Turn AI activity into business results.

An AI strategy is a plan for how a business will use AI to produce specific business outcomes, and it stands on four pillars. Vision and goals: what the business should look like in three to five years, and which outcome it is actually chasing, whether that is cost, productivity, revenue or risk. Initiatives and data: which use cases to back, and what proprietary data the business holds that a competitor cannot get hold of. Technology and infrastructure: which platforms to bet on, and what the data underneath them has to support. People, organisation and governance: how the work gets organised, who builds the capability, and how AI use stays responsible. In the Kellogg framework the fourth pillar is argued to be the most important of the four, because AI initiatives are sociotechnical. The technology and the people have to move together, or the initiative stalls no matter how good the tooling is.

Three questions in order. Where does the business genuinely stand, which is a maturity assessment across strategy and leadership, data and infrastructure, how solutions get built and deployed, talent, and governance. Where should it play, which is a map of the business data across four quadrants, customer, operational, administrative and risk, scored for how specific that data is to the business and how much better it is than what competitors hold. Then what should it do first, which ranks the opportunities that map onto the richest data. The strategy also has to name the failure patterns to avoid. The Kellogg framework calls them pathologies, and there are four: plenty of data with no governance over it, a solution hunting for a problem, pilots that never become production, and leadership that agrees AI matters without resourcing it.

OmniPulse is an AI strategy advisory business based in Sydney, Australia. We build AI strategies for mid-sized businesses across Australia and New Zealand, using the framework from the AI Strategies for Business Transformation program at Kellogg, Northwestern University. We are independent of technology vendors and take no commissions.

Mid-sized private businesses in Australia and New Zealand, typically between 50 and 300 staff. The businesses we work with usually have AI in use somewhere already, without a strategy behind it. Common sectors include construction, manufacturing, engineering, healthcare groups, wholesale, distribution, transport and logistics.

Shadow AI is any AI tool doing work inside a business that the business cannot see, measure or manage. It usually arrives through personal accounts rather than through procurement, which is why it never appears on an IT asset register. Most businesses have it, and most cannot say which tools are in use or who is using them.

Most businesses already are, whether or not anyone decided to. The more useful question is which parts of the business are worth pointing AI at, and what has to be fixed first. AI multiplies what is already there, so pointing it at a process built on scattered data and manual steps usually creates more correction work than it saves.

It depends on the account. Consumer accounts and business accounts handle data differently, under terms that change. The practical risk for a business is not the tool, it is that nobody has decided which tools staff may use or what information may go into them. That is a governance question rather than a technology question.

An AI readiness assessment scores how prepared a business is to get a result from AI. It reads leadership alignment, whether a specific problem has been named for AI to solve, the state of the data, whether core systems can share it, how the organisation adopts new tools, budget, and governance. The output is a maturity read rather than a technology recommendation, because the same tool succeeds in one business and fails in another depending on what sits underneath it. OmniPulse runs a 14-question version that takes about four minutes and returns a report by email.

One document, in five parts. A readiness assessment scoring where the business sits across five areas, with the specific gaps that would sink an AI project if they were ignored. A ranked shortlist of opportunities, mapped to the data behind each one, with the reasoning for the order. A blueprint for each initiative worth doing, covering what it is, how it works, who does what, and where people stay in the loop. A staged roadmap with the markers that say the business is ready for the next stage. And the governance layer: what data AI may touch, who owns it, what good looks like, set against the Privacy Act and the Voluntary AI Safety Standard. It ends with a 30-60-90 day action plan.

Four weeks, across five phases. Phase one assesses what the business can actually support. Phase two works out where AI is worth doing and in what order. Phase three designs the blueprints, with the client in the room. Phase four sets the sequence over time. Phase five covers the safety, governance and change-management standards the whole plan is held to. You finish with a strategy your own team can act on without us.

Not by default, and that choice stays with the client. We produce the strategy and select the products in it. From there the client can run the work with their own team, use a partner we refer them to, or have us deliver it white-label through a build partner. We identify what could realistically be built and proven inside the first 90 days. We do not build it, and we do not put a fixed price on the build, because that gets scoped by whoever implements.

Sydney, Australia. The office is at Level 3, 478 George Street, Sydney NSW 2000. We work with businesses across Australia and New Zealand.