Conversational AI Platform

My main use case for Accenture Conversational AI is as an enterprise tool that I use to build virtual assistants and chatbots that handle client and employee interactions.

A specific example of how I use it for client or employee interactions involves it having a bank of information that we use for employees to reference and chat with, allowing them to pull up information at any time on each of their personal machines.

I decided to implement a virtual assistant for employee support rather than sticking with traditional methods like email or phone support because it was more efficient, allowing employees to self-serve and get the information they needed quickly and efficiently.

The best features Accenture Conversational AI offers include consistently delivering great, steady, reliable data and predictable responses for automated tasks, as well as having instant support that provides 24/7 immediate answers to repetitive queries, which frees up our human employees.

The reliability and instant support of Accenture Conversational AI have made a difference in my day-to-day work by freeing up our human agents, enabling them to focus on more interactive tasks that are more complex instead of repeating the same things over the course of the day, adding more engagement to their day and making their work more inviting.

One feature that stands out and I appreciate is that this platform is versatile, definitely streamlining diverse workflows ranging from what we have on our client end for engagement all the way to data troubleshooting.

I think Accenture Conversational AI can be improved by addressing query misclassification, as there are unclear user phrasings that can sometimes cause the system to misclassify intents unless you spend a lot of time extensively optimizing it.

I have been using Accenture Conversational AI for three years.

Accenture Conversational AI is stable and very reliable.

Accenture Conversational AI's scalability is very good.

The customer support is exceptional.

I did not previously use a different solution before Accenture Conversational AI.

I did not purchase Accenture Conversational AI through the AWS marketplace.

My company does not have a business relationship with this vendor other than being a customer.

From automating those HR processes, we were able to save 200 man-hours over the course of a year, which translated into about $10,000 worth of savings.

We have seen a return on investment, as we were able to save 200 employee hours over the course of our time using this platform.

I have no issues with pricing, setup cost, and licensing.

Before choosing Accenture Conversational AI, we did not evaluate other options.

My advice to others looking into using Accenture Conversational AI is to look at different products that might be more suitable and smaller for your needs, as you need to be an enterprise-level company to make the most out of this platform. I would rate this product a 6.

Our organization deals with online retail POS solutions here in East Africa, and Accenture Conversational AI is now integrated with our invoicing system and reporting to ensure that our customers get the best value out of the POS system itself.

Our POS is used in multiple branches and with multiple users, so whenever the manager or the admin wants to see the reports of the particular branch performance or with respect to particular employees or with respect to the overall sales in a given month, Accenture Conversational AI helps not just as a chatbot, but as much more than a chatbot, to get the results and proper, valid results on the fly.

Accenture Conversational AI helps not just on the current POS data or the customer data, but it also does forecasting for helping us in competitive analysis and giving responses as if we are chatting with an AI bot, so it helps us to grow our business with respect to competitive analysis and add more value to it.

The best feature and the most important feature of Accenture Conversational AI is ease of usage, as it is hosted on the cloud and tightly integrated with our systems, summarizing all the conversations, even forecasting, and getting our reporting or any results we want about our company, suppliers, sales or invoices on the fly.

Since Accenture is a big organization, they can improve the cost metrics by charging per user integration and also the most optimal use of tokens.

Support is an important aspect here, especially in the African market, as Accenture is not directly present in Kenya, so sometimes online support does not work out because people need the physical presence, requiring a kind of physical touch. Thus, support is a bit lacking and needs improvement.

I have been using Accenture Conversational AI for the past one year.

Accenture Conversational AI has responded positively in terms of our customers' feedback, as they can easily migrate to get the reports on the fly and even get assistance in any kind of complex reporting or complex sales processes, helping to reduce the overall time to market and improve some of the KPI indicators, such as the sales conversion cycle or spending more time with the customers.

Setting up those integrations with our existing systems was a one-time integration with a simple REST API we used to integrate Accenture Conversational AI, so it was pretty straightforward.

The CSAT score improved from 40% to almost 90%, and the average call handling time has been reduced so that customers save their time on more productive activities instead of just fetching reports from the old systems.

I would advise anyone with a startup or if it is an SME to definitely prefer using Accenture Conversational AI, as the integration is very easy to do, and there are obviously a lot of other features which I have mentioned.

My main use case for Accenture Conversational AI has been in the insurance industry, helping several companies mainly with their voice assistance and chatbots. With generative AI emerging, we have been using a lot of NLP and ensuring that we keep operations alive even though there is no human being manning it.

A specific example of how I use Accenture Conversational AI for voice assistance in my insurance projects is mainly for consultations, where someone might find that what they are looking for is not found during a normal online consultation. They have the option to choose a voice assistant, which will help them customize a package in terms of what they want to insure and what they want to leave out. It is mainly used for custom packages that are not freely available.

The best features Accenture Conversational AI offers include its integration with legacy systems, which is quite complex because a lot of things are set in stone and you need a lot of innovation and technical ability to integrate these systems. I think it is a great accelerator in the insurance industry because it makes everything a little bit faster, way more accessible to users, and for the people receiving the information, it is easier to categorize and see and separate the data easily. I can see where our customer base is heading towards, what they are liking more, and what they like to include in their packages.

The accessibility and speed of Accenture Conversational AI have impacted my day-to-day operations by allowing us to work at speed without compromising quality. We appreciate that we are able to give our clients peace of mind.

The only thing that I have seen with Accenture Conversational AI is that for long-term operations, it comes a little bit more expensive. However, I am very thankful that working with companies that have been in the industry for so long makes it easier to integrate with legacy systems and gives a little bit more extensive support than other conversational AI solutions that I have worked with.

Accenture Conversational AI can be improved as it often requires custom development for implementation, which brings us to higher implementation costs. The costing around implementation is a very big conversation that we have been trying to get over that hurdle. Though the return on investment has not been that bad, the initial implementation costs are a little bit higher than other conversational AI solutions.

In terms of needed improvements, working with the Accenture team for technical implementation has been brilliant, but they just need to help us with the costing when it comes to implementation. In terms of features, we are quite happy with what we have, and they do give a lot of global support depending on where we are and what type of implementation we are doing.

I have been using Accenture Conversational AI for quite some time, about two to three years.

In my experience, Accenture Conversational AI has been stable, with no downtime or issues. Clients are loving it, and any hiccups have usually occurred during implementation and testing.

Accenture Conversational AI is quite easy to scale up or down depending on my needs, particularly if I am on cloud or private cloud. It is straightforward to communicate with the support team about pricing, space, and capability when it comes to scaling.

Regarding Accenture Conversational AI's capabilities, I think its governance and security are really good, as we have not had any issues security-wise. How it is governed is in line with all the GDPRs and the POPI acts, with no significant issues on that front.

The accuracy and reliability of output from Accenture Conversational AI have been very consistent. I think it is one of its greatest strengths, and we are able to get great data in terms of that. The NLP setup is easier than most, and it also has some agent assist capabilities, which are very helpful.

I have used other solutions before Accenture Conversational AI. Recently, we have been trying out Microsoft Copilot, which is cheaper, but most of the capabilities we are looking for are not there, making Accenture Conversational AI good in comparison.

We previously evaluated no other solutions before Accenture Conversational AI, as it was the only option we knew at that time, and we just went with it.

Currently, I think Accenture Conversational AI is really great due to how we can customize the implementation, making it easy for us to align with different settings or scenarios. So far for me, it has been great, and we are going to start our third implementation soon, with each implementation having its unique nuances based on the company's wants, needs, and business goals.

My experience with pricing, setup costs, and licensing for Accenture Conversational AI has been really great, as the team has been very helpful. However, I think the initial pricing is quite heavy. Hopefully, we can come to some agreement to reduce the original price as we get deeper into these different implementations. I have a good team of developers who understand what is needed and can meet deadlines, and Accenture's support team is also fantastic in teaching us how to handle things that might be new to us.

We have seen a return on investment from using Accenture Conversational AI, especially money-wise. In terms of agents needed, that has become less, and companies are pivoting toward employing people who are technically sound in the setup of Accenture Conversational AI instead of relying heavily on consultants. While I do not have the exact numbers, the waiting time and conversion rate sit currently between thirty and forty-five percent.

In terms of metrics on how much time has been saved or conversion rates improved since I started using Accenture Conversational AI, I think we have cut down those calls to about thirty percent, which is quite good. In terms of converting a client, those numbers have been up by about thirty to forty-five percent, paving a new way of doing business for the insurance companies that we are consulting for.

I rate Accenture Conversational AI an eight out of ten because, while it helps a lot, it is not an out-of-the-box product where you can just learn and implement on your own. You still need a lot of help from the Accenture team, plus the implementation cost plays a role. My overall review rating for Accenture Conversational AI is eight out of ten.

We have a culture operating system where we provide a B2B application for organizations to log their culture, their values, and their behavior. We measure those values, the culture, and culture KPIs using Accenture Conversational AI's platform to query, letting users query their culture data. This provides a chatting interface for our users so that they can chat with their culture data.

For example, if a chief people officer or chief culture officer wants to see how their organization is doing on a metric called innovation or psychological safety, they can directly chat with this interface. In the backend, Accenture Conversational AI figures out the query structure, queries our backend, and shows the answer.

There are many use cases, such as onboarding health checks to see how many employees have been onboarded and how many employees have signed up their culture values. We had all this data in our database, and Accenture Conversational AI was used to facilitate all types of conversations on our interface in Instill Chat.

The best feature Accenture Conversational AI offers is orchestration. It can understand the query really well, including the person, entity, and all other things from the semantic side.

It improved the experience for getting data in a natural language pattern in an NLP form, rather than through a chart or other formats, which was very useful.

Our NPS score actually improved by eight points by introducing this feature, Instill Chat, which is built on Accenture Conversational AI. That is one metric, and efficiency-wise, it was really good. The speed was good, and accuracy was fantastic.

The accuracy was phenomenal. Once we understood the UX, it was easy, but it took some time to familiarize ourselves with the platform. The accuracy and speed were phenomenal.

It felt pretty secure, and we had all the certificates from AWS and Accenture. Accenture Conversational AI was pretty reliable and accurate; I would rate it ten out of ten.

Accenture Conversational AI needs to fix some UX bugs, simplify the engineering onboarding, and reduce the cost.

The debugging of the tool needs to be simplified. When we were working with Accenture Conversational AI, we were not able to see the logs, debug the code, and address the errors we faced. The UX needs to be simplified for debugging.

Reducing the cost is another improvement needed for Accenture Conversational AI.

We have used Accenture Conversational AI for quite a while, but not for an extended period. When it came out in 2024, we started using it for a year, then we switched to our internal platform.

Accenture Conversational AI is stable.

Accenture Conversational AI seems pretty scalable to us, and we did not face any issues.

Customer support was really good; they were there whenever we had a bug or UX issues, such as when we were not able to find the logs, and they were really helpful.

I did not previously use a different solution before Accenture Conversational AI.

Before choosing Accenture Conversational AI, we were looking to build in-house, but we did not have the engineering expertise to build something like that.

The setup process was straightforward for the setup costs and licensing.

We were selling our product much more easily, so our NPS score went up by eight to ten points. Those are the two metrics, and our revenue increased.

We were using it for one year, and we paid a substantial amount.

Accenture Conversational AI is now very costly, and there are other cheaper solutions available in the market. We could actually build something in-house as well.

We started using Accenture Conversational AI, and feature-wise, it is great, but the engineering side of this platform is really heavy, and the cost is very substantial. We had to switch to a cheaper platform, and right now we have built our own internal tool. We started with Accenture Conversational AI, but because of the UX issues, the bugs, and some issues with the engineering side, we had to move away. I would rate this product an eight out of ten.

The main use case for Accenture Conversational AI is that while scaling Hiretual, we evaluated multiple infrastructure options and needed to address repetitive queries such as interview status, scheduling, and application status from both candidates and recruiters. We needed improved response time and enhanced candidate experience, which is why we integrated this bot with the Node.js backend APIs.

Accenture Conversational AI helped with those repetitive queries between candidates and recruiters by allowing candidates to check application statuses through our application handle and conduct interview scheduling from the enterprise side. We used the AI bot to automate candidate support, as candidates were raising repetitive queries via email and manual support. We needed to reduce dependency on human intervention, so we built the chatbot with predefined dynamic responses, resulting in 60% to 70% of the queries being handled automatically and achieving faster resolution times.

In addition to the main use case, we also focused on intent recognition and understanding users' query patterns, along with entity extraction for details such as job ID and candidate ID. We maintained conversational flow and addressed issues where chatbot responses were generic or inaccurate, leading us to improve intent definitions and train the chatbot with various candidate queries and contextual flows. For instance, we ensured predefined answers for frequently asked queries, which significantly enhanced accuracy and reduced user frustration.

The best feature of Accenture Conversational AI is its ability to redefine intent. Candidates in Hiretual ask similar questions in different ways, such as what is my application status or what stage am I in right now, so we employed the platform's intent recognition capability to train and refine responses over time, leading to high accuracy and improved understanding of user queries.

Accenture Conversational AI has positively impacted my organization by handling 60% to 70% of common candidate queries automatically, which reduced reliance on manual support and improved accuracy after several iterations. The structured intent and entity framework, along with a user-friendly interface for training phrases and easy integration with Node.js backend APIs, played crucial roles in this success, though the setup requires continuous improvement rather than being a one-time effort.

Training and refining the intent recognition on Accenture Conversational AI has not been straightforward, as we needed extensive data for training the AI chatbot and faced a learning curve in managing diverse candidate queries during the implementation process. Initially, the training was moderately easy thanks to the structured intent and entity setup, and we created various training phrases such as check status and application status.

Accenture Conversational AI can be improved due to the initial learning curve for training data, as it sometimes misclassified user queries, especially with varied phrasing, prompting a need to enhance intent recognition accuracy. We diversified training phrases for each intent, improved accuracy through NLU training, and minimized fallback rate for user queries, along with enhancing context handling for better continuity in conversations.

On the user experience front, there should be clear, human-readable responses and a user-friendly conversational design to avoid confusion, especially with long and unclear responses that are not beneficial for user interactions.

I have been using Accenture Conversational AI for around 1.5 years.

My advice for others considering Accenture Conversational AI is that if your application has many repetitive queries that are unlikely to change, it is highly beneficial. For example, in educational platforms where students might frequently ask about their marks or CGPA, this solution fits well. However, if your platform involves frequently changing data or requires dynamic interactions, it may present challenges for the AI.

Accenture Conversational AI is excellent since it easily integrates with cloud backend services such as AWS, offering flexibility across various setups, including cloud-agnostic environments or deployment on AWS, Azure, Google Cloud, or even on-premises depending on business needs. I rate this product an 8 out of 10.

I have been using Accenture Conversational AI for about a year and have had the opportunity to explore its features and capabilities in various projects.

I primarily used Accenture Conversational AI for building chatbots and virtual assistants and was also building customer support automation, such as handling FAQs, booking flows, and basic troubleshooting.

I am happy to share a specific example of a project where I used Accenture Conversational AI for customer support automation. One project that comes to mind is when I worked with a large e-commerce company to build a conversational AI-powered chatbot that could handle customer inquiries and provide personalized product recommendations. The chatbot was designed to automate tasks such as answering FAQs, helping customers with order tracking, and providing basic troubleshooting for common issues.

I also explored using Accenture Conversational AI for employee support, creating virtual assistants that help with internal processes and workflows.

One of the best features Accenture Conversational AI offers is the intent recognition combined with contextual understanding. Additionally, the ability to integrate seamlessly with the back-end API is a significant advantage. The platform feels quite enterprise-ready in terms of scalability and customization.

I have seen significant benefits from Accenture Conversational AI's intent recognition and contextual understanding in my project, particularly in terms of improving user engagement and reducing support queries. For instance, in one project, I used this feature to develop a conversational interface that could accurately identify and respond to customer inquiries, resulting in a thirty percent reduction in support tickets and a twenty-five percent increase in customer satisfaction. I did notice significant improvements in user experience and efficiency, particularly in terms of reduced support queries and increased customer satisfaction.

The analytics dashboard provided good visibility into user interaction and drop-offs that helped us continuously refine conversational flows.

From a business perspective, Accenture Conversational AI significantly improved customer experience by providing instant responses. It also reduced operational costs by lowering support ticket volume.

We saw approximately a thirty to thirty-five percent reduction in support workload and about twenty percent cost savings on customer support operations. Development time for new conversational flows also dropped by around twenty-five percent.

One area that could improve is ease of debugging complex conversation flows. Sometimes tracing why a specific intent failed is not very straightforward. Additionally, initial setup can feel heavy.

Better documentation with more real-world examples would help greatly, especially for edge cases.

I would give Accenture Conversational AI a solid eight out of ten. It is powerful and scalable, but there is room for improvement in developer experience and debugging.

The platform has been instrumental in streamlining our support process, but there is still room for improvement, particularly in developer experience and debugging, and also in terms of natural language processing and integration with other systems.

I think one area for improvement could be enhancing the natural language processing capabilities to better handle nuanced user queries.

I am working as a full-stack developer for the last two years.

Overall, Accenture Conversational AI is quite stable. We rarely face downtime issues, and even under heavy traffic, it performed reliably.

Scalability is definitely one of its strongest points. We handled a spike of thousands of concurrent users without major issues. It scaled seamlessly.

Support was generally helpful, especially for critical issues. Response time was decent, though sometimes for smaller queries, it took a bit longer.

Before this, we were using a more basic rule-based chatbot system. It lacked scalability and contextual understanding, which is why we moved to Accenture Conversational AI.

Setup required some learning curve, especially around configuration, but once done, it was quite stable.

We did not purchase Accenture Conversational AI through the marketplace. We actually worked directly with Accenture to implement the solution. I believe this approach allowed us to get more customized support and integration with our existing system.

The return on investment was quite clear within a few months. We saved time on development, reduced support costs, and improved user satisfaction. Efficiency gains were noticeable across teams.

Pricing felt a bit on the higher side initially, but it made sense for enterprise use case.

We looked at Dialogflow and Microsoft Bot Framework. While they were good, Accenture Conversational AI felt more aligned with our enterprise-scale requirement and integration.

I would suggest investing time in designing conversation flows properly from the start. Additionally, make sure your back-end integrations are clean and well-structured. It really helps maximize the value of Accenture Conversational AI.

Overall, Accenture Conversational AI is a solid platform for building scalable conversational systems. It is especially useful for enterprise use cases where reliability and integration matter a lot. I gave this product a rating of eight out of ten.