A Collaborative approach to analytics that brings together the skills, experiences, and knowledge of people in a variety of roles delivers benefits to the organization and to individual team members. The opportunities for insights multiply as you bring more minds to bear on a given problem.
It also emphasizes the problem-solving process, correctly identifying that data analysis that generates the most valuable insights doesn’t happen in a vacuum. Without the input of people who have a thorough understanding of the industry, are talking with customers, working on product development, managing production, etc., data analysts are operating without context.
Collaborative AI Defined
Collaborative AI is a new model of work that enables employees to perform their job functions faster and with more insight as a result of teamwork with AI systems. Otherwise known as a Digital Workforce or a Hybrid Workforce, Collaborative AI frees humans from mundane, repetitive tasks in order to focus on more high-value or unique tasks.
According to Harvard Business Review, “Their research involving 1,500 companies, they found that firms achieve the most significant performance improvements when humans and machines work together”.
“Through such collaborative intelligence, humans and AI actively enhance each other’s complementary strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter".
What comes naturally to people (making a joke, for example) can be tricky for machines, and what’s straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans. Business requires both kinds of capabilities.”
With Collaborative AI technologies in place, companies will begin to reorganize corporate structures so humans can manage and improve upon AI systems that are performing basic tasks, such as data entry, answering common customer service queries, and more.
Let’s examine two critical reasons Collaborative AI has become possible and how these changes will impact the modern workforce.
Conversational AI is Fully Democratized
Amelia, a global leader in Collaborative AI predicted 2019 would be the year technical novices could begin building AI use cases. With IT experts working alongside so-called “citizen developers,” users would be able to focus on specific use cases and flesh this process out while technology experts make sure the process is functioning properly.
Two years later, citizen developers no longer need technology experts looking over their shoulders. As we’ve seen with our Digital Employee Builder, AI use cases and fully-functional roles can be built and deployed in a complete process in less than 30 minutes, with no code required. As the year takes shape, more businesses will deploy AI systems using this no-code model.
We’ve seen this trend take hold in most other facets of the IT ecosystem. For example, 70% of enterprises already have policies in place for citizen developers, according to Gartner’s Citizen Development is Fundamental to the Digital Workplace report. By 2024, Gartner predicts more than65% of application development will be from low-code development. This percentage could be even higher for Conversational AI.
In fact, 47% of businesses say difficulty integrating cognitive AI projects with existing systems and processes is their biggest hurdle to AI initiatives, according to the Harvard Business Review. Companies also cannot find the talent required to integrate and deploy AI systems on their own. The same report revealed that fewer than half of businesses (45%) have a high skill level around integrating AI technology into their existing IT environment.
No-code Conversational AI systems will help line-of-business leaders with moderate or novice technology skills develop Collaborative AI use cases. These new systems will incorporate APIs, RPAs and visual components via conversational wizard-assisted design processes. By simply responding to the AI system’s suggestions and guidance— either through chat or voice-based conversations — employees will deploy Collaborative AI use cases mostly without help from the IT team.
The Corporatization of Conversational AI
Human Assisting Machines
Humans need to perform three crucial roles. They must train machines to perform certain tasks; explain the outcomes of those tasks, especially when the results are counterintuitive or controversial; and sustain the responsible use of machines (by, for example, preventing robots from harming humans).
Training
Machine-learning algorithms must be taught how to perform the work they’re designed to do. In that effort, huge training data sets are amassed to teach machine-translation apps to handle idiomatic expressions, medical apps to detect disease, and recommendation engines to support financial decision making.
Consider Microsoft’s AI assistant, Cortana. The bot required extensive training to develop just the right personality: confident, caring, and helpful but not bossy.
Similarly, Conversational AI “digital assistants” aimed at the general public (e.g., Siri, Alexa, Google Assistant), have empowered the average smartphone user to speak, rather than type, search-based queries. These experiences have been positive enough to persuade users to ditch the Google search bar in favor of conversing with Conversational AI.
A survey from Pew found that 46% of Americans regularly use these digital assistants, with the most popular reason (83%) being the ability to “use a device without my hands.”
AI can boost our analytic and decision-making abilities and heighten creativity.
Interaction
As the general public slowly becomes more accustomed to interacting with these digital assistants, information workers are operating alongside Collaborative AI systems — or Digital Employees — to perform business processes and to solve IT problems. These systems are more sophisticated than the digital assistants that people use to hear weather reports or locate movie times; they’re fully-trained workers who can process information, perform tasks, solve issues and collaborate with their human co-workers to help businesses run better.
AI agents like Cortana, for example, can facilitate communications between people or on behalf of people, such as by transcribing a meeting and distributing a voice-searchable version to those who couldn’t attend. Such applications are inherently scalable—a single Chabot, for instance, can provide routine customer service to large numbers of people simultaneously, wherever they may be.
Just as the consumerization of IT brought personal IT tools, such as email and chat to the enterprise, the corporatization of Conversational AI will see these more complex and intelligent conversational systems transition from the work place into our personal lives.
Scale
In fact, AI is predicted to power 95% of all customer interactions by 2025, including live telephone and online conversations, according to Servion Global Solutions. The ability to perform high-value commercial tasks through conversational systems is in such demand that 43% of millennials say they would pay a premium for a hybrid human-bot customer service channel, according to PwC.
Tool capabilities that enable collaboration
What does collaborative analytics software look like in action? There are a few capabilities that facilitate the process.
Teamwork spaces— Team workspaces governed by permissions and controls that ensure security allow employees with teams and across teams to collaborate.
Reusable workflows— Datasets used and analyses conducted by one team are able to be saved and reused by others.
Single source of truth— Data is centralized and available via a single access point, ensuring that everyone is using the same version of the data.
Chat — Built-in or API-integrated collaboration tools allow team members to ask questions, make comments, and tag others for feedback.
Visual, collaborative data modelling— a visual approach to data modelling allows business users to participate without writing code. Schemas and tables are available for all users to explore, and business users can create or contribute to new data models and add their input to existing datasets.
Sign off note
For Conversational AI to move from the business into our home lives, users will have to rely on these systems to do more than report the weather. Businesses will have to allow customers to purchase products and receive white glove advice and recommendations through Collaborative AI. Insurance companies will need to empower AI to guide users through policies, make payments and even settle disputes. Banks will need to let users apply for mortgages or make changes to their accounts through Collaborative AI. The list of use cases is infinite.
We’ve already seen companies deploy these use cases to great success. As 2021 progresses, these use cases will become the norm, rather than the outliers, within their respective industries.
Source: Harvard Business Review article.
