Accenture Conversational AI Reviews

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.

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.

I used Accenture Conversational AI to resolve customer inquiries. I started working as a customer support analyst focused on ticketing, and the tool was used to resolve customer inquiries, automatic tier one support triage, and streamlining conversational routing to human agents. It was used to filter the queries we were getting on a daily basis so that queries were filtered and sent to the direct competent agent.

We received different types of queries, and Accenture Conversational AI helped with customer inquiries and ticket triage. There were queries regarding onboarding, transaction status checks, and transactions paused for admin action. The AI was configured to answer most of these queries that did not need human intervention. For queries requiring us to react, such as onboarding requests, the AI was escalating to the direct support agent. Most of our queries regarding transaction status checks, to understand if a transaction was completed or failed, were resolved quickly by this conversational AI tool.

Accenture Conversational AI helped my team reduce the volume of tickets we were getting on a daily basis in a significant way. It sped up our resolution time because most of the issues that a human being could take five minutes to handle were tackled in seconds by the AI tool. After resolving issues, there was a data analysis component where the AI could tell us the number of requests we were getting on a daily basis and what we could do to prevent those requests from coming. In addition to supporting with real-time answers, the AI was providing us with real-time data.

The best features Accenture Conversational AI offers in my experience include support with responding to agents and pre-built industry configuration. Based on our industry, the AI was configured to use the language we use on a daily basis. Someone working in FinTech cannot have the same language as someone working in the hotel industry.

Accenture Conversational AI provided conversation analytics and insights on a weekly or daily basis. The tool could provide us with a detailed dashboard tracking containment rates, drop-off points, and intent accuracy. It clarified customer sentiment so that we could build on different features we were already offering to our partners. We were also able to create features based on different needs picked up by the AI from the conversations it was receiving. Robotic Process Automation was very important when it came to native connectors that let conversational flow trigger back-end transactional workflows like balance lookup, account update, ticket generation, and transaction status checks. Those were very impactful features for my team.

Accenture Conversational AI impacted my organization positively by revealing that the volume of queries a human agent was treating had reduced considerably and customer satisfaction rate had increased because the AI was able to provide direct responses to our partners. The majority of our requests were about transaction posts for admin action and transaction status checks. When the AI started answering these tickets, it was a great period for us because the AI was working toward providing timely responses and our partners were happy. We have also seen an increase in client acquisition because of word-of-mouth marketing. When people are satisfied with your product and support team, they tell others that the company is doing well because responses are always available. The good thing about Accenture Conversational AI is that it works even at night and on weekends, every day and any time. This has impacted our organization by revealing that we are closer to our partners.

Nothing is perfect in the world, and I can say Accenture Conversational AI can be improved in several areas. The initial setup, training dataset integration, and edge case prompt tuning have a steep learning curve. Custom integration with proprietary back-end database APIs often requires significant technical support. The tool is better suited for medium to enterprise support teams dealing with high volume inquiries. I would like them to increase the customization capabilities so that the tool can grow as our company grows.

I have used it for three years.

I have not experienced any downtime with Accenture Conversational AI. Maintenance was planned ahead, and we were informed before it occurred. Every software undergoes maintenance, and this was frequent, but we were informed of the specific period when maintenance would happen. We informed our partners in advance. I did not experience any downtime where the software went down without being informed. The tool is very stable.

Accenture Conversational AI is very scalable. We have grown with it, and we noticed a huge number of partner acquisitions. I give scalability a rating of eight out of ten.

Accenture Conversational AI's customer service team has been helpful at some points, but I give them a rating of seven out of ten because there were times we contacted them and did not get a response, even though we were in need of quick support.

I have used Sprout Social and Zendesk AI before Accenture Conversational AI. I am not the one who makes decisions regarding tool selection. Even though I am a senior partner support analyst, I work under a partner support manager and head of support. The decisions are taken at a higher level. Even though we provided essential feedback on the necessity of having such an application, those making the decision ultimately chose to switch to Zendesk. After using Accenture Conversational AI for three years, I believe the organization has enjoyed the benefits. We have switched to Zendesk, so perhaps Zendesk has more to offer, but I cannot confirm this because I was not part of the decision-making process.

The major challenges we faced were when we started using the application. There was significant miscommunication regarding training and user onboarding for my team, as we were the ones handling integration. We did not receive very strong support when it came to integration. The learning material was not complete. It took considerable time for us to fully integrate and use the application at its full capacity. I recommend that they work well on the onboarding process, as this will make the application shine and encourage people to subscribe to the tool.

For the thousands of customers we were handling on a daily basis, Accenture Conversational AI was great because my team was not large. When it comes to money saved, I can mention customer retention at the high percentage I provided. Regarding efficiency, we were able to handle all customer queries without neglecting or ignoring any partners. Premium and default partners were all served well. The return on investment is great and very positive.

I was not involved in the acquisition process. The acquisition occurred before I joined the company, but they acquired it for my customer support team. Regarding pricing, I believe it is very good because the company supported the pricing throughout the three years we used it. The setup was complicated when it came to human configuration, but the licensing was good and the pricing was correct. Given our budget, it was good, which is why I think we used it for three years.

I did track specific metrics and numbers regarding reduction in ticket volume, faster response times, and improvements in customer satisfaction. Accenture Conversational AI has improved our customer satisfaction rate by ninety-five percent because we are always resolving queries. What pushes partners to give complete satisfaction is when their request is resolved in less than five minutes, in less than ten minutes, or is resolved within the SLA resolution time. It also sped up our full-time resolution time. Even new employees who were not well-trained were supported by this tool. We noticed a ninety-five percent improvement in customer satisfaction rate. We achieved one hundred percent full-time resolution for the different queries the AI was able to resolve, such as transaction status checks and many other issues we trained the AI to tackle. We also noticed a very significant improvement in customer retention because all our customers remained with us because they were happy. I can say we achieved one hundred percent retention rate. Regarding acquisition, I cannot quantify this because I do not work in the commercial department, but I have seen many partners joining because of word-of-mouth marketing.

I give Accenture Conversational AI a rating of eight out of ten because the application is very good and is working for the services we are paying for. I removed two points because there is room for improvement regarding onboarding and personalization of the application according to the company. They have much to do in these areas. Additionally, we are not getting timely support from them, which is something they should consider.

Accenture Conversational AI's AI capabilities demonstrate strong governance and security, and we felt safe using the application. We did not notice any data breach or privacy issues. I could give a rating of nine out of ten for security because the application is very good, only authorized people can access it, and we did not experience any security concerns. During these three years, we felt confident about privacy and compliance, ensuring that everything was secure.

I will give Accenture Conversational AI a rating of ten out of ten for accuracy because the responses are very accurate. All responses given to customers are correct and are based on our FAQ. I have never seen Accenture Conversational AI providing a wrong response. Accuracy is one hundred percent.

For a company that does not have a large budget for designing support based on AI, Accenture Conversational AI is an excellent choice because it is very affordable and very reliable. I do not see any downtime, and even though the support team is not fast when we need them, they are there and eventually solve the issues raised. It is very good and integrates well. For a company with five hundred members and one thousand customers, this tool would be a very good fit. What I can tell them is that Accenture Conversational AI is trainable and easy to train so that it can adapt to your industry terms and language and react as needed. My overall rating for Accenture Conversational AI is eight out of ten.

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.

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.

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.

I also use Accenture Conversational AI to hire people for me, and I use it to keep track of the project and explain the project to me, the progress of the project, and how the project is working on a daily basis. I keep track of it.

I do not need to code anything with Accenture Conversational AI; it is just automated. Everything is there, and I just have to use the service for my own work, which is very nice and easy to work with.

Accenture Conversational AI has positively impacted my organization, as I need to spend more time myself. Since it is an automated OS and automated process orchestrator, we basically have to spend less time on our participant or teammate or yourself.

We should also need an explainable AI on top of Accenture Conversational AI for more transparency on the model and the confidence.