From chaos to clarity: How to get AI delivering value

AI is disrupting both the digital and the Australian business landscape. On the surface, there are unlimited use cases and opportunities to incorporate AI into business processes, and that is continuing to drive adoption. But many organisations now face challenges that threaten to derail the enthusiasm that's powered their adoption journeys so far. 

A key challenge is that AI is evolving at a pace that exceeds the ability of most organisations to keep up. 

Every day there are new tools, updates to existing tools, new AI models and frameworks, and new technical terms to contend with. Organisations start building internal capability in one ecosystem or type of AI, only to see the frontier shift and the goalposts move within a few short months. 

An adopter of AI in the past year will have seen the leading edge of AI shift from 'copilot' type digital assistants to autonomous AI agents, to multi-agent orchestration and now to model context protocol (enabling AI to connect to external systems). There are a lot of factors and parameters to be considered when adopting any one of these - and this is the chaotic environment that many AI adopters now find themselves in. 

As the complexity of the AI landscape grows, so does confusion. There are a lot of questions without clear answers. Will this new flavour of AI fit my particular use case? Will adopting it and building capability to work with it create enough value to justify the investment? Can data, security and other risks be appropriately managed and addressed?

Organisations are increasingly coming to terms with the chaos of participating in the AI space. What they need is a way to regain clarity and control over their AI adoption journeys.

This is an opportunity to pause, rethink and put some structure around their AI adoption: setting a clear direction and roadmap for how they can continue to incorporate AI into business processes today and scale AI adoption to create maximum value into the future.

The role of a roadmap

Discovery workshops are a good starting point to cut through the chaos of current-day AI adoption. Through this process, the pain points that the organisation is trying to address with AI will become apparent. It's important to understand the current landscape, in terms of people, process and technology: what AI tools it is using today, for what purpose, and the extent of internal capability to work with that particular technology. 

Once the current state and landscape is understood, the organisation's goals and intended future state should be discussed and documented as well. The ability of AI to help deliver those goals can then be canvassed. Asking thought-provoking questions can help the organisation determine whether they really need AI, where exactly AI is suitable as a solution and how it can be integrated, and which AI models are likely to be the best fit and/or the most transformative. 

All of this activity is then reflected in a roadmap that offers a structured approach to adoption. 

An effective roadmap has several key features or roles. 

First, it represents a measured approach to AI adoption. Especially early on, this will typically take the form of 'baby steps', starting small, realising value, and then progressively building on that to drive and embed value-accretive AI adoption deeper into an organisation's operations.

Initially, the AI is likely to operate in a 'copilot' rather than 'autopilot' mode, with the AI being injected into a process to assist with efficient transaction delivery. Copilot-style AI also emphasises the joint role of artificial and human intelligence in executing business processes, ensuring human intelligence sits on the top of the artificial intelligence as a check-and-balance that the AI is behaving as it's expected to. 

The roadmap is not just about setting the pace for technology adoption; it shows the reciprocal investment in staff training and capability that is needed as time goes on, and as AI becomes more embedded. This ensures that internal capability is developed and maintained, and value can be realised from progressively more challenging uses of AI technology.

Another role of the roadmap is that it helps organisations to determine whether or how to adopt advances in AI technology. In the AI space, there is always something new, and the pace of change can make it hard to work out where to start and what to use. Additionally, for every evolution of AI, such as from generative to agentic, an organisation may be tempted to drop what they've been doing and move to the newer technology, which can stifle value creation. 

Having a roadmap ensures that organisations can stay the course and play a long, measured game on AI. All technology improves over time. Many early adopters of a technology will find that down the track, a more efficient and cost-effective way of doing what they've already achieved emerges, and it makes sense to adopt that to be more operationally efficient. We're already seeing this happen in the multi-agent orchestration space, with new cloud-based services acting as an efficient replacement for earlier open-source tools. 

Switching out modular AI components or services does not mean that organisations are discarding what they've done before; it's simply an opportunity to make an AI-augmented process even more efficient by modernising parts of it. The roadmap ensures that the business case for modernisation is clear and consistent with the organisation's set direction and goals.

In conclusion, a structured approach to AI adoption is necessary to shield organisations from the chaos of the rapidly evolving AI space and bring clarity and value to AI use and deployments.

In a world where AI evolves faster than strategy, a roadmap isn't just helpful, it's essential. It's the difference between chasing trends and creating lasting value.