One post tagged with "Technological Innovation"

While many companies are still exploring the potential of generative AI, some trailblazers have already woven it into their core operations, achieving impressive results. OpenAI's latest report, "AI in the Enterprise," distills seven universal principles for successful AI adoption in businesses, drawing from in-depth research into industry leaders like Morgan Stanley, Indeed, and Klarna. This isn't just a technological achievement—it's a shift in mindset, collaboration, and business value.

Seven Insights: From Exploration to Scalable Implementation​

1. Start with Rigorous Evaluation (Evals): Prioritize "Control" Before "Growth"​

Adopting AI isn't an overnight process. Before rolling it out widely, establishing a thorough, measurable evaluation system is crucial for success.

Take financial giant Morgan Stanley as an example. With sensitive client operations at stake, they didn't just follow trends blindly. Instead, they developed a multi-dimensional evaluation system focusing on three core areas—accuracy in language translation, quality of information summarization, and comparison with human expert answers. Only when the model was deemed "controllable, safe, and beneficial" did they gradually introduce it to frontline operations.

This cautious approach has paid off: now, 98% of Morgan Stanley's financial advisors use AI daily; the document hit rate in their internal knowledge base has soared from 20% to 80%; and client follow-ups that once took days are now completed in hours.

2. Deeply Embed AI into Product Experience, Rather Than Adding a Chatbot​

The most successful AI applications are those that seamlessly integrate into existing products, enhancing the core user experience. It should feel as natural as water or electricity in daily life.

Indeed, the world's largest job site, exemplifies this approach. Instead of merely adding a job search chatbot, they used GPT-4o mini to automatically generate personalized "recommendation reasons" for each system-matched job. This seemingly small tweak directly addresses job seekers' "why me" questions, significantly improving matching efficiency and user experience. As a result, job seekers' application initiation increased by 20%, and the employer successful hiring rate rose by 13%.

3. Act Early to Enjoy the "Compounding Snowball" of Knowledge and Experience​

AI's value grows through continuous iteration and learning. The earlier you start, the more your organization can benefit from this "compounding" effect.

Swedish fintech company Klarna's AI customer service system is a vivid example of this principle. In just a few months, AI customer service has handled two-thirds of customer chat sessions, effectively taking on the workload of hundreds of human agents. More impressively, the average resolution time for customer issues dropped from 11 minutes to 2 minutes. This initiative is expected to generate $40 million in annual profit growth for the company. Today, 90% of Klarna employees use AI in their daily work, enabling faster innovation and continuous optimization across the organization.

4. Tailor and Fine-tune to Align with Business, Creating a Moat​

General large models are powerful, but true competitive advantage comes from "tailoring to fit." By fine-tuning the model on your unique data and business scenarios, it can better understand your business.

Home improvement retailer Lowe’s faced a challenge with the vast amount of supplier-provided, inconsistently formatted product data when optimizing its e-commerce search function. By using OpenAI's API to fine-tune the GPT-3.5 model, Lowe's trained it to become an "expert" in deeply understanding home improvement industry terminology and consumer search habits. The fine-tuned model improved product label accuracy by 20% and error detection capability by 60%.

5. Empower Frontline Experts to Drive Innovation from the Bottom Up​

Those who understand business pain points best are often the frontline employees dealing with issues daily. Providing them with simple and easy-to-use AI tools can foster the most practical solutions from the bottom up.

Global bank BBVA adopted this "expert-led" strategy by opening ChatGPT Enterprise to all employees. In just five months, employees spontaneously created over 2,900 customized GPT applications. These applications cover a wide range of scenarios, from credit risk assessment and legal compliance Q&A to sentiment analysis of customer NPS surveys. Many analysis and reporting processes that used to take weeks are now completed in hours.

6. Break Developer Bottlenecks by Delivering AI Capabilities Through "Platformization"​

In many enterprises, R&D resources are the main bottleneck for innovation. To break this deadlock, establishing a unified, efficient AI development platform is crucial.

Latin America's largest e-commerce and fintech company Mercado Libre created an internal AI platform called "Verdi". This platform integrates language models, APIs, and other development tools, allowing the company's 17,000 developers to quickly build, deploy, and iterate AI applications using natural language, like "building with Lego". Platformized delivery has brought astonishing efficiency improvements: the speed of listing and cataloging product inventory has increased 100-fold, and the accuracy of detecting fraudulent products is nearly 99%.

7. Set Bold Automation Goals to Free Up Human Resources for High-Value Work​

Every enterprise is filled with repetitive, tedious processes. Instead of viewing them as necessary operational costs, set a bold goal: completely automate them with AI agents.

OpenAI itself is a practitioner of this concept. They built an internal automation platform to handle the daily tasks of the support team. This platform can automatically access customer data, read knowledge base articles, draft email responses, and even directly update account information or create support tickets in the system. Today, the platform automatically handles hundreds of thousands of tasks every month, freeing employees from repetitive labor to focus on more creative and strategic high-value work.

Common Success Patterns​

Looking at these success stories, the key lies not in pursuing the most cutting-edge models or technologies, but in a set of common strategic thoughts:

• Evaluation-Driven: Use rigorous evaluation as the "gatekeeper" for project initiation and iteration.
• Product Mindset: Treat AI as an intrinsic capability to enhance core product experience, not an add-on feature.
• Continuous Investment: Recognize that AI's value lies in compounding, willing to invest resources, accumulate data, and cultivate organizational capabilities over the long term.
• Platform Governance: Use platformization to safely, compliantly, and efficiently empower the entire organization with AI capabilities.

The path to success is similar: focus deeply on high ROI scenarios first, then use accumulated data, experience, and organizational learning to feed back into the next larger-scale iteration.

Practical Checklist for Technical Teams​

1. Conduct eval like a "requirement review": Before project launch, use quantitative metrics to assess potential risks and benefits.
2. Make every module "natively support AI": Consider how AI integrates into product design from the start, rather than adding a chat window afterward.
3. Start the "compounding flywheel" early: Begin accumulating high-quality business data, streamlining core processes, and cultivating employees' AI mindset.
4. Establish a "model-as-product" fine-tuning pipeline: Automate and streamline the model fine-tuning process to create a unique, hard-to-replicate differentiation moat.
5. Empower the frontline with low-threshold tools: Deliver AI capabilities to business departments through Custom GPTs and other forms, allowing real-world scenarios to drive platform capability improvement.
6. Provide a unified "scaffold": Offer developers a unified framework for security, compliance, monitoring, and routing to lower the innovation threshold.
7. Target "three high" processes: Prioritize automating processes that are highly repetitive, low in subjective judgment, and high in cross-system to maximize the value of AI agents.

Action Recommendation: Choose a long-standing pain point in your business now and initiate the first round of eval evaluation. Starting with small victories, your AI compounding curve has quietly begun on this land full of opportunities.