NineTwoThree Studio

AI can’t just patch every problem for your business. Manual inefficiencies, strategy gaps, and poor data quality—AI won’t fix those leaks. But when it comes to: - Predictive analytics for smarter decisions - Personalized customer experiences - Automating repetitive processes That’s where custom AI solutions are uniquely powerful. Know what AI is built to solve—and what it’s not.

Two weeks ago, OpenAI rolled out advanced voice capabilities for ChatGPT. Allowing it to engage in real-time, lifelike conversations. Here’s what this means for businesses exploring AI solutions: 1) Interactive Experiences: Think virtual customer support that feels natural and hands-free, or AI assistants that can help users on the go. 2) Broader Use Cases: Ideal for building voice-activated apps, accessibility tools, or any service that benefits from high-quality voice interaction. 3) Current Availability: Advanced Voice features are available for Plus and Enterprise users on iOS and Android, with plans to expand access in the coming months. This upgrade allows businesses to transform how they connect with customers.

OpenAI announced 5 new updates for developers. Here’s what they mean for business leaders: 1️⃣ Realtime API: Faster response times for customer interactions and support. 2️⃣ Prompt Caching: Reduced costs and quicker responses for common queries. 3️⃣ Model Distillation: Deploy smaller, high-performing models in more places. 4️⃣ Vision Fine-Tuning: Tailor visual AI for unique business needs. 5️⃣ New Playground Features: Experiment, refine, and optimize faster.

“A hammer will see everything as a nail” Not every problem requires an AI solution 😅 Here’s a reference guide to know when to use AI and when you may not be ready.

Sometimes ChatGPT doesn’t do the trick 😅 Off-the-shelf AI is great for standard use cases like chatbots, basic data analytics, or marketing automation. Custom AI is non-negotiable if you have business needs that require high accuracy and tailored models (e.g., niche data processing, proprietary algorithms). Expect higher investment but deeper integration and competitive differentiation. Here’s a simple question to ask yourself: Will an existing solution get me 80% of the way there, or do I need to solve a problem only my data can address?

Data isn’t the new oil. Quality data is. AI may get the spotlight, but data pulls the strings behind the scenes. If you have access to quality data in a specific domain, you can become the go-to source for information. If you know how to leverage that data effectively.

Taco Bell's AI has rolled out to over 100 stores. There's only one problem. It's missing strong safety guardrails (see video). This is a reminder to any company considering launching AI solutions: LLMs are not perfect. They have knowledge cutoffs. They can hallucinate. There are ways to combat these challenges such as using RAG (Retrieval Augmented Generation). This powers an output that can be based on more than what’s in the LLMs training set. To learn more about this, comment “RAG” and we’ll send you our free guide on Retrieval Augmented Generation.

7 AI terms that every executive should know. What did I miss? ♻️ Share to spread AI knowledge, instead of just AI hype.

It’s overwhelming to think about updating your website let alone fine-tuning a Large Language Model. This knowledge gap can limit problem solvers' ability to leverage all the AI technology available. Here’s a simple framework we use to avoid technology pitfalls during product strategy (WHHW): - What problems are we prioritizing and what’s their ROI? - How will we measure success? - How will users interact with the product - What will we build in which order? This may seem pretty straightforward, but it’s worth remembering that common sense isn’t always common practice. These four questions will serve as the compass through the development process.

AI has fallen victim to the paradox of choice: “The more options you have, the less likely you are to choose any of them” The options are endless: - OpenAI or CoPilot - Custom or Pre-Built - Internal or External This is why many companies choose to simply do nothing or build ineffective tools. After building successful AI solutions for the last 7 years we couldn’t sit by and watch. This sparked the creation of our free AI Implementation Guide. This 28-page guide reveals our step-by-step process for implementing AI into dozens of established brands and funded start-ups across several sectors. Here’s a sneak peek of what you’ll learn: - Product Strategy - Model Building - User Testing - Interface Building - Maintenance & Optimization - Estimated Timelines If you’ve considered building an AI solution or you are just curious about what the process would look like, download our free AI Implementation Guide by commenting “AI” on this post. We will send it to you for free. Here’s to avoiding the hype and creating real solutions.