Artificial intelligence (AI) is something you should be working on now, are your board directors ready?

For board directors, the world of AI can feel complex and intimidating. This blog post is your roadmap to getting started with AI. We’ll explore real-world examples across industries and equip you with key questions to ask as you consider AI for your company.

Boards need to understand what questions they should be asking.

AI in Action : Industry Examples

• Manufacturing: AI-powered robots are taking over repetitive tasks, improving efficiency and safety. They can also analyse sensor data to predict equipment failures, minimising downtime.
• Finance: AI algorithms power fraud detection systems, protecting institutions from financial crime. They also personalise investment recommendations and streamline loan approvals.
• Retail: AI personalises product recommendations on e-commerce platforms, boosting sales and customer satisfaction. It can also analyse customer sentiment to improve marketing campaigns.
• Healthcare: AI assists doctors in diagnosing diseases by analysing medical images. It can also develop personalised treatment plans and predict patient outcomes.

General Use Cases For AI Augmentation

AI excels at crunching vast amounts of data and identifying patterns, but it lacks the human touch – creativity, critical thinking, and strategic decision-making. Here’s how AI can augment some common aspects of business and how to measure this dynamic:

AI Augmentation for Knowledge Workers:

• Enhanced Research and Analysis: AI can sift through mountains of data, surfacing relevant insights and trends for knowledge workers to analyse and interpret.
• Smarter Workflows: AI-powered automation can handle repetitive tasks like data entry and scheduling, freeing up valuable time for knowledge workers to focus on higher-level thinking.
• Improved Decision-Making: AI can generate simulations and forecasts, equipping knowledge workers with data-driven insights to inform strategic decisions.

Metrics for Measuring Success:

• Knowledge Worker Productivity: Track metrics like the number of projects completed, turnaround times, and client satisfaction to assess if AI is streamlining workflows and boosting output.
• Quality of Work: Monitor metrics like error rates and rework needs to see if AI-powered automation is improving accuracy and reducing human error.
• Innovation Rate: Measure the number of new ideas generated and implemented to see if AI is helping knowledge workers identify opportunities and develop creative solutions.
• Employee Satisfaction: Conduct surveys to gauge how knowledge workers feel about AI integration. Are they feeling empowered and supported, or overwhelmed and disengaged?

AI Augmentation for Operational Workers:

• Enhanced Safety and Efficiency: AI-powered sensors and equipment can monitor conditions and predict potential hazards, keeping operational workers safe. AI can also optimise workflows and logistics, reducing errors and inefficiencies.
• Improved Quality Control: AI-powered vision systems can inspect products for defects with greater accuracy and speed than human inspectors, ensuring consistent quality.
• Predictive Maintenance: AI can analyse sensor data from machinery to predict equipment failures before they happen. This allows operational workers to perform proactive maintenance, minimising downtime and production losses.

Metrics for Measuring Success:

• Operational Worker Safety: Track the number of accidents and near misses to see if AI-powered safety features are effectively reducing risks.
• Process Efficiency: Monitor metrics like production throughput, cycle times, and rework rates to assess if AI is streamlining workflows and boosting output.
• Quality Output: Track defect rates and customer satisfaction scores to see if AI-powered quality control is improving product consistency.
• Equipment Uptime: Measure the percentage of time machinery is operational to see if AI-based predictive maintenance is minimising downtime.

AI Augmentation for Operational Excellence:

• Automated Workflows: Repetitive tasks like data entry, sorting, and basic inspections can be automated by AI-powered systems, freeing up operational workers for more strategic activities.
• Predictive Maintenance: AI can analyse sensor data from machinery to predict equipment failures before they happen. This allows for proactive maintenance, minimising downtime and production losses.
• Real-Time Optimisation: AI algorithms can analyse real-time data on factors like inventory levels, resource allocation, and production bottlenecks. This enables dynamic adjustments to optimise workflows and resource utilisation.

Metrics for Measuring Operational Productivity with AI:

• Throughput and Cycle Times: Track the number of units produced per unit of time to assess if AI-powered process optimisation is increasing output. Cycle times for individual tasks can also be monitored to identify further efficiency gains.
• Error Rates and Rework: Measure defect rates, product returns, and the need for rework to see if AI-powered automation and quality control are improving accuracy and reducing errors.
• Reduced Downtime: Monitor the percentage of time machinery is operational to see if AI-based predictive maintenance is minimising downtime. Track the time saved resolving equipment failures compared to traditional methods.
• Cost Savings: Analyse the cost of implementing and maintaining AI systems against the cost reductions achieved through improved efficiency, reduced waste, and minimised downtime.

Essential Questions for Directors

• Strategic Alignment: How does AI align with our overall business strategy and goals?
• Value Proposition: What specific problems can AI solve for our company, and what value will it create?
• Data Readiness: Do we have the necessary data infrastructure and expertise to implement AI effectively?
• Ethical Considerations: How will we ensure fairness, transparency, and responsible use of AI in our operations?
• Governance and Oversight: Who will be responsible for overseeing AI projects, and how will we manage potential risks?

Getting Started: Your Next Steps

• Education: Encourage board members to participate in AI literacy workshops or seminars.
• Expert Consultation: Engage with AI consultants who can assess your company’s needs and recommend suitable solutions.
• Pilot Projects: Start small with pilot projects in specific areas to gain experience and build confidence.
• Focus on People: Remember, AI is a tool to empower your workforce, not replace it.

In conclusion, embarking on the journey of integrating artificial intelligence into your business operations is an exciting opportunity for growth and innovation.

As an AI Ambassador, I offer a unique blend of expertise and insight to guide you through this transformative process.

With my assistance, you can navigate the complexities of AI implementation with confidence. Whether you’re seeking education on AI fundamentals or need support in scoping out potential use cases for your company, I am here to help you every step of the way.

About UpSw!ng

The content in this series is my perspective, open to discussion and healthy challenge, and welcomes ideas and collaboration should you wish to get involved.

It is my objective to represent diverse thinking and inspirational leadership towards a fundamental shift in the way technology and innovation can grow profitable organisations, whilst making positive impacts on the environment and society.

What may feel dramatic now, will be considered visionary in the future!