7 essential steps to safely and effectively implement AI in your business
Implementing artificial intelligence (AI) can provide numerous benefits, but it's not a straightforward task. Ensuring safe and effective implementation requires a keen understanding of AI and thoughtful planning. Here's a comprehensive guide to help you communicate with your team or management about incorporating AI, featuring real-world examples from both large and small businesses.
1. Understand the value of AI for your business
Before you start, it's crucial to comprehend the potential of AI in your particular industry or business setting. AI can streamline processes, automate repetitive tasks, enhance customer service, provide insightful data analytics, and much more. Explain these benefits clearly to your team or management to set a positive tone for the discussion.
For instance, the streaming giant Netflix uses AI to personalise recommendations, significantly enhancing user engagement and retention. They estimated that their recommendation system is worth $1 billion a year. On a smaller scale, Stitch Fix, an online personal styling service, uses AI to provide personalised outfit recommendations. The AI learns customers' preferences over time, improving its recommendations and making the service more valuable.
2. Define clear objectives for your AI implementation
Identify what problems you want AI to solve in your business. Do you want to enhance customer experience? Optimise supply chain management? Improve data analytics? Clear goals will help the team focus on what's important, and it will make it easier to measure the success of your AI implementation later on.
Coca-Cola uses AI to analyse its sales data and social media to understand customer preferences and trends, focusing its AI efforts on predicting what customers want before they know it themselves. Similarly, Zest.ai, a small fintech company, uses AI to assess credit risk more accurately. They've clearly defined their AI's role, helping to streamline their processes and make better lending decisions.
3. Invest in adequate training and education
It's crucial that everyone involved understands AI's fundamental concepts, even if they won't be directly working on it. This fosters a culture of awareness and facilitates informed decisions regarding AI usage. Moreover, those working directly with AI will require technical training to use the tools effectively and safely.
IBM launched its AI Skills Academy, a program aimed at providing training for faculty members to help them integrate AI into their courses. This enables their teams to understand AI better and harness its capabilities effectively. In a similar vein, Blue River Technology, a small agricultural company, trained its team to use AI in developing a device that can identify and eliminate weeds, reducing the need for herbicides. Through their investment in training, their team was able to build an effective and innovative solution.
4. Evaluate your data readiness
Good quality data is the fuel that powers AI. You need to ensure that your business has enough data to train the AI models, and that this data is clean, diverse, and unbiased. Make it clear that without a sound data management strategy, any AI initiative could falter.
For instance, American Express leverages its vast transaction data to develop predictive models for fraud detection. This example demonstrates the need for massive amounts of clean, reliable data. A smaller example is Yelp, which, although a popular platform now, started small, and they have always used their user-generated data to improve their recommendation algorithms. The ability to collect and utilise this data has been key to their success.
5. Address ethical concerns and data privacy
Transparently address any ethical issues tied to AI, including data privacy, potential bias in AI algorithms, job displacement fears, and more. Assure your team that any AI deployment will adhere strictly to local and international data privacy regulations, and that robust measures will be taken to ensure the AI system operates ethically and fairly.
Microsoft has an entire committee dedicated to responsible AI use, ensuring ethical guidelines are followed and privacy concerns are addressed. They emphasise transparent and fair AI systems, especially after lessons learned from their 'Tay' chatbot incident. Similarly, X.ai, a small company offering an AI personal assistant that schedules meetings, is transparent about their data practices, explaining that their AI respects user privacy and only accesses the necessary information to perform its task.
6. Stress the importance of regular testing and iteration
It’s important to understand what your baseline measures of effectiveness are before you implement any tool, so that you can assess what impact your chosen AI tools are having.
AI is not a "set it and forget it" technology. It needs constant fine-tuning and monitoring to ensure it's providing the intended value and not producing unintended harmful outcomes. Emphasise that regular audits and updates are part of the package, not an optional extra.
Google's AI system AlphaGo, which defeated the world champion Go player, underwent continuous refinement and testing. After its victory, DeepMind continued to refine the system, resulting in AlphaGo Zero, an even more powerful version. Similarly, Descartes Labs, a geospatial analytics startup, uses AI to analyse satellite imagery for predicting crop yields. Their models are continuously updated and refined based on the latest data, underlining the importance of regular iteration.
7. Plan for scalability
From the start, discuss how the AI system might need to scale up in the future. Will your infrastructure support this? What about your budget? It's important to keep in mind the longer-term implications of your AI deployment, even as you plan for the initial rollout.
Amazon's AI scalability is exemplified in its product recommendation engine. Starting as a basic feature, it has evolved over the years to handle vast amounts of data and complex patterns, showing the importance of planning for scalability from the start. A similar story can be found in Grammarly. This writing-enhancement platform started small but had a vision for growth. Their AI-powered writing assistant has become more refined as the user base has expanded, showing how scalability has been factored into their long-term plan.
Remember, the key to successful AI implementation lies in open, clear communication with your whole team, strategic planning, and a keen focus on ethical considerations.
By following these steps, you can create a strong foundation for AI in your business, delivering powerful results while mitigating potential risks. Both large and small businesses have reaped significant benefits from this approach, demonstrating that companies of any size can leverage this powerful technology effectively and ethically.
Want to learn more about how to plan for and implement AI technologies in your business, safely, and effectively?
Join us at our next Power Panel - Are You AI Ready? Future-Proof Your Sales & Marketing.
Wednesday 19 July 2023 4:00pm – 6:00pm
Covert Theatre, 51 Mackelvie Street, Ponsonby, Auckland
