Accreditation & Credibility

Benchmark Six Sigma is an Accredited Training Provider of Exemplar Global (formerly RABQSA), a globally recognized certification body and part of the ASQ (American Society for Quality) family.

Exemplar Global accreditation ensures consistent standards, credible assessments, and global acceptance — validating capability without replacing it.

Who It’s For / Entry Requirements

For working professionals who want both sides: materially more capable with AI in their own work, and able to design AI solutions that other people use. Neither credential is a prerequisite for the other, so you may start with either. Enrolment is open.

Entry requirements

• No programming, in either programme.
• Basic computer literacy, and comfort with everyday office applications and web-based AI tools.
• Familiarity with any of ChatGPT, Claude, Gemini or Microsoft Copilot helps but is not required.
• A laptop is recommended throughout. CAISA is hands-on and you build a working agent in it.
• No paid AI subscription is needed to attend or qualify. Trying some demonstrated features yourself may need a paid or organisational account.
• No prior AI qualification. Existing CAISA holders may take CAAP alone; existing CAAP holders may take CAISA alone.

Learning Outcomes

In your own work (CAAP)

• Examine evidence across sources and establish what you can rely on.
• Make sense of numbers and complex information, and find what matters.
• Frame the problem, challenge assumptions and develop choices you can stand behind.
• Prepare for and handle the conversations and messages that carry consequences.
• Hand recurring work to AI and keep the approval.
• Direct substantial multi-step AI work, with limits you set.

In solutions others use (CAISA)

• Translate a business need into AI requirements and a structured BRD.
• Judge technical feasibility, and whether an AI solution is justified at all.
• Design knowledge bases, LLM integration and context-aware conversational flows.
• Choose between a simple knowledge-base solution and an agentic system, and avoid over-engineering.
• Build, test and present a working AI agent without writing code.
• Govern the result: risk, data handling, human checkpoints and adoption.

What the combination gives you. The judgement to tell the two apart. You can say which work you should direct AI through yourself, which work deserves a built solution for other users, and what that solution has to be able to do — and then deliver either one.

Accreditation. Benchmark Six Sigma is an Accredited Training Provider of Exemplar Global (formerly RABQSA, established 1991), part of the ASQ (American Society for Quality) family. Both credentials are issued by Benchmark Six Sigma, an Exemplar Global Accredited Training Provider. Accreditation ensures consistent standards, credible assessments and global acceptance — it validates capability without replacing it.

Verifiable. CAAP is issued as a digital credential on Accredible with QR verification and does not expire. CAISA carries lifetime validity.

Track record. Professionals from organisations including Google, Boeing, Adobe, Coca-Cola, JP Morgan, EY, Accenture, Tata Motors, Adani and WNS have trained with Benchmark. Over 15,000 verified testimonials across Benchmark programmes; CAISA holds a 4.9 average from 54 participant reviews.

Practitioner-built. Both programmes use the platforms and methods Benchmark itself uses to build AI solutions for clients. Neither requires coding, and neither substitutes a tool tour for judgement.

Target Audience (Professional Roles, Experience Level)

Experience level. Mid to senior professionals, in roles where you both do knowledge work yourself and influence what gets built for others. The bundle suits people with enough standing to propose an AI solution, not only to use one.

Professional roles

• Business managers and functional leaders.
• Operations, quality and business excellence professionals.
• Consultants and independent professionals.
• Business analysts and data-oriented professionals.
• Project, programme and product managers.
• Digital transformation and AI adoption leads.
• Functional heads in HR, finance, marketing, sales, service and customer experience.
• Entrepreneurs and business owners.

Particularly suited to anyone expected to answer both questions in the same meeting: can AI do this? and should we build it?

CAAP and CAISA answer two different questions. CAAP is about what you can do with AI in your own work — selecting the right capability for a business task, using it on real work, and judging the output well enough to act on it. CAISA is about what you should build for other people to use — turning a business need into requirements, deciding whether an AI solution is justified at all, designing it, and building a working prototype. Each has a ceiling without the other: applied capability stops at your own desk, however well you direct AI, while architecture without applied depth produces designs that look sound and miss what the work actually needs. Bought separately, these are two decisions, two approvals and two prices. The bundle makes them one.

You decide once. One enrolment, one approval, both places held. No second business case to make six months later, and no exposure to a change in fee or schedule between the two.

You choose the order. Neither credential is a prerequisite for the other. Start with CAAP if you want to be more effective in your own work first; start with CAISA if there is a solution you already need to design. Your schedule is confirmed at enrolment.

Your claim changes. Separately, two certificates. Together, demonstrated capability across the full span — from applying current AI capability to your own work, to architecting a solution other people depend on. That span is what distinguishes someone who can lead AI adoption from someone who can only participate in it.

You stay current while you build. The year of AI Ahead runs after CAAP certification, so the applied side keeps refreshing while you put the architecture side to work. This matters more than it sounds: AI solution designs date quickly, and last year’s assumptions about what a model can do produce over-engineered architecture.

60 PDUs, from 60 live online hours.