Impact of AI Wearable Implementation on Different Industries
Trends In AI Payments Technology Changing How The World Transacts
Gaurav Tewari, founder and Managing Partner of Omega Venture Partners.
gettyThe internet accelerated the pace of information flow, and payments technology innovated in parallel to allow transactions to match that pace; in 2023, there were $1.3 trillion in non-cash transactions. AI promises to further accelerate technological adoption, with the number of non-cash transactions expected to hit $2.3 trillion by 2027.
The bottleneck for future growth revolves around the need to maintain levels of fraud prevention and security standards in every transaction. At the same time, consumers and businesses both seek tailored payment solutions that seamlessly integrate into their workflows without compromising on transaction speed. Much like the internet brought about the conditions to innovate payments to suit the new paradigm it created, AI-driven payment technologies promise to fundamentally change the way the world transacts.
Key Areas Of AI Adoption In PaymentsAt my company, we're already seeing strong adoption of AI across several key areas:
1. Fraud MitigationFraud prevention remains one of the primary concerns in digital payments, and AI is now an important tool in the endless battle against fraud. Companies can leverage machine learning to analyze vast amounts of transaction data in real time, allowing payments processors to detect anomalies and flag suspicious activity. By aggregating patterns around emerging threats within one platform, organizations can make sure they are prepared for what is coming next.
2. Transaction Optimization And SpeedEmerging technology is also being used to drive efficiency in transaction processing by optimizing payment routing. AI algorithms can analyze transaction size, destination and historical patterns to deduce the optimal path for any given payment. This can create faster transaction speeds while also reducing unnecessary delays, making payments more efficient and reliable.
Visa's network engine is an example of such innovation, using AI-powered systems to optimize payment routes. The complexity of the global networks that Visa operates is immense, but the continual evolution of the tech driving the network continues to accelerate transaction volumes.
3. Embedded FinanceEmbedded finance is changing the way digital commerce is conducted, with various financial services (payments, loans, invoicing, etc.) integrated directly into software platforms. By combining native data (e.G., transaction histories, behavior insights, preferences) with AI, businesses can deliver personalized and relevant financial products that are tailored to individual needs.
Uber illustrates the power of embedded finance, tying together payments and the user experience to change the way customers interact with the platform. Uber integrates digital wallets and payment systems across its various services (ride share, food delivery, freight) and as a result can leverage machine learning models to drive offers and micropayments that increase engagement and satisfaction across its network.
Barriers To AdoptionAI is accelerating innovation and adoption of payments trends, but key barriers around system modernization, privacy and diverse regulatory environments remain.
1. Modernizing Legacy Payment SystemsMany financial institutions and businesses are still working with outdated payment infrastructure, and these legacy systems were not designed with modern use cases in mind. As volumes increase and consumer expectations evolve, the slow pace of modernization is becoming apparent. For example, the decision to implement FedNow, the Federal Reserve's instant payment system, took four years to launch. However, while slow, I believe these developments are important to continue the pace of payments innovation.
2. Data Privacy And SecurityPayment systems handle some of the most highly sensitive data, and so AI systems must meet the strictest privacy standards. Privacy regulations (GDPR, PCI DSS, etc.) and the need to work with trusted vendors can increase the barriers to entry, increasing the already pervasive benefits of scale that have historically limited new entrants.
3. Regulatory ComplianceThe regulatory landscape for payments varies by jurisdiction, with different regions requiring specific licensure for certain payment solutions. This creates a barrier to the global rollout of payment solutions and makes cross-border payments an acute pain point for many multinational corporations. Global standards like ISO 20022 can reduce the burden of attempting to operate across multiple jurisdictions, but regionally, specific standards remain.
Future Trends • Rise Of Virtual CardsVirtual cards are gaining popularity for the security and flexibility that they offer, with the North American market growing from $200 billion in 2019 to an expected $500 billion in 2025. The ability to quickly spin up new digital cards with specific rules and traceability is enabling businesses to granularly control spend and increase the efficiency of the reconciliation process.
The rise of banking-as-a-service is pushing this trend forward by bringing more innovation to the ecosystem. As emerging fintechs partner with regional banks to bring new platforms to market, the ability of downstream businesses to build innovative new solutions should increase.
• Predictive Fraud PreventionThe increasing speed of digital payments is a double-edged sword, in that payments lost to fraud are now much harder to recover. AI is increasingly being leveraged to build predictive fraud prevention solutions to ensure that payments are not lost in the first place.
• Payments Driven By AIAccounts payable (AP) automation software is transforming how organizations manage their financial workflows by combining cloud-based platforms with AI. Many modern AP systems now offer advanced features (automated invoice processing, dynamic approval workflows, etc.) that remove the need for manual workflows while streamlining cash flow management. As AI-driven workflows are combined with other emerging innovations (like card virtualization), I expect the volume of spend managed by these platforms to increase.
Concluding ThoughtsThrough fraud detection, transaction optimization and embedded finance, AI-driven systems are increasingly becoming integral to the modern payments ecosystem. The staying power of legacy payments infrastructure, along with privacy concerns and complex regulatory environments, continue to pose significant obstacles. However, despite these challenges, AI is already proving to be a tremendous accelerant and innovation catalyst. As we look to the future, I believe AI will continue to push the boundaries of what is possible in payments and fintech.
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Make Money With AI — 5 Resources To Learn How
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Learning how to use AI-powered tools effectively can help you make more money at your current job by becoming more valuable. You can also use these tools to bring in cash on the side.
I'm a Self-Made Millionaire: Here's How I Use ChatGPT To Make a Lot of Money
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According to Resume Now, 48% of employees want AI training for their jobs, as many are worried about the potential impact of the new tools that have been coming out. The 2025 AI Disruption Report revealed that 89% of workers have concerns about their job security, with 43% noting that they know someone who has lost a job due to AI. The report found that employees are interested in learning about how AI impacts their roles.
Here are five different resources to learn how to use AI to make money if you want to increase your income at work or look into side hustles.
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1. Courses on Udemy or Coursera"Participants can start their AI education through Coursera alongside Udemy as foundation-building AI platforms," said Hone John Tito, co-founder of Game Host Bros. He pointed out that the most popular introductory course for AI is AI For Everyone by Andrew Ng because it teaches you how to implement AI-powered techniques in practical settings.
You'll want to take some time to explore the courses offered on these platforms by reading the reviews to see which would be most beneficial to your current situation. Ensure that you take a course that's catered towards your industry so you can stay informed of the technology's impact.
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2. LinkedIn Learning AI TutorialsMindgard's chief marketing officer and AI security advocate Fergal Glynn shared that he has worked with many successful professionals who use LinkedIn Learning's AI tutorials. He added, "They're perfect for upskilling in AI through structured, beginner-to-advanced courses."
Glynn pointed out that these courses include tutorials on topics like automating reports or analyzing data, and tips on how AI can be used in actual work environments. If you want to make more money in your current role, you'll want to start by learning about the basics of AI, and then look into specific courses that apply to your field.
3. AI Communities"The process of learning AI for business revenue generation requires dual attention to practical experience alongside membership in thriving AI communities," remarked Tito. He noted that he has learned from forums, Slack groups and virtual meetups.
Glynn elaborated that you can enhance your AI skills by joining AI-focused Reddit communities or LinkedIn groups because members of such communities often share free resources and income-generating tips. Since AI-powered tools are rapidly evolving, you'll want to be around others who have embraced them so that you can stay ahead of the curve.
4. Prompt Chatbots To Help YouYou can directly prompt a chatbot to explain how to make money with AI. If you want to get better at using AI-based tools, the best place to start is with chatbots so you can see how easy it can be to get the help you want.
Here are a few prompts worth testing out on ChatGPT to learn about making more money at your day job and outside of it:
I work as a [insert job]. How can I use AI to make my job easier?
I work as a [insert job]. How are people in my field using AI to make extra money?
What are different ways to make money with a side hustle with [insert your skill]?
Analyze the best ways to monetize [insert your skillset] based on current market trends.
I need help streamlining [insert specific tasks] to become more productive at my job.
What are popular AI-powered tools for [insert your job title/role]?
Once you play around with a platform like ChatGPT, you'll discover that using AI to make your job easier or to simplify tasks is much more accessible than you realized. You can try different prompts based on your job and what you want to improve.
5. Test Out Different ToolsThe experts noted that there's no substitution for real-world applications since AI-powered tools are rapidly changing. While education helps, you'll want to start experimenting with the various platforms to see if you can operate them independently. Here are a few suggestions for doing so:
Use AI-powered chatbots to speed up content creation or research on a topic.
See if AI can improve a task for you and make your job easier. For example, test Grammarly to see if it can improve your writing, emails and overall communication.
Try out Tidio AI to outsource customer service if this is part of your job.
Use OpusClip to make viral videos from one of your clips.
Glynn believes Canva is the best platform to use AI-powered tools directly because you can make sellable graphics. You can also use Jasper.Ai for free templates for writing projects. The goal is to experiment with popular AI tools to see how you can make money based on your skillset and current industry. The tools that you test out will depend on the type of work you do. If you're ever feeling stuck, you can prompt a chatbot to provide a list of tools you should try.
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This article originally appeared on GOBankingRates.Com: Make Money With AI — 5 Resources To Learn How
AI As A Service: Unleashing Growth By Applying Traditional Success Principles To A New Revolution
Jayant Walia is Head of Business Development and GTM at Gainbridge, an insurtech revolutionizing the insurance & retirement industry.
gettyWith the continuous innovation and advancement in AI solutions and AI infrastructure, it is not a far stretch to assume that most companies will offer AI capabilities in the future. While extremely exciting, we have seen similar trends in the recent past, particularly in financial services. In fact, well-known fintech VC Angela Strange once popularly declared that every company, even ones that have nothing to do with financial services, will become a fintech company.
Whether that famous prediction will ultimately play out or not, the fact remains that it has become much easier and more accessible for companies to embed financial services—across payments, lending, investing, etc.—into their offerings, driven largely by the power of APIs. The ability to integrate seamlessly with other financial services systems (across banking cores, payment processors, credit ledgers) means that companies can connect with core financial systems and build their offerings on top of it without having to develop the backend infrastructure themselves.
We are seeing similar trends play out within artificial intelligence where there are a number of AI infrastructure-as-a-service (AIaaS) and LLM-as-a-service (LLMaaS) companies emerging. AI platforms will allow companies to embed natural language processing (NLP) and generative AI capabilities to enhance their offerings without having to build complex models themselves.
By understanding the strategies of companies that have successfully embedded and scaled financial services during the embedded finance revolution, we can identify core principles that companies should follow to successfully embed AI capabilities into their solutions, potentially creating a much larger impact.
Embedded Finance: Introducing A New Wave Of Financial Services CompaniesOver the last decade, many non-traditional financial services companies—including e-commerce companies, technology platforms, retailers, etc.—became hugely successful by offering financial services as part of their offerings. A major reason why this was possible was the availability of APIs and the emergence of fintech infrastructure companies that integrated with core financial services systems, providing access to these APIs. This enabled companies outside of the financial services sector to integrate offerings such as payments, lending and banking into their core business.
However, just because companies were able to offer financial services did not guarantee success. While the potential was significant, successful companies were able to scale their financial services offerings because they deeply understood their core customer needs and the problem they were solving through these financial products while using the most efficient customer acquisition strategy.
AI As A Service (AIaaS): A Broader, Far-Reaching RevolutionThe applications of AI will be far-reaching, impacting every industry and function—from automating customer service and optimizing internal operations to offering more personalized services to end users across various touchpoints. With the emergence of LLMaaS platforms such as OpenAI, Cohere, Google Cloud and many more, businesses can now integrate sophisticated AI models into their offerings without requiring deep expertise in AI development.
Even though the impact of AIaaS is much wider and pervasive than embedded finance, conceptually the principles for success may be similar for a lot of companies integrating these solutions. Just like embedded finance, the success will depend not just merely on offering AI-powered solutions but on strategies used by companies to effectively solve real-world problems for their users/clients.
B2B Companies: Focus On Specifics To Drive Maximum ValueFor B2B companies, success may come from focusing on specific, high-impact use cases that have a high total addressable market (TAM). While AIaaS platforms open up a world of possibilities, B2B customers are not looking for generic one-size-fits-all solutions. They would rather be looking for solutions that could help solve unique pain points for companies that are pervasive in their respective industries and functions.
B2B companies would need to adopt a consultative approach to sales, helping clients understand how they can implement specific AI solutions in their workflows. This is particularly important as many B2B customers may be looking to leverage AI in their business but are still not fully sure what that solution is and how they can use it. This approach has served well in the embedded finance space, where many tech-first companies started offering specific financial services, such as lending or card payments, to specific industries that thrived because they addressed well-defined needs.
D2C Companies: Enhance Efficiency, Personalize Offerings And Lower CACFor D2C companies, the goal remains the same: scaling revenue and users while keeping customer acquisitions low.
However, as we've seen with embedded finance, offering similar features as your competitors does not guarantee success. Companies that understand their customers really well and offer targeted products and features will create a strong competitive moat for sustainable growth. For example, consider Revolut's focus on competitive foreign exchange rates, Chime's early direct deposit and no-fee overdraft and CashApp's peer-to-peer service that focuses on the underbanked segment. All are tailored to their target customers' unique needs.
While a lot of features offered by these companies might overlap over time, the timing of the specific product launches, the anchoring of specific features offered by respective companies and their customer messaging show how these successful companies understand what matters most to their user base. Likewise, D2C companies that embed AI services/solutions in their platform will need to deeply understand their users and create a growth strategy that resonates with their target audience.
ConclusionJust as embedded finance revolutionized the financial services industry, introducing a new wave of companies that offered financial products and created innovative partnerships, AIaaS is set to do the same in a larger and more pervasive manner.
Companies that not only embrace this change and remain disciplined in not getting enamored by the AI hype but also focus on how to leverage AI while homing in on solving their customers' real pain points will ultimately win. This would mean complete disruption for some industries and a very complementary solution in others. The companies that understand this early and execute accordingly can ultimately deliver the most value in the future.
Disclaimer: The opinions expressed in this article are solely those of the author and do not necessarily reflect the views or opinions of Group 1001.
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