Leadership · Half day or 1 day
AI for Business Leaders
A concise course for senior leaders and directors: where AI creates actual value, where it does not, and how to judge investment, risk and organisational readiness.
Half day or 1 day · Leadership · Leadership · Senior leaders, directors and business decision-makers
Overview
Business leaders are saturated with claims about AI and short of a method for judging them. Some proposals are genuine operational improvements. Others are vendor hype, impressive-looking automation that does not change the work, or work that would be cheaper as conventional software. This course gives you a way to tell the difference.
The course covers current capabilities and limits, automation versus augmentation, use-case identification, value, operational impact, risk, governance, privacy, cost and organisational readiness. Vendor claims are treated as claims: they have to survive contact with data quality, process reality and the cost of being wrong.
The teaching is commercial and operational. You work through a structured way to find, score and sequence opportunities, including workforce implications and the difference between a tool rollout and a change to how work is done.
You leave able to ask better questions, reject weak proposals quickly, and sponsor a small number of uses that can actually be delivered.
Audience
Senior leaders, directors and business decision-makers who sponsor AI work, approve spend, or have to set organisational direction without needing to design the system themselves.
Prerequisites
No technical background is required. Bring a view of your organisation’s processes, constraints and current AI pressure, even if that view is incomplete.
Duration
Half day or 1 day
The half-day version concentrates on judgement and prioritisation. The full day adds deeper use-case work and a first roadmap.
Learning outcomes
- 01
Describe current AI capabilities and limits in operational terms rather than product language.
- 02
Distinguish automation from augmentation, and identify which is actually being proposed.
- 03
Evaluate vendor and internal claims against data, process, risk and the cost of error.
- 04
Compare build versus buy at a business level, including lock-in, operating cost and dependency.
- 05
Judge organisational readiness: skills, data, process ownership and governance.
- 06
Prioritise a small set of use cases with a clear view of value, risk and complexity.
- 07
Commission next steps that a technical team can actually execute.
Outline
- 01
What the technology can do in a business
- A grounded account of current capabilities: language, documents, code, images, and tool use.
- Limits that affect operations: hallucination, inconsistency, latency, and the need for a human owner.
- Automation versus augmentation, and why “replace the process” is often the wrong first move.
- Exercise: rewrite a hyped proposal as an operational description.
- 02
Value, risk and the organisation
- Where value actually appears: time, quality, coverage, cycle time, and new work that was previously uneconomic.
- Operational impact: exceptions, handover, accountability and the work that remains after a model is introduced.
- Risk, privacy, governance and the questions a board or executive team should insist on.
- Workforce implications: skills, roles, and the difference between a copilot and a headcount plan.
- Exercise: identify who is accountable if a generated output is wrong.
- 03
Investment choices
- Vendor claims, proofs of concept, and the difference between a demo and a production path.
- Build versus buy, including the cost of integrating AI platforms into existing systems.
- Cost: licences, usage, integration, evaluation, change and ongoing operations.
- Organisational readiness: data, process owners, approved tools and the capacity to change work.
- 04
Use-case prioritisation and a first roadmap
- A scoring method: value, feasibility, data, risk, complexity and reversibility.
- Selecting a small number of uses rather than a catalogue of ideas.
- What “good” looks like after 90 days: owners, measures, and a decision to continue or stop.
- Exercise: rank a set of opportunities and agree the next commissioned piece of work.
Practical work
You work through case examples and, where possible, your own pipeline of ideas. You score opportunities, name owners and risks, and leave with a ranked shortlist rather than a brainstorm. The half-day version uses a tighter set of examples; the full day includes a more complete roadmap exercise.
Takeaways
- A decision lens for capability, value, risk and readiness
- A use-case scoring template
- Questions to put to vendors and internal teams
- A ranked shortlist and recommended next steps
Delivery
Cognitecta delivers private corporate training, on-site or as remote live training. Courses can be run as published, or adapted to your organisation’s stack, domain and experience level.
Instructor
Nicholas Johnson, AI architect and software engineer. He has a degree in Artificial Intelligence and around twenty years of professional technology training, including hundreds of courses for engineering teams and large organisations. About.
Related courses
1 day · Leadership
AI for Technical Leaders
A decision-making course for CTOs, engineering managers, architects and senior technical product leaders. How to choose architectures, vendors and investments that will still make sense in production.