How businesses can adopt AI for profitability?

To adopt AI for profitability, businesses need to move beyond experimentation and into strategic, scalable implementation. Here's a breakdown of what companies should be doing right now:

1. Define a clear AI strategy

• Align AI with business goals: Focus on where AI can reduce costs, increase revenue, or improve customer experience.
• Portfolio approach: Combine quick wins (e.g., automating tasks) with ambitious “roofshots” (e.g. new services) and “moonshots” (e.g. AI-driven business models).
• Embed AI into core operations: Treat AI as intrinsic to your business model, not just a tool.

 2. Invest in people and skills

• Upskill your workforce: Nearly half of employees say AI training is essential, but many feel under-supported.
• Create AI-specific roles: Data scientists, ML engineers, AI product managers, and ethics officers are in high demand.
• Foster a culture of experimentation: Encourage teams to explore AI use cases and share learnings across departments.

3. Prioritise high-ROI use cases

• Start with proven areas:
• Customer service (chatbots, AI agents)
• Marketing and sales (personalisation, lead scoring)
• Operations (predictive maintenance, inventory optimisation)
• Finance (fraud detection, forecasting)
• Track KPIs: Measure productivity gains, cost savings, and revenue impact to justify scaling.

4. Build scalable infrastructure

• Modernise your data architecture: Focus on high-quality, relevant data rather than quantity.
• Use cloud-native AI platforms: These offer flexibility, scalability, and integration with existing systems.
• Leverage open-source and pre-trained models: Especially useful for startups and mid-sized firms to reduce costs.

5. Establish governance and risk management

• Create AI oversight structures: Assign executive sponsors and ethics committees to guide responsible use.
• Mitigate risks: Address hallucinations, bias, and IP concerns with robust testing and monitoring protocols.
• Ensure compliance: Stay ahead of evolving regulations on data privacy and AI transparency.

6. Move from pilots to production

• Avoid pilot purgatory: Only 1% of firms consider themselves “AI mature” because most projects stall at the pilot stage.
• Scale successful use cases: Use ROI data to expand AI across departments.
• Adopt agile methods: Iterate quickly, gather feedback, and refine models in real time.

Bonus: Prepare for the rise of AI agents

• AI agents (autonomous digital workers) are expected to double workforce productivity by handling routine tasks and supporting complex workflows.
• Plan for orchestration: Humans will need to manage, train, and collaborate with these agents.