Artificial intelligence has been heralded as the ultimate disruptor—a force capable of bulldozing outdated systems, rewriting business strategies, and supercharging human potential like rocket fuel on an open flame.

But let’s keep our feet on the ground. Amara’s Law reminds us of a painful truth: we’re great at overhyping the short-term impact of breakthrough technologies and absolutely terrible at grasping their long-term consequences.

Generative AI sits right in this tension. It’s the internet in the ‘90s all over again—massive promises running headfirst into obstacles like integration headaches and ethical quandaries. The hype cycle may be cooling, but don’t let that fool you. This is just the quiet before the real storm of innovation.

Enter Menlo Ventures’ 2024 report, The State of Generative AI in the Enterprise. This isn’t another tech buzzword salad; it’s based on hard data from 600 U.S. enterprise IT decision-makers. The findings? The race is heating up. Pilots are evolving into full-scale production systems, and the gap between the trailblazers and the latecomers is becoming a canyon. The winners? They’re the ones locking in practical, ROI-backed wins while building the scaffolding for larger-scale transformations.

This week, I’ll dive into the report to uncover where generative AI delivers real, measurable results today. We’ll examine how these early victories are laying the foundation for a future of game-changing innovation. Buckle up—this is just the beginning.

Top Opportunities for AI Improvements

Generative AI is changing workplace productivity by addressing critical bottlenecks and automating labor-intensive tasks.

Below are the top use cases delivering measurable ROI and strategies for leveraging them to boost efficiency as determined by the survey from Menlo Ventures' 2024 report, The State of Generative AI in the Enterprise.

Code Copilots: Empowering Developers (51% Adoption)

How It Works: AI copilots like GitHub Copilot, Codeium, and Cursor assist developers by generating code, suggesting improvements, and automating repetitive tasks. Task-specific copilots, such as Harness’ AI DevOps Assistant and QA Assistant, streamline pipeline generation and test automation.

Productivity Gains:

• Reduced development time through automated code suggestions.

• Improved code quality with AI-driven reviews.

• End-to-end development support using advanced AI agents like All Hands.

Implementation Tip: Identify repetitive tasks in your software development lifecycle and deploy copilots to optimize those areas, freeing up developers for more complex problem-solving.

Support Chatbots: Scalable Customer and Employee Support (31% Adoption)

How It Works: Tools like Ada, Dante-AI (the one I use on TheAIE.net), and Intercom offer 24/7 customer support, while Observe AI provides real-time guidance for contact center agents. These chatbots interact directly with customers and internal teams, delivering knowledge-based support.

Productivity Gains:

• Faster resolution times for customer and employee inquiries.

• Reduced workload for human support agents, enabling them to handle complex cases.

• Cost savings through automation of high-volume, low-complexity queries.

Implementation Tip: Begin with frequently asked questions or high-volume tasks. Train the chatbot with historical support data to ensure seamless responses.

Enterprise Search and Data Retrieval: Unlocking Knowledge (28% Adoption)

How It Works: Tools like Glean and Google Gemini for Workspaces enable semantic search across emails, messengers, and document repositories. They integrate with existing data systems to provide unified access to organizational knowledge.

Productivity Gains:

• Reduced time spent searching for information.

• Better decision-making through faster access to relevant data.

• Cross-functional collaboration facilitated by unified knowledge repositories.

Implementation Tip: Conduct an audit of data silos in your organization. Deploy enterprise search solutions to centralize access and leverage AI-powered insights.

Data Extraction and Transformation: Streamlining Workflows (27% Adoption)

How It Works: AI systems automate the extraction, organization, and transformation of data from unstructured sources, enabling quick analysis and integration into workflows.

Productivity Gains:

• Eliminates manual data entry errors.

• Accelerates insights generation by reducing processing times.

• Frees teams to focus on interpreting data rather than managing it.

Implementation Tip: Apply these tools to workflows with high volumes of unstructured data, such as invoice processing, customer surveys, or compliance reporting.

Meeting Summarization: Saving Time in Collaboration (24% Adoption)

How It Works: Tools like Fireflies.ai (my favorite) and Otter.ai generate automated meeting summaries. In healthcare, Eleos Health integrates directly with electronic health records (EHRs) to simplify documentation.

Productivity Gains:

• Reduces time spent taking notes during meetings.

• Ensures actionable takeaways are documented consistently.

• Allows professionals like healthcare providers to focus on core tasks.

Implementation Tip: Integrate summarization tools into your meeting platforms (e.g., Fireflies) to automatically capture and distribute key points.

Strategic Steps to Maximize ROI Across Use Cases

I tried to codify how to maximize the impact of generative AI. I created the S.M.A.R.T. Framework to help you efficiently delegate tasks to AI and optimize your daily workflow, allowing you to focus on high-impact activities.

• Sort: Categorize tasks (e.g., repetitive, data-driven, or strategic) and prioritize them.

• Match: Pair tasks with AI tools (e.g., Sanebox for email, ChatGPT for data analysis).

• Automate: Implement AI tools and monitor their effectiveness.

• Refine: Continuously improve workflows by simplifying and iterating processes.

• Take Control: Focus on high-value, strategic tasks that require creativity and decision-making.

By aligning these use cases with organizational goals, enterprises can harness generative AI’s transformative power to unlock productivity and operational efficiency at scale.

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The State of Generative AI

I love to talk about how to save money or improve quality using AI. However, when you can find a really compelling example of how much from an enterprise I think it should inspire all of us to find the same kind of gains.

Klarna, the Swedish buy-now-pay-later giant, has taken the lead in leveraging AI to streamline operations and bolster profitability. Amid its preparation for a potential IPO, Klarna revealed that its AI initiatives have yielded remarkable results.

From January to September 2024, sales and marketing expenses dropped 16% to $161 million, while customer service and operations expenses fell 14% to $140 million. These savings were achieved alongside a 23% increase in revenue—an impressive feat in an inflationary, cost-sensitive environment.

Key AI initiatives driving these results include:

1. AI-Powered Chatbots: Klarna's OpenAI-powered customer service bot, launched in January, performs tasks equivalent to 700 human agents, significantly reducing labor costs.

2. Automated Marketing: AI-generated content and imagery have slashed marketing expenses, eliminating reliance on external firms for translation and production.

3. AI-Assisted Development: Despite cuts in other areas, Klarna has increased its tech and product development spending by 17% to $227 million, emphasizing the importance of maintaining an innovative edge.

The results speak for themselves. Klarna’s operating expenses decreased by 2% over the period, and the company posted a modest net profit in Q3—a milestone it attributes largely to its AI-driven efficiencies. These cost reductions offset rising challenges, such as a 44% increase in loan defaults and a 67% rise in borrowing costs.

The State of Generative AI in 2024

In 2024, generative AI crossed a critical threshold, evolving from an experimental novelty to an indispensable enterprise technology. Businesses poured $13.8 billion into generative AI, a 6x increase from the previous year, signaling a decisive pivot from pilot projects to large-scale implementation. With 72% of decision-makers expressing optimism about broader adoption, generative AI has begun embedding itself across departments and industries, unlocking productivity and reimagining operational processes.

Investment Surges and New Priorities

The spending surge underscores a shift in enterprise priorities. Initially driven by innovation budgets, 40% of generative AI investments now come from reallocated operational funds—a testament to the technology’s tangible value. While foundation model investments still command significant attention, the application layer has emerged as a growth frontier, drawing $4.6 billion in investment, an 8x increase year-over-year.

Companies are identifying multiple use cases—ten on average—with 24% prioritized for immediate deployment. Leading applications include code copilots (51% adoption), chatbots (31%), enterprise search tools (28%), and meeting summarization solutions (24%). These tools drive measurable returns, optimize workflows, and cut costs while enhancing employee productivity.

Early Successes and Growing Pains

The shift to production is not without challenges. Many organizations are still navigating technical hurdles, data privacy concerns, and unclear ROI paths. Notably, technical integration remains a bottleneck, often underestimated during initial planning. Despite these obstacles, the broadening scope of generative AI adoption—from healthcare to finance to media—signals a commitment to long-term transformation.

Key verticals are pioneering adoption:

• Healthcare: AI-driven tools streamline clinical documentation, triage, and revenue cycle management.

• Legal: Generative AI simplifies contract review and litigation preparation, transforming historically tech-averse workflows.

• Media and Entertainment: Content creation tools are enabling both professional studios and independent creators to scale production. Models like RunwayML, along with OpenAI’s Sora, show us the potential for how AI may generate movies in the future, but not without some hiccups.

Generative AI’s Competitive Landscape

The competitive landscape is heating up. Enterprises are embracing pragmatic, multimodel strategies, often deploying three or more foundation models tailored to specific use cases. Open-source solutions, led by Meta’s Llama 3, hold 19% of the market, while closed-source models still dominate at 81%. OpenAI’s market share has declined, with Anthropic’s Claude 3.5 gaining traction as enterprises prioritize security, cost-efficiency, and expanded capabilities.

The Road Ahead: Opportunities and Risks

2024 has also seen the emergence of agentic architectures, powering 12% of implementations. These systems, capable of autonomously managing complex, multi-step tasks, signal the next wave of enterprise automation. Additionally, retrieval-augmented generation (RAG) has become the dominant design pattern, adopted by 51% of enterprises for efficient, context-aware AI deployment.

The challenges ahead are as significant as the opportunities. A talent drought in AI-specific roles is intensifying, creating fierce competition and driving salary premiums. Enterprises must also address the ongoing risks of model hallucinations, ethical considerations, and scalability as they expand their AI ecosystems.

Conclusion: Generative AI’s Transformative Potential

2024 marks a turning point. Generative AI is no longer a speculative future technology but a foundational business tool. As enterprises refine their strategies, the focus shifts from experimentation to execution, paving the way for long-term value creation. The question for organizations is no longer whether to adopt AI but how to maximize its transformative potential in a rapidly evolving landscape.

As Klarna demonstrates, AI is more than just hype—it is a practical, transformative tool capable of driving measurable business outcomes. Yet the journey is not without challenges. Companies must navigate technical integration hurdles, rising development costs, and the growing complexity of AI governance.

For enterprises considering AI adoption, Klarna’s approach offers a clear lesson: the long-term rewards can outweigh the short-term costs, provided that AI investments are aligned with strategic business objectives. As generative AI continues to evolve, the question is no longer whether companies should invest but how to do so most effectively to secure lasting advantages.

Further Reading

• 5 New Ways to Build A.I. Into Your Company’s Core - Bringing A.I. into your organization? Try these unconventional ideas.

• Asana's Head of AI: It's All About How AI Supports People - Asana Head of AI Paige Costello joins Caroline Hyde and Ed Ludlow to discuss AI's impact on hiring trends and on IT teams, and how it can support employees' work.

• Softr - Turn your spreadsheets and databases into client portals and internal tools. No code is required. I’ve been looking at this for a personal project.

• Lookie—Lookie is designed to consume knowledge on YouTube, which often takes too much time and makes it challenging to organize key information. The process becomes 100x smarter and faster with just a simple share, turning YouTube into a personal knowledge hub.

Gift Ideas for the Holiday with ChatGPT Search

Today is the biggest shopping day of the year, Black Friday. While Google may provide the best shopping deals for a product. It’s only going to give you results for a gift search. (If you ask Google Gemini what the best gift for a tech-savvy professional or dog lover is, you will get decent results though, see below.)

How To Use this Prompt

You can use this prompt to look for a gift and replace the terms in the square brackets using ChatGPT. This will help you search but also allow you to chat with the search results to refine them and provide the perfect gift.

Search for unique gift ideas for [specific recipient, e.g., 'tech-savvy professionals,' 'dog lovers,' 'children under 10,' etc.] within a budget of [$amount]. Focus on options that are practical, creative, and aligned with [specific interest or lifestyle, e.g., 'productivity,' 'gaming,' 'outdoor adventures,' etc.]. Provide a brief description and where to buy each item.

I created this to be timely for Black Friday, but you could change it. Here’s an example of using ChatGPT to search for AI tools.

Search for innovative AI tools and resources for [specific professional audience, e.g., 'marketing professionals,' 'data scientists,' 'HR teams,' etc.] within a budget of [$amount]. Focus on solutions that enhance [specific objective, e.g., 'workflow automation,' 'data analysis,' 'team collaboration,' etc.]. Provide a brief description of each tool, its key features, and where to access or purchase it.