AI career paths
• AI for Cybersecurity convergence area (the inverse direction)