These expert insights highlight the varied approaches needed to develop AI strategy across industries, whether focusing on regulatory compliance, predictive analytics, or quick wins.
1. Chief Data Officer (Retail Sector):
“To effectively create AI strategy, businesses need to start by understanding their data ecosystem. Many organisations overlook the importance of clean, structured, and accessible data. In retail, for example, AI thrives on customer behaviour insights. Without robust data management, even the best algorithms will fall short. A phased approach works best, where data readiness is prioritised before jumping into complex AI projects. Retailers that refine their AI data processes early can improve sales forecasting and inventory management.”
2. AI Solutions Architect (Healthcare Industry):
“When organisations in healthcare produce AI strategy, they must consider regulatory compliance as a cornerstone. AI systems dealing with sensitive patient data require adherence to strict privacy standards. Start small by automating administrative tasks, like patient appointment scheduling, before moving on to diagnostic tools. Building trust with stakeholders—patients, doctors, and regulators—is critical for successful implementation. In the healthcare field you often require to show tangible benefits early in the process.”
3. Head of Innovation (Financial Services):
“One mistake financial institutions often make when attempting to develop AI strategy is focusing too heavily on technology rather than business goals. AI should solve specific pain points, whether it’s fraud detection or personalised investment advice. Collaboration between technical teams and decision-makers is crucial to align capabilities with strategic priorities. Additionally, ongoing model monitoring ensures accuracy as market conditions evolve. Financial institutions that maintain transparency in their AI processes often gain higher trust from customers.”
4. Machine Learning Engineer (Manufacturing):
“In manufacturing, the key to successfully developing AI strategy lies in predictive analytics. By analysing equipment data, AI can anticipate maintenance needs and prevent costly downtime. However, it’s important to integrate AI solutions into existing workflows seamlessly. Employees should be trained to interpret AI insights, ensuring the technology enhances operations rather than disrupting them. Clear training programs help workers trust and rely on AI-generated insights for smoother operations.”
5. Digital Transformation Lead (Logistics):
“The logistics sector benefits immensely when companies produce AI strategy with a focus on real-time analytics. AI can optimise routes, reduce fuel consumption, and enhance delivery timelines. However, scalability is a challenge. Businesses should pilot AI tools in a limited capacity before deploying them across the entire supply chain. Data-sharing agreements with partners also play a vital role in maximising AI’s impact. Collaborative AI strategies in logistics often lead to better resource utilisation and faster deliveries.”
6. Chief Technology Officer (Start-ups):
“Start-ups need to take a pragmatic approach when they develop AI strategy, because they often lack extensive resources. Instead of building everything in-house, leveraging pre-trained models and cloud-based AI solutions can be a game-changer. Start-ups should prioritise quick wins, like automating customer support or streamlining internal processes, to demonstrate value early. This approach not only saves costs but also boosts investor confidence.”
7. Cybersecurity Specialist (Tech Industry):
“AI’s role in cybersecurity has grown exponentially, but when businesses develop AI strategy for this area, they must prioritise adaptability. Cyber threats evolve rapidly, and static models quickly become obsolete. Investing in dynamic, self-learning AI systems that detect anomalies in real time is essential. Collaboration with cross-functional teams ensures comprehensive threat mitigation. A strong focus on cybersecurity strategies can significantly reduce the risk of breaches and downtime.”
8. Sustainability Manager (Energy Sector):
“Developing AI strategy in the energy industry requires a dual focus on efficiency and sustainability. AI can forecast energy demand, optimise grid distribution, and identify areas for renewable energy integration. The challenge lies in processing vast amounts of data from diverse sources. Long-term success often comes from integrating AI with emerging renewable energy technologies.”
