From Months to Days: How AI is Compressing Innovation Cycles

📣 Just Launched: AI Accelerator for Agencies

Last we launched our first event, inspired by work we did this spring for one of the world’s leading branding agencies, our AI Accelerator for Agencies. 5 leading AI experts — Justin Massa, Kate Cook, Tahnee Perry, Fred Grinstein, and Robert Hatta — are running lean-in workshops to help you grow revenues, better serve clients, cut costs, and create value. 

We have a surprise coming in the next week. In the interim, here’s the details + a code to save some money:

🗓️ Date: July 16
🕐 Time: 1–5 PM ET
📍 Format: Virtual, private
👥 Spots: 12 senior agency leaders
💵 Cost: $750 (use code STU10 for 10% off)

Register here.

Spots are limited by design. First come, first confirmed.

Ps. I’m working on a private equity event. If you’re interested, lmk.

🎙How AI is Compressing Innovation from Months to Days: A Deep Dive with Amir Ouki

The Managing Director of Applied AI at BOI reveals how companies are transforming their innovation cycles using synthetic consumers and AI systems that can reason through complex problems.

When Amir Ouki tells business leaders they can compress their product development from 6-8 months down to just days, the skepticism is immediate. But as Managing Director of Applied AI at BOI (Board of Innovation)—a global AI innovation partner that delivers unbiased expertise from strategy to deployment—Ouki has the results to back up the claim.

Working with major businesses across consumer goods, healthcare, financial services, and manufacturing, Ouki's team tackles a critical challenge: slow, manual innovation cycles that kill competitive agility. His approach combines strategic thinking with cutting-edge AI systems to deliver measurable business outcomes at scale.

The Innovation Bottleneck

Most large companies face the same growth paradox. They need to scale and innovate faster, but can't rely on hiring more people. Traditional innovation processes create bottlenecks that slow them down exactly when speed matters most.

"Companies today know growth is critical," Ouki explains, "but scaling via headcount alone is no longer viable. Traditional methods stall agility and create bottlenecks that reduce efficiency and effectiveness."

The numbers tell the story. A typical business innovation cycle —from initial signal to validated product concept—takes 6-8 months of manual work (especially in the enterprise). By the time companies validate ideas with real consumers, markets have shifted and opportunities have passed.

But there's a deeper problem. Teams spend most of their time on process work rather than actual innovation. "What we see is teams investing in ideas that are not ready to go to market," Ouki notes. "They're spending so much time on the funnel process instead of prototyping and making ideas real."

The AI-Powered Solution

Ouki's approach starts with a fundamental principle: always begin with the business challenge, never with the technology.

"We always start from the challenge first—not AI," he emphasizes. "You have to know what your challenges are, be able to articulate them, and deconstruct them into sub-challenges. Then you ask: what AI technologies will help us solve this?"

His team has built what they call "end-to-end innovation capabilities"—AI systems that can take raw signals and transform them into validated product concepts. Here's how it works:

1. Signal Intelligence

The system ingests multiple data sources: internal company data, external market signals, and third-party datasets. AI identifies patterns and opportunity spaces that would take human analysts months to uncover.

2. Concept Generation

Using these insights, AI systems generate and refine product concepts, service ideas, and business model innovations tailored to specific market opportunities.

3. Synthetic Consumer Testing

Here's where Ouki's approach gets particularly innovative. Instead of waiting months for traditional consumer research, his team uses "synthetic consumers"—AI systems that mirror real customer behaviors, motivations, and decision-making patterns.

Think of synthetic consumers as digital focus groups that work 24/7. They're not replacing real customers, but they're getting teams 95% of the way to market-ready concepts before any human testing begins.

Watch: Case Study → How Synthetic Consumers Accelerate Product Development with AI | 5-Minute Breakdown

4. AI Systems That Reason

The backbone of this process is what Ouki calls "AI systems that can reason through problems"—a simpler way to think about what others call "agentic systems." These are AI tools that can break down complex challenges, evaluate multiple solutions, and coordinate different actions to reach a goal.

"We think of it as a spectrum of reasoning capability," Ouki explains. "Multiple AI systems working together to make decisions, deconstruct problems, evaluate outcomes, and coordinate actions."

Real-World Results

The transformation is dramatic. One CPG client now gets from raw market signals to factory-ready product specs in days rather than months. A manufacturing client cut their quoting process from six weeks to same-day turnaround, directly impacting revenue.

But Ouki is honest about the limitations. "Synthetic validation gets us close but isn't a replacement for real consumers," he notes. "Successful innovation always involves combining synthetic validation with real-world insights."

The cultural shift is equally important. Teams that once spent months on manual research and analysis now focus their energy on prototyping, testing with real customers, and bringing winning concepts to market faster.

The Numbers That Matter

• Innovation cycle compression: 6-8 months reduced to days

• Process efficiency: Teams spend more time on high-value prototyping and testing

• Market responsiveness: Near-immediate adaptation to changing consumer demands

• Revenue impact: Faster quoting processes alone have eliminated six-week delays

Why Off-the-Shelf Tools Fall Short

When asked why companies can't just use existing AI tools, Ouki points to two critical barriers that business leaders consistently face:

Data Security: "Every Fortune 500 company we work with cannot let their data leave their environment, let alone go into a SaaS tool. They end up using external tools without their most valuable asset—their own data."

Customization Limits: Generic tools can't adapt to specific business logic, existing workflows, or unique competitive advantages. "We're thinking about the specific commercial logic relevant to each team, how they work now, what makes them strong, and how to unblock their specific obstacles."

This is why Ouki's team builds custom, secure, IP-owned AI solutions rather than recommending vendor tools for strategic capabilities.

The Mindset That Unlocks Value

The biggest barrier to AI success isn't technical—it's mental. Ouki calls it "ChatGPT thinking."

"ChatGPT has been both a boon and a curse for AI adoption," he explains. "It's trained people to think of AI as a chatbot, as a way to answer questions. But what's more helpful is thinking of AI as a way to improve decision-making."

Watch: 1-minute Clip → Why ChatGPT Thinking Limits Business AI Potential | Amir Ouki's Quick Insight

The unlock comes from recognizing that every role involves hundreds of decisions daily. AI's real value is making as many of those decisions more effective as possible.

"That augmented decision-making is the compounding unlock of AI at the enterprise level," Ouki notes. It's about building systems that consistently help teams make better choices, faster.

Your Implementation Roadmap

For leaders wondering where to start, Ouki offers a clear framework:

Start with Growth, Not Efficiency

While most teams think about AI for automation, Ouki sees three better opportunities:

1. New Revenue: Finding new segments, audiences, and products

2. Revenue Optimization: Getting more from existing products and services

3. Efficiency Gains: Automating existing processes

"Even though efficiency is where people start, it's usually new revenue and revenue optimization that drive meaningful investment," he says.

Focus on Quick Business Wins

Look for friction points in your current sales or innovation processes. Manufacturing companies, for example, often struggle with slow quoting times—a perfect AI use case with clear ROI.

Build Custom for Strategic Capabilities

Use off-the-shelf tools for general productivity, but build custom solutions for your competitive advantages. "There's no right or wrong in buy versus build," Ouki notes, "but for strategic capabilities where data security and customization matter, custom solutions usually win."

Start Small, Scale Smart

Begin with one brand, one product line, or one process. Prove value with a minimum viable solution, then expand systematically. "We always approach building from an MVP perspective—start small, focus on proving key hypotheses, then build in a way that's scalable and modular."

The Road Ahead

As AI capabilities advance, Ouki sees the gap widening between companies that embrace systematic AI innovation and those that don't. The winners won't just be using AI tools—they'll be building AI-native capabilities that compound over time.

"Leadership's role in clearly communicating AI's strategic value is essential," he concludes. "A clear vision from the top significantly accelerates organizational adoption and innovation outcomes."

For business leaders, the question isn't whether AI will transform innovation cycles—it's whether you'll lead that transformation or be left behind by competitors who do.

Watch the Full Interview → How AI Compresses Innovation Cycles from 6 Months to Just Days

Amir Ouki leads Applied AI at BOI, working with middle market and Fortune 500 companies across consumer goods, healthcare, financial services, and manufacturing. His team specializes in building secure, custom AI capabilities that solve real business challenges at scale.

🙋Ask Just Curious AI: What’s the biggest hidden cost when rolling out AI to non-technical teams?

Over the last week, I found myself deep in the weeds, building my own retrieval-augmented generation (RAG) pipeline using Supabase and Python. This was less vibecoding and more muscling through.

I made a ton of mistakes (my desktop has 100+ screenshots of cryptic errors) and logged countless hours debugging, learning firsthand the complexities and tradeoffs involved in an AI 'build vs. buy' decision. Ultimately, I decided on Delphi for ease, efficiency, and scalability, but the journey was immensely valuable.

Inspired by this adventure, we're kicking off a new regular feature: 'Ask Just Curious.' Each edition, I'll answer a key AI question from our community or pose one from our latest insights. My goal is to unpack nuanced challenges and share actionable strategies to guide your AI journey—helping you avoid those 100 error screenshots!

Check it out and help us make it better: 

This week’s question: What’s the biggest hidden cost when rolling out AI to non-technical teams?

(This is pasted directly from our AI chat.)

🌐 My Favorite Thing: Jimmy Iovine on Tetragrammaton with Rick Rubin 

Listen here →

After seeing it referenced on David Senra’s Founders podcast, I revisited an amazing conversation between Jimmy Iovine and Rick Rubin on the Tetragrammaton podcast.

First, this is just a great listen, full of stories about the music industry in the 70s and 80s and what growing up in Carrol Gardens was like in the 50s and 60s.

But also, some lessons:

• Embrace Beginner’s Mind: Iovine highlights the value of innocence and ignorance in creative breakthroughs. SMB leaders should start AI transformations with an open mind, focusing on simple, clear objectives rather than overcomplicated solutions.

• Prototype Fast & Learn from Errors: Iovine stresses hands-on experimentation. Leaders should quickly prototype AI projects, embracing trial-and-error to accelerate learning and improvement.

• Trust the Creative Process: Iovine emphasizes confidence in the face of uncertainty. SMBs should trust their teams and data-driven insights, even when outcomes are initially ambiguous.

• Visionary Leadership Shapes Culture: Iovine underscores the importance of visionary leadership in setting a creative and adaptive culture. SMB leaders must clearly articulate their vision for AI adoption and foster an environment where experimentation is encouraged.

Ultimately, the conversation with Jimmy Iovine reinforced that successful AI-driven transformation in SMBs requires clear strategic vision, disciplined execution, and a culture open to innovation and continuous learning.

Also, it’s full of amazing stories.