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List of AI Business Ideas for Entrepreneurs

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Incorporating artificial intelligence business ideas completely transformed the trading landscape and gained widespread acceptance in the past few years. Many organizations are actively leveraging AI, employing it as a standalone solution for multiple tasks or seamlessly integrating it into enterprise software systems that are capable of managing fundamental business processes.

This dual approach reflects the versatile nature of AI, serving both as a specialized tool for addressing the business’s specific needs and as an enhancer of overall operational efficiency across various domains. This trend signifies the maturation of AI technologies and how it’s becoming a regular part of modern business strategies, fostering innovation and optimizing workflows.

Even though AI has established itself as a technology that is a growth and popularity magnet, every innovation-driven entrepreneur is still on a wild quest and actively seeking AI based business ideas to dive into the world of this disruptive technology.

86% of CEOs say AI is a regular part of their work, and it’s not robots or big machines – it’s software that helps with everyday tasks. AI can guess what customers will do and even automate manual tasks, which means it’s becoming really important for businesses in ways we haven’t seen before.

If you, too, are looking to incorporate AI into your business or start AI businesses, we can offer you the most comprehensive and profitable AI startup ideas that can align with your goals and the latest industry trends. Additionally, forming an LLC can provide your AI startup with legal protection and operational flexibility, making it a crucial step in your entrepreneurial journey.

Artificial intelligence for business could add approximately $15.7 trillion to the global economy by 2030, according to a PwC report. Their research also suggests that 45% of these economic gains will come from product enhancements, which will drive consumer demand through increased personalization, greater variety, and improved affordability over time.

This guide will further help you understand the top ways in which artificial intelligence business ideas can help get maximum ROI. This will give you a head start in your AI journey while offering you a good look into what to expect while diving into this lucrative industry.

Before we dive into these exciting details on the artificial intelligence business opportunities, let’s look at the current state of the AI market and how you can leverage this technology for coming up with the top AI business ideas.

A Good Look AI Market Structure

According to a report by Grand View Research, the global AI market is anticipated to reach $1,811.75 billion by 2030, expanding at a remarkable CAGR of 36.6% from 2024 to 2030. This growth is fueled by factors such as advancements in machine learning, broader adoption across industries, and increasing demand for automation and data-driven solutions.

The latest McKinsey Global Survey reveals a notable increase in AI adoption, with 72% of organizations now utilizing AI, up from around 50% in previous years. The use of generative AI has nearly doubled in the past ten months, with 65% of respondents regularly employing it. High expectations persist, with three-quarters of respondents forecasting significant or disruptive changes from generative AI. AI adoption is now widespread globally, with over two-thirds of organizations in nearly every region implementing AI, particularly in the professional services sector.

Furthermore, a PwC report estimates that AI could add approximately $15.7 trillion to the global economy by 2030. Their research also suggests that 45% of these economic gains will come from product enhancements, which will drive consumer demand through increased personalization, greater variety, and improved affordability over time.

How Businesses Are Using Artificial Intelligence in Their Operations

According to a Forbes Survey, businesses across multiple domains increasingly rely on AI to enhance and optimize their operations. Leading applications include customer service, with 56% utilizing AI for this, and cybersecurity and fraud management, adopted by 51% of businesses.

In addition to this, the importance of artificial intelligence business ideas can be seen in multiple other areas, such as customer relationship management (46%), digital personal assistants (47%), inventory management (40%), and content production (35%). Moreover, businesses are also looking to harness the power of AI solutions for product recommendations (33%), accounting (30%), supply chain operations (30%), recruitment and talent sourcing (26%), and audience segmentation (24%).

Business owners use Artificial Intelligence

Coming to improving the customer experience with the help of AI business ideas, 73% of businesses are either utilizing or planning to employ AI-powered chatbots for instant messaging. Furthermore, 61% of companies leverage AI to enhance email optimization, and 55% incorporate AI for tailoring personalized services, such as product recommendations.

All these statistics are capable of highlighting the growing adoption of AI in business. These trends are capable of letting entrepreneurs and startups easily understand the value of AI in improving customer engagement, operational efficiency, and overall competitiveness.

Most Lucrative AI Startup Ideas to Explore

In this dynamic digital landscape, AI has emerged as a driving force, allowing businesses to reach new heights of success. Let us look at multiple business ideas with AI for a startup that can pave the way for maximum ROI.

AI-Based Retail Assistance Solution

Artificial intelligence in business examples like AI-powered retail assistants are revolutionizing the retail industry by offering personalized customer support, recommendations, and seamless shopping experiences. These assistants utilize natural language processing and machine learning to understand customer preferences and provide tailored product suggestions. The global AI in the retail market is projected to reach $40.74 billion by 2030, which makes AI retail assistance a highly lucrative business opportunity for retailers looking to enhance customer engagement and drive sales.

AI-Based Entertainment Platform

The entertainment industry is undergoing a transformative change with the integration of AI. The technology is revolutionizing content creation and audience engagement through AI-generated content and personalized recommendations. By 2030, AI in the entertainment market will reach $99.48 billion, presenting lucrative artificial intelligence business opportunities for entrepreneurs and entertainment businesses to leverage AI technologies.

Even though AI has established itself as a growth and popularity magnet, every innovation-driven entrepreneur is still on a wild quest, actively seeking AI for business ideas to dive into the world of this disruptive technology.

AI-Based Recruitment App

One of the best AI business ideas is to develop an AI-based recruitment platform. By effectively filtering and shortlisting individuals, AI-driven recruitment apps are streamlining the hiring process for businesses. HR professionals can save money and time using AI algorithms to evaluate resumes, conduct interviews, and forecast member fit. Furthermore, they can expedite the hiring process, dedicating more time to strategic tasks. In addition, AI-powered recruitment portals can help businesses reduce recruitment costs by eliminating the need for lengthy manual screening processes and minimizing the risk of hiring the wrong candidate.

AI-based recruitment portal

AI-Based Logistics and Supply Chain Management Solution

AI-driven logistics and supply chain management helps businesses optimize operations, improve efficiency, and reduce related business costs, making them one of the suitable AI startup ideas of the century.  It is one of the most sought-after business ideas in artificial intelligence that uses AI for demand forecasting, optimize routes, and monitor inventory, allowing businesses to achieve seamless supply chain operations.

AI Healthcare Platform

AI-powered healthcare tools can transform the medical ecosystem, paving the way to bridging the gap between patients and doctors. Furthermore, AI-powered healthcare apps can help professionals with easy diagnostics, treatment planning, and patient care, making them one of the top AI business ideas in today’s world.

There are several use cases around the inclusion of AI in healthcare setups. AI has proven to be a boon for all three subsets of the sector – patients, doctors, and healthcare agencies. Whether you look at AI-backed automation in EHR, scheduling for doctors, or health tracking for patients, the role of AI in the domain is massive.

AI Marketing App

AI-based marketing apps leverage AI algorithms to provide businesses with valuable insights and automation capabilities. These apps are a few of the notable examples of business ideas using AI that enable businesses to precisely target their audience and deliver personalized experiences. From developing campaigns to analyzing customer behavior and preferences, AI-powered marketing apps help businesses stay ahead of the competition and create a lasting impact on their customers. This makes them one of the lucrative AI business ideas in this competitive space.

Generative AI-Powered Content Creation Tool

Generative AI for business is transforming the landscape of content creation, presenting a compelling business opportunity for companies aiming to maximize RoI through innovative technology.

Being one of the superior Generative AI business ideas, content creation tools like ChatGPT, Jasper, Writesonic, etc., empower users to produce diverse content forms, from articles and graphics to personalized videos, significantly streamlining the content creation process.

Furthermore, by developing a generative AI-powered content creation tool businesses can tap into a lucrative market opportunity, catering to a wide range of industries that demand rapid, high-quality content production. This capability of integrating generative AI in business is crucial for maintaining a competitive edge in the fast-paced digital landscape. 

By leveraging this technology, businesses can  not only meet the growing content needs across various platforms but also personalize experiences for their users, enhancing the overall brand loyalty and engagement. 

According to a Bloomberg report, the generative AI industry is expected to become a $1.3 trillion market by 2032. This significant growth highlights an ideal timing for businesses to embrace generative AI ideas for business and invest in developing AI content creation tools. 

As the demand for streamlined, automated content production escalates across various sectors, businesses that innovate in this space can capture substantial market share. This makes Generative AI one of the most suitable AI business ideas of the decade. 

[Also Read: Cost of developing an AI content detection tool in 2024]

AI Content Creation Tool

AI content creation tools have the power to revolutionize content production, paving the way for businesses to produce high-quality content with ease, rapidly, and in large quantities. These Generative AI tools for businesses help both users and businesses with a wide range of content needs, including writing articles, making graphics, and creating videos, thereby saving time and resources for content creators.

[Also Read: How Much Does It Cost to Build an App Like ChatGPT]

The content marketing market is estimated to reach $69.8 billion by 2030, making AI content creation a profitable investment for businesses that want to meet the expanding demand for valuable content. This makes them one of the most suitable AI and machine learning business ideas of the decade.

AI eLearning Platform

AI-powered eLearning platforms are meeting the increasing demand for online education by providing personalized learning experiences, making them one of the superior AI business ideas for entrepreneurs to drive into. These platforms use AI algorithms to analyze learner behavior and preferences, allowing them to adapt the content accordingly. The platform can create personalized learning paths that optimize knowledge retention and engagement by understanding each learner’s strengths, weaknesses, and preferences.

AI Energy Optimization Solution

One of the most powerful AI and machine learning startup ideas, the AI-powered energy efficiency optimization solution is like a smart tool to help businesses save energy and money. It uses clever algorithms to study energy use and finds ways to reduce waste. By using this technology, businesses can lower their utility bills and contribute to protecting the environment.

It’s a great way to show that your business cares about being eco-friendly and can improve your brand image. The AI in the energy market is expected to reach $19.8 billion by 2031, thus highlighting how these types of AI SaaS ideas are a few of the lucrative options for entrepreneurs to dive into.

AI-Based Smart Finance Robotic Process Automation App

Businesses can also enter the AI ecosystem by launching an AI-based Smart Finance RPA app. This app automates repetitive finance tasks using AI technology, saving time and cost, reducing errors, and improving compliance. Businesses can streamline their financial operations and enhance efficiency by automating processes like invoice processing, reconciliation, and tax filing. With a well-developed RPA app, businesses can provide cutting-edge financial solutions to meet users’ needs and stay at the forefront of the AI revolution, which makes it one of the unique AI startup ideas to consider.

AIoT App

Businesses can also initiate an AIoT startup that utilizes smart sensors and cloud computing to transform industries. One of the lucrative AI business ideas for a startup in this fast-pacing ecosystem is to offer solutions such as predictive maintenance for industrial equipment, energy management for buildings, and environmental monitoring for agriculture. To ensure a successful launch, identify a specific problem your target customers face. Develop a prototype of your AIoT device and thoroughly test your AI product ideas using real data to present it well to potential investors and customers.

AI-Driven Cybersecurity App

AI-driven cybersecurity is becoming increasingly valuable in the recent digital era ruled by cyber threats and data breaches. This area represents a significant and promising opportunity in the realm of artificial intelligence business ideas. This technology utilizes machine learning algorithms to identify and address cyber threats in real-time, protecting businesses against malware and security attacks while safeguarding sensitive information. Being an AI business idea worth diving in, the AI cybersecurity market is expected to reach $93.75 billion by 2030.

AI-Based Smart Home Solution

An AI-based smart home solution is an amazing AI startup idea that leverages advanced artificial intelligence to create highly automated and intelligent home environments. By integrating machine learning and IoT devices, this solution can offer predictive maintenance, personalized energy management, and enhanced security. It helps homeowners reduce energy costs, improve safety, and enhance convenience, making everyday living more efficient and enjoyable.

AI-Powered Video Analytics Solution

An AI-powered video analytics solution is a highly promising AI startup concept that utilizes machine learning algorithms to analyze video footage in real-time. This technology can be applied to security, traffic management, and customer behavior analysis.It helps businesses and municipalities by automating surveillance, improving security measures, optimizing traffic flow, and providing valuable insights into consumer behavior. These advancements lead to enhanced safety, better resource management, and more informed decision-making, making it a key area for business ideas using AI to drive innovation and efficiency.

Get AI-based solutions can help your business

How to Estimate the Cost of AI App Development

The cost of AI app development varies based on factors such as feature complexity, data processing, and system integration. A basic AI app, like a chatbot or recommendation engine, typically costs $20,000 to $50,000.

More advanced applications, such as predictive analytics or voice recognition, range from $50,000 to $150,000. Highly sophisticated AI solutions, including deep learning-based automation or virtual assistants, can exceed $150,000 to $500,000+. The final cost is influenced by AI model development, data training, UI/UX design, testing, and post-launch maintenance.
Beyond development, businesses should account for expenses like data collection, cloud hosting, API usage, compliance, and ongoing updates. Choosing between in-house development and outsourcing can also impact costs and project timelines. While AI app development requires a significant investment, it delivers long-term benefits by enhancing automation, improving customer interactions, and optimizing operations.

Also Read: Intelligent App Development Cost Estimation Guide

How AI Business Ideas Can Drive Revenue?

The only true answer to how AI startups make money is the combination of their access to data sets and capabilities.

For your AI project management practices to be truly successful, it is advisable to collaborate with a dedicated AI consulting firm and make these three facets of your business very strong.

Data Sets

The success of your AI startup ideas and the answer to how AI services companies make money ultimately depends on the data set they work with. The more qualitative it is, the greater the playing field for AI engineers will be.

But, gathering data is not easy. Everyday security breaches, multiple phishing attacks and malwares have made it all the more difficult for a startup app development company to gather relevant data.

The solution to this crippling restriction is a partnership. Several tech giants are known to partner with hospitals, payment companies, or even enterprises to get data for them to analyze.

Domain Expertise

More often than not, insights that create a breakthrough innovation come from an in-depth idea of the industry or domain. Only when you know an industry inside out, you will be able to identify areas where AI will create a breakthrough.

Skilled AI Talent

AI development company in Australia, US and of other regions and researchers will ultimately drive your AI business. The more skilled your associated manpower is, the greater the chances of you delivering intelligent services to industries that will make money with Artificial Intelligence.

Now that you know how to make money in AI with the best AI and machine learning business ideas, it is now time for the Final Step. The step that will bring you on the path of earning guaranteed high revenues and business popularity. By choosing from the list of the best AI business ideas and implementing a strategic approach, you can build a profitable, future-ready venture that leverages the power of artificial intelligence to drive innovation and market growth.

From AI-based app development to smart chatbots, predictive analytics, and generative AI consulting, Appinventiv provides artificial intelligence consulting services which help AI-based businesses design diverse digital solutions and implement AI for business ideas that cater to diverse industries. We as an  innovative artificial intelligence development company are not only capable of enhancing the overall business efficiency and productivity but also guarantee to drive customer engagement and maximum ROI. Get in touch with our AI engineers to start or scale your AI journey.

FAQs

Q. How to start an AI business?

A. To embark on your AI business journey and to know how to start an AI business, consider partnering with a dedicated AI app development firm like Appinventiv. Our experts have almost a decade of experience when it comes to transforming your AI ideas into reality. Start by discussing your AI-related startup ideas, business vision, and goals with our skilled AI professionals, who can guide you through the development process while helping you identify the best solutions per your custom business needs. With our proven track record and technical prowess, we can help you create a top-notch AI business idea that can help you stay ahead in this competitive market.

Q. How much does it cost to build an AI-based business solution?

A. The cost of developing an AI-based business solution can vary between $50,000 and $300,000. Several factors can directly affect the cost of development, such as the overall complexity of the app, the location of the app development firm, the time frame for development, features to be integrated into the app, etc. Get in touch with our AI developers to get a clear cost of your AI-based startup ideas as per your custom business requirements.

Q. What are the top 10 best AI business ideas to start?

A. AI-Based Retail Assistance Solution: The AI retail market is expected to grow to $ 40.74 billion by 2030.

AI-Based Entertainment Platform: Using AI to create the content and offer customized suggestions, the market is projected to surpass 99.48 billion by 2030.

AI-Based Recruitment App: It automates the process of hiring by sifting through resumes, interviewing, and forecasting the compatibility of candidates, which saves expenses and time.

Smart Finance RPA App: An AI robot automates financial processes, such as invoice processing and tax filing, making them more efficient and compliant.

Artificial Intelligence (AI) in Logistics and Supply Chain Management: Route optimization, demand forecasting, and stock tracking. Use artificial intelligence to conduct efficient operations across the supply chain.

AI Healthcare Platform: Helps improve diagnostics and treatment plans, as well as patient care, and reduces the dissimilarity between patients and physicians.

AI Energy Optimization Solution: It decreases the energy waste and expense, and the global AI energy target market is likely to be well worth 19.8 billion dollars by 2031.

Artificial Intelligence-Based Cybersecurity Application: Real-time identification and protection of a cyber threat, with the potential market estimated at 93.75 billion in 2030.

Intelligent Smart Home: Transforms home automation with predictive maintenance, energy management, and security.

Artificial Intelligence with Video Analytics: It can analyze video data using artificial intelligence to enhance security, traffic control, and consumer patterns, and make better decisions.

Q. How can AI improve customer experience in businesses?

A. AI enhances customer experience by personalizing interactions, streamlining support, and optimizing business operations. Here’s how AI transforms customer engagement:

Predictive Customer Insights: AI anticipates customer needs by analyzing data trends, enabling businesses to proactively enhance service and retention.

Voice & Sentiment Analysis: AI evaluates customer interactions to gauge sentiment, helping businesses address concerns and improve satisfaction.

Personalized Recommendations: AI analyzes customer preferences and behaviors to suggest relevant products and services, boosting engagement and satisfaction (e.g., Amazon, Netflix).

AI-Powered Chatbots & Virtual Assistants: Intelligent chatbots provide instant, 24/7 customer support, resolving queries efficiently and improving response times (e.g., ChatGPT, Siri, Google Assistant).

Automated Self-Service Solutions: AI-driven self-help portals and FAQs empower customers to find quick solutions without human intervention.

 

 

 





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The “Boring” AI Business Model Making Millionaires in 2025

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In 2025, while the world gushed about flashy robots, talking cars, and futuristic gadgets, a group of young people quietly built an AI business so unremarkable at first glance that almost nobody noticed it. It wasn’t glamorous, it didn’t have a viral video, and it didn’t try to predict the next big trend. But it was making millionaires out of regular people… people who had nothing but grit, curiosity, and patience.

This is the story of Maya and her “boring” AI model, and how it reshaped not only her life but the lives of thousands who followed her blueprint.

Maya was 27 in early 2024, living in a one-bedroom apartment above a grocery store in a crowded city. She had a degree in literature, not engineering, and worked as a night-shift clerk in a local print shop.

She had no investors, no startup culture contacts, and no idea how to code beyond simple website builders. But she had two assets few people had: relentless curiosity and the habit of looking for problems that nobody wanted to solve.

One evening, while scrolling through forums about small business challenges, she noticed dozens of owners complaining about repetitive, mundane tasks: sorting invoices, tagging emails, filing customer questions, and moving data from one platform to another. It wasn’t glamorous work. It wasn’t on the cover of magazines. But it was expensive and time-consuming.

And something clicked.

“What if AI could quietly do all these boring tasks?” she wondered.

While the tech world chased billion-dollar breakthroughs in self-driving cars and virtual universes, Maya started tinkering with the “unsexy” side of artificial intelligence. She didn’t try to build a sentient assistant. Instead, she built small, focused AI “micro-tools” that automated the ugly, tedious back-office work of everyday businesses.

Maya’s first tool wasn’t impressive. It was a simple AI model that read PDF invoices, extracted key data, and entered it into spreadsheets for small business owners. She found an open-source language model online, watched tutorials for weeks, and cobbled together an interface using no-code platforms. She spent nights testing it on free samples and begging café owners to try it.

Her first paying client was a family-run furniture shop. They hated doing paperwork at the end of every week. Maya’s tool reduced their 6-hour weekly process to 30 minutes. They paid her $50 a month. She danced around her apartment with joy.

But Maya didn’t stop. She built a second micro-tool to categorize customer emails into urgency levels. A third to predict low inventory items. Each small AI bot solved one tiny, boring problem. Together, they saved small businesses hundreds of hours.

By mid-2024, Maya had 47 clients paying $30-$200 a month. Not life-changing, but enough to quit her print shop job. She then created subscription tiers and white-labeled her micro-tools so other freelancers could sell them too. Her pitch wasn’t sexy; it was practical. “Save time. Save money. Grow quietly.”

This was the turning point.

A former schoolteacher named Daniel, 32, bought a license to resell Maya’s AI bots in his own city. He signed up 20 businesses in a month. He made more than he’d made teaching full-time. A retired accountant named Lucia, 58, did the same. She introduced the tools to her network of small retailers and built a six-figure income in a year.

The “boring” AI model had become a movement… not of tech moguls, but of ordinary people solving ordinary problems.

Maya’s philosophy was simple:

Don’t chase hype.

Solve persistent problems.

Keep costs low and margins healthy.

Let others partner and profit.

Instead of selling one giant software platform, she sold dozens of tiny, niche AI “workers” that anyone could subscribe to individually. This modular approach allowed even small-town businesses to adopt AI at their own pace.

By early 2025, hundreds of resellers around the world were using Maya’s framework to deliver micro-AI services. Some ran one-person operations; others built small agencies. They weren’t Silicon Valley founders… they were baristas, teachers, retirees, and college kids who saw a need and used Maya’s blueprint.

One such story was Sophie, a 21-year-old student who had grown up watching her parents run a bakery. Sophie bought Maya’s AI invoicing and scheduling tools, customized them with her own branding, and started selling them to bakeries and cafés in her region. Within six months, she’d replaced her part-time job income. Within a year, she was making $12,000 a month… enough to pay off her student loans before graduating.

Then there was Amir, 44, a former mechanic who lost his job during an economic downturn. He learned how to use Maya’s training materials, packaged AI bots for auto shops, and made more money in his first year of self-employment than he ever had before.

The model worked because it wasn’t glamorous. No flashy ads. No wild claims. Just steady value. Maya called it “AI plumbing”… building the pipes that let small businesses run smoother.

She focused on four principles:

1. Accessibility: Make it cheap and easy enough for non-tech people.

2. Education: Offer plain-language training and support.

3. Flexibility: Let resellers white-label and adjust pricing.

4. Community: Encourage sharing improvements and templates.

By mid-2025, Maya herself wasn’t just running a business. She was leading a decentralized movement of AI micro-entrepreneurs. Her own income grew into the millions, but she always reinvested in building better training and tools.

And yet Maya stayed humble. She still lived in a modest apartment, still answered customer support emails personally, and still said no to investors who wanted to “scale aggressively.” She believed the real revolution wasn’t another billion-dollar tech giant but thousands of small, empowered entrepreneurs earning honest incomes from useful AI tools.

Her success attracted skepticism. Some said it was too simple. Others thought the big companies would crush her. But Maya knew she was in a different lane. She wasn’t trying to win a popularity contest… she was trying to solve real problems.

And as the economy shifted in 2025, her approach turned out to be exactly what people needed: stability, low overhead, and the ability to start small.

By late 2025, analysts began to notice. Articles described the phenomenon of “boring AI” making quiet millionaires. But those inside the movement already knew: it wasn’t about hype. It was about mindset.

Maya often told her community:

Find a problem. Build the simplest AI solution. Offer it to the people who need it most. Repeat. Don’t try to impress. Try to improve.

It was a model anyone could adopt. A 19-year-old in Manila built AI tools for local fishermen to predict tide patterns. A 63-year-old in Nairobi used AI bots to help farmers monitor soil moisture. A single mom in Toronto built an AI appointment scheduler for local hair salons. The stories poured in, all rooted in the same principle: simple solutions, consistent effort, and community sharing.

Maya had proven something profound:

You don’t need to invent the next flying car to become successful. You can build “boring” tools that make life easier… and people will pay for that forever.

Moral of the Story

In a world obsessed with hype and spectacle, quiet consistency and practical problem-solving can outlast trends. The “boring” AI business model shows that success isn’t about dazzling innovation… it’s about meaningful impact. When you stop chasing fame and start solving real problems, you unlock a sustainable path to wealth, freedom, and purpose. Even the most ordinary ideas, applied persistently, can change lives… including your own.



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The AI Movie Factory Is Ramping Up

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“Because I know the rooster.”

Those were the words of a Baghdad-based director named Hasan Hadi when asked how he was able to corral not just a host of non-actor children for his new movie but a particular kind of junglefowl.

Hadi – his The President’s Cake will come out this fall from Sony Pictures Classics and was just chosen as the official Iraqi Oscar submission – made the comment to a pair of reporters at a dinner at the Toronto International Film Festival. While among the more colorful – and barnyardy – of the remarks uttered at the important early-September gathering, it was far from the only one emphasizing the uniquely human qualities of filmmaking.

Across the Canadian city, directors made statements that, as the algorithm rises, almost take on a political cast. Richard Linklater and Ethan Hawke stood in front of an audience and described the painstaking rehearsal for their movie about Lorenz Hart. (“Ethan and I have done our share of dialogue-ntensive movies,” Linklater said, “but this was something else.”) Nia DaCosta talked about how her feelings on Ibsen animated her need to redo Hedda Gabler. Paul Greengrass left audiences breathless with his latest neo-verite adventure that has Matthew McConaughey as an embattled bus driver saving children in the 2018 Paradise wildfires.

None of them mentioned AI explicitly. They didn’t have to. Their pro-human vehemence was evident in every quote and frame.

But a different vision of Hollywood was also playing out at the industry’s big convocation, as tech entrepreneurs pitched their own vision to the entertainment decisionmakers. People from Largo, which builds models to test movies using virtual audiences. Luma AI, whose executives think studios can deploy their video-generation tool to ramp up production (and ramp down sets). Genny, which uses Google’s VEO-3 to help documentarians create re-enactment footage with the push of a button. All of them were at TIFF too, trying to enact their own vision of the entertainment future. And while they rarely crossed paths with the humanists, they clashed with them ideologically just the same. Hollywood may only be big enough for one them.

Pull the camera back and you’ll suddenly see the same battle playing out everywhere, in boardrooms and courtrooms. Warner Bros. has just sued Midjourney, making similar allegations as Disney and Universal before it against the image-generation startup. Anthropic has just agreed to settle with three authors who sued the AI company for training its models on their books. If the settlement is approved, it could result in the company paying a total of $1.5 billion to hundreds of thousands of authors – but the judge in the case also cleared the way for tech companies to engage in such training without permission so long as they bought retail copies of the books.

Seeking to convey the stakes, two activists, Guido Reichstadter and Michael Trazzi, have gone on hunger strikes outside the San Francisco office of Anthropic and London office of Google’s DeepMind respectively. They say they won’t eat any food until the companies stop developing all new AI models, giving both a visual and historical dimension to the conflict.

Meanwhile, the startup Showrunner, with investment from Amazon, made waves when it said it would use AI for an internal experiment to restore some 43 minutes of lost footage from Orson Welles’ The Magnificent Ambersons. The announcement generated a backlash from the company managing Welles’ estate, which an official there calling the move a “purely mechanical exercise” that lacked “uniquely innovative thinking.”

And of course The Sphere just opened an AI-enabled re-formatted The Wizard of Oz, aided by Google and $80 million (a budget $15 million higher than the original’s in 2025 dollars). While eliciting rave reviews, the project also added in cameos for the CEOs David Zaslav and James Dolan who were not, according to most film historians, present on the 1939 MGM set.

After years of companies building tech and raising money, the introduction of AI into the house of storytelling is finally here. And media players need to decide whether they want to make up the guest bedroom.

It would also be a mistake to think AI will only be used on classic films – on films with few stakeholders. The tools pitched and implemented would be used to create what was once done by hand on sets and in marketing departments, automating the analogue, with all the labor and cultural consequences to go with it.

At a hearing for the Anthropic settlement, one of the author plaintiffs, Kirk Wallace Johnson, said he saw the proceeding as the “beginning of a fight on behalf of humans that don’t believe we have to sacrifice everything on the altar of AI.” Johnson is the author of The Feather Thief, a critically acclaimed 2018 true-crime book about a heist that made off with scores of centuries-old historical bird skins. You could say that he, too, knows the rooster.

This story appeared in the Sept. 10 issue of The Hollywood Reporter magazine. Click here to subscribe



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How To Un-Botch Predictive AI: Business Metrics

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Predictive AI offers tremendous potential – but it has a notoriously poor track record. Outside Big Tech and a handful of other leading companies, most initiatives fail to deploy, never realizing value. Why? Data professionals aren’t equipped to sell deployment to the business. The technical performance metrics they typically report on do not align with business goals – and mean nothing to decision makers.

For stakeholders and data scientists alike to plan, sell and greenlight predictive AI deployment, they must establish and maximize the value of each machine learning model in terms of business outcomes like profit, savings – or any KPI. Only by measuring value can the project actually pursue value. And only by getting business and data professionals onto the same value-oriented page can the initiative move forward and deploy.

Why Business Metrics Are So Rare for AI Projects

Given their importance, why are business metrics so rare? Research has shown that data scientists know better, but generally don’t abide: They rank business metrics as most important, but in practice focus more on technical metrics. Why do they usually skip past such a critical step – calculating the potential business value – much to the demise of their own projects?

That’s a damn good question.

The industry isn’t stuck in this rut for only psychological and cultural reasons – although those are contributing factors. After all, it’s gauche and so “on the nose” to talk money. Data professions feel compelled to stick with the traditional technical metrics that exercise and demonstrate their expertise. It’s not only that this makes them sound smarter – with jargon being a common way for any field to defend its own existence and salaries. There’s also a common but misguided belief that non-quants are incapable of truly understanding quantitative reports of predictive performance and would only be misled by reports meant to speak in their straightforward business language.

But if those were the only reasons, the “cultural inertia” would have succumbed years ago, given the enormous business win when ML models do successfully deploy.

The Credibility Challenge: Business Assumptions

Instead, the biggest reason is this: Any forecast of business value faces a credibility question because it must be based on certain assumptions. Estimating the value that a model would capture in deployment isn’t enough. The calculation has still got to prove its trustworthiness, because it depends on business factors that are subject to change or uncertainty, such as:

  • The monetary loss for each false positive, such as when a model flags a legitimate transaction as fraudulent. With credit card transactions, for example, this can cost around $100.
  • The monetary loss for each false negative, such as when a model fails to flag a fraudulent transaction. With credit card transactions, for example, this can cost the amount of the transaction.
  • Factors that influence the above two costs. For example, with credit card fraud detection, the cost for each undetected fraudulent transaction might be lessened if the bank has fraud insurance or if the bank’s enforcement activities recoup some fraud losses downstream. In that case, the cost of each FN might be only 80% or 90% of the transaction size. That percentage has wiggle room when estimating a model’s deployed value.
  • The decision boundary, that is, the percentage of cases to be targeted. For example, should the top 1.5% transactions that the model considers most likely to be fraudulent be blocked, or the top 2.5%? That percentage is the decision boundary (which in turn determines the decision threshold). Although this setting tends to receive little attention, it often makes a greater impact on project value than improvements to the model or data. Its setting is a business decision driven by business stakeholders, representing a fundamental that defines precisely how a model will be used in deployment. By turning this knob, the business can strike a balance in the tradeoff between a model’s primary bottom-line/monetary value and the number of false positives and false negatives, as well as other KPIs.

Establishing The Credibility of Forecasts Despite Uncertainty

The next step is to make an existential decision: Do you avoid forecasting the business value of ML value altogether? This would prevent the opening of a can of worms. Or do you recognize ML valuation as a challenge that must be addressed, given the dire need to calculate the potential upside of ML deployment in order to achieve it? If it isn’t already obvious, my vote is for the latter.

To address this credibility question and establish trust, the impact of uncertainty must be accounted for. Try out different values at the extreme ends of the uncertainty range. Interact in that way with the data and the reports. Find out how much the uncertainty matters and whether it must somehow be narrowed in order to establish a clear case for deployment. Only with insight and intuition into how much of a difference these factors make can your project establish a credible forecast of its potential business value – and thereby reliably achieve deployment.



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