Capturing value from AI
AI creates two types of opportunity for your organisation. The first is low-hanging fruit, quick wins you can capture by giving your existing teams AI capabilities. The second is new business models, ways to fundamentally rethink how you deliver value to customers. This page will show you how to:Find quick wins
Identify low-hanging fruit across your teams
Scale AI adoption
Move from individual use to team-wide impact
Manage risks
Understand the five governance challenges
Build policy
Create frameworks for responsible AI use
Low-hanging fruit: Self-serve automation
The fastest path to AI value is simple. Give each staff member access to the three AI hires we covered earlier: Assistant, Thinker, and Creator. This unlocks self-serve automation across your organisation.What self-serve means
Staff members solve their own problems with AI. No IT tickets, no waiting for developers. They draft, analyse, and create using AI tools directly.This is where most organisations see immediate ROI.
Why it works
The people closest to the work know what needs automating. When you give them AI tools, they find uses you never anticipated.Adoption spreads organically as people share wins.
Quick wins by function
Every team has tasks where AI delivers immediate value. Here are the common patterns we see across organisations.Sales
Drafting personalised outreach, summarising call notes, preparing meeting briefs, and researching prospects. AI handles the preparation so salespeople focus on relationships.
Marketing
Creating content variations, analysing campaign performance, drafting social posts, and generating ideas. AI accelerates the creative process without replacing human judgment.
Engineering
Writing documentation, debugging code, explaining complex systems, and drafting technical specifications. AI acts as a knowledgeable pair programmer.
Product Development
Synthesising user feedback, drafting requirements, competitive analysis, and brainstorming features. AI helps product teams move faster from insight to action.
Strategy
Market research, scenario planning, summarising reports, and preparing board materials. AI handles the heavy lifting of analysis and synthesis.
Operations
Process documentation, training materials, policy drafts, and compliance checklists. AI captures institutional knowledge and keeps it current.
The Innovator’s Dilemma
Beyond quick wins lies a bigger question. AI is creating new business models that could disrupt your industry, or let you disrupt others.The challenge
Successful companies struggle to adopt disruptive technologies. Your existing business model is working, so why change?But competitors without your legacy can build AI-native offerings from scratch.
The opportunity
The same technology that threatens your current model can power your next one. Companies that move early capture the new S-curve of growth.The question is whether you disrupt yourself or wait to be disrupted.
New business model patterns
AI enables business models that were not possible before. Here are the patterns we see emerging.Hyper-personalisation at scale
AI makes it economical to tailor products, services, and communications to each individual customer. What once required expensive human attention can now scale infinitely.Example: Insurance companies offering truly personalised policies based on individual risk profiles rather than broad demographics.
Expert services democratised
AI brings expert-level advice to markets that could never afford it before. Legal guidance, financial planning, and medical triage become accessible to everyone.Example: Small businesses accessing the same strategic analysis that was once reserved for large enterprises.
Automated operations
Back-office functions that required large teams can run with minimal human oversight. This changes the economics of entire industries.Example: Customer support that handles 90% of inquiries automatically, with humans focusing only on complex cases.
Intelligence as a service
Companies can package their domain expertise into AI-powered products. Your knowledge becomes a scalable asset rather than a constraint.Example: Consulting firms offering AI tools that deliver their methodology to clients continuously, not just during engagements.
Not every organisation needs to pursue new business models. For many, capturing the low-hanging fruit delivers enough value. Know which game you are playing.
Responsible AI: The five risks
AI adoption creates real risks that need active management. These are the five challenges we see most often in organisations.1. Shadow AI
1. Shadow AI
Staff members using unapproved AI tools without IT knowledge or governance. They sign up for free accounts, paste company data into public tools, and create security blind spots.The problem: You cannot secure what you do not know exists. Shadow AI bypasses your data policies and creates compliance exposure.The solution: Provide approved tools that meet people’s needs. If your official AI tools are worse than free alternatives, people will use the free ones.
2. Unproductive Use (AI Slop)
2. Unproductive Use (AI Slop)
AI that generates volume without value. Long, generic outputs that waste more time to read than they save to create. Content that sounds professional but says nothing.The problem: AI makes it easy to produce mediocre work at scale. This creates noise, erodes quality standards, and frustrates recipients.The solution: Train people to use AI as a starting point, not a finish line. Emphasise editing, judgment, and quality over speed and volume.
3. Underestimating Change
3. Underestimating Change
Treating AI as a minor productivity tool rather than a fundamental shift in how work gets done. Planning for incremental improvement when the change is exponential.The problem: Organisations that underestimate AI get disrupted by those that do not. The gap between AI leaders and laggards is widening.The solution: Track AI capabilities actively. What was impossible last year is routine this year. Build AI literacy across leadership.
4. Not Investing in the Future
4. Not Investing in the Future
Focusing only on today’s quick wins without building capability for tomorrow. Failing to develop AI skills, infrastructure, and culture.The problem: Quick wins plateau. Organisations that do not invest in deeper AI capability get stuck while competitors advance.The solution: Balance immediate value capture with longer-term capability building. Treat AI adoption as ongoing development, not a one-time project.
5. Oversharing
5. Oversharing
Putting sensitive data into AI systems without understanding where it goes. Customer information, strategic plans, and proprietary methods flowing into systems you do not control.The problem: Data shared with AI tools may be used to train future models, exposed to other users, or stored in ways that violate regulations.The solution: Know your tools’ data policies. Use enterprise versions with proper data handling. Train staff on what should and should not go into AI systems.
Building your AI governance framework
Good governance does not slow AI adoption. It accelerates it by giving people confidence to experiment within clear boundaries.AI Acceptable Use Policy
Internal document for staff. Defines what AI tools are approved, what data can be shared, and what uses are prohibited.This is your foundation. Without it, you have Shadow AI.
AI Principles
External-facing statement of how your organisation uses AI responsibly. Covers transparency, human oversight, and ethical boundaries.This builds trust with customers and stakeholders.
Experimental AI Policy
Framework for testing new AI use cases safely. Defines how teams can experiment, what approvals are needed, and how to scale successful pilots.This enables innovation without chaos.
What to include in your Acceptable Use Policy
Your AI Acceptable Use Policy should address these key areas.Approved tools
List which AI tools staff can use. Include both general-purpose tools like ChatGPT or Copilot and any specialised tools for your industry.Be specific about which versions are approved. Free tiers often have different data policies than enterprise versions.
Data classification
Define what data can go into AI systems. Public information is usually fine. Customer PII, financial data, and trade secrets need stricter controls.Match your existing data classification scheme where possible.
Human oversight requirements
Specify where human review is mandatory. Customer-facing content, legal documents, and financial decisions typically need human sign-off.Be clear about who is accountable for AI-assisted work.
Prohibited uses
Name specific uses that are not allowed. This might include generating content that impersonates individuals, making autonomous decisions about people, or bypassing approval processes.Clear prohibitions prevent problems before they occur.
Your policy should evolve. AI capabilities change rapidly, and your governance needs to keep pace. Plan for quarterly reviews at minimum.
Making AI secure, local, and private
Enterprise AI tools offer security features that consumer versions do not. Understanding these helps you choose the right tools for sensitive work.Data residency
Where your data is stored and processed. Enterprise tools often let you specify Australian data centres, which matters for regulatory compliance.Consumer tools typically process data wherever is cheapest.
Training opt-out
Whether your data is used to train future AI models. Enterprise agreements typically guarantee your data stays private and is not used for training.Consumer tools often use your data by default.
Audit logging
Records of who used AI tools, when, and for what purpose. Essential for compliance, incident response, and understanding adoption patterns.Consumer tools rarely offer meaningful audit trails.
Access controls
Integration with your identity management. Enterprise tools connect to your existing SSO and can enforce role-based access to different AI capabilities.Consumer tools cannot distinguish between your employees and the general public.
Quick checkpoint (you’re done when…)
Quick wins
You can identify low-hanging fruit in your teams
Business models
You understand how AI enables new value creation
Five risks
You can name the governance challenges to manage
Policy framework
You know what belongs in an AI Acceptable Use Policy
Ready to practice?
Complete the mini challenges to apply these concepts to your organisation