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🔁 AI Transforming Business: Insights from Gartner's 3Q 2023 Report

Revolutionizing Operations, Security, and Engagement in the Digital Age

🔁 AI Transforming Business: Insights from Gartner's 3Q 2023 Report

Let's dive into how Artificial Intelligence (AI) is changing the business world in 2023. This article covers everything from AI's effect on the environment to how it's shaking up business strategies and security. We'll also see how AI is changing the way businesses talk to customers and manage their teams. Get ready to explore the exciting world of AI in business today!

1. Generative AI's Environmental Impact:

The rapid growth of generative AI, like ChatGPT, has raised concerns about its environmental impact. Despite potential sustainable uses, generative AI can increase greenhouse gas emissions and significantly consume electricity and water. It relies on large data models, leading to high energy demands. Presently, ICT uses less than 1% of global electricity, but predictions suggest it may reach 6.4% by decade's end. By 2030, about 75% of CIOs might face electricity limitations due to the increasing power needs of advanced technologies. Balancing the environmental challenges with the advantages of generative AI is crucial

Analysis of Generative AI Use-Case Examples

2. AI in Business Strategy

Generative AI affects businesses differently, so leaders should think about how it will change their specific organization and workforce. Important points include:

  1. Demand Drivers: This influences if businesses should grow or scale back their services, affecting where they invest in people and tech.

  2. Technology Drivers: How a company uses tech affects how it organizes work.

  3. Changing Tasks: Generative AI will transform tasks like writing content or coding.

Leaders should figure out how much demand they expect for their services and how they plan to use AI, which could range from making jobs easier to creating new business models.

CEO’s Top 10 Strategic Business Priorities for 2023 and 2024

3. AI Adoption and Regulation

When it comes to using AI and following its rules, businesses have some important things to think about:

  • Legal and Corporate Leaders: They need to prepare for upcoming AI regulations and understand how these will affect business operations and strategies.

  • Global Regulatory Approaches: Different regions, including the EU, Canada, and China, are focusing on transparency, risk management, governance, and data privacy in AI.

  • Navigating Evolving Regulations: Organizations are advised to ensure transparency in AI use, engage in continuous risk management, uphold human oversight, and maintain data privacy.

  • The overarching theme is the importance of ethical and effective AI use in the business sector.

Common Principles in Global AI Regulations and Guidance

4. Generative AI in Information Security

Generative AI is changing information security by improving how businesses detect and respond to threats. However, it also brings new challenges like potential biases and the need for strong data protection. Chief Information Security Officers (CISOs) are key in using AI effectively in security strategies.

  • Generative AI in Security: It's transforming how we handle information security.

  • AI's Role in Threat Detection: AI excels at identifying and responding to security threats.

  • Challenges with AI in Security: Despite its benefits, AI presents new security challenges, like potential biases and the need for robust data protection.

  • CISOs and AI: Chief Information Security Officers (CISOs) play a key role in integrating AI into security strategies.

5. AI and Customer Experience

AI's role in Customer Experience is transforming how businesses engage with their customers. It's making interactions more personal and services more efficient. This includes using AI for better customer communication and handling challenges like data privacy. It's all about using AI wisely to improve how customers feel about their interactions with businesses.

  • Enhancing Customer Interactions: AI significantly improves how businesses interact with customers. It helps create personalized experiences and efficient customer service.

  • Operational Efficiency: AI aids in automating responses and customer engagement, streamlining operations.

  • Challenges and Opportunities: While AI offers numerous benefits in customer service, it also poses challenges like data privacy and adapting to customer needs.

  • Strategic Implementation: Businesses are strategically implementing AI to optimize customer experiences, ensuring a balance between technological advancement and customer satisfaction.

Primary Focus of Generative AI Initiatives

6. Impact on Talent Strategy

Generative AI is being used to make businesses more efficient, especially given concerns about talent shortages and inflation in 2023:

  • Summarization: AI tools like ChatGPT offer quick solutions to customers, freeing up staff for more tasks.

  • Content Creation: AI aids in crafting personalized responses and marketing materials, speeding up sales processes and enhancing customer engagement.

  • Marketing Efficiency: AI helps create targeted messages and improves webpages and products, boosting sales.

However, it's important to start with small AI projects and watch out for risks like data privacy issues, biased responses, and copyright violations. This approach balances AI's advantages with its potential challenges.

7. ABB Electrification Enhances Financial Strategy with Advanced ML Models

Machine learning (ML) is transforming financial planning at ABB Electrification (ABB EL), making forecasts more efficient and accurate. Alessandro Marchesano, head of FP&A, highlights the need to understand ML and the role of humans in model development. ABB EL's pilot program successfully integrated external factors into ML models and plans to expand ML use in financial activities and across ABB.

  • Driver-Based ML Models at ABB EL: Uses complex external drivers like GDP and consumer sentiment in their ML models to explain forecasts, identify relationships, and adapt to market changes.

  • Self-Correcting Models: Changes in external drivers train the ML model to self-correct, allowing for quick business response to market shifts.

  • Three-Step Process for Refining ML Models:

    1. Source external drivers from experts.

    2. Validate drivers using ML and statistical models.

    3. Create algorithms with validated drivers.

  • Regular Algorithm Refinement: ABB EL regularly assesses ML performance to improve accuracy and model business areas effectively.

  • Framework for Algorithm Improvement: Questions guide refinement, focusing on impactful inputs, algorithm accuracy, trend models, and optimal business area modeling.

  • Outcome: Improved speed and objectivity in ABB EL’s financial planning through iterative improvement of ML models.

8. How to Serve Your AI-Powered Customer?

The shift in customer dynamics due to AI is significant, moving beyond human customers to machines making purchases. This trend includes AI bots negotiating deals and handling tasks like shopping and comparing, changing the market landscape:

  • Machine Customers Emerging: AI-powered bots, like Walmart's negotiation software, are increasingly conducting transactions.

  • AI in Various Sectors: From Ticketmaster’s bot issues to Nike’s sneaker bots, AI's role is expanding.

  • Generative AI Adding Complexity: AI tools like ChatGPT are becoming part of everyday applications, aiding in tasks from travel planning to shopping.

  • Market Growth Opportunities: AI is driving market growth by streamlining purchasing processes, exemplified by HP Inc.'s Instant Ink service.

This AI-driven shift towards machine customers is fundamentally changing how purchases are made and markets operate.

Machine Customer Decision Path

To benefit from AI-driven machine customers, executive leaders should:

  • Make product/service info accessible to machine customers: Ensure data is available for various search variables and provide API access.

  • Incorporate machine customers into digital commerce strategies: Be outstanding in digital commerce, where machine customers will likely make purchases.

  • Form a multi-departmental commercial partnership: Create scenarios for AI machine customer interactions and ensure a responsive supply chain.

  • Hire and train staff for AI interactions: Staff should understand AI algorithms and technology, with some requiring data science knowledge.

  • Train staff to recognize machine customers: They might already be interacting via chat or voice synthesis.


In conclusion, "AI Transforming Business" highlights AI's profound impact on businesses. It's reshaping efficiency, security, and customer relations, showing how crucial it is for companies to adapt and integrate AI strategically. This change calls for balancing technological growth with ethical and strategic considerations, key to future success.

For more information and deeper insight, please check out here. All credit for this research goes to the researcher of this project.

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