Full digital transformation begins with AI
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How to select contact

center AI use cases

What AI does your contact center need?

Most companies experience challenges when implementing AI. From stakeholder buy-in, inaccessible data, a shortage of resources and lack of integration, there’s many stumbling blocks that can deter any AI project. But, one of the largest impediments to successful AI is right from the start: identifying AI use cases that provide solutions and return on the investment. This guided planner will help you weigh your organization’s AI readiness with your specific goals to help you better determine which AI use cases are most fitting for your contact center today.
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Do you have a specific problem to solve or
business goal to accomplish?
Is the solution to your problem better
achieved through automation or does it
require machine learning?
Before you can ask "What AI?", you need to first determine if AI is a fitting
solution.
AI is fitting if:

1) You have a vendor or technology that can support the use case or data if you're
building internally
2) You can justify the business value
3) Your organization's AI maturity supports AI
As an initial step, create a list of the business goal(s) or problem(s) where AI can
help by asking: Where are the friction points in your customer journey or process?
                Automation
Repetitive, high-volume tasks with pre-defined
processes with few or fixed outcomes defined
processes currently executed by humans.
I'm not sure
       Machine learning (ML)
Machine learning involves processing and analyzing
data patterns to identify trends and make decisions
with or without human intervention. It's best for
solutions where you can use your current data to
predict or classify future data. For instance, using
last year's agent schedules to forecast the number
of agents to schedule this year.
How AI ready is your organization?
Will you use a vendor or technology that doesn't
require you to bring or train any data or are you
planning to build an AI solution from scratch?
Using advanced AI-based technology requires an established
foundation. Launch AI responsibly by selecting AI use cases
aligned to your organization's maturity. Take this quick Forrester
Consulting assessment to determine your AI-Readiness.
AI-Averse
AI-Aware
AI-Driven
AI surfaces trends and looks for patterns in large datasets. To put
AI to work, you'll first need to be sure your data infrastructure is
prepared for AI by making sure all the data you need is
warehoused in a single place rather than segregated into different
applications or reporting systems. Once your data is integrated,
you can transform the raw data into insights using AI. If you're
unsure of how ready your data infrastructure is for AI, you can
often determine this based on how easy or difficult it is for you to
create reports from your data.
Vendor
DIY
How easy is it for you to create
reports from your data?
Do you have the data you need
to solve the problem or
goal?
No
Not sure
Yes
Unable to report or high
level of effort
Reporting requires moderate
effort
Reporting is low effort
Is your goal solution
needed within the
next three months?
What or who is most impacted
by your problem or business goal?
Are your ACD, QM
and WFM analytics
and systems unified
across all channels?
Customers
Agents
Operations and Insights
Natural-language Virtual Agent
Voice Authentication
Voice Bot (Conversational IVR)
Agent Assistance
Real-Time Quality Management and Training
Advanced Routing
Real-Time Omnichannel Customer Insights
Is your data structured
and automatically
clustered and classified?
Is your solution better for
Unsupervised Learning?
Unsupervised learning helps you categorize unlabeled data.
If you already have a general sense of what the categories or
criteria should be and the issue is lack of resources to
manually label your data, unsupervised might not be the
best solution.
Predictive Content Recommendations
Proactive Virtual Agent
Scale and Improve Digital Channel
Experience
Agent Assistance
What or who is most impacted
by your problem or business goal?
Customers
Agents
Operations and Insights
Sentiment Detection and Analysis
Proactive Virtual Agent
Agent Assistance
Real-time Quality Management
Scale and Improve Digital Channel
Experience
AI-powered Forecasting
What or who is most impacted
by your problem or business goal?
Agents
Customers
Operations and Insights
Chatbot
Conversational IVR
Robotic Process Automation (RPA)
AI Analysis of Customer Interactions
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