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1、 TELECOMS&CONNECTIVITY GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING Whitepaper GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 1 TELECOMS&CONNECTIVITY 1.1 Introduction Conversational AI is a technology that enables the automation of customer interactions by a

2、llowing customers to respond to an enterprise using natural language.It is having a significant impact across several industries;being used to power chatbots or virtual assistants for use cases such as customer service,marketing and commerce.The technology works when a user inputs text or leverages

3、speech recognition technology and the conversational AI understands the natural language using NLP to extract the intent and then translate this into structured data.It then uses natural language understanding(NLU)to process this data,understand the intent and extract specific pieces of information.

4、A response is consequently generated based on the intent and context identified,by using predefined rules or using generative AI to form more dynamic and contextually relevant responses,using information from the enterprises knowledge systems.Conversational AI has gained significant momentum over th

5、e past two years,with the rise of chatbots that use LLMs,following the launch of ChatGPT in late 2022.Following this launch,the usage of chatbots has grown significantly and therefore enterprise demand for this technology has also grown.Whilst conversational AI technology existed prior to the develo

6、pment of LLMs,the use of conversational AI alongside generative AI highlighted the potential of the technology to be used for a range of use cases;one of which is the capability to automate a larger proportion of customer conversations,whilst interacting in a more natural way.1.2 Future Market Outlo

7、ok Conversational AI Juniper Research has forecast that the conversational AI market will grow substantially over the next five years;driven by heightened enterprise demand for conversational AI services,owing to the ongoing digitalisation of businesses as well as the increasing trend towards conver

8、sational business messaging.The largest region for conversational AI revenue in 2025 is Far East&China,followed by North America.The US is the leading country in terms of conversational AI revenue,which can be attributed to the large investment into conversational AI services in this country and the

9、 presence of vendors which are established leaders in the market,including Google,IBM and Microsoft Azure.Chinas advancements in AI have closely followed those of the US.In China,there has already been large adoption of conversational AI services within retail and eCommerce,as well as banking,which

10、contributes to the high spend on conversational AI services.In this country,WeChat is a key platform for conversational AI services,as the superapp offers social media,payments,and eCommerce within the platform.The number of chatbot users in each market will impact the future opportunities to engage

11、 with consumers via conversational AI technology.There are a high number of chatbot users in regions where many enterprises have implemented chatbots for different use cases,thereby increasing the opportunities for consumers to access chatbot technology.Whilst these chatbots may not necessarily use

12、conversational AI technology,instead being rule-based chatbots,there will be more opportunities in these markets for conversational AI vendors to offer the technology;especially as these enterprises may look to upgrade from rule-based chatbots to conversational AI systems.The region with the highest

13、 proportion of the population which are chatbot users is North America,followed by West Europe and Far East&China.Therefore,these are the regions in which Juniper Research anticipates there to be the greatest opportunity for conversational AI vendors.Conversely,the region with the lowest proportion

14、of the population using chatbots is Africa&Middle East,consequently,this region is not expected to provide a significant opportunity for the provision of conversational AI services in the near future.GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 2 TELECOMS&CONNECTIVITY Figure 1:

15、Total Number of Chatbot Users in 2025(%of Global Market),Split by 8 Key Regions Source:Juniper Research 1.2.1 Key Drivers of the Conversational AI Market There are several key factors that Juniper Research has identified to be key in driving the growth of the conversational AI market over the next f

16、ive years.Technological advancements will create a higher value proposition for enterprises as well as increase the affordability for small to medium-sized enterprises(SMEs);enabling higher adoption of conversational AI.Moreover,the increased consumer demand for conversational messaging will also be

17、 key in driving growth of the conversational AI market in 2025.i.Technological Advancements Driving Conversational AI Adoption The technological advancement which Juniper Research believes will be most valuable in driving the growth in the conversational AI market over the next year will be the depl

18、oyment of AI assistants which leverage both rule-based and generative AI technology.There will also be an increased availability of agentic AI frameworks for greater autonomy;increasing cost reductions for larger enterprises,which will also drive market growth in the future.a)Generative AI Allows Gr

19、eater Automation of Conversations As previously mentioned,the demand for the use of generative AI to form responses to customer inputs,following recognition of intent by conversational AI,has grown substantially.Over the last two years,many LLMs for a range of use cases have been developed,thus thei

20、r potential use for automating conversations is growing.Generative AI can be used to generate responses based on training data,and in multiple languages.Incorporating generative AI in tandem with predefined flows within a conversational AI system will have a significant impact on the potential of th

21、e technology for enterprise use.Enterprises can use this capability alongside predefined responses for contextual responses;reducing the number of enquiries that require agent handoff,in turn reducing the workload for live agents and improving customer experience.Conversational AI vendors must ensur

22、e they make a range of LLMs available to enterprises,to provide flexibility.With the market advancing at a rapid pace,enterprises do not want to be tied in to using one LLM and want to be able to change this seamlessly to take advantage of the latest and most efficient LLMs for the use case.Therefor

23、e,it must be made simple for enterprises to choose the most effective LLM for the use case they require,which must be easy to later change.GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 3 TELECOMS&CONNECTIVITY b)Improving Speech Recognition to Implement Conversational AI in Conta

24、ct Centres The use of conversational AI within contact centres is expected to be an important use case for growing revenue from the technology over the next few years.Enterprises will look to replace existing interactive voice response(IVR)technology with voicebots which are powered by conversationa

25、l AI.Thus,it is important that speech recognition technology is improved to better understand consumers with a range of different accents and identify the customer sentiment with speech.This is essential to ensuring that conversations are routed to the appropriate voicebots,and that conversations ar

26、e escalated at the right time.Conversational AI vendors must partner with voice recognition vendors that specialise in speech to text conversion and natural language processing for voice-based interactions.These vendors can assist with creating conversational AI for voicebots that is able to integra

27、te into a range of systems and technologies;offering the flexibility required for enterprises that want to leverage conversational AI within their contact centre.c)Agentic AI Frameworks Will Automate Tasks and Increase Cost Reductions for Larger Enterprises Agentic AI is the latest buzzword used whe

28、n referring to customer experience automation.The technology leverages multiple LLMs for the automation of tasks,with minimal human oversight.The technology will be used for use cases such as enabling more intelligent message management,message personalisation,and improved security.Agentic AI will c

29、omplement the use of generative AI in the mobile messaging space.For example,generative AI will be used for content generation for a messaging campaign,and the agentic AI will be used to determine when the message must be sent to ensure maximum customer engagement.By enabling greater task automation

30、 when it comes to managing interactions with customers,this will further reduce reliance on human agents,who can instead focus on overseeing these interactions and handling more complex scenarios.This will lead to further cost reductions for enterprises.Juniper Research therefore believes that now i

31、s the time for conversational AI vendors to start investing into agentic AI,and to form partnerships with AI vendors that will help them to launch this technology.However,we do not believe that agentic AI frameworks will contribute to much of the growth in the conversational AI market in 2025.Instea

32、d,conversational AI vendors must ready themselves for when enterprises are ready to implement this technology.Ensuring that agentic AI frameworks can be easily integrated into their current communications stacks must be of priority to conversational AI vendors in 2025.d)Improvements in Contextual Aw

33、areness Improvements in contextual awareness are enabling more intelligent message orchestration.Conversational AI enables messages to be routed based on intent,interaction history and customer preferences.Moreover,the identification of customer sentiment is improving;allowing messages to be routed

34、to human agents at the right time.For this improved contextual awareness,several conversational AI vendors are using retrieval augmented generation(RAG).This framework integrates real-time information retrieval by indexing data taken from a range of data sources;determining the intent and context of

35、 a query and then retrieving the relevant information.The prompt is then augmented by combining the retrieved information with the query and is passed to an LLM to produce a contextually relevant response.Using RAG eliminates the need to retrain an LLM when new information becomes available,ensuring

36、 that the conversations always remain contextual.To offer this hyper-personalisation of customer interactions,conversational AI vendors must look to integrate RAG with customer data platforms to leverage the data stored within these platforms and generate personalised responses to customers.Offering

37、 redirection to channels where the customers identity can be captured will also be key to creating contextual conversations.For example,enabling click-to-chat with WhatsApp will enable the customers mobile number to be collected by an enterprise,which allows them to reach out to the customer later v

38、ia this channel.GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 4 TELECOMS&CONNECTIVITY At present,there are many players in the conversational AI market offering similar services.This competitive landscape is driving innovation within the market;resulting in the technological adv

39、ancements previously mentioned.However,the conversational AI market will not sustain this large number of players and,therefore,there is expected to be a period of consolidation.The conversational AI vendors which will emerge as leaders in this market will be those offering the technology across a w

40、ide range of channels,who make this technology self-serviceable to consumers,and offer this at a competitive rate.ii.Reduced Cost and Cost Predictability of Conversational AI Services will Drive Higher Adoption Rates As the technologies used with conversational AI are refined to optimise cost and ha

41、ndle a larger proportion of customer interactions,it will be important that the cost of the technology is predictable for enterprises.Lower costs and spend predictability will create a higher value proposition,and enterprises will look to automate a higher proportion of conversations as they start t

42、o realise the cost savings that can be made.a)Generative AI:Fine-tuning LLMs is Reducing Cost Enterprises which currently use structured,rule-based bots would benefit from leveraging LLMs to increase the number of interactions that can be handled by a chatbot.By using natural language generation to

43、create human-like text or speech responses based on the identified intent and context of the input,this improves customer experience and allows queries to be responded to quickly.However,until recently,many of the LLMs available were general purpose,which are slow,expensive to run,and have limited u

44、nderstanding of industry-specific terminologies.This therefore created a low value proposition to enterprises.Over the last year,conversational AI vendors have been fine-tuning LLMs to improve efficiency and to reduce the cost of running a chatbot that leverages these LLMs.This has been done by trai

45、ning foundation models,such as the GPT models offered by Microsoft OpenAI,on industry-specific data.This creates a higher value proposition for enterprises looking to automate a larger number of customer conversations,rather than relying solely on predefined flows.For conversational AI vendors,incre

46、asing the number of interactions that can be automated will increase revenue as enterprises will be purchasing a higher volume of traffic.One of the other issues surrounding the use of generative AI alongside conversational AI is the non-deterministic nature of the technology;meaning that it can pro

47、duce different outputs from the same inputs under different conditions.Fine-tuning the models parameters can also help ensure more consistent outputs;ensuring the model is more predictable,thereby overcoming concerns surrounding this.This will be of great concern within the banking and finance indus

48、try.Therefore,either creating more prebuilt flows for this industry,or fine-tuning LLMs for key use cases,must be of focus to conversational AI vendors if they want to increase revenue from this industry.For example,for identified intents for business-critical use cases,the AI must always direct to

49、the appropriate flow and produce the desired output,to ensure that issues do not arise with inaccuracies.b)Focus on Making Conversational AI Solutions Self-Serviceable Conversational AI vendors must focus on making the deployment of AI solutions self-serviceable,which will reduce the cost of convers

50、ational AI deployments by enabling the enterprise to create and manage conversational AI solutions more independently.One of the ways that conversational AI vendors must look to do this is by offering prebuilt templates for a range of industries and specific use cases.This will enable enterprises to

51、 more quickly set up and customise conversational AI solutions.Another way to make conversational AI more self-serviceable is by offering no-code platforms for building.This enables enterprises without developer expertise to create and deploy these conversational AI solutions,such as SMEs and enterp

52、rises that are not in a technology-related field.Conversational AI vendors must also look to implement generative AI to help with the creation of predefined flows,where the enterprises can input a phrase explaining how they want the flow to work,and the generative AI being used to create this.iii.In

53、creased Consumer Demand for Conversational Messaging With the high number of smartphone users globally,Juniper Research is anticipating a trend towards more conversational messaging between brands and customers.GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 5 TELECOMS&CONNECTIVIT

54、Y This is being facilitated by the growth in the number of users of rich media messaging channels which support enterprise conversational messaging use cases.Moreover,there is a growing preference amongst consumers for engaging across these channels,with the number of messages delivered across OTT m

55、essaging apps growing year-on-year.China is a good example of where OTT messaging has been particularly successful.In this country,there are over one billion users of the messaging app WeChat,and the high penetration of this app has resulted in high enterprise adoption in the country.This app is suc

56、cessful in the number of features it integrates within the channel;making it possible for enterprises to engage with consumers for a number of different use cases.WhatsApp,which is popular in India,Brazil,and Indonesia,is implementing similar features to that of WeChat,with the WhatsApp Pay feature

57、being available in India.In the US,whilst there are a high number of OTT messaging users,they are spread out across different applications.Adoption of OTT messaging channels has therefore not been a priority for enterprises in this country,instead enterprises must adopt an omnichannel approach.RCS m

58、essaging will not necessarily become a channel of preference amongst consumers for peer-to-peer(P2P)messaging,however it replicates the way that consumers are used to engaging.As the ecosystem develops,Juniper Research anticipates a growing demand for the use of RCS business messaging for conversati

59、onal use cases.Conversational AI vendors must look to support the channel,especially in the US,where high adoption of RCS business messaging is expected.Figure 2:Countries with the Highest Number of OTT Messaging Users in 2025(m)Source:Juniper Research 1.3 Future Market Outlook Conversational Commer

60、ce Conversational commerce is the process by which end users of conversational devices,such as smartphones,can leverage them for commerce purposes,including retail and banking,through a chat interface.It assists customers throughout the entire purchasing journey,including from pre-transaction,where

61、a brand can offer promotions and recommendations to customers,to during transaction,where a customer is making a payment,and to post-transaction,which can include delivery updates,customer satisfaction,returns and exchanges.GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 6 TELECOM

62、S&CONNECTIVITY Juniper Research has forecast that the total spend over conversational commerce will grow substantially nearly 90%between 2025 and 2029.In China,WeChat is a messaging app which has evolved to integrate social media,payments,eCommerce,and other features.Users can do several things with

63、in the app including paying for bills,booking ticketing,and online shopping.This is a key messaging channel for conversational commerce in China and contributes to the high spend observed in the Far East&China region.Also in China,customers use Alipay,which was originally a payment platform but has

64、expanded to include a range of services including shopping and food delivery,further contributing to the high spend observed.Also,within the Far East&China region,KakaoTalk is a messaging app that has also developed into a superapp in South Korea.The messaging app offers a range of services includin

65、g KakaoPay for payments and KakaoBank for banking,which contributes to the high conversational commerce spend in South Korea.The Far East&China region is therefore a key market to focus on for conversational commerce opportunities.However,with WhatsApp introducing more and more features for enterpri

66、ses for business messaging,the app is expected to grow in use for conversational commerce over the next few years.Conversational AI vendors must therefore focus on WhatsApp in the countries where it is most highly penetrated,as a key channel for enabling conversational commerce for enterprises.1.4 N

67、avigating the Regulatory Landscape There are different approaches to the regulations of AI in major markets.Whilst several markets have set out clear regulations for the use of AI,there is still a lot of ambiguity in how this is applied in other markets.However,Juniper Research believes that as AI i

68、s increasingly used for a range of applications,stricter regulations will be put in place to ensure safety and security with AI.Many countries are due to set out their plans for the regulation of AI in 2025,and therefore,this may have an impact on how enterprises look to implement conversational AI

69、and generative AI over the next two years.At present,there is a fragmented approach across regions for AI regulation;regulations can differ across both countries and sectors.The different privacy considerations that are being addressed by these regulations,and therefore what conversational AI vendor

70、s must be aware of,are as follows:Data collection and consent:with conversational AI enabling a new way for enterprises to collect customer data through conversations,there will need to be considerations on how this data is collected and processed.Conversational AI vendors must therefore consider re

71、gulations surrounding data collection and consumer consent when developing conversational AI solutions that are designed to personalise customer interactions.Data protection and security:conversational AI vendors must establish clear data use policies and regularly evaluate the use of personal data.

72、Vendors must ensure use data is encrypted,and security measures are reviewed regularly to identify vulnerabilities.Transparency:regulations will also include ensuring that there is transparency with the use of AI.Conversational AI vendors must help to ensure consumers are aware of the interaction be

73、ing with an AI system,maintain documentation about how the AI model is trained,and be able to provide clear information about the systems functionality,data sources,and training.Algorithmic bias:regulations must be introduced to combat algorithmic biases when using conversational AI systems.This wil

74、l include mandating that the datasets used for training generative AI models are diverse and representative of a range of backgrounds.i.Provide Guidance when Creating Guardrails Different guardrails must be provided by conversational AI vendors to foster confidence in enterprises looking to implemen

75、t conversational AI solutions.The different AI safety tools must include:Agents in the loop:this will allow agents to monitor how chatbots are engaging with consumers;allowing agents to identify and mitigate potential biases or issues with the AI system.Conversational AI vendors must ensure that con

76、versations are GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 7 TELECOMS&CONNECTIVITY routed to agents at the right time,to ensure that potentially risky or nuanced situations are handled by human agents.Payment card industry(PCI)and personally identifiable information(PII)maskin

77、g:conversational AI vendors must prevent access to sensitive information when using AI systems to limit the risk of data breaches.Vendors must be compliant with general data protection regulation(GDPR),the California consumer privacy act(CCPA)and industry specific standards such as the payment card

78、industry data security standard(PCI-DSS).Masking the data will be important to enable data analysis without compromising privacy.Hallucination detection:this will be essential to ensuring reliability when generative AI is used for responses to customer inputs.Conversational AI vendors must provide h

79、allucination detection tools,through partnerships with specialist vendors who provide these tools,to identify hallucinations with high accuracy.These must be integrated within conversational AI solutions that leverage generative AI for responding to customers.1.4.2 Industry Demand i.Banking and Fina

80、nce The banking industry is highly regulated,with banks subjected to both geographical regulations as well as internal policies and procedures.Therefore,the implementation of conversational AI within this industry will be useful both for employees in the banking industry that need to keep up to date

81、 with new policies and regulations,as well as for powering customer-facing chatbots.There is evidence for high adoption of conversational AI solutions amongst enterprises within the banking and finance industry.Juniper Research has identified that some of the larger banks will opt for in-house devel

82、opment of conversational AI solutions,whilst also integrating external LLMs for advanced capabilities.On the other hand,smaller banks will require prebuilt solutions,with no-code/low-code flows,to help them to implement conversational AI without the need for experienced developers.a)Implementation o

83、f Conversational AI by Large Banks JP Morgan Chase launched LLM Suite in July 2024,an internal AI assistant,to more than 200,000 employees.Also,Bank of America developed a proprietary conversational AI solution called Erica,which assists customers with banking tasks,provides financial advice,helps w

84、ith transactions,and identifies savings opportunities.In April 2024,it was announced that Erica had surpassed more than two billion interactions since it was first launched in 2018 and has helped more than 42 million customers.The first billion interactions occurred over the first four years,with th

85、e virtual assistant reaching the second billion 18 months later.This indicates a rising demand amongst banking customers looking for immediate responses to queries via virtual assistants.The top enquiries from Bank of America clients that use its virtual assistant include:Requesting an account numbe

86、r or routing number Finding transactions Assistance with money transfers and bill payments ii.Retail Whilst like the banking industry,in that enterprises within the retail industry will also demand conversational AI solutions for deploying chatbots,the specific use cases for chatbots in this industr

87、y will differ and will require conversational AI vendors to tailor chatbots to ensure that they fulfil the need of enterprises within the industry.Retail chatbots will require a different tone of voice,as they will be required to support customers with different use cases,therefore the tone of voice

88、 must be customisable to the brand voice that customers associate with that brand.a)Implementation of Conversational AI Amongst Retailers Conversational AI will be implemented by retailers in different ways,to automate processes to boost operational efficiency and enable significant cost savings.Con

89、versational AI will be integrated within internal systems as well as customer support systems to provide support to both employees and customers.Larger retailers will have a higher budget when compared to smaller retailers,therefore GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING

90、8 TELECOMS&CONNECTIVITY there is greater opportunity for these large retailers to automate different processes with the use of conversational AI.Conversational AI vendors therefore can solve several problems for retailers and must focus on offering solutions beyond customer-facing chatbots.iii.Healt

91、hcare a)Implementation of Conversational AI in Healthcare Conversational AI will be used in the Healthcare industry for the following use cases:Patient care:conversational AI will be used within the healthcare industry for automated appointment scheduling and reminders,providing initial patient asse

92、ssments prior to appointments,and reminders about medications.Administration:conversational AI can also be used for administrative purposes within the healthcare industry,for example,for automated billing and insurance claim assistance.Remote patient monitoring:conversational AI technology can be ut

93、ilised alongside remote patient monitoring tools,to identify potential health issues and assist doctors during consultations.iv.Transport a)Implementation of Conversational AI in Transport Within the transport sector,conversational AI-powered chatbots will be able to automate the following:Real-time

94、 updates and information:enterprises in the transport industry can use conversational AI chatbots to provide customers with updates on their travel,including department and arrival times and any potential delays to customer journeys.Ticket booking and payment:conversational AI chatbots can also be u

95、sed in the transport industry to facilitate the purchasing and delivery of tickets within the chatbot interface.GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 9 TELECOMS&CONNECTIVITY 1.5 Market Forecast Summary:Total Conversational AI Revenue Juniper Research has forecast that th

96、e total revenue from conversational AI services will grow from$14.6 billion in 2025,to more than$23 billion by 2027;generating a total of$57 billion globally over the next three years.Figure 3:Total Conversational AI Services Revenue in 2025($m),Split by 8 Key Regions Source:Juniper Research Convers

97、ational AI revenue,which originates from enterprise spend on conversational AI platforms,will be driven by the benefits of implementing agentic AI to services.Agentic AI is a subset of AI that enables solutions to act independently to attain a preset objective,whilst learning from previous interacti

98、ons.Specifically,agentic AI enables enterprises to automate tasks such as service enquiries and appointment scheduling over conversational channels;reducing reliance of human agent intervention.Conversational AI vendors must integrate agentic AI into their communications technology stack;creating en

99、terprise solutions that automate customer interactions across messaging channels.To allow agentic AI to manage these interactions across the complete customer journey,integration with business support systems where customer data is stored is essential,The level of autonomy given to agentic AI must b

100、e carefully considered,with human oversight of actions a necessity during early-stage implementations.Issues around liability arising from hallucinations or erroneous communications must be avoided before enterprises trust in agentic AI can be established.GENERATIVE AI AND AGENTIC AI:THE FUTURE OF A

101、UTOMATION IN MESSAGING 10 TELECOMS&CONNECTIVITY Order the Full Research Discover how technological advancements including generative AI and agentic AI are impacting the market in this brand-new conversational AI report.Featuring forecasts split by chatbot users and conversational commerce spend by w

102、eb,app,OTT,RCS,and voice,this essential research delivers a thorough assessment of industry demand,split by multiple sectors.With a Competitor Leaderboard that reveals 22 leading vendors,and data split across 61 countries,this report provides stakeholders with key information on how to succeed in th

103、e conversational AI market,particularly as the market moves at a rapid pace.Key Features Market Dynamics:Detailed insight into the future outlook of the conversational AI market;assessing key drivers,including the impact that generative AI and agentic AI will have on the future of business communica

104、tion automation.Key factors addressed include the impact of growth of the conversational AI market this year,including the technological advancements driving adoption,the importance of cost predictability,and increased enterprise demand for conversational messaging use cases.The research also explor

105、es enterprise demand for conversational AI services across key industries,including banking and finance,retail,healthcare,and transport.Key Takeaways&Strategic Recommendations:In-depth analysis of key development opportunities,industry trends and findings within the conversational AI market;accompan

106、ied by key strategic recommendations for stakeholders.Benchmark Industry Forecasts:Market size and forecasts for conversational AI,including 5-year forecasts for total number of enterprises implementing conversational AI,and total revenue from conversational AI.It also forecasts the total number of

107、chatbot users and accesses by chatbot type,including web-based,app-based,over-the-top(OTT)-based,and rich communications services(RCS)-based chatbots,as well as voicebots.It also looks at conversational commerce revenue across each chatbot type.Juniper Research Competitor Leaderboard:Key industry pl

108、ayer capability and capacity assessment for 22 conversational AI vendors,via the Juniper Research Competitor Leaderboard.Whats in this Research?1.Market Trends&Strategies Detailed analysis and strategic recommendations for the conversational AI market;assessing key factors driving conversational AI

109、adoption over the next two years,including which technological advancements will have the greatest impact.It also provides insight into industry demand for conversational AI,and the tools that conversational AI vendors must look to offer for specific industries and use cases.2.Competitor Leaderboard

110、 In-depth analysis of the capabilities of 22 conversational AI vendors,via the Juniper Research Competitor Leaderboard(PDF).3.Data&Forecasts The forecast suite includes a breakdown of the conversational AI market and includes the total number of businesses implementing conversational AI,and the tota

111、l revenue from conversational AI services.It also forecasts the total number of chatbot users,split by web-based,app-based,OTT-based,and RCS-based chatbots,as well as voicebots.The conversational AI spend across each chatbot type is also forecasted.4.Interactive Forecast Excel Highly granular datase

112、t comprising over 31,000 datapoints;allied to an interactive scenario tool;giving users the ability to manipulate Juniper Researchs data.GENERATIVE AI AND AGENTIC AI:THE FUTURE OF AUTOMATION IN MESSAGING 11 TELECOMS&CONNECTIVITY 5.harvest Online Data Platform 12 months access to all the data in our

113、online data platform,including continuous data updates and exportable charts,tables,and graphs.Publication Details Publication Date:February 2025 Author:Molly Gatford Contact:For more information contact Juniper Research Ltd,9 Cedarwood,Chineham Park,Basingstoke,Hampshire,RG24 8WD UK Tel:UK:+44(0)1256 830002/475656 USA:+1 408 716 5483(International answering service)http:/