Marketing Smarter with AI

Marketing leaders are increasingly embracing artificial intelligence not as a replacement for people, but as a powerful tool to amplify productivity, improve decision-making and unlock greater strategic value.

That was the key takeaway from a recent breakfast panel, where senior leaders working at the forefront of AI shared practical insights into how the technology is transforming marketing, sales and customer engagement. Rather than focusing on future possibilities, the discussion centred on real-world implementation, lessons learned and what organisations should prioritise next.

The strongest message of the morning was clear: organisations achieving the greatest success with AI are pairing hands-on experimentation with strong executive sponsorship, shared organisational capability and practical governance.

The Panel

The discussion featured four industry leaders with complementary perspectives on AI adoption:

Nik Sproal, Head of AI and Intelligence at The Garden, brings more than 25 years of property marketing experience and now leads the development of AI-first product suites.
Laura Craig, Founder and Managing Director of Vivo Marketing, has built an AI-powered outsourced marketing business supporting more than 350 brands, delivering approximately five times conventional output through purpose-built AI skills and templates.
Tony Petruzalek, Head of Marketing at Oreana, has embedded AI into the daily operations of a lean marketing team, rebuilding internal systems and launching multiple in-house platforms focused on practical, rapid delivery.
Candy Hertz, broadcaster and event MC, moderated the discussion.

Together, the panel offered three distinct viewpoints-enterprise AI development, AI-enabled marketing services and client-side implementation within the property sector-demonstrating how organisations can combine technology, people and processes to create measurable value.

AI is Delivering Immediate Business Results

One of the clearest themes throughout the discussion was that AI is already producing tangible outcomes.

Rather than simply reducing workloads, organisations are building bespoke project management tools, developing simple design capabilities and creating internal marketing platforms that reduce software costs while improving operational efficiency.

Importantly, AI adoption has not resulted in widespread job losses. Instead, businesses are expanding their capabilities.

The Garden, for example, has added five to six specialist roles, including engineers and a part-time Chief Technology Officer, to support growing demand and platform development.

Across the panel, the pattern was consistent: automate operational tasks first, then invest in technical expertise to scale AI beyond tactical wins.

Shared Skills Create Scalable Success

Another recurring message was the importance of centralising AI knowledge.

Rather than allowing individuals to work with isolated prompts and personal experiments, organisations are achieving greater success by creating shared libraries of validated prompts, workflows and reusable templates.

Successful AI use becomes repeatable when teams document tasks, map workflows and convert proven approaches into reusable organisational assets.

Memory-enabled AI tools further strengthen these systems by improving outputs over time, reducing duplicated effort and creating consistent quality across teams.

Better Context Produces Better AI

The panel argued that successful AI depends less on clever prompts and more on providing meaningful context.

Detailed information about brand voice, customer needs, emotional positioning, functional benefits and project constraints consistently produces stronger outputs than one-off requests.

Speakers recommended a layered approach:

Strategic brand context first.
Workflow design second.
Templates third.
Human refinement as the final step.

They also cautioned organisations against becoming overly reliant on any single AI platform.

With technology evolving rapidly, investing in transferable skills and repeatable workflows will ultimately provide greater long-term value than building processes around one vendor.

Quality Still Requires Human Oversight

While AI can dramatically increase output, quality assurance remains essential.

Panellists explained that high-performing organisations continuously collect customer feedback, internal reviews and performance data before feeding those learnings back into prompts and workflows.

Simple classification systems also help determine which tasks can be automated and which require human review.

Practical governance measures-including token usage limits, approved AI models and monitoring heavy users-help maintain quality while managing costs.

Governance Should Enable Innovation

Governance was widely viewed as a competitive advantage rather than an obstacle.

The panel recommended establishing internal AI steering committees made up of senior leaders and early adopters to oversee platform selection, approved AI skills, data residency requirements and spending controls.

Finding the right balance is critical.

Overly restrictive governance risks slowing innovation, while insufficient oversight increases exposure to hallucinations, customer trust issues, escalating costs and ethical concerns surrounding voice and image cloning.

Well-designed governance enables safe experimentation through sandbox environments while maintaining clear organisational guardrails.

Human Judgement Remains the Difference

Despite rapid advances in AI capability, every panellist agreed that judgement, creativity and strategic thinking remain uniquely human.

Rather than replacing experienced marketers, AI is enhancing senior capability by removing repetitive work and creating more time for higher-value thinking.

The discussion also highlighted a growing challenge for junior marketers.

Without proper training, AI can reinforce shallow thinking and limit skill development.

The recommended solution is deliberate investment in coaching-teaching junior team members how to use AI effectively while developing strategic thinking, critical evaluation and customer empathy.

A small group of internal experts should build and validate AI skills, with proven templates then distributed across wider teams for consistent execution.

Knowing Where AI Works-and Where It Doesn't

The panel identified several areas where AI is already proving highly effective, including:

Database re-engagement campaigns.
Large-scale advertising variations.
Templated marketing deliverables.
Image and render generation.
Meeting notes.
Repeatable content experimentation.

However, speakers were equally clear about where caution is required.

Poor use cases include large-scale human-facing phone interactions, complex live customer service without robust safeguards and unsupervised AI agents operating in sensitive environments.

Questions around voice cloning, image replication, undisclosed AI interactions and hallucinated content require clear organisational policies and transparent communication with customers.

The overarching principle was simple: deploy AI where work is structured, measurable and repeatable, while keeping people at the centre of experiences requiring trust, empathy and authenticity.

Looking Ahead

The panel outlined three priorities for organisations at different stages of AI maturity.

Short term: Centralise AI skills, standardise workflows, establish governance through steering committees and spending controls, and free senior leaders to focus on strategic work.

Medium term: Invest in technical capability, develop internal AI platforms and move beyond isolated tool experimentation towards productised AI solutions.

Long term: Preserve human judgement as the true competitive advantage while using AI to increase productivity, accelerate experimentation and protect brand authenticity.