To win at AI, close the gap between strategy and execution

It used to be that the big eat the small. But as we enter an extraordinary moment of technological acceleration it's become the fast eat the slow. Every sector faces a unique set of challenges. But all share the same imperative: to transform boldly now. Across all industries studied in the Guide to Next 2026 Report, leaders overwhelmingly consider AI critical, yet execution is lagging. 

Despite substantial investment, many organisations are only seeing localised, staff-level improvements rather than enterprise-wide productivity gains needed to shift growth trajectories or influence share price. When companies take a federated approach instead of embedding AI into the systems, workflows and management decisions, their initiatives become a patchwork of stand-alone tools, disconnected from core operations and unable to fully realise the potential of their technology spend.

Organisations are also grappling with broader obstacles. Talent shortages are an issue for around 40% of organisations globally, with half struggling with privacy and compliance concerns. Legacy systems, insufficient data and cultural issues are also impeding progress.

Challenges are not unique to any industry

These barriers are underlining the need for enterprise grade solutions that are ready to scale. A conservative risk-averse culture in many organisations that is still focused on narrow proof-of-concept projects is also hindering progress.

A cautious AI pilot approach, coupled with weak data foundations, is leaving Australian organisations vulnerable to competition with innovation cycles often exceeding twelve months. Half of Australian retailers (50%) cite integration challenges and risk and compliance concerns as barriers to scaling. They also lag global peers when it comes to AI as a strategic priority with only 23% having 'significant, ongoing investment' in AI versus 50% in the US and 47% in the UK.

For Australian telcos, the main barriers to broader AI adoption are integration with legacy systems (47%) and regulatory/privacy risk (47%). Meanwhile, half of Australian media companies (50%) are struggling with regulatory and privacy issues when it comes to next-gen ad format.

To address all these challenges, four key steps are vital:

1. Close the strategy-execution gap

A key issue is the gap between knowing and doing. There should be clear owners for new strategic priorities, whether AI adoption, data modernisation or new service lines. These leaders must rigorously track execution milestones, and make it fast. For example, if AI is deemed critical, set targets for scaling pilots to production within quarters not years. Treat AI optimisation (AX) as a core marketing function and make AI visibility a KPI. Tie management team goals and incentives to achieving these targets.

Scale what works. If an AI model is improving fulfilment or personalisation in one banner or region, invest to roll it out widely. 

2. Invest in people and culture

Technology without talent and agility is doomed to fail. Upskilling your workforce in data science, as well as investing in new data science talent, is vital, given the need to strengthen data integration in many organisations.

A more start-up like mindset, where speed is valued as much as reliability, will help speed up pilot cycles and encourage experimentation. This might mean restructuring teams, simplifying governance for faster decisions and celebrating lessons from failures.

3. Put trust at the core

Customer trust is paramount in the age of AI. Organisations must champion privacy and ethics initiatives, not merely for compliance but as strategic differentiators. Offer privacy-centric features and ensure AI decisions such as recommendations are explainable. Platforms such as Amazon and YouTube enable users to ask: "why was I recommended this?" as well as enabling users to further refine their recommendations by deselecting topics or product categories they may not be interested in.

Implement an AI governance board that can approve and review AI use cases for ethical and brand alignment. Consider using progressive consent UX to give users control and insight into data use in exchange for personalisation.

4. Balance partnerships with independence

Leveraging ecosystems and partnerships can be a quick route to fill capability gaps.  Our research identified heavy pilot funding from third parties. But while pursuing alliances can be advantageous, it's important to retain strategic control. Partnerships should include knowledge transfer or bolster proprietary assets, such as co-developing platforms rather than completely outsourcing. Otherwise, dependency can undermine long-term competitiveness.

5. Focus on data

Data quality and integration issues are pervasive across industries both in Australia and globally. Data isn't AI-ready and technical data debt is significantly delaying CX innovation. Without investing in data foundations, even the best tech strategies will falter. Make IT modernisation and data integration a top strategic program (with Board oversight). This could mean building out first party data, consolidating customer data onto cloud platforms, or breaking down internal silos and investing in AI-driven analytics.

With many challenges shared across all sectors, leaders should maintain a cross-industry lens. Solutions in one sector can inform another. A telecom executive could learn from a retailer's personalisation playbook, while a media company's approach to data consent could inform a consumer goods CEO. Having a cross-pollination mindset will help anticipate trends and avoid insular thinking.

Incremental change won't close the transformation gap between strategy and execution. "Status quo strategies" are fading in effectiveness. From wider and deeper AI adoption to cultural agility, leaders must invest in scalable AI rather than narrow solutions, and make the bold decision to accelerate the implementation cycle.