More than any time in our industrial history, people today expect always available access to research, advice, commerce and support from any location, device or platform they prefer. As explained in the first of this blog series, conversational AI provides companies with the technological means for meeting that expectation in a way never before possible.
Already, we’re seeing our clients move past the exploratory stage and now view conversational AI as an imperative for brand differentiation. As these innovation leaders move beyond theoretical evaluations of the technology to outcome-based projects delivering real business value, they’re also seeing the need for an entirely new business plan to guide their thinking. We call this a “conversational strategic plan.”
Typically led by digital commerce leaders, chief marketing officers and CIOs, the conversational strategic plan defines the goals, approaches and tactics for leveraging not only conversational technologies but also the many other AI-enabled innovations now coming into focus. With such a nascent and fast-evolving space, this will be a first-time exercise for most and a steep learning curve for anyone just getting introduced to AI.
To help these leaders know what to anticipate over the next couple years, we’ve compiled the following 10 predictions for conversational AI.
- The need for “academic” experimentation is fading. This year marks the beginning of the commercialization phase for conversational technologies and cognitive agents. Lessons learned were not lost on the fast-movers and innovators, who are now well into the first phases of implementation. (For our list of lessons learned, gleaned from our clients’ proofs of concept and pilots, see the second blog post of this series.)
- Conversational technologies are the launching pad for next-gen user interfaces, such as augmented reality, gesture controls, biometrics, visualization and intelligence. Expect much more immersive experiences to be realized using conversational AI technology. This will include proliferation of vision recognition as an AI service, leading to integration of vision and voice domains, as well as increased interest in augmented reality for virtual sales, training and support.
- Contextualization and self-learning will rapidly improve. Currently even the leading AI services platforms expect contextualization to be implemented as a customization feature; however, we expect these technologies to evolve, gaining the ability to handle context innately and self-learn faster and more effectively than they currently can.
- IOT voice integration will accelerate in devices, including automotive, wearables, home automation, personal health monitoring, drones and consumer electronics. This development will increase functionality, drive replacement and upgrade cycles and, of course, produce rivers of data to feed analytics, machine learning and business intelligence.
- The business reality of $15-per-hour labor makes the ROI of “conversational AI automation” impossible to ignore. Early lessons learned are already revealing both cost savings and revenue opportunities for those with determination and creativity.
- Language coverage will improve. Currently, language capabilities differ greatly among the Tier One cognitive agent technologies. For markets outside the U.S. and UK&I (especially Europe and APAC), non-English language coverage is key for success. Expect an increased number of supported languages and dialects and vast improvements in the proficiency of individual non-English languages.
- We’ll see smoother hand-overs between cognitive agent, live chat agent, call center agents and other channels. The user experience across channels should be consistent and aligned, whereas today the technology is not (yet) capable of handling all conversations on all channels. Ultimately, cognitive agent programs will not be implemented stand-alone but rather as a part of an overall interaction strategy that allows interlinked technology and change management across all platforms and channels.
- Configuration will get easier and faster. Robust and high-quality conversational AI implementations require considerable effort to design, test, deploy and maintain. Leading providers know that to keep lowering business case barriers and achieve adoption at scale, the technology must become easier and faster to implement. Other factors that will increase adoption include the evolution of products and frameworks, further leveraging of standards, best practices and reusable components by system integrators, and one-to-many/as-a-service solutions.
- Other AI technologies will be added to cognitive agent solutions. As cognitive agents become more intelligent, they will further improve user satisfaction, sales effectiveness and cost efficiency. This will happen by embedding other AI technologies, including video, sentiment and emotion recognition, improved recommendation engines and user-desirable integrations, such as social platform, weather, traffic, news and mapping APIs.
- Speech and images will infiltrate text-based chat. Initially, conversational implementations have primarily focused around text-based chat. Meanwhile, intelligent speakers are proliferating, and speech recognition capabilities are improving, so speech-based cognitive agents are quickly growing in importance. Dedicated multi-modal devices incorporating text, voice and video are gaining traction, and mobile phone/tablet technology already provides a ready platform for this triple play. Soon, integration among the three formats will be an expectation from users, and must be accommodated in forward-thinking solutions and customer experience design.
The bottom line is, it’s all changing fast, with frameworks morphing, architecture standards and best practices evolving, and new use cases appearing daily. Your conversational brand strategy needs to be a living entity in order to accommodate all of these considerations and more.
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