Hi! I'm Natalia Brattan
- Harvard executive education in AI
- Certified risk management professional
- Member of the American Society for Quality
- AI consultant certification (coming 2026)
- Author of multiple articles on AI and innovation
- Host of SME-focused AI webinars
- Experienced advisor to small businesses
- Former audit lead at a leading tech giant
Hi! I'm Natalia Brattan
- Harvard executive education in AI
- Certified risk management professional
- Member of the American Society for Quality
- AI consultant certification (coming 2026)
- Author of multiple articles on AI and innovation
- Host of SME-focused AI webinars
- Advisor to hundreds of SMEs
- Former audit lead at a leading tech giant
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My Perspective on AI
AI is everywhere in business now. Some people compare it to new electricity or the internet. I don’t. AI is more like new nuclear power or the steam engine. They can spiral out of control quickly, at any level.
AI hallucinations have devastated many businesses, while AI has helped other businesses grow and prosper. As a business professional, I must know what makes the difference. My background helps me answer that.
How My Background Factors In
I graduated from the National Technical University of Ukraine as an electrical engineer specializing in automated controls. From there I shifted to quality management and ISO 9001 standards training, consulting, and auditing. I spent the last thirteen years in audit and risk management as a certified audit and risk professional. I’ve worked at BlackBerry and Cisco. I’ve completed executive education in AI at Harvard and earned several professional AI certifications.
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I feel fortunate to have this background because AI is experiencing its “Detroit in the ’80s moment” now.
What is “Detroit in the ’80s”? In the 1980s, North American automakers woke up to a brutal reality: Japanese cars were simply better. Buyers traded Fords and Chryslers for Toyotas and Hondas. The quality crisis was undeniable. This is when quality management evolved from a back-office function into a business survival strategy
AI is in the same place today. AI manufacturers are prioritizing speed-to-market over robust implementation. Capability is accelerating. Accuracy is not.
The defect rate is huge. AI hallucination rates run 50% to 82% across major models, hit 88% in legal queries, and reach 64% in medical summaries without safeguards. Code generation invents fake libraries in up to 99% of certain prompts.
Detroit’s answer to its quality crisis was Total Quality Management: testing, auditing, risk management, and statistical process control. The same playbook is needed today.
The Biggest Misconception About AI
Dario and Daniela Amodei, the brother-and-sister co-founders of Anthropic, sat down with Oprah. At one point Dario brought up their newest model, Mythos. He explained that Anthropic had “discovered through testing” that Mythos is exceptional at finding security vulnerabilities.
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Take another example. Two high school students, Zach Yadegari and Henry Langmack, launched Cal AI, a photo-based calorie counting app. Snap a picture of your plate, log calories and macros. The app runs on a mix of image models from Anthropic and OpenAI, layered on retrieval-augmented generation over open food databases. They didn’t pick one model and ship. They tested. “We have found that different models are better with different foods,” Yadegari told TechCrunch.
This is the part most small business owners are not prepared for. Traditional software was built to do a specific thing. Your CRM was a CRM. Your accounting software was your accounting software. You bought it, learned the buttons, and used it for the job it was designed for.
AI is not like that. You cannot know what it’s good at and where it fails unless you test it. Different models are better at some tasks and worse at others. Even foundation model founders like Dario Amodei acknowledge that some key AI capabilities are only discovered post-production through rigorous testing. The most important, none of AI models were tested by manufacturers for your specific workflow.
What Makes AI Work in Business
How many small business owners thoroughly test their AI tools’ capabilities? How many audit the outputs, review the risks, and have a structured process for matching tasks to tools?
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This is one of the biggest factors behind successful and failed AI adoption.
The winners had several things in common.
- Matched each task to the right tool through testing.
- Automated a few high-value tasks, instead of going broad.
- Tested every AI-enabled workflow thoroughly before pushing it live.
- Built proper controls around every output before it shipped.
Companies that fail at AI adoption do the opposite.
How I Help SMEs
I do a significant amount of AI research. Every week, I read dozens of AI newsletters, scan industry news, and follow major model and product updates.
I test new tools and features which can improve business operations, and share my feedback.
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My goal is to understand AI deeply enough to use it safely and profitably, and to help small businesses do the same.
Most AI content online is filled with noise including personal stories, hype, acquisitions, funding announcements, and interesting-but-impractical news. I filter through that noise to extract practical insights small business owners can actually apply in their day-to-day operations.
I share those insights every Wednesday through my free newsletter. I also run occasional workshops and webinars for small business centers, and publish articles.