A cross-border eCommerce platforms enable businesses to sell in international markets by connecting sellers and buyers from different countries or regions. To do so, these platforms provide a variety of features that facilitate cross-border transactions, such as global market access, payment processing, logistics and shipping support, compliance assistance, and multi-language support. In addition, these platforms are scalable, reliable and user friendly which helps businesses to reach global customers seamlessly and grow sales. These platforms are leveraged across various industries, including supply chain, logistics, health and beauty, automotive, and retail consumer goods.

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There is a need for cross-functional and enterprise-wide automation with integrations across so many of these sectors. I hesitate to choose one, even though I am coming from the legal side.

I see a lot of use cases for Automation in the Operations space (Commerce/ Marketing), where organizations are using some RPA (Robotic Process Automation) products to help

We've done a lot of automation in our AP processes and will start translating those benefits into AR as well.  However I think the area where we have the largest opportunity is in our order processing - there are so many touchpoints today, and so many different models, that we likely need to do some process re-engineering first... but then will have a large opportunity to automate.

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It depends a lot on how much automation you have already completed.  We automated a lot of HR, IT, Procurement, Accounts Payable and Receivables already.  They could be more done there but the obvious and easy projects are done so my vote was for Sales.

Automation of an organization&#39;s business operations, such as manufacturing, distribution, or specialized product development like drug discovery, can do the most to provide a competitive edge.


With myriad datasets, and ever changing needs, HR has a lot of potential to optimize, streamline and automate data processes.

The one with the highest cost per resource. 

Interesting that marketing is ranking so highly in this...


I would say with the advent of Generative AI that marketing is becoming easier to automate, but that&#39;s not to say that everything should be.


The opportunity for companies to really go against the grain and ramp up the &#39;human&#39; side of their marketing is big right now. 


Podcasts, video, thought leadership, community building, all of the stuff the bots can&#39;t do.


From a finance perspective. All low level mundane data entry activities should be automated, to free up time for more strategic pursuits. 


AP in the form of invoice capture, and AP automation in the form of direct bank links are quick wins.


AR in the form of debt collection automation, dunning, reminders etc is also fertile ground for automation.


Gives the machines the stuff that they&#39;re good at, so we can focus on the stuff that we&#39;re good at as humans.

They all should use automation properly to become more efficient. Sales should use it so they can spend more time doing what matters most, having conversations with people who can buy from them. 

I&#39;d define automation as many functions have also suffered from automation for the sake of automation solutions.
Even to date I can give any day a handful of supply chain and ops processes at any given company that were wrongly automated or not automated for no reason.
Any business function can do well with automation that&#39;s defined on what value it brings and not just money and efficiency, but also how it contributes to the overall strategy.
In marketing or sales, one can automate for efficiency and lose customers at the same time.
So this topic is really much deeper and I wish company leaders would take this more seriously when they discuss to evaluate real impact and not just the cost side.

There is a need for cross-functional and enterprise-wide automation with integrations across so many of these sectors. I hesitate to choose one, even though I am coming from the legal side.

I see a lot of use cases for Automation in the Operations space (Commerce/ Marketing), where organizations are using some RPA (Robotic Process Automation) products to help

Interesting how the Marking dropped suddenly in the last quarter

We've done a lot of automation in our AP processes and will start translating those benefits into AR as well.  However I think the area where we have the largest opportunity is in our order processing - there are so many touchpoints today, and so many different models, that we likely need to do some process re-engineering first... but then will have a large opportunity to automate.  

Which business function do you see the most need for automation?

There is no doubt that AI and Machine Learning have had a tremendous impact on business. It fact, it is being applied to almost every industry there is.&nbsp;Here are some quick examples of how AI/ ML have created business value across several industries:Financial Services (My industry) – The Financial services industry relies heavily on AI/ ML algorithms to help detect fraud in real time, saving companies millions in losses. They also use these algorithms to give their customers (e.g. merchants/ SMBs) access to online business trends and peer benchmarking – adding real value to these businesses every day.Retail – This is an industry that has been on the forefront of this tech in order to improve customer experiences. They use AI/ML to create a seamless experience between online interactions and in-store purchases. Think about Walmart and their experiments with facial recognition technology to determine if their customers are happy or sad at their point of purchase.Farming – Yes AI/ ML has also reached farming. John Deere has actually invested in building robots that make decisions to treat plants with pesticide based on immediate visual data – taking weather and infestations into account! Manufacturing – Auto manufacturers have done a good job with collecting data from cars to predict when parts would fail or when they need servicing. This not only allows them to uphold their safety records but also allows them to create products to improve driver and passenger needs.&nbsp;

For any online advertisers, particularly ecommerce, using AI/ML to monitor and optimize digital advertising campaign effectiveness. Understanding how/where to best spend that money to maximize ROI. 


It's applying the same thinking as you noted Anil within financial services fraud of finding irregularities in the data, but just applied in the context of ad spending.

I would agree with Anil that at this stage there are plenty of areas where ML/AI helps to create the insight that fosters better business decisions (e.g.leading to efficiency and cost savings) true business value such as innovation, resiliency, increased revenue or growth have yet to be demonstrated across industries. 

I'm certainly not a naysayer but the inconvenient truth is ML/AI are still nascent and emerging technologies with an Achilles heel, the inherent biases in data used to build their models.

Understanding your customers’ collective and individual preferences in order to present better products and effectively price discriminate.

The impact varies across sectors, but its significance is substantial. In the realm of investment, AI/ML's capacity to analyze vast datasets and take decisive actions proves invaluable to investment firms. Similarly, in the healthcare sector, the capability to identify health trends and enhance patient care plays a pivotal role in propelling healthcare forward.

The most common use case I see across industry is on customer service delivery value proposition. Multiple techniques are used here, the most popular AI technique is NLP or natural language processing. These GenAI techniques helps in creating quicker responses to customer cpmplaints thereby nullifying requirement for manual intervention, hence lowering costs

AI and Machine Learning can create significant business value across various industries. Here are some examples:

1. **Improved Customer Service:** Chatbots and virtual assistants can provide 24/7 customer support, resolving routine inquiries and issues efficiently.

2. **Personalized Marketing:** AI can analyze customer data to deliver tailored marketing messages and product recommendations, increasing conversion rates.

3. **Predictive Maintenance:** AI can predict when equipment or machinery is likely to fail, enabling proactive maintenance and reducing downtime.

4. **Supply Chain Optimization:** AI can optimize inventory management, demand forecasting, and logistics, leading to cost savings and better service.

5. **Financial Fraud Detection:** Machine learning algorithms can identify fraudulent transactions in real-time, saving businesses from financial losses.

6. **Healthcare Diagnostics:** AI can assist in medical image analysis, early disease detection, and personalized treatment recommendations.

7. **Content Recommendation:** Streaming services and websites use AI to recommend content to users, increasing engagement and retention.

8. **Quality Control:** AI-powered vision systems can identify defects in manufacturing, ensuring product quality and reducing waste.

9. **Human Resources:** AI can streamline recruitment, automate administrative tasks, and enhance talent management.

10. **Energy Efficiency:** AI can optimize energy consumption in buildings and industrial processes, reducing costs and environmental impact.

11. **Legal Research:** AI-driven tools can assist lawyers in legal research, contract analysis, and due diligence.

12. **Risk Assessment:** In insurance and finance, AI can assess risks more accurately, leading to better underwriting and investment decisions.

13. **Language Translation:** AI-driven translation services facilitate global communication and business expansion.

14. **Agriculture:** AI can optimize crop management, automate harvesting, and monitor soil conditions, improving yields.

15. **E-commerce Pricing:** AI can dynamically adjust pricing based on demand, competition, and other factors to maximize revenue.

These examples showcase the diverse ways in which AI and Machine Learning can add value by enhancing efficiency, reducing costs, improving decision-making, and delivering better products and services. The specific application of AI depends on the business's needs and goals.

Here are five examples of how AI/ML is creating value for business:

1. Improving customer service: AI-powered chatbots are now commonplace on many business websites, providing 24/7 customer support and answering questions quickly and accurately. AI is also being used to personalize the customer experience, such as by recommending products or services based on a customer's past purchase history and browsing behavior.


2. Automating tasks: AI can automate a wide range of tasks, from simple data entry to complex decision-making processes. This can free up employees to focus on more strategic and creative work, leading to significant cost savings for businesses.


3. Optimizing operations: AI can be used to optimize all aspects of a business's operations, from supply chain management to manufacturing to marketing. For example, AI can be used to predict demand for products and services, optimize inventory levels, and identify and address bottlenecks in production.


4. Developing new products and services: AI is being used to develop new products and services that would not be possible without it. For example, AI is used to develop self-driving cars, medical diagnostic tools, and personalized educational platforms.


5. Making better decisions: AI can help businesses make better decisions by analyzing large amounts of data and identifying patterns and insights that would be difficult or impossible for humans to spot on their own. For example, AI is being used to predict fraud, identify high-risk customers, and optimize marketing campaigns.

Here are some specific examples of how large companies are using AI to improve their business:

Netflix: Netflix uses AI to recommend movies and TV shows to its users based on their viewing history and preferences. This has helped Netflix to increase customer engagement and reduce churn.


Amazon: Amazon uses AI to personalize its product recommendations, optimize its supply chain, and detect fraud. This has helped Amazon to become one of the most successful companies in the world.


Walmart: Walmart uses AI to automate inventory management, pricing, and forecasting tasks. This has helped Walmart to reduce costs and improve efficiency.


Google: Google uses AI to improve its search results, develop new products and services, and personalize the user experience. This has helped Google to become one of the most valuable companies in the world.


BTW, Fred used AI to create the outline for this answer because it is efficient. AI is an accessible tool that can drive efficiency in text-based responses when the author prompts leveraging personal experience, and fact checks/edits the outcome based on expertise and human understanding.

If 2023 was the year when AI truly captivated everyone's attention, 2024 is when AI will start chipping away at every aspect of business value creation in industries such as e-commerce and marketplaces in 2024, including:

- Productivity - AI will automate repetitive tasks and software development, freeing up employees to focus on higher value work. For example, AI could generate reports, process returns, or handle simple customer inquiries. This will boost productivity.

- Infrastructure - Behind the scenes, AI will help optimize infrastructure like cloud costs, server loads, and delivery routes. This "AI infrastructure" will reduce costs and delays.

- Applications - AI-enabled apps will transform experiences. Product recommendations, search, personalized promotions, and chatbots will all become smarter and more intuitive through AI.

- Content - AI tools will help generate product descriptions, blog posts, emails, ad copy and more. They'll allow faster iteration to find top performing content. Humans will still oversee for quality.

- Automated Communication - More communication between software systems will happen automatically through AI APIs rather than human setup. This includes order information, inventory updates, analytics reports and more being shared seamlessly.

Utilize AI in consulting, composing, guiding, and documenting across all levels of healthcare professionals to minimize human errors and enhance efficiency.

One example is the creation of new things, e.g. a prototype of a system, a new way of understanding a set of data, or of crafting a reply or a response to a certain legal argument or set of arguments. 

What are some examples of business value creation through AI/Machine Learning?

Many AI-based applications are finally making the jump across the hype cycle from ‘promising’ to ‘delivering’ real value. Chatbots are one that have made a dent in 2017 and will continue to do so in 2018. Messaging has proved to be a simple, and extremely effective way to communicate and interface with information. Message based apps leverage a commonly used medium to provide a conversational interface to complex workflows. This has already dramatically simplified how enterprise users interact with applications and collaborate at work, especially as more and more millennials enter the workforce with drastically different expectations for software than their prior generation. But adding AI to chat takes us beyond just simple question and answer commands. Instead of typing “File HR Ticket” to a SupportBot, an employee can simply describe their actual challenge, e.g. “what is our maternity leave policy?” Bots can handle complex NLP, decompose the question, search for potential answers across multiple applications/ backends, and instantly provide the user with the right answer. This greatly simplifies workflow and massively boosts productivity and customer/ employee satisfaction. Zooming out a bit, the bigger picture going into 2019 is very clear. AI will be built in as a layer across every application going forward — just as “mobile” used to be a standalone category, it is now seen as a necessary component of any software product.

This could easily be a million, billion or a trillion dollar question. if I knew the answer to this, I wouldn't be posting it on Gartner, I would create a company to make money from it.


The reality is that most enterprises are already using AI. How many chatbots does your firm operate? That's AI. Microsoft is trying to use AI to help people with their products. If you're an Excel user, how often do you Google "how do I do ..." Microsoft wants to remove Google from that and keep you inside their own offering. Having used Microsoft office for a while, their help function was great many years ago, the information was locally hosted and you usually ended up at a page in the built-in helpfile with a series of steps. It worked well for me. Later on Microsoft moved this online and I needed an active connection. They also decided to point to a 30 minute video. I get it, a video is more detailed than a series of steps but I never had 30 minutes, I just needed to figure something out and move on. That's when I switched over to Google. These days will still check Google first.


AI is not new and has gone through many waves. In the past there was a burst of innovation and we were going to have thinking machines in a few years only for things to crash and for an "AI winter" to emerge. Then there would be a new innovation and the cycle would start again. We've been through a few of these. Is this time different? Perhaps, perhaps not. With each cycle, we seem to learn more and we can do more. The early version of Office had a basic spell checking tool, it had a hard time knowing whether you used "red" vs. "read" correctly since both words are spelled "correctly." Grammar check was added later and now we have a slightly smarter spell check that can help you with this.


The trouble with things like AI is that we place expectations on it that are unreasonable and that leads to problems. The internet was supposed to lead to a world of frictionless e-commerce but we have some very large gatekeepers (i.e. Amazon.) There are alternatives and we'll see what happens with AI today. We're still in the experimentation phase and we're clearly fumbling with it. Its good for somethings but not everything. We don't know yet. We may be amazed by AI doing something that its not very good at but its an improvement from the current state. We may not find something AI is very good at for a little while, but we may find that out later.


I couldn't imagine my job without the internet but I'm not at the point where I will share everything with the internet. Could you imagine someone dumping a bunch of customer data online and asking google how to craft a sales pitch? But I can imagine someone googling how to create a sales deck or googling how to do something in PowerPoint or researching the target firm/key people in the audience. AI will be similar. I think we'll see AI extend itself into basic tools for repetitive work and likely enter a pause for a while. We'll then see the next wave.


I can say more but I think my answer is long enough for now. If anyone has any questions or wants to speak about the finer points, feel free to reach out.

In many cases they are already consuming it, and most SaaS solutions are embedding into their platforms, that to me will be the bulk, in-workflow-AI. Then, whenever you see a business that 1) has proprietary data 2) data is reasonably good shape 3) data can augment business model or product or service, then you have second group of opportunities the in-product-AI.

AI technology is revolutionizing enterprises across the globe. Its applications range from automated decision-making, enhancing customer experiences, predictive analytics, process automation, to bolstering cybersecurity. These functionalities allow businesses to improve operational efficiency, personalise client interactions, forecast trends, automate routine tasks, and secure their digital frontiers. 

In essence, AI is a transformative tool for enterprises, accelerating their transition into the digital era and fostering a proactive, efficient, and secure business environment. As technology continues to evolve, it's clear that AI's potential in enterprise settings will only increase, becoming an essential component of successful business operations.

I believe enterprises are poised to integrate AI across diverse fronts: from personalized customer experiences powered by AI-driven insights, to streamlining operations through predictive analytics. However, amidst this transformation, it's crucial to remember that we're in control of what goes into training AI engines. As AI becomes a seamless part of decision-making, let's exercise caution and remember it's a tool meant to enhance human judgment, NOT replace it. By using it judiciously, we ensure that AI becomes a force for responsible innovation, amplifying human expertise while respecting ethical considerations.

Artificial intelligence is, without a doubt, a game-changing innovation. Like the introduction of the internet or big data, it will spawn new business methods. The real value of artificial intelligence will be evident when applied to every facet of an enterprise, from processing massive amounts of data to identifying important insights and anticipating future trends. Not only will this aid in cutting costs, but it will also open up new doors rapidly and precisely. 


It's nothing new; AI has been there for a while, quietly improving things in the background. With the advent of GenerativeAI, however, enterprises will now be able to create content using LLMs and Deep Learning assisted models and then present it in a way that is more accessible to humans. AI will penetrate every level within the enterprise because of the adaptability of the technology and the many ways to gain deeper insights at a lower cost. 


In fact, I've noticed that several businesses are investing in the necessary infrastructure to allow numerous AI models for be used or consumed across their whole enterprise.

What I have been learning.. enterprises are integrating AI across diverse areas, such as data analysis, customer service, and supply chain management, to gain insights, enhance efficiency, and optimize decision-making. AI-driven chatbots handle customer inquiries, while predictive analytics aids in anticipating trends. Industries like healthcare benefit from AI-assisted diagnoses and personalized treatments. Manufacturing and quality control benefit from predictive maintenance and defect detection. AI enhances recruitment and employee engagement in human resources. In finance, AI powers algorithmic trading and portfolio management. Energy management improves with AI-driven grid optimization and consumption prediction. Enterprises can adopt AI through in-house development, partnerships, or cloud services, aligning with industry goals and resources.

We are working on implementing Advanced Search, Image recognition and Chatbots as priority features for our product ( a collaboration/data entry tool used by various businesses).  These features appear to be the most practical in that order

Artificial intelligence is, without a doubt, a game-changing innovation. Like the introduction of the internet or big data, it will spawn new business methods. The real value of artificial intelligence will be evident when applied to every facet of an enterprise, from processing massive amounts of data to identifying important insights and anticipating future trends. Not only will this aid in cutting costs, but it will also open up new doors rapidly and precisely. 


It&#39;s nothing new; AI has been there for a while, quietly improving things in the background. With the advent of GenerativeAI, however, enterprises will now be able to create content using LLMs and Deep Learning assisted models and then present it in a way that is more accessible to humans. AI will penetrate every level within the enterprise because of the adaptability of the technology and the many ways to gain deeper insights at a lower cost. 


In fact, I&#39;ve noticed that several businesses are investing in the necessary infrastructure to allow numerous AI models for be used or consumed across their whole enterprise. 

What are real ways enterprises will consume AI?

Best Cross Border eCommerce Platforms Reviews 2024 | Gartner Peer Insights

As a Shopify Markets user, I'd say the experience has had its ups and downs but has been mostly positive. It is an innovative platform that allows for easy online store management and provides opportunities for small and even medium size businesses. 

1. Unify cross-border e-commerce platform, which enables sellers to sell across multiple countries in a short time
2. Support multiple local e-payment channels to easy the collection for sellers
3. Quick & easy setup & integration
4. Good support both direct & indirect (from the 3rd party partners)
5. Good brandname to create the trust for the buyers, which is quite a key thing to improve the sellers' brands as well

Shopify is best solution for ecommerce and dropshipping business. Our company started dropshipping and we found the overall best solution is Shopify. In terms of store management, no other CRM is even come closer to Shopify. We have great experience with this platform.

Shopify

FedEx Logistics

BigCommerce is a fantastic composable ecommerce platform that we replatformed onto and have been using for the last 18 months. The ability to leverage partner apps and customisations allows it to be the perfect platform to cater for the unique aspects of our business. 

A simple to use ecomm platform with a few limitations that are discovered after you have reached a turning point.

Finally a high-performance, customizable amd scalable product

My overall experience about FedEx Internation Connect is they deliver worldwide internationally with single label process which helps the retailers to avoid the barriers of global trade.

FedEx is a reliable source when it comes to international shipping

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