For the first time in decades, the biggest advantage of a tech revolution isn’t reserved for the tech-savvy. AI has levelled the field. You don’t need to code to create impact. The opportunity now lies in how leaders apply it. Tools like ChatGPT, Copilot, and Gemini offer quick wins, but the real gains come when leaders push beyond the basics and build on them with a broader stack.
Used together, the AI stack sharpens decision-making, clears workflow bottlenecks, and frees people to focus on higher-value work. The advantage compounds from active use, not passive awareness. Leaders who experiment directly, get comfortable, and lead by example develop sharper instincts and unlock more opportunities.
The Great Leveller: Democratised Access
What makes this moment unique is that AI doesn't only boost productivity, it has flattened who can access these benefits. New Zealand is already among the highest per-capita users of frontier models like GPT-5 and Claude Sonnet 4.5. The barrier to entry is almost zero: no capital outlay, no specialist training, just a laptop or phone and an internet connection. As Frances Valintine CNZM (Founder of academyEX) puts it, “AI is a gift because it enables smaller businesses to do more things, to be more personalised, to digitise in new ways”.
This is the levelling effect: AI has moved from the domain of technical experts to the reach of every leader. Access is now democratised. Leaders can apply it in context and orchestrate use across teams and workflows. With AI already built into Microsoft and Google workspaces, the excuses for ignoring it wear thin. The real risk is vendor lock-in, where leaders stop at default tools and miss the explosion of AI-native platforms that don’t just speed up work but can fundamentally transform a business.
Before we dive into the tools themselves, it’s worth stepping back to see how AI can fit into everyday business life for a Leader across three core areas:
How you think (Leadership practices): using AI to test assumptions, surface blind spots, and draw insights from complex data. For example, consolidating feedback from hundreds of employees into clear themes before an offsite.
How you work (Workflows): redesigning day-to-day execution by automating routine steps, spotting anomalies, and keeping projects moving. Instead of teams spending hours on reports, AI produces them instantly so people can focus on decisions.
Where you’re going (Strategy): shaping future direction by modelling market shifts, stress-testing scenarios, and simulating strategic choices. AI might highlight the customer segments most likely to respond to a launch or forecast the costs of expanding into a new region.
The payoff isn't some abstract "ROI" figure... Measuring AI depends on what you need / where / when / how. It could manifest itself in many ways, such as:
Clearer choices backed by data, measured in forecast accuracy and the share of decisions supported by AI.
Faster execution with fewer manual delays, tracked by cycle times and hours saved.
New capacity for growth, seen in time shifted to strategic work and the number of new initiatives launched.
This is the levelling effect of AI. The edge now comes from how leaders choose to apply it. We’ve seen three dimensions of that: how you think, how you work, and where you’re going. Effective adoption requires leaders to consider carefully how and where to apply it.
From Novelty to Application
Leaders should view adoption through two lenses: strategic and tactical.
Strategically, think of AI as a pyramid of capability. Each layer supports the next: skip a step and the structure collapses. Start with simple, reliable tools, then build up to advanced systems that enable real transformation.
Layer 1: AI-augmented individual productivity. Co-pilots, assistants, and summarisation tools help people think better, draft faster, and analyse more effectively. People still do the work, but with sharper insight and less effort. It is like swapping a blunt pencil for a precision instrument.
Layer 2: AI-driven workflow re-engineering. Leaders redesign processes from the ground up, stripping out legacy steps and reshaping how teams deliver outcomes. Here, automation and augmentation intersect: AI runs repeatable processes end to end while people make higher-value judgments. It is like moving from hand tools to an assembly line, the output is the same, but streamlined and scalable.
Layer 3: AI-unlocked core capabilities. At this level, AI redefines what an organisation can do. It might mean reinventing customer service with 24/7 intelligent agents, personalising offers at scale, or redesigning finance and supply chains to cut costs and boost efficiency. It is like evolving from an assembly line to a full factory ecosystem. Suddenly you can create things that were not possible before.
Automation (tasks AI can handle end to end) vs augmentation (AI supporting human judgment) is the thread that runs through each layer of the pyramid. In Layer 1 it starts almost entirely as augmentation, in Layer 2 it becomes a mix of both, and in Layer 3 it creates entirely new possibilities through a blend of automation at scale and augmented intelligence for strategic decisions.
This framing helps leaders map where a workflow sits in the pyramid, but the next question is whether AI delivers real value in practice. The way to find out is by testing. That’s where the 1-Week Value Test comes in.
The 1-Week Value Test
The 1-Week Value Test is a simple experiment to show whether AI creates meaningful improvements in a workflow. Value can be measured in time saved, costs reduced, errors avoided, quality improved, or people freed for higher-priority work.
It runs in two steps:
1. Run the chosen workflow once in its normal state without AI to capture a baseline. This gives you a reference point for time taken, cost, error rate, or level of effort.
A leadership team might take two days to consolidate feedback into a strategy deck
A sales leader might wait four hours while their team qualifies leads manually
A CFO might need a full day’s work from staff to produce a compliance report.
2. Run the exact same workflow again, this time with AI support. The comparison between the two shows where AI adds tangible benefits.
Leadership insights can be summarised in minutes with Fireflies recording discussions and ChatGPT clustering large sets of feedback
Sales leaders see qualification time drop to one hour when AI scans the customer database for high-fit patterns
Finance leaders see compliance reporting reduced to one hour as AI extracts data and auto-formats reports with fewer errors.
Tips for making it work:
Choose the right workflow: something repeated often with clear inefficiencies.
Define value clearly: hours saved, accuracy improved, costs reduced, or capacity created.
Keep it small and time-bound: a test that can be done within a week.
Act on results: if AI delivers clear benefits, scale it. If results are mixed, refine prompts, try other tools, or narrow scope. If there’s no improvement, move on and test another workflow.
The purpose is to give leaders practical evidence of where AI makes a real difference. These small, tactical tests become the building blocks for moving up the strategic stack.
Tools Leaders Can Use
Once leaders grasp the mindset and frameworks, the next step is grounding this in tools. These are accessible to anyone, but worth explaining because “AI tools” often sounds abstract.
ChatGPT: for rapid ideation, drafting, and productivity. A leader might draft a client proposal in minutes or generate alternative phrasings for a campaign.
Gemini (Copilot mode) & Copilot: integrated into work suites, able to summarise long documents into concise board-ready points.
Perplexity: a research and synthesis engine for cross-checking trends and compiling competitive insights quickly.
NotebookLM: Google’s knowledge assistant. Teams can upload large sets of documents and instantly query them for insights.
Manus: an agent tool that executes tasks with little input. Useful for refining writing, orchestrating workflows, or delegating repetitive tasks like document summarisation.
Wispr Flow: a voice-first workflow tool for capturing meeting notes hands-free and triggering tasks automatically.
Granola and Fireflies: transcription tools that capture, transcribe, and summarise meetings, removing note-taking and ensuring follow-ups are not lost.
Whisk: a Google Labs tool for rapid visual exploration. Combine subject, scene, and style to generate draft visuals for campaigns, concepts, or board discussions in minutes.
Napkin AI: a visual storytelling tool. Paste text or ideas and turn them into clear visuals for strategy decks, board updates, or team communications.
Gamma: a presentation builder that structures ideas and generates draft decks. Often used for ideation before polishing in PowerPoint or Google Slides.
Google’s Nano Banana: advanced AI image generation and editing. Excels at precision edits with natural language prompts while keeping characters and styles consistent.
Automation and Agent Builders (Relay, Autohive, Zapier, Make, n8n): connect apps and automate workflows, for example moving new sales leads from a form into a CRM and triggering follow-ups.
From Tools to Leadership
What makes AI powerful is not any single tool but how they combine. Productivity gains are only the first step. True transformation comes from redesigning workflows and unlocking new capabilities.
Leaders do not need to master the technology itself, but they must master its application and integration. Those who embed AI into decision-making, communication, and strategy will move faster than those who leave it to specialists. Every decision now demands the same question: what does AI do to this?
Too much learning today happens in theory, in workshops, or in isolation from real work. The result is knowledge that rarely sticks or changes behaviour. The shift comes when leaders test AI directly against live challenges such as board reporting, scenario planning, market analysis, or workflow bottlenecks.
This is the principle behind programmes such as the Master of Technological Futures at academyEX. Instead of separating study from work, leaders test tools on real problems, simulate decisions, and embed new ways of working as they learn. Workflows are classified as automation or augmentation, short value tests are run, and tools like ChatGPT, Perplexity, and NotebookLM are applied in context.
Confidence builds through safe experimentation, no coding required, while values guide choices about what to scale and how to govern it. Done in cohorts, this approach creates shared intelligence across industries and helps leaders move faster together than they could alone.
This article was co-created in partnership with academyEX, sponsor of The AI Corner newsletter and podcast.
Written by Mike ✌
Passionate about all things AI, emerging tech and start-ups, Mike is the Founder of The AI Corner.
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