Unlocking AI’s True Business Potential

The pace of AI innovation in Australia and New Zealand can be overwhelming. Less than two years after generative AI went mainstream, an even more significant game-changer has arrived in the form of agentic AI. 

Many organisations don’t yet have a clear AI strategy, let alone one for agentic AI. Businesses need strategic frameworks to set ambitions, prioritise investments, and assess competitive threats. It’s time to shift from the ‘art of the possible’ to the ‘art of the practical’. Understanding  the role AI should play in your organisation’s operating model is critical for choosing when to invest, and assessing where you will benefit more from generative AI solutions or intuitive, autonomous tools like Agentforce.

The key questions to ask are: which of your current systems is trapping the most value; could an AI investment allow you to break through that constraint; and, given your infrastructure, are you in a position to act on this opportunity? This is all about trapped value, differentiation, and speed.

Renowned tech sector consultant Geoffrey Moore has championed building value-based coalitions between visionaries and pragmatists within an organisation, to truly drive material impact with new technologies, and this is especially true for AI. In this article, we will give you a snapshot of a Whitepaper collaboration by Geoffrey Moore with Salesforce, combining his renowned methodology and our AI and CRM expertise to offer an actionable framework for developing a robust enterprise AI strategy.

Moving Beyond the AI Hype

AI is not a silver bullet, but used wisely, it can change the nature of the problems a business faces, and allow leaders to address problems that were simply unaddressable in the past. Signs of trapped value – where capacity or system complexity limit the ability of your organisation to meet expectations – will help you prioritise where to apply these innovations, eliminate roadblocks and unlock that value at scale.

You don’t need to start big: apply “small brain” solutions that accelerate “big brain” innovation. Create focused, manageable AI solutions for specific needs rather than trying to build an all-encompassing “Swiss Army knife” AI. Designing an agent that requires comprehensive knowledge of every aspect of your business is a recipe for over-complexity and failure. Instead, scope the agent’s role to a manageable size (creating a “small brain”), allowing it to be effective within a constrained but  well-maintained data environment that business users can help to curate and steward. This frees up precious data science resources to focus on enabling disruptive, differentiating reasoning power (aka “big brain” innovation) to drive better-informed decisions and more power agents.

The Art of Differentiation and Combination

Once you’ve identified the job to be done, the next step is choosing and combining the right AI tools. Different types of AI have unique impacts and requirements, so aligning its application with specific business and industry needs is essential. The three main types of AI are:

Predictive AI uses data and machine learning to identify patterns and anticipate future patterns. Predictive AI has delivered decades of success providing actionable insights, albeit limited to local systems where the data is available. 

Generative AI analyses data to generate new content– many people would be familiar with generative AI in some form, as it is primarily deployed in human-in-the-loop applications and the quality of its answers still often requires review. 

Agentic AI refers to a series of AI systems and models that follow directions, process information, take actions, learn from errors, and continuously improve performance– without constant human prompting. 

This is why Agentforce is an absolute game-changer as it orchestrates workflows and augments human capacity to unlock that trapped value. 

By aligning the most appropriate AI capabilities with your organisation’s strengths and infrastructure, you can unlock new operating models and deliver value where it matters most.

Speed, Competition, and Adaptability

How fast do you need to move to stay ahead of your competition? The answer to this question depends on how far the disruptive innovation has progressed through the technology adoption life cycle in your geography and industry. 

When considering new technology, early adopters are driven by the promise of disruption, while late adopters usually want their tech widespread and risk-free. But Geoffrey Moore teaches us that every market reaches a moment when the game changes. This is the point he calls “crossing the chasm,” when a technology reaches critical mass in a sector and finds its “killer app” — the must-have solution to a problem so critical that adoption accelerates at speed.Timing your adoption of AI correctly depends on sensing that moment in your industry when predictive, generative or agentic AI unlocks a business problem you’ve had for a generation. Miss that window, and every competitor will leave you behind. So the right move isn’t necessarily to act the fastest, but to sense when AI is set to unlock trapped value — and act decisively.

Conclusion: The Roadmap to Success

Trapped value, differentiation, and speed are just parts of the AI journey. The real question is how to turn these insights into a practical, actionable strategy.

The white paper, Building an AI-Powered Business in 2025: Six Key Questions for Executives, dives deeper into these questions and themes. Download your free copy today and set your business up for AI success in 2025.