Chatbots use rule-based logic for simple, predictable tasks.
Conversational AI leverages ML and NLP for dynamic, human-like interactions.
Scalability is higher with conversational AI for complex, multi-turn dialogues.
The cost of chatbots is lower initially, but AI offers better long-term ROI.
User Experience is enhanced with conversational AI’s personalized engagement.
You have seen the demos. You are aware that the future of interaction with clients, assistance with employees, and efficiency of operations will be conversational. However, when you reach the crossroads of innovation, one very important question comes up: Are you developing a mere chatbot or are you developing true conversational AI?
This is not semantic. It is a positioning move that will determine how your brand will talk, the quality of relationship that you will have with your customers, as well as how you will scale your business in the digital era.
The roadmap goes in one direction to a transactional, script-based answer tool that may answer the FAQs but not much more.
The other opens a smart, understanding, and ever-growing partner to your business, able to process nuance, context, and surprises.
The choice you make today for Chatbots vs conversational AI will determine whether your business is seen as a forward-thinking leader or just a mere company trying to automate a phone tree. Ready to find out which one is right for you? Let’s dive in.
Market Forecast 2030: Chatbots vs Conversational AI
The chatbot and conversational AI market will grow exponentially, and this is likely to drive both sectors by the year 2034 with the development of AI, natural language processing (NLP), and the rising demand for automation in customer service, online shopping, and others. A more in-depth prediction is provided below using available data and a comparison with chatbots and conversational AI wherever possible.
Chatbots
Conversational AI
The overall global chatbot market size is likely to reach USD 27.29 billion by 2030, with the annual growth rate hitting a CAGR of 23.3 percent between 2025 and 2030. (Source: Grand View Research)
In 2024, the world conversational AI market size was estimated and valued at USD 11.58 billion, which will rise to USD 41.39 billion in 2030 with a CAGR increase on 23.7% between 2025 and 2030.
Both markets will be dominated by the demand of automation, the development of NLP, and regional expansion in North America and Asia-Pacific. Customer service and retail/e-commerce are the most likely to remain dominant applications with conversational AI relating to voice and IoT-linked use cases.
Join Appinventiv to Dominate Conversational AI!
With conversational AI set to hit $61.9B by 2034, outpacing chatbots, partner with Appinventiv to build innovative AI solutions and lead the future.
Chatbot vs Conversational AI For Business: A Strategic Comparison
Businesses must understand the basic difference between traditional chatbots and conversational AI so that they can make well-informed strategic decisions. The differences determine their potential use in different operational requirements and the intended outcomes of their customers, as well as the customer experiences they aim to provide.
Technological Foundations
Old school chatbots are based on fixed rules, decision trees, and so forth, as well as on keyword recognition. Their workings are based on scripts and logic streams carefully modelled by developers.
Conversational AI for enterprises, in turn, uses a far more sophisticated artificial intelligence relying on the use of Machine Learning (ML), Natural Language Processing (NLP), Natural Language Understanding (NLU), and Large Language Models (LLMs) to enable its communication processes.
Language Comprehension
Classical chatbots only know certain keywords and phrases and can often become quite confused by issues with language variety, slang, or contextual information. They require precise language to be used by the user to provide a proper one.
However, conversational AI is particularly good at natural language understanding, discerning intent, bringing context into the equation, and even understanding sentiment inside a conversation. It can retrieve relevant entities out of messages, and it can form more significant engagements.
Learning & Adaptability
The traditional chatbots do not learn or adapt on their own about how to improve their interaction; they speak the same way whenever a user uses them.
In contrast, conversational AI keeps learning and evolving its operation using each interaction and experience.
Interaction Complexity
The classic chatbots are only effective in simple, predictable, linear, or one-turn exchanges, and they have a decision-tree-like structure.
Conversational AI, however, is meant to handle complex, dynamic, and multi-turn conversations with context spanning many turns.
Customary chatbots may have easy and quick deployment, and in most cases, the initial setup of a chatbot and the maintenance costs are not as high. Nevertheless, they need to be hand-updated.
Conversational AI usually requires high deployment costs and an extended implementation timeline, as AI management, data processing, security, and system updates can be rather sophisticated. It also requires constant training and data insertion to sustain and improve its performance.
Scalability:
The traditional chatbots are not very scalable since accommodating new rules and having to manage an increasing knowledge base will prove to be cumbersome and time-consuming.
Conversational AI, on the other hand, is exceptionally scalable, supporting more interactions regularly with minimal effort required.
Conventional chatbots may provide a more mechanical experience, which may end in frustration for the user if the query is not covered within their programmed scenarios. Users probably feel that they are riding with a chatbot.
Conversational AI, however, represents a more human-like and personalized conversation experience, and it is much more natural and human and provides much greater satisfaction to the user.
Although traditional chatbots require lower initial investment and can be deployed quickly, the disadvantage directly correlates with limited capabilities and the increased likelihood of a user being frustrated in complicated situations.
The lengthier process of investing and the complexity linking conversational AI, conversely, allow sophisticated functionality, the best user experience, and solid, long-term scalability. To have a brief overview of those differences, the table below gives an overview of those differences as a side-by-side feature comparison:
Aspect
Rule-based Chatbots
Conversational AI
Technology
Rule-based logic, decision tree, and keyword recognition.
AI, ML, NLP, NLU, LLMs
Language Understanding
Recognition of keyword only; cannot cope with variation, context, sentiment
Contextual awareness, intent, sentiment, free form input
Learning Aptitude
Does not learn; unable to do anything except update manually
Learns and grows all the time through communication
Interaction Complexity
Simple, linear, predictable, single turn
Dynamic, multi-turn, context-sensitive
Deployment Speed
Rapid and simple to deliver
Increased start-up expenses, lengthier arranging time
Initial Cost
Less up-front cost
Could cost more
Ongoing Maintenance
Script update manually; time-consuming
Constant re-training, information feed, and observation
Scalability
Low scalability; Expansion is hard to scale up
Extremely scalable; copes with growing volumes easily
User Experience
Stiff, possibility to get frustrated; ‘chatting with a chatbot’
Chatbot vs Conversational AI: Industry Applications
Both traditional chatbots and advanced conversational AI have quite different and powerful uses in a wide variety of industries. The knowledge of what each of the technologies does best could be taken to assist businesses in the application of their strategy.
Chatbot For Business in the Past: Traditional Uses of Chatbots
A rule-based chatbot for business can only cater to a particular industry and situations where the repetition of the inquiring questions is high and predictable. They are very simple and low cost, which makes them suitable:
Customer Service: This treatment is directed to respond to often asked questions (frequently asked questions) regarding the time of business, returns of items, or shipment delivery. They can deliver quick, consistent answers to frequently asked questions, lowering the processing load of the human agent in routine actions.
Lead Generation: Chatbots can match users against simple-qualification processes, e.g., asking a few pre-determined questions to build contact information or pre-qualify interest.
Internal Operations: For simple employee self-service, such as password resets, creation of IT tickets through a menu-based interface, or resolution of frequently asked HR questions.
Customer Support at E-commerce Store: It involves answering general queries regarding the status of orders made, terms of returns, or simple questions about products.
Patient Management in Healthcare: Helping patients book appointments, enter tests, or simply answer their questions regarding the most frequent symptoms. Simple cases can also be tracked through health records and monitoring medicine intake with their help.
With a more detailed understanding and learning ability, conversational AI is used in industries that demand more engagement, personalised and dynamic communications. It has a wider applicability, including:
High Quality Customer Care:
Being available 24/7, minimizing the wait time, and processing a broad range of requests, taking away the mundane tasks to be done by human agents and allowing them to concentrate on more sophisticated tasks.
The ability to solve complex problems about the retrieval of data and then relating it to several sources, e.g., a customer order history, and a support ticket.
More positive active prevention of negative consequences is achieved by ensuring this based on detecting customer dissatisfaction with the help of sentiment analysis and increasing it with a live agent in case.
Sales and Marketing:
Servicing individual marketing messages and product suggestions according to user behaviour, and previous purchases and preferences.
Directing consumers through sophisticated purchase cycles and communicating with potential clients on a one-on-one basis.
Qualification of the leads for the sales team through the involvement of the potential customers, data gathering, and evaluation of interest levels.
Facilitating employee self-service with HR and IT questions, benefit management, and the help (often confounding) of internal processes.
Serving as the virtual assistant to human agents, it provides immediate insights, proposing reactions, and making note-taking much easier and translating in real-time.
Helping supervisors detect agent performance patterns, conversations to review, and notifying them about agents who require assistance.
Assisting managers in identifying the potential of automation, establishing procedures for troubleshooting, and improving knowledge bases.
Retail:
Providing customized product suggestions, predicting sales, and maximizing dynamic pricing.
Order tracking and updates, as well as processing returns and exchanges.
Proactive Engagement: While proactive engagement, or triggering a conversation or action based on triggers or predictive analytics, might involve sending an appointment reminder, shipping update, or making someone feel welcome and assisted on your website.
Data Capture: Gathering the necessary information about the user or feedback, like when people are onboarded or are asked after purchase.
Transactional Capabilities: Enabling activities that involve transactions, such as making a purchase over an e-commerce store, booking a ticket, or reserving a hotel.
Build a custom conversational AI to transform your industry niche!
Partner with Appinventiv for smart, scalable solutions that enhance engagement and drive growth.
Chatbots vs conversational AI: Choosing the Right Solution for Your Business Needs
The choice between Chatbot vs conversational AI for business demands a profound evaluation of the exact needs of the industry, as well as the resources and the strategic objectives of the business. The choice is not just a technological but a strategic choice that will define how the company deals with the customers and its operational efficiency.
Those are the main things you will want to take into consideration when making your decision:
Dynamics of Interactions:
In tasks that are simple and repetitive with predictable answers, like frequently asked questions, basic order status, and single-turn queries, a rule-based chatbot may be a sufficient solution.
When it comes to conversations that are speculative, comprehensive, multi-turn, require contextual comprehension, complex problem-solving, or tailored advice, conversational AI becomes a requirement.
Wanted Customer Experience:
A rule-based chatbot can be enough, provided that a predictable functional experience is acceptable.
Nonetheless, when it comes to offering a human touch, empathetic, personalized, and seamless experience, conversational AI takes the day, particularly given that customer experience has become a deciding factor in many areas beyond product quality and price.
Budget and Resources:
Rule-based Chatbots tend to be cheaper and easier to implement, which is an appealing characteristic to businesses that have limited startup capital.
Conversational AI, in turn, requires a more substantial initial investment, including the collection of data, and the initial training of intricate models.
There is also a difference in ongoing costs and training: rule-based bots have to be updated manually, whereas conversational AI requires constant training and data input as well as monitoring to maintain and improve its performance. These are recurring expenses and may prove to be quite high, especially where they have to be sourced outside.
Moreover, companies should determine how well they can handle the complex AI systems within their domains.
Replacing and upscaling:
When future growth plans presuppose the growth of more complex and diverse customer requests, conversational AI can be more scalable.
Most Conversational AI systems have more advanced integration features with outlying CRM systems, ERP, and other business systems. This high level of integration facilitates very personalised responses and the potential to automate most workflows in several departments.
Strategic Alignment:
A conventional chatbot is adequate when the business objective is simple and concise, and when someone wants it to serve as a resource to answer frequently asked questions or to direct the user to human operators effectively.
It is also appropriate where few conversational examples are available before training, or where quick implementation and a low initial cost are the most important factors. It is even possible that some users enjoy the predictability of a bot having blatant restrictions.
On the other hand, conversational AI is becoming a pivotal choice when extensive customer engagement and building long-term relationships are important. Otherwise, it becomes vital when businesses strive for innovations and communication that are human-like or when issues that were difficult to resolve are complex and require information from several teams. The ability to scale the support in multiple channels (website, mobile apps, SMS, voice) also indicates conversational AI usage.
Lastly, when dealing with a larger scope of automating work processes and increasing productivity beyond basic interactions with customers, then a more suitable conversational AI can be used.
Look at the Hybrid Approach:
Companies that are starting their automation process usually begin with a simple chatbot to visualize the present discomfort and collect the first data, maybe on the pilot project level.
They will also be able to graduate to more sophisticated conversational AI solutions for businesses as their needs change and they expand their experience. This step-by-step strategy reduces risk and can afford more cautious investment.
Go Data-Driven:
A data-driven approach is core to the success of any conversation solution. Understanding the current customer interactions, the pain points, and data analysis is very important.
Some of the commonly asked questions may be the basis for developing conversational AIs, and the analytics team can use the data from web and chat interactions to improve the system. This emphasizes why data analytics and customer insights are so essential to the development of a successful conversational AI plan.
Cost to Develop: Chatbots vs Conversational AI
The level of the required expense necessary to create and introduce chat conversational solutions is very distinct between the regular chatbots and perfect conversational AI. Businesses that are contemplating automation should understand such cost implications.
Less Initial Investment: These chatbots will usually be associated with a lower initial investment, say between $30,000 – $200,000.
Quick Deployment: They are simple to set up and test, and often require little to no coding knowledge. By automating the interactions with the customer, the businesses will be able to achieve this in a short time, without involving the IT department.
Easier Maintenance: Maintenance is generally simple, and all that is necessary is to make manual increments to their scripts and rules as the needs of the business change.
Reasonably Forecasted Costs: As each has limited functionality owing to the programmed conditions of use, their development and use costs are usually readily predictable.
Cost to Develop Conversational AI
More complex and with considerably more capabilities, conversational AI solutions are more expensive to invest in since they are more intricate, require the use of more advanced technologies, and need to be kept continually up to date.
Greater Upfront Cost: Conversational AI development and implementation normally entail a larger initial expenditure of between $45,000 and $500,000.
More Deployment Cycle: The complexity of AI management, such as data management, security, and integrations with other systems, tends to result in a longer deployment cycle.
High Data Expenses: The foundation of conversational AI is the collection of data, and this can be expensive, particularly when the data required is not collected internally and must be bought externally.
Increased Training Costs: Various kinds of data need a specific amount of training, and therefore, the more complicated the data, the more expensive it is to train the AI model.
Continuous Optimization: Constant Training and Maintenance: There is a constant need for training and data input to optimize conversational AI, requiring constant resources and supervision continuously.
Complexity in the development: The development of a practical conversational AI requires advanced coding and advanced knowledge of machine learning and Natural Language Processing (NLP).
Particular Cost Ranges: After the initial setup, potential decision makers should expect the monthly expenses of AI solutions to range between $50 and $5000, depending on the magnitude and the complexity. Multimodal AI models that combine different data modalities such as text, image, and video may cost approximately twice as much per token compared to Large Language Models (LLMs) that work with textual activities.
Measuring Impact: ROI and Performance Metrics
It is imperative to measure the worth of conversational solutions as it is a key to proving its usefulness and the rationale for investing in it to continue. These will consist of monitoring key performance indicators (KPIs), as well as computing the return on investment (ROI).
Calculating Return on Investment (ROI)
It is easy to get a definite financial picture by calculating the Return on Investment (ROI).
The normal ROI equation is:
ROI = (Characterized by costs – Costs) / Costs x 100%.
Expenses: Businesses should take into consideration:
Initial implementation costs and setup fees.
Recurring monthly Payment.
Training and daily training and maintenance, e.g, non-stop data input and trace.
They include costs of data collection, particularly in situations where external data has to be purchased.
Benefits: The benefits of chatbot & conversational AI for businesses are multidimensional and include:
Cost Savings: Costs are cut back on the staff (can cut contact center costs by up to 60 percent), training costs, and HR overheads. Another voice-to-messaging call deflection reduces costs.
Revenue Growth: More sales, expansion in conversion, and product suggestions adapted to the customer that drive average order value.
Increased Customer Retention and Customer Lifetime Value ( CLV): A direct effect of better customer satisfaction.
Efficiency: Gains are achieved by the ability to respond quickly and release human agents regarding lighter, less complex jobs.
Discover Your Custom AI Investment
Consult our AI cost experts at Appinventiv to get a precise estimate for your tailored Conversational AI platform.
The Future Trends in Chatbots and Conversational AI for Enterprises
Conversational interfaces are an emerging environment that is experiencing an even faster evolution thanks to the innovative trends leading the modern world. They are destined to transform the way companies engage with their clients and implement strategies into business processes.
Hyper-personalization is shifting gears beyond simple suggestions to develop extremely individual experiences. This is done through AI, machine learning, and real-time data analytics with consideration of browsing behavior, location, preferences, and even things such as weather. Future AI would look at forecasting the needs of the customers and providing support before they formally request it.
Proactive AI, in which systems make contact to provide support whenever an event occurs as a signal or through predictive analytics, and turns help support into proactive.
Another noteworthy trend is Multimodal Interactions, as opposed to the primitive text-bound business chatbots of yesterday, which represent a growing sophistication in the interactions with the chatbots through an amalgamation of voice, text, visual images, visual video, and body language. It enables more natural and intuitive communication, and, therefore, provides a user with the opportunity to communicate in their preferred way.
The idea of Emotionally Aware AI is a exciting one, with AI systems able to detect and react to human emotions through advanced sentiment analysis or NLP. This will allow more compassionate reactions and proper escalation with human agents in case of frustration or distress being identified.
There is also a marked direction in the future in terms of Human-AI Collaboration, redefining the role of human agents. In an upward spiral, an increasing number of repetitive, monotonous queries are being taken on by AI, leaving the human agents to focus on complex emotional issues that require empathy as well as critical thinking. AI acts as a copilot or a virtual assistant to the human agents and allows them to see real-time insights, recommend responses, simplify note-taking, and assist with real-time translation.
With AI being increasingly incorporated into the operations of the business and customer interactions, Ethical Considerations take precedence. It is important that there is transparency and that customers know they are speaking to a bot. Strong data privacy and accessibility to AI ethics, perpetual observation, and address of AI bias are not negotiable.
Conclusion: Which is Best: Chatbot or Conversational AI?
This overview of the possibilities and uses of business chatbots and conversational AI reveals an undeniable reality: there does not exist a definitive solution. Rather, the right or perfect decision depends solely on the circumstances of the particular needs, strategic goals, and desired results of the customer experience of your company.
In case of Foundational Automation:
Conventional, rule-based chatbots are suitable within the contexts that require simplicity, predictability, and scalability to an extent where routine queries are performed in bulk. When the main use case is answering simple questions on demand, guiding the user through a simple service, or deploying a simple information retrieval service, a chatbot can serve as a cost-effective solution that can be deployed rapidly.
They are the best fit when the flow of conversation is properly established and there is little necessity for dynamic and subtle insight.
In the case of Advanced Engagement and Transformation:
Conversational AI involves highly complex, dynamic, and highly personal conversations with the help of advanced AI, machine learning, and natural language processing. Let us assume that you run a business that needs to handle complex customer cases, deliver personalised product recommendations, automate complex processes, or be like a human agent that empathises, learns, and gets better with time.
The strategic imperative in that sense is conversational AI. It is an investment in a more intelligent and richer customer journey and massive scale amplification of operations.
At the end, the choice is a strategic technological assemblage to the individual demands and dreams of your business. The complexity of your interactions, your budget, scale requirements, and the level of customer experience you aim to deliver will provide a clear path to choosing the right conversational solution, whether you are serving a small, medium-sized, or large enterprise.
Appinventiv: Your Tech Partner That Empowers Your Business with Intelligent Conversations
It is not easy to bring simplicity into automated communication; you need to have a trusted and experienced partner like Appinventiv. Having established expertise in the field of AI chatbot solutions for business, we enable companies to implement smart conversational technologies through our bespoke AI Chatbot Development Services aligned to their specific needs.
The excellence and innovation that we commit to are captured through our capabilities and performances:
Extensive Experience: We have 1600+ professionals from different fields of technology to provide all-encompassing and progressive solutions.
Proven Success: The success of our clients is at the core of our business, as evidenced by our 3000+ successful deliveries, which have transformed the way companies handle their operations and enabled them to have greater customer engagement in diverse business areas.
Certified Quality: We are a Certified ISO 9001/27001 company, hence robust and reliable solutions are guaranteed, since we hold the highest international standards in terms of quality management and information security.
Award-Winning Excellence: Our tireless effort in the smooth execution of the AI development services in different industry sectors has earned us global recognition and the most prestigious awards in the award-winning world.
Spring 2025 Worldwide Clutch Awards
Fastest-Growing Companies of 2025 list
Leading Custom Software Developing Company 2025
The fastest-growing AI development companies of 2024
Deloitte Fast 50 India 2023 Award
Strategic Partnerships: We have a strong channel of partnership with industry leaders such as AWS Sagemaker, Adobe, ServiceNow, and MoEngage, enabling us to provide complete and advanced conversational services.
Success Stories: Check out these success stories for learning how we help businesses:
Mudra Budget Management App: Using our adaptive AI-powered Chatbot App, the Mudra budget management solution provides deep insights into user spending behavior by analyzing their spending on linked debit and credit cards, alongside historical data and their spending habits, thus empowering users with actionable insights.
AI in Banking Industry: We worked for a large European bank to solve their problem with AI and ML adoption for volumetric challenges in large datasets and their 6% annual home loan churn rate. Within 10 weeks, we built a multilingual AI-powered chatbot, a churn prediction engine, a chatbot with a scale from 1 to 10, and ML-driven ATM cash-out optimisation for over 10M data points.
This cut manual processes by 35%
Boosted accuracy by 50%
Improved ATM service by 92%
20% retention increase
Whether you’re looking to implement a foundational chatbot or a sophisticated conversational AI platform, we at Appinventiv are your strategic partner to help you with basic foundational chatbots or develop complex conversational AI systems, transforming your business with intelligent, efficient, and engaging communication experiences.
FAQs
Q. What is conversational AI?
A. Conversational AI refers to systems technologies that allow for automated dialogue with users in a way that comprehends and responds to natural language. It is a combination of natural language processing (NLP), machine learning, and contextual awareness of language and actions, which are meaningful and goal-oriented, as in providing responses, offering recommendations, and performing assigned tasks. It is implemented in chatbots, virtual assistants, and voice interfaces.
Q. What is the difference between chatbots and conversational AI?
A. Conversational AI is designed to enable advanced interactions through chatbots, which interface with users via text and voice. It integrates NLP, machine learning, and contextual understanding of voice and text to enhance chatbot productivity. While basic and instruction-bound, chatbots with conversational AI capabilities interact more intelligently like human users and intuitively adapt to users’ speech patterns, thereby elevating the overall user experience.
Q. How does chatbot vs conversational AI for business impact customer support?
A. In conversational AI vs chatbot, Chatbots are effective for handling routine customer queries with scripted responses, ideal for basic support. Conversational AI for business excels in complex support scenarios, understanding nuanced queries and providing personalized, human-like responses.
Q. How does conversational AI vs chatbot handle complex queries?
A. Conversational AI can understand and respond to complex, nuanced queries by leveraging NLP, context retention, and reasoning capabilities, often improving over time through learning. In contrast, a chatbot is generally limited to handling straightforward, predefined queries and may struggle with ambiguity or context outside its programmed rules.
Q. What challenges do chatbots face compared to conversational AI?
A. Particularly, rule-based chatbots or automated voice systems struggle to grasp context and varied linguistic expressions throughout lengthy dialogues and with user-imposed complexity or vague notions. They follow a rigid protocol, which leads to a lack of variety.
On the other hand, advanced NLP and applied machine learning solve the issues of conversational AI, nuanced linguistics, context retention, and discerning user intentions. From an engagement and accuracy perspective, chatbots still struggle to resolve limited scalability, inability to integrate, and insufficient processing power, AI in general, and machine intelligence in particular, for such advanced systems.
Q. Which is easier to maintain in chatbot vs conversational AI for business?
A. Chatbots need frequent script updates but are simpler to maintain for basic tasks. Conversational AI requires ongoing training but can self-improve, reducing manual maintenance over time.
Archie Mayani is the chief product officer at GHX, a global supply chain company that uses data and cloud-based technologies to connect healthcare providers like hospital systems and their suppliers.
For more than 20 years, Mayani has worked on clinical and supply-chain health technologies at companies like Change Healthcare and United Health Group.
At GHX, Mayani works to ensure that the company develops technology that can help hospitals procure patient supplies — like implants and IV fluids — as seamlessly as possible. By using AI-powered technologies that can anticipate supply chain disruptions, prioritize them in order of most critical, and identify substitutions, hospitals can be better equipped to provide effective patient care.
Business Insider interviewed Mayani about what sets healthcare apart from other industries when it comes to AI implementation.
This interview has been edited for length and clarity.
Rachel Somerstein: How is healthcare unique as an industry, particularly when we think about the integration of AI?
Archie Mayani: I’m based in Silicon Valley, where everybody wants to fail fast and move forward. But healthcare is very different from other sectors using AI.
When you are building a dating app and your AI hallucinates, it’s kind of funny and makes a great first-date story. When you have a patient on the operating table and you don’t have the right supplies delivered at the right time, it’s scary.
Can you talk about the goals of AI implementation in healthcare supply chain management?
Healthcare is about patient safety and how you use technologies responsibly, always putting the patient in the center. When we think about supply chain management, it’s almost like an invisible operating system in this shared ecosystem of patient care and delivery.
GHX’s mission with AI implementation revolves around delivering the right supplies at the right time to improve the quality of care and make it more affordable.
How did you arrive where you are now?
We have been leveraging AI and machine learning for the last 15 years. A lot of our work during the pandemic involved making supply disruptions more visible, with the goal of making supply chains more resilient and proactive.
One of the most important cases we thought about, coming fresh off the pandemic, was, “Can we look at backorder anticipation?”
It doesn’t matter what the cause is — it can be geopolitical conflict or meteorological tragedies. It could be that a trailer was dislodged and now we’ve lost the supplies on the freeway. But if we can anticipate back orders, we can anticipate disruption.
If the system is intelligent enough, it could recommend nearby substitutes within your distributed area. We started there, on a path of, “We’re going to build this machine-learning model that’s going to be intelligent, anticipate these disruptions, and make substitution recommendations.”
Where is AI in supply chain management working best right now?
We have an agile development approach at GHX, where our customers give us live feedback. We had an “aha” moment from our customers: They said, “This is absolutely what we’ve asked for for the past 20 years. You are starting to predict all of these disruptions, but the disruption of a Band-Aid is not the same as a disruption of IV fluid.”
They asked, “Can you make this technology even more intelligent for what I need, depending on where I think my most critical risks are and what kind of care delivery is most important to my organization?”
So we came up with the idea of clinical sensitivity and a confidence score, essentially to validate whether disruptions are clinically relevant to specific customers.
That was one of the things that changed the trajectory of our AI implementation road map: Just because we can deliver insights doesn’t make them useful; they have to be predictive and personalized.
What does the future of AI in healthcare supply chain management look like?
Since healthcare is different and unique from other industries, our approach is to automate workflows as much as possible using agents while keeping a human in the loop. Once the customer feels confident, we can start fully abstracting those workflows so that AI agents are handling them entirely.
The other place gaining traction is copilot environments. For example, we have a product called the perfect order dashboard, which marries data insights. A customer may say, “Show me the view of my world, of where the supplies are, of where I’m doing an exceptional job with my suppliers getting those supplies on time, making sure that the orders and invoices are paid on time, and show me all of the discrepancies.” Still, that’s not enough.
The copilot allows you to tell a story with that data, very similar to a ChatGPT-like experience: “Show me the top three defaulting suppliers not delivering supplies on time.”
Once you have those supplier lists generated, you can say, “Send an email to XYZ supplier, making sure we have a quarterly business review scheduled, and please attach the perfect order dashboard view showing the last quarter’s trend.”
It might seem small, but it’s a huge value-add. It used to take maybe three or four hours to understand the data, extract insights, and drive follow-up actions and decisions. Now, it takes minutes.
What advice do you have for others in your position or who hope to be?
The hardest or most useful thing you can do is to say no.
In healthcare, everything is urgent — and it truly is. But not everything matters equally. So, the ability to say no to the right things and ensure that you’re focusing on the highest value-added items for your customers is critical when you’re in healthcare.
Big Tech, or even a smaller tech startup, can innovate as research labs and fail. We don’t have that option. So understanding what matters now, what will matter in 10 years, and finding the right balance to focus on the right innovations, becomes critical.
It’s about having the right data, the right governance and mechanisms, and always thinking about performance, security, and privacy. It’s also about making responsible choices on where to invest your energy, so that you’re ultimately not working on the sexiest, coolest, or hardest things.
It comes back to the patient: making care affordable and of the highest quality possible.
After a busy day at the Goldman Sachs tech conference earlier this week, I sat down with the firm’s internet analyst Eric Sheridan to take stock. His main takeaway: “No one’s slowing down.”
Despite spending massively on AI infrastructure, almost every tech exec told him AI demand is outstripping their ability to supply intelligence, he said.
This was summed up by executives at CoreWeave, which builds and runs AI data centers. “Unrelenting,” they said, while noting there’s been yet another upward inflection in AI demand in the past four to six weeks.
During the dot-com boom of the late ’90s, internet infrastructure was built out massively based on eyeballs — just the fact that people were looking at websites. This time, there’s actual revenue from consumers and companies paying for AI services, Sheridan noted.
The conference headliner was OpenAI CFO Sarah Friar. The room was packed for her talk. Even the overflow room was full, with many analysts and investors sitting on the floor. I’ve never seen so many loafers and crossed legs at the same time.
OpenAI CFO Sarah Friar
Mike Segar/REUTERS
OpenAI is on course to generate $13 billion in revenue this year, but the company is “still massively compute constrained,” she said. That leads to tough decisions such as holding back new products, running some services intentionally slower, and having to choose which research projects get resources and which ones must wait.
This situation is also creating “strange bedfellows,” Sheridan told me. At the Goldman conference, Meta CFO Susan Li said the tech giant is working with Google, an arch rival. Friar mentioned OpenAI is also tapping Google’s cloud for capacity. Those two are going to the mat over the AI search market.
Related stories
Business Insider tells the innovative stories you want to know
Business Insider tells the innovative stories you want to know
In the new world of autonomous software development, it’s now easier to create bespoke software in-house. “Why wouldn’t I code the kind of software that is exactly what OpenAI needs,” the CFO said. “That is going to change the whole face of how software is developed.”
I felt a shudder ripple across the room as attendees considered how much of the world AI might consume in the coming years.
“Short everything,” someone muttered beside me as the audience got up to leave. Analysts laughed nervously as we filed out in a long, slow line.
Artificial Intelligence (AI) is transforming our lives and business models at an unprecedented pace. A recent local bank survey shows that over 70% of customers use generative AI daily, and businesses are actively embracing AI to enhance customer experiences, optimise data analytics, and explore new avenues for growth. As Hong Kong’s largest digital marketing community, IAB Hong Kong announces that its annual flagship event “C25” will take place on 24 September 2025 at the Hong Kong Convention and Exhibition Centre (HKCEC), bringing together industry elites from Hong Kong and around the world to explore the latest trends in AI within digital marketing and seize future growth opportunities.
Under the theme “AI in Action”, C25 2025 will showcase real-world AI business applications through keynote speeches, expert panels, and practical case studies. Attendees will gain actionable insights into how AI is enhancing customer engagement, boosting efficiency, and unlocking fresh growth opportunities in Hong Kong and beyond.
A key highlight this year is the launch of the inaugural “Agentic AI Challenge”, a groundbreaking competition designed to fast-track the industry’s move from theory to practice. Participants will develop advanced AI agent solutions capable of autonomously planning, executing, and optimising complex tasks—accelerating local adoption of automation and AI-driven marketing strategies.
C25 Organising Committee Chair, Tania Lau, “Agentic AI has transcended its role as a supporting tool to become a transformative force, redefining how businesses operate and roles are shaped. In Hong Kong, we’re witnessing workflows revolutionized, roles reimagined, and new opportunities unfolding at an unprecedented pace. This evolution doesn’t replace human creativity—it elevates it, empowering marketers to achieve more, drive groundbreaking innovation, and set new benchmarks for our industry.”
Prominent Speaker Panel Leading Industry Discussions
C25 features a world-class lineup of over 50 industry leaders and tech innovators, including marquee names such as Amazon Web Services, Google, Meta, WPP, Microsoft and Baidu. Attendees will access insights and witness practical expertise through 30+ dynamic keynotes, panels, and live case studies, focusing on the future of digital marketing and the business impact of AI.
The conference is structured across four action-driven tracks:
AI HORIZONS – Big-picture trends and future-shaping innovations, including keynotes from top AI chiefs on search, disruptive applications, and practical deployment.
AI DRIVERS – Sector-focused strategies with deep dives into finance, retail, travel, and global video content—showcasing how AI delivers personalisation, efficiency, and overseas expansion.
AI ACCELERATORS – Breakthroughs in health, entertainment, and culturally-inspired marketing, illustrating how AI is transforming industry practices and audience experiences.
AI CATALYSTS – Innovative solutions for smarter analytics, omni-channel ad automation, and AI-enabled influencer strategies that maximise business effectiveness.
New Features: “Agentic AI Challenge” and “VIP Speaker Lounge”
This year’s C25 introduces the first-ever “Agentic AI Challenge“, where participants will design innovative AI agent solutions capable of understanding, planning, and executing complex tasks. The competition is structured in two phases:
Proposal Submission – Contestants must submit their concepts by the designated deadline. A panel of judges, including university professors and experts from Google, Adobe, Microsoft, HKT, Cherrypicks, and Votee, will shortlist up to 10 finalists.
Live Presentations – Finalists will present their solutions during the afternoon of C25. An expert jury will evaluate and award the Champion, 1st Runner-up, 2nd Runner-up, and Merit prizes.
Winners will not only receive cash prizes but also industry accreditation, signalling both technical excellence and strategic creativity. Designed to merge innovation with practical application, the Challenge aims to encourage the local industry to move beyond theory and embrace AI-powered marketing execution.
More details: C25 Agentic AI Challenge
VIP Speaker Lounge: Driving Deeper Industry Conversations
C25 2025 also debuts the VIP Speaker Lounge, a premium and exclusive networking platform designed to foster collaboration across industries. The Lounge will bring together senior leaders from brand management, advertising, technology startups, and academia for in-depth dialogue, interactive discussions, and small-group forums. By encouraging knowledge exchange and thought leadership, the Lounge will aim to expand networks, spark partnerships, and accelerate the healthy development of Hong Kong’s AI marketing ecosystem.
Exhibition Zone: Experience Next-Gen AI AdTech
The C25 Exhibition Zone brings together advanced AI and advertising technology solutions from top local and global providers, including intelligent SEO tools, AI-powered analytics platforms, and digital transformation services. Attendees can try out these innovative platforms firsthand, explore their technical advantages, and connect with solution experts for tailored advice. Throughout the event, AI-driven real-time translation will ensure seamless communication, fostering technology-business integration and active industry collaboration.
