Chapter 5. How AI can improve Business Process

合集 - the business case for ai(10)

1.Chapter 6 Optimize decision making with AI - Simple versus Intelligent Data Analytics2024-12-252.Chapter 1: The Promise of AI2024-12-253.Chapter 7 ML development life cycle2024-12-254.Chapter 8, 9 B-CIDS: 5 pillars of AI preparation → Jumpstart Approach2024-12-255.Chapter 10-11-12. Find AI Opportunities - 4 Stages2024-12-256.Chapter 3-4. 5 tips for AI success & 5 myths of AI2024-12-25

7.Chapter 5. How AI can improve Business Process2024-12-25

8.Chapter 13 Build or Buy2024-12-269.Chapter 14. Measure Success2024-12-2610.Book notes: The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications2024-12-26

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AI can improve Business Process (customer service & HR)

Customer Service

• Workload Reduction: Employee burnout is a significant issue in customer service.
  AI Assistants: Virtual AI assistants can decrease support request volume by providing 24/7 support and handling multiple inquiries across various channels.
  • Example: Exelon uses an AI assistant to answer billing and service outage questions for 10 million customers.
• Productivity Improvement: AI can enhance customer service operator productivity.
  MetTel developed the Next Best Action (TNBA) AI system to streamline ticket routing.
  TNBA analyzes service tickets and predicts next steps with 75% accuracy, reducing labor-intensive tasks.
  Answer Suggestions: AI can suggest answers to operators, reducing time spent searching for information.
  Even with 50-60% accuracy, AI significantly boosts operator productivity, allowing for faster customer responses and an increased capacity to handle inquiries.

Human Resources

• Faster Recruitment:
  Recruiting qualified candidates is time-consuming and requires careful attention to job needs and candidate qualifications.
  AI tools can efficiently sift through millions of candidates and various data points to identify suitable individuals.
  • Example: Vodafone uses AI to assess candidates through video responses, evaluating factors like voice intonation and body language, which reduces vetting time by half.
• Personalized Learning and Development:
  Personalized L&D opportunities can help retain employees and support career growth.
  LinkedIn Learning provides course recommendations based on individual skills and industry trends.
  Companies can use AI to connect employees with mentors and create customized learning paths tailored to career aspirations, enhancing employee engagement and retention.
• Promotions and Rewards:
  Employee promotions and rewards can be biased and lack transparency, leading to turnover.
  AI can streamline the promotion process by recommending candidates based on performance, pay trajectory, and tenure.
  AI can also predict employees at risk of leaving by analyzing churn patterns, allowing for timely incentives to improve retention.
  Establishing a fair manual process is crucial before implementing AI to avoid perpetuating existing biases.

AI can improve Business Process (Sales)

Sales

• Creating an Accurate Prospect List:
  AI can automatically generate accurate prospect databases, reducing the need for manual corrections of missing or outdated information.
  It intelligently populates databases, merges multiple sources to eliminate duplicates, and ranks prospects by relevance, enhancing the efficiency of prospecting efforts.
• Increasing Leads:
  AI can improve lead generation by actively engaging with prospects and qualifying leads at scale.
  For example, Terrapinn uses an AI sales assistant to reach out to thousands of potential customers, significantly increasing the number of qualified leads and improving the sales team's meeting rates.
• Predicting Sales Actions:
  AI tools assist sales personnel by recommending tailored actions for individual customers based on their buying journey.
  By analyzing past purchases and customer engagement, AI can predict the next steps, allowing sales representatives to focus on nurturing relationships rather than spending time deciding on actions.

AI can improve Business Process (Marketing)

• Personalized Recommendations:
  Amazon's recommendation engine contributes to one-third of its revenue by suggesting alternative products, leading to increased purchases.
  Other platforms like Twitter and LinkedIn recommend content and connections, enhancing user engagement and retention.
  Personalized recommendations build brand loyalty, gather valuable customer data, and encourage user-generated content, potentially boosting revenue.
  Businesses can recommend tutorials based on support inquiries or compatible product add-ons during purchases to further increase sales.
• Churn Reduction:
  Churn rate measures the percentage of customers who stop using a service, and a slight reduction can significantly enhance profits. (a 5% reduction in churn can produce a 25% increase in profits)
  AI can predict which customers are likely to churn and provide targeted incentives to retain them.
  For instance, a telecommunications company in Southeast Asia saved $10 million monthly by using a churn prediction model to send high-value offers to at-risk customers.
  AI can also analyze unstructured data to identify root causes of churn, helping businesses address issues that lead to customer loss.
• Uncommon Uses of AI in Marketing:
  An AI pipeline can streamline workflows by automatically suggesting keywords for market analysis, saving time and improving efficiency.
  Content marketers can use NLP to identify content gaps by analyzing customer search logs, generating topic ideas quickly. (SOE)
  AI can also assist in customer nurturing through predictive outreach, enabling personalized engagement with customers who need support, thus strengthening relationships.

AI can improve Business Process (IT Operations)

• Preventative Maintenance:
  In October 2021, Meta experienced a significant service outage due to network maintenance mistakes, resulting in millions lost in advertisement revenue for both Meta and its customers.
  Traditionally, IT monitoring has been reactive, addressing problems after they occur. However, machine learning (ML) allows for a preventative and predictive approach.
  AI-driven solutions can alert IT teams hours or days before a potential incident by detecting patterns that precede IT infrastructure failures.
  For Meta, predictive capabilities could have identified the risk of an outage based on commands and configuration changes during maintenance.
• Event Noise Reduction:
  IT teams often face an overwhelming number of daily alarms from monitoring systems, many of which are redundant or false.
  These excessive alerts can slow down IT operations as teams spend time searching for issues instead of resolving them.
  AI can prioritize alerts based on their business impact, ensuring critical issues, such as network problems affecting customer-facing platforms, are addressed promptly.
  Fannie Mae uses an AIOps tool to reduce alert noise, which groups similar alerts and identifies common root causes, resulting in a 35% reduction in incidents.The use of AI has also decreased problem resolution times by 25-75%, allowing issues that previously took hours to fix to be resolved in minutes.

AI can improve Business Process (Manufacturing)

• AI in Manufacturing Operations:
  At a BMW assembly plant in Germany, an AI tool compares vehicle order data with live images of produced cars to ensure model designations match order specifications, alerting the final inspection team if discrepancies are found. This intelligent automation improves efficiency in tedious tasks requiring high attention to detail.
• Predictive Quality Control:
  AI is particularly valuable in detecting defects throughout the manufacturing process, which can be costly due to the need for replacement components, rework, and delays.
  By leveraging data from machine sensors and cameras, machine learning (ML) models can be trained to detect defects more accurately than human inspections, even identifying flaws invisible to the naked eye.
  Seagate has implemented ML in its hard disk manufacturing, transitioning from human analysis of microscopic images to a deep learning approach that processes images in real time, identifying defects early and minimizing production impacts. This has led to a 10% reduction in manufacturing time and up to a 300% ROI.
• Predictive Maintenance:
  Unplanned downtime is a significant challenge for manufacturers, costing an estimated $250,000 per hour in lost production.
  Predictive maintenance uses data collected during machine operation to forecast potential failures, allowing for timely maintenance planning and reducing unwarranted downtime.
  This strategy enables maintenance to be scheduled during low-impact periods, extending machine life and avoiding costly replacements.