ways to make money with AI
Artificial Intelligence

15 Ways to make money with AI

Mandi D 27 November 2023
Table of contents

Ways entrepreneurs can build and monetize AI, include providing services like development/consulting, selling AI products/apps, enhancing businesses with AI, investing in AI companies, and supplying data/compute infrastructure. Key opportunities exist in leveraging AI for predictive analytics, personalization, automation, conversational interfaces, and developing innovative applications.

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Introduction

Artificial intelligence (AI) represents an unprecedented opportunity for entrepreneurs and businesses to drive growth, increase efficiency, and deliver greater value to customers. As AI technologies become exponentially more powerful due to advances in computing power, new algorithms, and more training data, they are transforming nearly every industry in profound ways. Savvy entrepreneurs who can capitalize on these emerging capabilities stand to reap outsized rewards.

Just a decade ago, many cutting-edge AI applications were confined to research labs and tech giants. But thanks to open source frameworks, cloud computing, and new toolkits, AI is now accessible to businesses of all sizes. Everything from computer vision, natural language processing, predictive analytics, robotic automation, recommendation engines, and beyond is now within reach.

Entrepreneurs who take the time to deeply understand modern AI and creatively apply it to real business problems can drive amazing results. The most successful entrepreneurs don’t just jump on AI buzzwords. Rather, they carefully identify their unique business challenges and how AI specifically can address pain points for customers or within internal operations.

The opportunities to leverage AI to provide new value are practically limitless. With custom machine learning models, businesses can uncover subtle patterns in data, make hyper-accurate forecasts, identify high-potential opportunities, deliver individualized recommendations, and automatize complex processes. This drives greater efficiency, higher quality decisions, and more delight for customers.

Integrating the right AI solutions into an existing business can deliver a tremendous competitive advantage. Even small improvements in things like customer churn, lifetime value, conversion rates, and operational costs directly translate into higher profits. AI-first startups utilizing AI from day one can completely disrupt traditional industries. Investors eagerly fund promising AI startups because the growth potential is enormous.

As entrepreneurs explore potential AI applications, they should ground their thinking in real business outcomes. The most promising AI solutions start with a clear problem and measure success based on tangible impact – whether it’s increased sales, lower costs, tighter risk management, faster turnaround times, etc. Making the business case for AI investment is critical.

In total, artificial intelligence promises to massively expand what humans and businesses are capable of. We have only scratched the surface of AI’s full potential. Its continued exponential progress will enable entrepreneurs to accomplish things previously unimaginable. Any business that doesn’t eventually tap into AI will get left behind.

Now is the time for forward-thinking entrepreneurs to capitalize on the AI revolution to generate new value and secure a lasting competitive advantage. Those who actively experiment with and thoughtfully integrate the AI capabilities emerging today will thrive in the new economy AI is accelerating.

Provide AI Services

One of the most lucrative and accessible ways for entrepreneurs to capitalize on AI is by directly selling their AI expertise and services. As more companies adopt AI capabilities, demand for talented AI practitioners vastly exceeds supply. Leveraging your AI skills to help organizations develop and implement AI solutions can be extremely profitable.

AI Consulting

AI consulting has quickly grown into a massive industry. Established management consulting firms like McKinsey, BCG, and Bain & Company have rushed to build out AI consulting practices. At the same time, many new AI pure play consulting firms have emerged to help clients navigate the AI landscape.

As an AI consultant, companies will pay you handsomely for your strategic advice and technical expertise implementing AI solutions tailored to their specific business challenges. You can consult across the full AI project lifecycle – from initial opportunity assessment, strategy development, vendor selection, solution design, systems integration, model development, and change management.

The most in-demand AI consulting skills include:

  • Business strategy: Analyze clients’ data, processes and objectives and identify high-potential AI applications. Quantify expected business impact and ROI.
  • Solution architecture: Design end-to-end AI systems and data pipelines tailored to clients’ needs.
  • Data strategy: Advise clients on how to structure, clean, enrich, and label training data. Set up processes for ongoing data collection.
  • MLOps: Help implement model development workflows, infrastructure, and monitoring for sustainable AI deployment.
  • Change management: Manage organizational change to ensure adoption of AI solutions and maximize realized benefits.

To succeed as an AI consultant, technical chops alone are not enough. You must also have strong communication skills to interface with leadership and clear business acumen to drive measurable results. Having experience in a specific industry like healthcare, manufacturing, retail, etc. can help you provide more tailored guidance.

AI consulting engagements often start from hundreds of thousands to millions of dollars, so this can be a very lucrative way to monetize your expertise. Many consultants even earn additional income from equity stakes or royalties from successful AI solution deployments.

AI Development

If you have strong software engineering and data science skills, you can offer end-to-end development of custom AI solutions for clients. Many companies have strategic AI initiatives but lack the internal technical talent to execute them.

Typical development projects include building things like:

  • Conversational chatbots and virtual agents
  • Recommendation engines for content, products, services
  • Computer vision systems for applications like quality inspection
  • Predictive maintenance systems for industrial equipment
  • Supply chain optimization algorithms
  • Predictive analytics and forecasting models
  • Sentiment analysis systems
  • Custom embedded smart devices with machine learning

Focus on industries and use cases where you have existing domain expertise. For example, developers with experience in manufacturing can build predictive maintenance apps for equipment failure, developers in retail can build customer segmentation models, etc. Understanding the nuances of an industry allows you to build AI that delivers maximum business value.

For larger engagements, consider hiring additional freelance data scientists, ML engineers, and front-end developers to help execute projects at scale. You can also partner with AI consulting firms to joint pitch clients and provide complementary services.

Data Services

The raw material for training AI systems is data. Cleaning and preparing data accounts for up to 80% of the work in many AI projects. Offering data-related services can let you focus on monetizing your data science skills without needing full software development capabilities.

Specific data services in high demand include:

  • Data collection: Gather relevant datasets from clients’ systems and external sources. Ensure adequate volume for model training.
  • Data cleaning: Fix missing values, duplicates, outliers, inconsistencies, and errors. Get data ready for analysis.
  • Data enrichment: Append useful attributes from external APIs and datasets. Adds context.
  • Data labeling: Manually label and annotate datasets for supervised learning. Essential for computer vision, NLP, etc.
  • Data transformation: Manipulate raw data into structures and formats optimized for AI use cases.
  • Data storage/hosting: Upload client data to your cloud environment for centralized access. Provides security, scalability, and governance.

Position yourself as a trusted data partner that can handle clients’ data needs from end-to-end. Invest in data infrastructure, security protocols, and tooling to ensure high-quality deliverables.

This is a great niche for technical founders looking to develop a steady stream of recurring services revenue. Be sure to secure proper data licensing and usage rights.

AI Training & Education

As organizations build out internal AI capabilities, there is surging demand for AI training and education. Conducting workshops, courses, and corporate training programs can be a scalable high-margin business.

Topic areas most in demand include:

  • Fundamentals of AI/machine learning
  • Hands-on ML/DL frameworks like TensorFlow and PyTorch
  • Computer vision and natural language processing
  • AI project management, deployment, monitoring
  • AI ethics, governance, and responsible development
  • Emerging AI trends and technologies

Target both technical and non-technical learners across all levels of expertise. Provide tailored sessions focused on specific business applications of AI. Developing a highly-rated catalog of on-demand video courses can help you passively monetize your expertise.

Separate yourself by focusing on practical, hands-on training – not just conceptual overviews. Give learners access to cloud notebooks and computing resources to actively build models. Share real code examples and case studies.

Once you establish your expertise and following, this becomes a highly scalable business. You can record courses once and sell them indefinitely. Develop partnerships with corporates looking to educate large workforces. Continuously expand your course catalog over time.

Sell AI Products & Applications

Rather than selling AI services, you can productize your own AI technologies and sell or license them as complete products or components. AI startups creating innovative applications, developer tools, and intellectual property around AI are attracting huge investments.

SaaS Products

One proven business model is to build your own software-as-a-service (SaaS) product with AI and machine learning capabilities baked in. Customers pay a recurring subscription to access your product through the cloud.

For example, you could create:

  • A predictive sales analytics platform that forecasts deals closing, high-value targets, churn risk, etc.
  • An AI-powered customer support platform that uses NLP to analyze tickets and suggest solutions.
  • A computer vision-powered visual inspection system for manufacturing.
  • A supply chain optimization platform with predictive logistics algorithms.

The key is to focus on a specific use case or industry vertical so you can tailor the AI to add maximum value. Deep domain expertise in fields like finance, healthcare, retail, etc. allows you to build specialized AI capabilities that generic AI cannot match.

Make your AI transparent to users – they simply access the predictive insights your product generates without needing AI expertise. Handle all model development, maintenance, and infrastructure under the hood.

SaaS businesses can be highly scalable and benefit from recurring revenues and cross-selling opportunities across customer bases. However, the technology risk is higher given the need to build and maintain complex software. Partnering with an expert development team can help mitigate this risk.

AI APIs

Rather than build complete products on top of your AI models, expose your AI capabilities directly via application programming interfaces (APIs) that others can integrate into their own products and workflows.

For example, offer APIs for:

  • Image recognition
  • Recommendation engines
  • Natural language processing
  • Predictive analytics
  • Speech recognition
  • Anomaly detection
  • etc.

Focus on fields where your proprietary AI offers significant advantages over commodity APIs. For instance, an AI startup Cnvrg.io offers computer vision models optimized for detecting defects in manufacturing.

Make integration seamless by providing SDKs and clear documentation. Offer tiered pricing plans based on usage volume.

The benefit of an API model is you don’t need to build full products and can focus just on the core algorithm development. And if your API powers the next killer app, you get to ride along with their growth.

AI Apps

If you have an idea for a consumer or business facing app enhanced by AI capabilities, you can build direct-to-user AI apps. Sell them through app stores or your own website.

Some examples:

  • A mobile calorie tracking app with photo recognition for food logging
  • An app that removes backgrounds from photos with a single click
  • A smart keyboard with next word predictions
  • A real-time language translation app
  • An app that colorizes old B&W photos

Leverage open source libraries and cloud services to quickly prototype and validate your app before investing heavily in custom AI development. Carefully consider your distribution strategy and go-to-market plan before building.

Apps can scale exponentially if they go viral, but the market is highly competitive. You will need great marketing and UX design. Integrating a subscription model can improve monetization.

This is ideal for founders with consumer marketing and mobile app expertise looking to add AI flair.

AI Tools & Components

Build standalone algorithms, frameworks, models, and other AI components developers can integrate into their own products vs. complete end-user apps. Offer them as commercial software toolkits, code libraries, SDKs, etc.

For example, you could sell things like:

  • Computer vision/NLP models trained on niche datasets
  • A toolkit for building conversational chatbots
  • A graph database optimized for AI development
  • Virtual agents and pre-built personalities
  • AI code libraries for IoT/edge devices

Position your products for programmers and data scientists. Make integration straightforward. Develop eye-catching demos to showcase capabilities.

This can be an attractive model because you avoid overhead of building full applications. But you need to invest heavily in documentation, samples, and community building to drive adoption. Viral open source projects that transition to paid models are ideal for this approach.

Enhance Existing Businesses with AI

If you already own or operate a business, integrating targeted AI solutions can deliver outsized benefits in the form of increased revenues, lower costs, tighter risk management, faster operations, and greater customer loyalty. Even simple machine learning models built using standard tools can yield impressive ROI.

When assessing AI opportunities, take an extremely business-centric view. Consider your biggest pain points and cost centers. Identify areas plagued by uncertainty and poor visibility. Look for tedious high-volume tasks. Talk to customers about unmet needs. Determine specific business objectives AI could help drive.

Here are some proven ways both large enterprises and small businesses can benefit from AI:

Personalization & Recommendations

Leverage machine learning techniques like collaborative filtering to understand your customers’ preferences and deliver hyper-personalized experiences. For example:

  • Build customer segmentation models that split your audience into groups with common behaviors/attributes. Create tailored interactions for each segment.
  • Implement product recommendation engines that suggest relevant items based on interests, purchase history, behaviors, etc. Dramatically improves conversion rates.
  • Analyze customer journeys to identify common paths and drop off points. Target high-value suggestion messages at each step.
  • Generate automated reminders when customers are due for purchases or appointments. Reduce churn.

The greater the personalization, the deeper engagement and ownership customers feel. Start collecting the data needed to fuel personalization.

Predictive Analytics

Apply techniques like time series forecasting, classification, and regression analysis to extract powerful insights from your business data. Specifically:

  • Forecast sales pipeline conversion rates more accurately based on past pipeline performance.
  • Flag high-risk accounts likely to churn based on predictive models. Proactively retain them.
  • Continuously analyze supply chain data to minimizing stockouts and writeoffs.
  • Identify subtle changes in key performance metrics that indicate future problems. Add monitoring guardrails.
  • Uncover correlations and patterns that would be impossible for humans to manually detect.

This transforms gut instinct decision making into data-driven strategic planning. Better anticipated outcomes translate directly into higher revenues and lower costs.

Process Automation

Use approaches like robotic process automation (RPA), smart workflows, and natural language generation to automate repetitive, high volume manual tasks. This frees up employees to focus on higher judgement work. Examples include:

  • Automating order processing, payment collection, appointment booking and other repetitive back-office tasks.
  • Automating customer service inquiries by using chatbots and NLP to parse requests then retrieve answers from knowledge bases.
  • Generating personalized sales correspondence or marketing emails tailored to customers.
  • Parsing documents and forms to extract key data needed for downstream processes.

Target the most mundane yet time consuming aspects of your business first. Time savings compound over years.

Conversational Interfaces

Implement conversational chatbot and virtual assistant interfaces powered by natural language processing to communicate with customers and staff:

  • Field common sales questions, take orders, provide support etc. 24/7.
  • Help customers self-serve instead of waiting on hold for agents.
  • Schedule meetings, interviews, appointments via natural dialogue.
  • Answer employee questions about HR policies, benefits, training, etc.

Today’s NLP models are sufficiently advanced for useful applications that deliver round-the-clock automated assistance.

Enhanced Offerings

Incorporate AI directly into existing products and services to add value:

  • Use computer vision in mobile apps for image analysis, augmented reality, and smart filters.
  • Apply NLP in documents and communication tools for text summarization, sentiment analysis, and translation.
  • Embed predictive analytics into software to forecast future scenarios and risk.
  • Add biometric security features like voice, facial recognition, and behavior profiling.

Delivering enhanced capabilities and experiences keeps your offerings differentiated and compelling. But focus on AI that aligns with your product and customers vs. gratuitous features.

The competitive advantage AI confers to businesses is fleeting. To stay ahead, you must commit to continuous iterative improvement powered by ongoing experiments and constant learning. View AI not as a one-time initiative, but a new way of operating.

How to Invest in AI Companies

Rather than building your own AI business from scratch, you can generate substantial returns by investing capital into promising AI startups and public companies. The surge in AI adoption has created a booming startup ecosystem and lucrative public markets.

AI Startup Funding

Providing seed or early stage private funding to AI startups allows you to own a piece of a potentially massive business before exponential growth occurs. Returns can eclipse 100X if the startup succeeds long-term.

Compelling startups to consider include those:

  • Developing innovative proprietary AI algorithms and technologies with sustainable competitive advantages. Look for specialization around industry verticals or hard technical problems.
  • Applying AI in novel ways to disrupt traditional industries like finance, healthcare, manufacturing, etc. Back teams intimately familiar with the domain.
  • Leveraging AI for cutting edge applications like self-driving vehicles, robotics, synthetic media, drug discovery, quantum computing, etc. Consider cross-disciplinary founding teams.
  • Gaining rapid market traction and revenue growth indicators like waitlists, pilot customers, strong word-of-mouth etc. Talk to early users to validate product-market fit.
  • Led by technical founders with elite AI credentials from organizations like OpenAI, DeepMind, FAIR, etc. Secondary founding teams can still excel.
  • Funded by top AI-focused VC firms like A16Z, Insight, NEA, Wing, etc. Pay attention to their portfolios.
  • Attracting world-class AI/data science talent and advisors. Assess the pedigree of early hires.

Conduct extensive due diligence before investing given the risks of early stage investing. Work with an experienced lawyer to secure preferred shares with strong rights. Consider syndicating with others investors you trust for better deal access and more leverage.

Seed stage AI startups often raise rounds under $5 million with moderate premoney valuations. Be prepared to hold 7-10+ years for maximum potential appreciation.

AI Startup Acquisitions

Once an AI startup has substantial revenues, customers, IP, talent, and market traction, acquiring it can instantly infuse your company with desired AI capabilities and savings years of internal development.

Microsoft, Google, Amazon, Facebook, Apple and other tech giants actively acquire mature AI startups to augment their offerings. For example, Salesforce acquired Tableau for $15B to gain data visualization capabilities powered by machine learning algorithms.

Pursuing acquisitions requires meticulous assessment of targets’ technology, cultural fit and expected synergies. Engage M&A and legal advisors with AI transaction experience. Develop an integration plan focused on retention and maximizing ROI.

The purchase price will depend on revenues, growth, margins, IP strength, competition, and strategic value. Given high demand for promising AI startups, expect substantial premiums.

If you operate a corporate venture capital arm, investing early in emerging AI startups can provide exclusive access to future acquisition targets at lower prices once they mature. This also gives you advance visibility into disruptive technologies.

AI Stocks

Publicly traded technology companies specializing in AI like Nvidia, Google, Microsoft, Amazon, Facebook, etc. offer more liquid exposure to the AI mega-trend.

Ways to gain AI stock exposure:

  • Individual stocks – Research and directly invest in stocks pioneering impactful AI work. Hold long-term for appreciation and dividends.
  • AI ETFs – Purchase AI-focused exchange traded funds containing baskets of relevant stocks. Diversifies risk.
  • AI Mutual funds – Invest in actively managed mutual funds run by managers specializing in AI companies. Pay for their expertise.
  • AI Indexes – Gain broad exposure through index funds tracking AI-related benchmarks like the ROBO Global Artificial Intelligence Index.

When assessing individual stocks, look for:

  • Sizeable AI research budgets, talented technical teams, and promising R&D pipelines.
  • AI product revenue growth, margins, and competitive moats. Seek platforms with synergistic “flywheel effects”.
  • Cloud infrastructure specialized for AI/ML workloads and tooling.
  • AI acquisitions and partnerships indicative of strategy.
  • Market dominance in relevant technical sectors like chips, computing, software etc.

Monitor new AI IPOs, spinoffs of AI divisions, and other opportunities driven by AI talent and intellectual property. Investing in the present leaders won’t necessarily capture the future innovators.

Provide Computing Resources

Given AI’s immense appetite for data and computing power, operating the infrastructure underpinning AI development can be a lucrative business. AI startups are eager to offload infrastructure management to focus on innovation. You can cater to their computing and data needs in several ways:

Data Centers

Construct and operate server farms that offer AI teams scalable data storage, cloud services, database hosting, and access to specialized high-performance hardware like GPUs, TPUs, and FPGAs for model training/inference.

Location near renewable energy sources like hydroelectric and solar can help lower power costs. Also consider climate controlled locations for hardware longevity. Partner with hardware makers for optimized AI servers.

Offer flexible storage tiers and bursting capabilities to support fluctuant workloads. High-speed connectivity enables low-latency data transfers and model serving.

Provide tools for monitoring resource utilization, securing access, managing costs, etc. Assist teams with data/model versioning, pipelines, and DevOps.

Develop vertical solutions around industries you know well so you can better customize infrastructure to client needs vs. one-size-fits-all.

Major upfront capital is required for physical facilities, hardware, connectivity etc. but long-term recurring colocation revenues can achieve profitability. Offer white glove support and SLAs to command premium pricing.

Co-Located Computing

If you operate existing data centers with unused capacity, offer co-location services to companies that need computing resources for AI initiatives but don’t want the overhead of full facilities.

Provide clients with rack space for their own servers alongside power, cooling, connectivity and physical security so they can colocate hardware within your data site. Offer flexible short-term leasing.

Let clients burst GPU/TPU computing capacity from your shared pools. Provide tools to monitor usage and spending. Offer consulting to assist with hardware selection, data pipelines, etc.

Start with pilot clients in industries you know well. Gather their infrastructure requirements and frustratements to guide enhancements. Keep iterating on power, space, and connectivity capacities to support next-gen hardware.

Cloud Services

Build and operate a cloud platform optimized for AI workloads like distributed model training/inference, AutoML, hyperparameter tuning, etc. Offer on-demand access to elastic GPU/TPU compute, auto-scaling, declarative machine learning pipelines, model registries/governance, and other capabilities tailored to the AI dev lifecycle.

Focus on ease-of-use, robust tools, and tight integration across data science, MLOps, HPC, container orchestration, and application services. Support open source frameworks.

Consider specializing around industry verticals you can uniquely cater to or specific ML techniques where you can offer performance advantages. Promote case studies.

Operate a global network that brings compute closer to data sources and end users for low latency serving. Partner with IaaS providers for backend infrastructure.

Offer consulting on ML best practices, architecture reviews, and training. Provide enterprise-grade support, security, compliance, and SLAs.

Edge Computing

While cloud hosts a lot of AI, there is a growing need to run inference on local devices and infrastructure to enable real-time decision making, privacy, and low latency response times. This is known as “edge AI”.

Offer products and services to help companies deploy and manage AI capabilities on edge devices like sensors, smartphones, cameras, IoT endpoints, 5G gear, robots, vehicles, etc. Provide tooling to simplify model deployment/monitoring/governance. Help handle data wrangling and communication with cloud. Develop specialized hardware and algorithms optimized for edge environments. Offer strategies for distributed learning across edges and cloud.

Proximity to data sources and low latency response times open many edge AI applications. But requires domain expertise across distributed systems, hardware, data pipelines, and model optimization.

Key Takeaways

  • AI business opportunities exist in providing services, building products, enhancing companies, investing, and supplying infrastructure.
  • Domain expertise in industries like finance, healthcare, retail, etc. allows you to build more tailored, higher-value AI offerings.
  • Look for ways to automate processes and apply AI techniques like machine learning, computer vision, NLP, robotics, and predictive analytics.
  • Focus on problems AI can solve uniquely well and areas where AI capabilities are evolving rapidly.
  • Partnering with technical co-founders or AI development firms can help bring product ideas to market faster.
  • Invest in AI safety, ethics, and best practices to build responsible AI systems that earn public trust.

Wrap Up

Artificial intelligence represents an unprecedented opportunity to build valuable businesses, enhance existing companies, invest for outsized returns, and supply the infrastructure powering this new era of technological capability. We are still in the very early days of the AI revolution – virtually every industry is ripe for disruption by entrepreneurial innovators who creatively apply AI to solve real problems.

While AI may seem intimidating from afar, most practical business applications don’t require PhDs or advanced degrees. Rather than focusing on complex math and algorithms, start with genuine business challenges and think laterally about how capabilities like predictive analytics, optimization, pattern recognition, automation, and natural language processing can drive transformational improvements. Leverage proven open source frameworks and cloud services to fast-track development.

When assessing ideas, aim for segments where AI can solve problems in wholly new ways rather than just incremental enhancements over the status quo. Look at areas with exponential growth in new high-value data generation. Seek out the most tedious or complex processes within organizations and consider how AI could eliminate bottlenecks. Think about how you could turn data into new revenue streams or SERVICES.

Remember that technology alone is rarely sufficient – you must still solve real customer problems and carefully engineer solutions suited to real-world conditions. Partnering with domain experts and seasoned engineers can help ground your thinking and convert concepts into products sustainably. Talk to many potential customers early to validate demand and refine approaches.

While the AI landscape shifts rapidly, enduring companies are built on principles like continuous learning, fail-fast experimentation, nimble adaptation, and relentless focus on delivering tangible value to users. Maintain healthy skepticism of hype and avoid distractions by the latest fads. Commit to responsible development practices that build trust.

The one guarantee going forward is that AI will continue advancing at an exponential pace, transforming business in novel ways each year. Therefore, continually educate yourself on emerging techniques, invest in your team’s capabilities, re-evaluate opportunities regularly, and maintain the agility to act decisively. Establishing an iterative flywheel where customer feedback informs rapid improvements compounds advantages over time.

The next major AI breakthrough could happen in your backyard – look for undiscovered voices, connect unique perspectives, and align incentives to transform great ideas into products that users love. Be bold and keep pushing boundaries. The opportunities for entrepreneurial AI businesses are boundless if you can imagine valuable applications others overlook.