In 2023, generative AI (GenAI) went mainstream. Companies of all sizes experimented with tools like ChatGPT, Midjourney, and GitHub Copilot. Workshops were held, hackathons flourished, and proofs-of-concept multiplied. But from 2024 and beyond, one thing is clear: the age of playtime is over.
The question has changed. It's no longer 'Should we be using GenAI?' but 'Why aren't we executing with it yet?'
For B2B organisations, the distribution gap is now less about access to technology and more about the will, skill, and structure to execute at scale.
Why execution is now mandated
There are three converging forces mandating a shift from AI experimentation to execution:
Customer expectation
Your buyers are more informed, demanding, and used to hyper-personalised, immediate experiences. GenAI has raised the bar.
Competitive pressure
Your competitors already integrate GenAI into their go-to-market strategies, operations, and product development.
Leadership and investor pressure
Some boards, investors, and executive teams now expect tangible returns from AI investments.
The bottom line: companies are no longer applauded for dabbling. They're evaluated by delivery.
From labs to operations: The four shifts that matter
One. From R&D to business units
AI isn't just an innovation department's playground anymore. Marketing, sales, HR, and customer success must integrate GenAI into their operations.
Two. From pilots to pipelines
Instead of endless pilot projects, companies need to plug GenAI into production workflows. AI must now be part of the revenue engine.
Three. From tools to teams
Organisations must invest in upskilling and reskilling. GenAI tools are only as good as the teams using them strategically.
Four. From 'Can we?' to 'How fast?'
The discussion is no longer about technological feasibility. It's about speed, scalability, and impact.
Execution in action
Marketing
B2B marketers use GenAI to generate and localise content at scale, optimise SEO strategies, and personalise newsletters and landing pages based on CRM segments.
Sales
AI enhances outreach by generating tailored messages. Sales reps are spending less time researching and more time closing.
Customer Success
GenAI helps analyse ticket data to preempt issues, generate support articles, and suggest upsell opportunities.
What's holding teams back and how to unblock progress
Common blockers
- Siloed data or tools.
- The ownership of AI within the organisation is unclear.
- There is a growing fear that automation will replace roles.
- There is a lack of clear KPIs for GenAI initiatives.
How to unblock
- Start with a cross-functional AI task force.
- Define high-impact use cases and build small, scalable workflows.
- Provide AI training for all relevant roles.
- Celebrate early wins and communicate impact.
To future-proof your AI execution approach to sustain momentum, companies need to scale with intention:
- Create AI governance frameworks to ensure ethical, secure, and compliant usage.
- Track ROI and business impact using clear metrics (e.g., content velocity, lead conversion lift).
- Keep humans in the loop, using AI to augment—not replace—critical thinking and creativity.
- Design for adaptability, recognising that AI models and platforms will continue to evolve.
Start delivering or start falling behind.
The experimentation era gave us valuable lessons. But now it's time to operationalise GenAI across the enterprise.
B2B leaders must ask: Where is GenAI already delivering results, and where should we double down? The tools are here, and the urgency is real.
The winners will be those who play with GenAI and AI and scale it purposefully.
So, what's your next execution step?
