May Habib

AI Deployment - How to Leverage AI Across Your Business

CEO and Co-Founder of Writer

Why a full stack approach makes for turn key LLM and generative AI deployment.

Full Stack Approach and Why it Matters

How to think about your business data, leverage what you currently have, and make your data work for you.

Data-Data-Data

How to get started with generative AI, move out of testing phases and into actual deployment across the business.

Move Out of Testing

Hear about some of the best use cases for generative AI today and future use cases for tomorrow.

Real World Applications of AI Today

Arming senior leadership teams with the AI literacy, practical skills and strategic acumen needed to guide their companies into the AI era.

March 2026 Cohort

AI Strategist // Trainer // Futurist

Mark Laurence

Founder and lead consultant at generative AI consultancy Ten Past Tomorrow.

February 2026 Cohort

April 2026 Cohort

“We're going to get to AI systems that are better than almost all humans at almost all tasks... That's the thing I think we're quite likely to get in the next two or three years." - Dario Amodei (CEO, Anthropic. However… ”Only 8% of employees reported they feel GenAI is already transforming their organisation.” - Deloitte - State of Generative AI in the Enterprise report, Q4 2025. Quite frankly, the gap between AI advancement and AI adoption that those two stats speak to is just too large. It’s no longer just a gap. It’s a chasm. And add to that… “In organisations where AI is openly embraced, the chance of developing 'AI Experts' and 'AI Practitioners' is halved when direct managers discourage its use, either directly or indirectly.” - Section School - The AI Proficiency Report. Merging those three storylines and you get a headline that says that senior leadership is the bridge that connects AI aspirations to organisational reality, and ultimately the trajectory of its integration at every level. In other words; true organisation-wide AI adoption doesn’t begin with technology or tools. It begins with leadership. AI literate and AI skilled senior leadership.

Close the AI capability gap: Exec-level training for senior leadership teams

Master AI prompting fundamentals

Evaluate and select AI tools strategically

Discover how to truly scale AI usage throughout your business

Develop a 5-step AI transformation roadmap

Lead AI change management

Implement AI governance and privacy practices

Senior leadership teams wanting to collectively, confidently, knowledgeably, skilfully and ethically lead an AI-forward company.

SLT teams who sense AI is far more than a technical challenge, that it demands leaders step forward to lead comprehensive strategic change.

Leadership teams excited by making a genuine commitment to fundamentally reshape their people, processes and businesses.

Students will learn the AI literacy to evaluate AI ideas, surface risk, and make sound judgments without becoming technical specialists.

BA-Cube Founder | LinkedIn Instructor

Angela Wick

25+ years practicing, shaping, and teaching analysis, product, and requirements.

AI Literacy for Business Analysts

AI risk often comes from poor assumptions about what AI can and cannot do. It also comes from poor analysis and organizational dynamics of leaders asking for solutions and pressuring teams to deliver solutions rather than solving real problems.Analysis professionals have a huge role in this dynamic that can change everything!Analysts are increasingly expected to participate in AI decisions, yet most training focuses on tools or stays abstract. This course fills the gap in understanding AI capabilities: Not from a technical point of view, rather a capability point of view; business uses and patterns of functional usage.You will build a grounded understanding of AI capabilities, limits, patterns, and failure modes, and learn how requirements shift toward hypotheses, experiments, and continuous outcomes in AI-enabled work.This course explores the skills of:🎯 Identifying the right AI use cases for specific business scenarios🎯 Distinguishing between deterministic and probabilistic systems and deciding where each should be applied in a workflow🎯 Questioning and communicating AI use case fit and risks—and defining clear hypotheses for AI solutions

Giving BAs AI Literacy to Understand, Evaluate and be Accountable for AI 

Major AI capability types and their appropriate uses

GenerativeAI is just one piece of AI, and much more than "chat".

What AI is good at and where it fails

Generative AI and its Data Sources (Public vs. Private)

How requirements shift to hypotheses and experiments

Where humans must remain accountable in AI-enabled systems

Analysts, Project Managers, Business Leaders and others involved in AI discussions or initiatives

Analysis Leaders responsible for AI-related risk

Analysts who want confidence without pretending technical expertise

Master Generative AI engineering by building production-ready LLM systems using RAG, agents, embeddings, and scalable AI architectures.

Training Engineers in AI & Data Science

Data Science Academy

Industry-focused academy training engineers in AI, LLMs, and data science.

Cohort 1

Generative AI is transforming every industry — but most engineers only learn how to use AI tools, not how to build real AI systems.Companies are urgently looking for engineers who can design production-ready LLM applications, implement Retrieval-Augmented Generation (RAG) pipelines, build AI agents, and integrate AI into real software products.This course bridges that gap.You will learn how to move from AI experimentation to real engineering, building systems that combine LLMs, embeddings, vector databases, APIs, and agents into scalable applications.By the end of this course, you will know how to:• Design reliable prompts and LLM workflows • Build semantic search systems using embeddings • Implement production-ready RAG pipelines • Develop full-stack LLM applications • Optimize systems for cost, performance, and scaleInstead of just learning theory, you will build the core architecture behind modern AI products used by startups and tech companies today.If you want to become an AI Engineer capable of building real Generative AI systems, this course will give you the practical foundation to do it.

Learn to Build Production-Ready Generative AI & LLM Applications

Understand how transformers and LLM architectures work

Design reliable prompts and structured LLM workflows

Build semantic search systems using embeddings and vector databases

Develop Retrieval-Augmented Generation (RAG) systems

Create AI agents and tool-enabled LLM systems

Build and deploy full-stack LLM applications

Software engineers who want to transition into AI engineering and build production-ready LLM and generative AI applications.

Data scientists and ML engineers looking to learn RAG, agents, embeddings, and modern LLM application development.

AI builders, startup founders, and product developers who want to integrate generative AI into real-world products.

Lead AI initiatives with clear goals, defensible evaluation, explicit risk decisions, and exec updates that drive alignment and investment.

CPO, GM xBraintrust, Nextdoor, OpenTable

Braintrust 🧠

Nextdoor

LiquidSpace I Work from where it works.

OpenTable

BlackRock

Elena Luneva

f-CPO. Product executive (Braintrust, Nextdoor, OpenTable, LiquidSpace)

Ex-Alexa AI Principal PM

Amazon

Elsevier

Schneider Electric

Polly Allen

Ex-Alexa AI PM turned founder at AI Career Boost

Magic

AI increases what your ICs can do. It also breaks the management systems most teams rely on.Prototypes multiply. Quality becomes subjective. Risk ships quietly. You end up in every review because no one is sure what "good" looks like — or who's accountable for evals, failure modes, and rollout decisions.If you're a Director, Head of Product, or VP with multiple AI initiatives in flight: you're managing something you didn't build a system for yet. Execs keep asking "How do we know it's good?" and "What's the ROI?" — and you're the one fielding it.This two-day workshop gives you the operating system. Not theory. Not frameworks. The actual playbooks, artifacts, and rituals high-performing, AI-native teams use — built during the workshop so you leave with real deliverables, not homework.Leave with:1. A 90-day AI portfolio — clear bets, owners, and decision gates so nothing gets orphaned2. A repeatable risk review process your team can run without pulling you in3. A shared quality bar — examples, acceptance criteria, and review gates so "is this good enough?" stops being a judgment call4. Reusable operating artifacts built during the workshop — actual deliverables, not slides

Get the operating system used by high-performing AI-native teams

Install an AI-native operating cadence that keeps speed and alignment

Run AI risk reviews that drive decisions and confidence

Own and drive evaluation metrics and process, before and after launch

Implement playbooks that let your team move with you, not wait for you

Director/Head of Product: Leads AI initiatives; turns prototypes into production with clear quality, gates, and exec updates.

Eng leader (Sr Mgr/Dir/VP): Ships AI fast while managing reliability, security, and cost with consistent eval + risk reviews.

Data/ML leader (Dir/Head): Owns evals and performance; aligns product and execs with shared language and decision-ready tradeoffs.