Ten Compelling Reasons Why You Need AI
So, your boss dropped by your desk this morning with a new company initiative and now you need to use Artificial Intelligence (AI). For something. It doesn’t matter what. There’s no particular problem he’s trying to solve, no well-defined measure of success, just that you need to use AI because the board of directors wants […]
So, your boss dropped by your desk this morning with a new company initiative and now you need to use Artificial Intelligence (AI). For something. It doesn’t matter what. There’s no particular problem he’s trying to solve, no well-defined measure of success, just that you need to use AI because the board of directors wants it, and now it’s in our business plan.
While that may sound like an irresponsible lack of focus, it’s an approach that offers a wealth of freedom. You can figure out what to use AI for and how to evaluate success! Here are some ideas to get you started.
1. Become more aware of what’s happening around you.
- The Why: Sentiment analysis can aid your mission and build trust with your customers.
- The How: See how we used Azure for real-time analysis of a Twitter feed. This can help you tap into ideas and opportunities from your customers.
- Measuring Success: Based on your goals, metrics can include increased revenue and customer retention.
2. Increase customer engagement with real-time generated content.
- The Why: Increase customer awareness of offers and opportunities like Marriott did with their award-winning Marriott Reward Moments.
- The How: Custom-build an API to feed advertisements showcasing available offers into dynamic video rendering software in real-time.
- Measuring Success: Higher conversion rates for clicks and purchases.
3. Provide better services with location-awareness.
- The Why: Needs and opportunities vary based on location—national region; urban, suburban, or rural; terrain; and climate. By detecting location information, you can offer more compelling services. Check out how the Electric Cooperatives are exploring these ideas.
- The How: Use geo-targeting and data aggregation of a series of services like locations of cell towers, Bluetooth beacons, and wifi networks. By considering which of these networks/signals are visible by the device, the signal strength of the signal, the location of all those networks/signals, along with a GPS reading, a system can surmise the location of a device to a pretty remarkable degree of accuracy. Bluetooth beacons can sometimes detect within inches.
- Measuring Success: Improved customer engagement metrics or purchases based on regions.
4. Anticipate and respond to changes in real-time (or near real-time).
- The Why: Make the most of today’s market opportunities by identifying and rapidly capitalizing on trends. If you monitor social media and other outlets for spikes, you can capitalize on unanticipated publicity or celebrity endorsements (think, the Kate effect).
- The How: Data streaming tools like Kafka and Hadoop paired with orchestrators like Zookeeper help you monitor streaming content like Twitter.
- Measuring Success: Track metrics such as decreased response times and increased sales.
5. Find out how to scale complicated business processes.
- The Why: Peter Senge argues the greatest competitive advantage is the ability to learn faster than our competitors – successfully scaling AI involves much learning and short feedback loops.
- The How: Divide the overall business processes into smaller segments and run machine models for each segment. Compare the individual segments to identify areas to focus on efficiencies.
- Measuring Success: Establish metrics to capture improvements in efficiency within your processes and evaluate if you are improving or not.
6. Proactively identify and address cybersecurity risks.
- The Why: A single data breach can cost you hundreds of millions of dollars in lost sales, legal costs and damaged reputation.
- The How: Use DataOps practices to build security into your data pipeline – at rest and in transit. Provide ways for the cybersecurity team to apply role-based data views and automate tests to validate. Implement tools to monitor and alert people to anomalies.
- Measuring Success: Mean Time to Recover, System performance and availability.
7. Reduce human bias and make better business decisions.
- The Why: AI can embed and reinforce biases. However, effective machine learning models can expose information that our biases may miss. Health and Human Services OIG had a large quantity of unstructured data that was time-consuming and complex to review for potential fraud, waste and abuse. AI now helps them sort through it faster and more accurately.
- The How: AI models can augment human decision making by quickly mining data for important information and highlighting areas that need more detailed review.
- Measuring Success: Increased ROI on time spent making decisions; for investigations or fraud analysis, better results for the time invested.
8. Deliver more value to customers with process automation.
- The Why: Customers want the benefits provided by your solutions, not the hours your employees work. Increase the value you provide by improving back-office processes.
- The How: Use Robotic Process Automation to standardize, accelerate, and where possible eliminate all the annoying red tape standing between you and your value delivery. Hear how the digital services division at U.S. Citizenship and Immigration Services improves customer service.
- Measuring Success: Happier customers and higher employee satisfaction.
9. Extract valuable information from your unused data.
- The Why: You have massive amounts of data but are uncertain how to make sense of it and use it. The Truck Safety Coalition faced a similar problem, with lots of data from the Department of Labor but no easy way to make sense of it all.
- The How: Process unstructured content such as audio files, emails and social media posts with modern techniques to unearth useful revelations about customers, competitors or internal operations.
- Measuring Success: Improved business decisions, validated with data, that lead to improved outcomes.
10. Provide a personalized customer experience.
- The Why: Regardless of the nature of their purchase, customers always want to know, “How long will it take?” How long before they see value? Sometimes, the details are so complex it can be impossible for a human to give an accurate answer. Personalized wait time estimates greatly improve the experience.
- The How: Use machine learning techniques and statistical modeling to derive an accurate answer based on the unique experience of each individual customer.
- Measuring Success: Improved customer satisfaction and retention metrics.
The World Is Your Oyster
Opportunities abound for data-based experimentation using artificial intelligence and machine learning, so it’s up to you where you want to begin your journey. The key is to make sure what you’re doing ties in with your organization’s mission, vision and goals. Use these to establish a clear definition of success. As with any idea, start small, deliver and measure and iterate. Let us know how you have used AI in your work!
Curious to read more? Find out how one hospitality giant used AI technology to win over a new generation of customers.
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