What is Artificial Intelligence, Machine Learning and Deep Learning

The idea of creating machines that are as smart as humans goes all the way back to the ancient Greeks myths of Hephaestus and Pygmalion incorporated the idea of intelligent robots (such as Talos) and artificial beings (such as Galatea and Pandora). Rabbi Judah Loew ben Bezalel of Prague, Czech Republic in 1580 coined the word “Robot” in a book called “Golem”, about a clay man brought to life.

In practical terms, however, the idea didn't really take off until 1950. Isaac Asimov published his Three Laws of Robotics. The phrase artificial intelligence was coined in 1956 by John McCarthy, who organized an academic conference at Dartmouth dedicated to the topic. The phrase "machine learning" also dates back to the middle of the last century. In 1959, Arthur Samuel defined machine learning as "the ability to learn without being explicitly programmed." And he went on to create a computer checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time. In that year, Alan Turing published a groundbreaking paper called "Computing Machinery and Intelligence" that posed the question of whether machines can think, in the famous Turing test. Joseph Weizenbaum (MIT) built ELIZA, an interactive program that carries on a dialogue in English language on any topic.

Like artificial intelligence research, machine learning fell out of vogue for a long time, but it became popular again when the concept of data mining began to take off around the 1990s. Data mining uses algorithms to look for patterns in a given set of information. Machine learning does the same thing, but then goes one step further – it changes its program's behavior based on what it learns. Other people prefer to use the term "machine learning" because they think it sounds more technical and a little less scary than "artificial intelligence." Someone on the Internet commented that the difference between the two is that "machine learning actually works." However, machine learning has been part of the discussion around artificial intelligence from the very beginning, and the two remain closely entwined in many applications coming to market today. For example, personal assistants and bots often have many different artificial intelligence features, including machine learning.

Over the past few years  artificial intelligence has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it. Stories about IBM's Watson AI winning the game show Jeopardy and when Google DeepMind’s AlphaGo deep learning program defeated S. Korean Master Lee Se-dol in the board game “Go”. These two examples and the appearance of BOTs have returned artificial intelligence to the forefront of public consciousness. Of course, "machine learning" and "artificial intelligence" aren't the only terms associated with this field of computer science. IBM frequently uses the term "cognitive computing," which is more or less synonymous with artificial intelligence.
However, some of the other terms do have very unique meanings. For example, an artificial neural network has been designed to process information in ways that are similar to the ways biological brains work. Things can get confusing because neural nets tend to be particularly good at machine learning, so those two terms are sometimes conflated.

The easiest way to think of their relationship is to visualize them as concentric circles with artificial intelligence — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s artificial intelligence  investment explosion.

Artificial Intelligence: Most Active VC Investors
Ranking, 2011 to 2016 year-to-date (as of June 2016)
1 Khosla Ventures
2 Intel Capital
2 Data Collective
4 Google Ventures
5 New Enterprise Associates
6 Andreessen Horowitz
7 Formation 8
8 Horizons Ventures
9 Accel Partners
10 Plug and Play Ventures
11 GE Ventures
12 Two Sigma Ventures
13 Samsung Ventures
14 Norwest Venture Partners
15 Bloomberg Beta

Khosla Ventures is the most active VC investor in AI-based companies. They 15 unique companies, including Atomwise, MetaMind (recently acquired by Salesforce), Scaled Inference, and LiftIgniter. 

Intel Capital backed startups including Lumiata, DataRobot, Perfant Technology and Parallel Machines and Data Collective (backer of Blue River Technology, Descartes Labs, SigOpt, and Nervana Systems).

Maybe the VC guys forgot the failure of Thinking Machines, let's hope whatever you call it, it will be successful.

Cheers,

James Wilson
+1-404 936 4000

Call Center Pros
james@call-center-pros.com

www.call-center-pros.com

3 IoT Issues to Consider Before Moving Forward With IoT

• 

  The IoT (Internet of Things) links together networks, devices, and data. It helps you learn more about the things vital to every level of your business. To build your IoT solution successfully you need agile products, services, and networks that enable you to gain real-time insights about the things your company values most. If you are deploying IoT-enabled products for customers or IoT-based devices in your own factory, consider these three issues before moving ahead with IoT implementation plans.
  1.    Software maintenance updates and software feature updates
  a.     IoT devices can be computing devices, software problems will occur at some point, and software updates will need to be applied to fix them.
  b.     In addition, new features are likely to be developed to further software updates are needed to enhance IoT operations.
  c.     IoT devices trigger alert by software updates, which have been ignored, so a call center with IoT connected software is needed to schedule a time for needed software update.
  d.     Issues are likely to arise around both connections to the devices and the legal rights to apply software updates to systems already in service.
  e.     Software updates can become even more complex if software updates actually cause unintended issues requiring on-site access by IoT personnel.
  f.      Make necessary verbiage in your product terms and conditions to cover all these cases.
  
  2.    Fast Restart and Flawless Authentication
  a.    IoT engineers need to build in restart utility to enables fast and flawless authentication.
  b.    In an industrial environment with hundreds or thousands of devices in difficult-to-find and reach locations, fast restart and flawless authentication can make the difference between shutting down an entire factory and waiting for IoT techs to perform manual resets.
  c.     The nature of IoT devices is to connect to other:
                                                     i.   Other IoT device connections
                                                   ii.    Other IoT users connections
                                                  iii.    Data storage connections
  d.    Authentication must be flawless and IoT reconnect without interference:
                                                     i.   Network environments change
                                                   ii.    Lose of connection of IoT devices
                                                  iii.    IoT devices restart
  e.    A call center with a human with a current escalation plan is always a smart plan.
  
  3. Handling failure
  a.    IoT sensors can trigger communicate both via SMS and email to proactively fix a problem before it becomes a failure and if both SMS and emails are ignored a human in a call center can proactively escalate.
  b.    It is always best for IoT technologies and services can rely on humans to be involved in a contact center to make the final decision.
  c.     Having IoT devices can greatly enhance operations and make them easy to observe and manage. It’s important to remember, adding IoT devices should never interfere with the working environment of the device the IoT is there to help monitor and control.
  d.    Failure of any segment of the IoT process to communicate must leave the IoT device in a nonmalignant state and as fully operational as achievable.
  e.    Build in the ability to allow the device to revert to manual operation as needed when fault conditions happen.
  f.      The computing portion of the IoT device should attempt to transmit error status codes when impending failure is detected.
  g.     When an IoT failure is detected and the equipment is dangerously still in use, it might take the IoT’s manufacture’s call center to actually call the factory and identifies the safety problem.
  h. Artificial Intelligence (A.I.) will help contact centers declutter the new torrent of additional IoT information by 80% for false alarms and 60% of repetitive work.
  
  Contact centers and Enterprises need to ask Call Center Pros or their software providers to provide A.I. sooner than later. Please contact us for any call center technology need or telecom challenge.
  
  Thank you,
  
  James W Wilson
  Call Center Pros
  james@call-center-pros.com
   http://www.linkedin.com/in/jameswwilson
  1-404-936 4000
  

  
  

4 Steps of Bringing Social Media in the OmniChannel Contact Center

Regardless of which step of involvement in social media in the OmniChannel contact center you’re currently in, there are tools to help you navigate the social media and the customer contact conundrum and social media. A variety of tools exist to help monitor, analyze, and route, social media interactions, providing reports, analytics and A.I. to help you understand how well your BPO or company is meeting your goals. These tools are appropriate for any BPO or company’s approach – no matter if your social media customer care is provided by the marketing department or contact center agents. A variety of proven products are helping companies hear what customers are saying, understand the context of these interactions, and respond to customers’ issues and questions, while maintaining the company brand and social identity. We found 4 steps of social media progression, what step are you in today, and what will it take to move to the next step?

Step 1: Manually Monitoring & Reactive Response
1)    Most companies starting out are using free or low cost tools such as Addict-o-Matic, HootSuite, Google Alerts, SharedCount, Social Mention, Trazzup  and TweetReach plus a variety of Facebook® free tools.
2)    These software utilities can look for key words and phrases, companies monitor public social media sites to determine what is being said about their company and products.
3)    Monitoring what their competitors’ customers might be saying? Finding a competitor’s unhappy customer is a perfect time to introduce that customer your product.
4)    By searching for keywords, company name, products or by following hash-tags on Twitter, as well as by entering in company or product-related names in Facebook, companies can see what is being said about them and their products.
5)    Usually marketing that is responsible for responding to public social media comments.
6)    Responses can range from:
a)      No response
b)    Simple acknowledgement of the comment
c)  An invitation to move the conversation from public to: phone, email, Facebook messenger or Twitter DM.

Step 2: Automated Monitoring & Manual Routing
1)    By using automated monitoring tools such as Brandwatch, Buzzient, Buzzsumo, CyberAlert, Keyhole, Mention, NetBase, Nuvi, Talkwalker, Trackur and other companies aggregate social feeds and get notification about when and how their brand is mentioned on social media sites.
2)    These monitoring tools provide real-time monitoring and alerting, allowing companies to respond to critical items promptly.
3)    Companies receive the alerts and information about the social media comments, which may include information on sentiment (e.g. is the customer very angry).
4)    The comments are then manually triaged and sent (usually via email) to the appropriate group (customer support, tech support, billing, collections or sales) for the next step.
5)    Depending on the specific relation and inquiry, communications can be manually sent to the right person on the customer’s need: language, time-zone or maybe a VIP team.
6)    The problem, these replies are all separate silos of information held by marketing

Step 3: Automated Monitoring & Automated Routing
1)    This goes a step beyond simply using monitoring tools to automatically routing the comments to an appropriate individual based on keywords and skills required to respond to the customer.
2)    In most cases, this involves integrating the social media monitoring tools with the company’s contact center technologies, such as skills-based routing, to ensure that the appropriately skilled person is handling the interaction.
3)    In this step where contact center agents start getting more involved with the process and the contact center technologies can be extended to the marketing department.
4)    When integrated with the company’s contact center platform, social media interactions can be treated as other customer service interactions: phone, email, or chat.
5)    Companies can then view contact center reports, indicating not only how many interactions came in via social media, but how long it took to respond to the customer, whether the situation was resolved, BPO’s might have different SLAs and more.
6)    Now, social media is mostly under control of the contact center and working well.
7)    Customers want to move between social media, chat, voice, email, SMS without having to repeat their situation with every new agent they meet.
8)    Customers want to access more channels which improves the customer experience (CX). And this is less customer friction through OmniChannel, but only when the channels are not forced or in separate silos.

Step 4: Full Integration with OmniChannel Contact Center Solution with A.I.
1) The customer having control through OmniChannel is the key to great customer experience.
2)  True OmniChannel always removes the separate silo channels, every agent can see every past conversation no maker which channel.
3)    The next step is connection the social media monitoring and routing with the rest of the contact center tools: WFM, CRM, CTI, scripting, ERP, BMP, databases, inventory, logistics with A.I.and finding repetitive commonly asked questions / giving suggested answers and finding the next money saving business trends.
4)    By integrating with the CRM system, companies can push up information about the customer to the CRM as a single silo.
5)  The IRV being smarter and properly set-up and actually being helpful and useful.
6)  And after a social media interaction (this requires being able to identify who the social media customer is, which isn’t always easy)
7)  Pulling all past communication from the CRM to give the agent the 100% view of the customers’ past.
8)  The agent needs to be provided with purchase history and any other context about through the OmniChannel interactions, again a single silo for everything.
9)  For example, agents should be able to see if the customer has already spoken with a contact center representative on this issue but didn’t get a resolution, or that this is an ongoing situation that hasn’t been resolved.
10)  Most often customers turn to social media as a last resort, and the individuals handling the interaction needs to understand the context of the past interactions.
11)  Artificial Intelligence (A.I.) in OmniChannel is where the magic happens.
a)    Experts are assigned for the A.I. to learn based their questions and replies.
b)    With about 200 most common questions and replies A.I. will start giving better suggested answers to the newer and less polished agents.
c)     These A.I. best suggested replies will get better and better with more time and more varied questions and answers.
d)    These A.I. best suggest replies will also be working in all other OmniChannels: email, chat, texting and even scripting for voice.
12)  Chat-Bots will gain enough information from the ‘expert’ agents to answer maybe 50%-80% of the repetitive questions from A.I. based on company expert’s answers.
13)  All true OmniChannel vendors have great APIs and will connect to any CRM or system.
14)  Email and mail with A.I. to give best suggested answers.
15)  There are not many true OmniChannel contact center vendors. I see three groupings:
a)    Some OmniChannel vendors are an ecosystem and best if you take most of it.
b)    Some OmniChannel vendors are “best of class” and only offer one part but they are great. Just like other single purpose vendors and started with recording and may have branched out to another contact center specality: WFM, Queue call back, PBX, Predictive dialer, CTI, Analytics, Social media monitoring and Social media answering to name a few.
c)     Some OmniChannel vendors are truly modular and have a great full offering as well as “best of class” modules to work with any systems you would like to keep.
16)  With OmniChannel will give better Customer Experience (CX) with these advanced solutions used right in the right way

Call Center Pros ran a small survey found 75% of BPOs and companies are still in the first two steps of social media usage for customer care, including both BPO and end user companies.

• 40% are in Step 1
• 35% are in Step 2
• 16% are in Step 3
• 9% are in Step 4

Still the large majority of BPOs and companies we spoke with said that they currently monitor and handle social media manually, but these BPOs and companies expect this will change in the near future.

• Analyze your customers needs and wants.
• Analyze your customer service operation.
• Analyze your social media strategy.
• Analyze your BPO and company’s goals.

Then decide the best way to move forward. Social media is here to stay, and you need to be ready to service your customers through the social sites that they’re using.

Call Center Pros says we shouldn’t even call it OmniChannel anymore…. That’s just how customer experience should be.
 
Please contact us for any call center technology need or telecom challenge.

Thank you,
James W Wilson
Call Center Pros
james@call-center-pros.com
 http://www.linkedin.com/in/jameswwilson
1-404-936 4000

How Can A.I. Help Contact Centers?

Customers have more choice of product goods and more communication channels than ever before and the customers are without a doubt, in charge. At the same time, companies are fighting with new digital products, which channels to market, business models, looking to get used to the hopeful digital world. While these market pressures were first noted in the travel, retail and financial trading services industries, they are rapidly becoming the norm in other industries. It is becoming progressively more apparent that companies must engage their customers on their choice of channels, or risk losing them to competitors who do offer those channels.

Enter OmniChannel, one of the hottest approaches to customer engagement in years. OmniChannel strategies seek to deliver a reliable and tailored experience to customers across all communication channels and store the info in a single silo. OmniChannel now affects customer service and support, as well as to all aspects of engagement in sales. The history of the word “OmniChannel” go back a few decades to leading-edge advertising firms, who then sought to deliver consistent communication across all marketing media (TV, radio, newspapers, magazines, billboards and so on) hence the name OmniChannel.

Achieving success in OmniChannel is not easy. The seamless combination of consumer experiences in a customer’s pathway that cross mobile devices, PCs, phones, SMS, email, chat and social media is a massive ordeal. This is especially true where companies were just adding these separate silos of communication channels, as needed just to catch up with customer’s demands.

How does A.I. work with OmniChannel?

1. Classifies and Allocates - Incoming transactions are analyzed and allocated to available employees according to topic, skill, and urgency.
2. 100% Vision of Transaction History - Relevant background information from existing systems is displayed for customer support, tech support and sales.
3. Response Suggestions - The expert responses are learned from historical expert replies and are offered a small selection of text templates.
4. Sending and Archiving - Select the best reply channel (chat, SMS, Social Media messaging or e-mail) and automatic archiving – according to content in a single silo.

Artificial Intelligence (A.I.), which involves the design and development of applications that ‘learn’ based on flexible, evolving analytical models. One of the (many) areas where A.I. can make improvement is through customer engagement, and particularly for those seeking insights to optimize OmniChannel. These on-going evolutions, accomplished through algorithms that learn from repetitious data, are what really distinguishes A.I. OmniChannel is to identify hidden patterns and unseen trends without precise programming to look for these patterns and trends. As A.I. is exposed to new data, they adapt automatically, looking to produce reliable and repeatable answers.
Advances in computing technologies have fueled A.I. advancement, allowing many established A.I. algorithms to be repeatedly applied to enormous data sets with orders of magnitude boost the cost saving results. Today A.I. can be demonstrated in such notable areas as A Hong Kong VC fund has just appointed an algorithm to its board, Google’s self-driving car, online recommendation engines (such as Netflix using Amazon's A.I. cloud ), social media using A.I. and bank fraud detection by A.I.
Here is a list of: A.I. / machine learning / deep learning technologies companies:

• Add-Structure
• Angoss Software
•  Ascribe
• Attensity
• Attivio
• Basis Technology
• Bitext
• Brainspace
• Cambridge Semantics
• Clarabridge
• Content Analyst
• Revealed Context (Converseon)
• Dell, Digital Reasoning, EPAM
• Etuma
• Expert System
• FICO
• Fractal Analytics
• Haystac
• HPE
• IBM
• Infegy
• KNIME
• Knowliah
• KPMG
• Lexalytics
• Linguamatics
• Luminoso
• MaritzCX
• Meaning Cloud
• Megaputer Intelligence
• Northern Light
• OpenText
• Rant & Rave
• RapidMiner
• SAP
• SAS
• SpazioDati
• Squirro
• SRA International
• Taste Analytics

Let’s explore where A.I. promises to help OmniChannel Contact Centers.

1.    A.I. understands the customers’ pathways through the customer service or customer support process. Understanding customer experience from an ‘outside in’ standpoint, requires a regimented, controlled view of customers’ communications. Analysis of these pathways can highlight where procedure need to be improved by removing obstacles to customers accomplishing their goal. A.I. could take this one step further, predicting the customer’s goal through their actions, and providing faster track to improve their experience. Additionally, A.I. could improve customers’ experience while encouraging up-selling. A.I. could also be used to guide the pathways, to achieve a ‘win-win’ for both customer and supplier.

2.    A.I. can most obviously help product and service recommendations. We’ve already mentioned Amazon and Netflix recommendation engines as successful deployments, but these are early examples. Using A.I. along with analysis, history and current environment, A.I. could truly personalized product recommendations and tailored communication.

3.    Improving service and support. OmniChannel service and support are equally important to retain customers. A.I. can help in multiple areas:
       a.    Self-help can remove much of the more routine questions; A.I. gives opened questions and not boxed in choices that are answered with “Press 1”.
       b.    Based on current context and history, it could be used to predict why the customer is seeking support, speeding the response.
       c.     A.I. can provide recommendations for problem solving, both from the perspective of recommending a particular pathways, but also providing recommendations to the customer and support staff on how to address the problem, improving support effectiveness.
       d.    A.I. through continuous learning, it can refine these pathways and recommendations, optimizing the customer experience (CX).

In summary, A.I. promises significant breakthrough in improving customer engagement and experience, and OmniChannel is a clear target in need of application.

Please contact Call Center Pros, LLC for any call center technology need or telecom challenge.

Thank you,
James Wilson, CEO
Call Center Pros
+1-404 936 4000
james@call-center-pros.comr
http://www.linkedin.com/in/jameswwilson

What is Cloud vs. Cloud-Washing?

In July, Seth Robinson from CompTIA released his new report on the state of cloud computing with some puzzling results. The survey’s data ran contrary to statements from every consultant and tech research firm. CompTIA’s new Trends in Cloud Computing report shows that while well over 90% of companies still claim to use some form of cloud computing, but the pace of progress appears to have slowed. In some cases, it even appears to have taken a step backwards. What accounts for this phenomenon? Have attitudes towards cloud everything cooled, even though cloud continues to be a prime reason in IT growth? The survey included 500 CIOs and IT executives in the CompTIA survey and the use of SaaS applications showed a decline since the last time CompTIA completed the survey in 2014.

“When the data came back it didn’t look like we were marching along the adoption path we’d defined and we didn’t quite know what to make of it,” said Seth Robinson.

• Business Sectors        2014   vs.    2016
• Business productivity 63%    vs.    45%
• Email  59%    vs.    51%
• Analytics/BI   53%    vs.    35%
• Collaboration 52%    vs.    39%
• Virtual desktop         50%    vs.    30%
• CRM   44%    vs.    37%
• HR Management       42%    vs.    29%
• Help desk       37%    vs.    29%
• Expense management            35%      vs.    29%
• ERP     34%    vs.    26%
• Call Center     31%    vs.    23%

Why “cloud washing” artificially inflates the implementation numbers
The contrarian statistics found two trends:

1. Gartner expects the public cloud services market to grow by double digits in 2016, with $204 billion in worldwide revenue representing a 16.5% increase over 2015’s $175 billion. For 2017, Gartner believes the market will continue expanding, with year-over-year revenue growing by 17.3%.
2. The survey shows the drop of Cloud adaptation from 2014 to 2016 is due to understanding the definition of “Cloud”. The new term "Cloud washing," or on-premises software re-branded as cloud software, and CIOs' lack of understanding of what constitutes a cloud service. The most basic cloud washing practice includes a vendor that hosts an implementation of their existing packaged software and calls it cloud because they are maintaining it in a virtualized data center.

First let's have a common agreement or understanding what is the “Cloud” as defined by the National Institute of Standards and Technology (NIST). 

Crucial characteristics as defined by NIST:

• On-demand self-service. A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider.
• Broad network access. Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations).
• Resource pooling. The provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, and network bandwidth.
• Rapid elasticity. Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time.
• Measured service. Cloud systems automatically control and optimize resource use by leveraging a metering capability1 at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

Service Models as defined by NIST:

• Software as a Service (SaaS). The capability provided to the consumer is to use the provider’s applications running on a cloud infrastructure2. The applications are accessible from various client devices through either a thin client interface, such as a web browser (e.g., web-based email), or a program interface. The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited userspecific application configuration settings.
• Platform as a Service (PaaS). The capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming.
• Deployment Models as defined by NIST:
• Private cloud. The cloud infrastructure is provisioned for exclusive use by a single organization comprising multiple consumers (e.g., business units). It may be owned, managed, and operated by the organization, a third party, or some combination of them, and it may exist on or off premises.
• Community cloud. The cloud infrastructure is provisioned for exclusive use by a specific community of consumers from organizations that have shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be owned, managed, and operated by one or more of the organizations in the community, a third party, or some combination of them, and it may exist on or off premises.
• Public cloud. The cloud infrastructure is provisioned for open use by the general public. It may be owned, managed, and operated by a business, academic, or government organization, or some combination of them. It exists on the premises of the cloud provider.
• Hybrid cloud. The cloud infrastructure is a composition of two or more distinct cloud infrastructures (private, community, or public) that remain unique entities, but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds).

CIOs are now savvier in their cloud choices
Many Contact Center as a Service (CCaaS) pitch it as cloud software just because it's hosted in their data center. They have not used something like CloudStack or OpenStack or built a proprietary cloud system to do the types of things to do the types of things that Amazon Web Services, Microsoft Azure or Google Cloud are doing. Cloud washing perpetuated as on-premises software providers and implementation partners told CIOs that they were adopting cloud. Some CIOs claimed they were using private clouds when they were actually depending on hosted data centers. So “cloud washing” was perpetuated.

The lower SaaS adoption numbers from his new study suggest CIOs have a better understanding of what comprises CCasS, SaaS, PaaS and IaaS and are becoming more savvy and making better educated technology choices. CIOs are still adopting cloud … they’re asking the right questions, before a making a decision.

Please contact us for any call center technology need or telecom challenge.

Thank you,
James Wilson, CEO
Call Center Pros
+1-404 936 4000
james@call-center-pros.com
http://www.linkedin.com/in/jameswwilson

Email and Mail with AI

No machine has yet been built with a human echelon of intelligence, A.I. technologies are now maturing to the point where this is not science fiction and more about reality. Did you know that some of the news you read may not be written by human beings? The Associated Press (AP) has been using A.I. technology for a majority of U.S. corporate earnings stories for their business news report. Did you know a Japanese venture capital firm Deep Knowledge recently became the first company in history to name an A.I. robot to its board of directors?

In many companies, the slower night shifts in a contact center are the ones that answer the days email and mail. If you think about amount of general company email and mail coming into companies every day, and the number of hours to reply and how long it takes to respond in a consistent manor, imagine the improvement that can be made with A.I. Right now most every step is a manual process for email and mail that takes place on the basis of confusing, complex rules, resulting in high labor costs. Up to 75% of the potential optimization lies in automatic classification and further processing of email and mail content.

The problem with Document Management Systems (DMS) and incoming correspondence has only supported the movement flow of documents in the digital mail-room and do not understand the content of the correspondence.

The A.I. solution analyzes incoming letters, forms or invoices, automatically identifies customer data and organizational terms, enriches customer transactions with information from third-party systems and distributes them automatically to business processes and employees based upon priority. The right A.I. solution would need to be automatic and not manual based on algorithms to recognize relevant content in your correspondence and transfer the message automatically and reliably to the follow-up processes of your company. The best A.I. email and mail system would be dynamic and non-rigid; it learns the behavior of selected “expert employees” during the dispatching and information retrieval process. The effectiveness of A.I. automation grows dynamically. The system learns and gets better.

How does Email and mail with A.I. work?

1. It classifies and allocates. Incoming transactions are analyzed and allocated to available employees according to topic, skill and urgency.
2. It validates and enriches. Relevant context is extracted. Discrepancies are detected automatically and submitted to available employees for validation.
3. In single click forwarding. The email is automatically indexed and forwarded to the recognized handling process. Most times the CSR will look at the incoming email and prepared reply and hit send.
4. Is the machine learning or A.I. processing. The system learns independently, because of its successful classification and processing, the automatic efficiency of the document processing is improved daily.

This is an fast, affordable, standalone product for mail with OCR and email. When combined with other modules, this can help with OmniChannel (documents, faxes, e-mail, apps, text messages, Facebook, and Twitter). The real future may be in man joining machine. Human and Robot interaction is emerging as a more practical, near-term application of A.I.
Please contact us for any call center technology need or telecom challenge.

Thank you,

James Wilson, CEO
Call Center Pros
+1-404 936 4000
james@call-center-pros.com
http://www.linkedin.com/in/jameswwilson

OmniChannel with AI

OmniChannel with AI is the application of technology that allows employees in a contact center to configure AI software to capture and interpret existing applications for automating a transaction, manipulating IoT or any other data sources, triggering automated responses and communicating with other digital systems. Automating and accelerating those processes won’t come from Siloed solutions who can’t see the big picture, or think holistically about the contact center and also the enterprise.
Any enterprise that uses labor on a large scale for general knowledge process work, where people are performing high-volume, highly transactional repetitive work, will boost their capabilities and save money and time with OmniChannel with AI. These solutions can be integrated into the enterprise contact center without the “rip-and-replace” mentality.

Just as industrial robots are remaking the manufacturing industry by creating higher production rates and improved quality, OmniChannel with AI is revolutionizing the way we think about and administer, customer care, business processes, workflow processes, remote infrastructure and other repetitive back-office work. OmniChannel with AI provides dramatic improvements in accuracy and cycle time and increased productivity in transaction processing while it elevates the nature of work by removing people from dull, repetitive tasks. OmniChannel with AI can be applied specifically to a wide range of industry sectors.

OmniChannel with AI and Process Automation
Exciting changes are happening: customer care will be everywhere and with IoT sensors that are embedded in everyday products; many interactions will be automated with virtual agents on common reoccurring activities. Deeper engaging and meaningful conversations with customers will happen when the CSR knows the issues from the displayed knowledge for the advanced calls. Companies will learn from every customer interaction, through every channel, and analytics will allow profoundly personalized exchanges that deliver immense customer satisfaction. If your CSRs have multiple systems to enter the same data, this can be automated. The more customer service history your OmniChannel with AI includes, the more it will learn. Certain agents who are stars at writing perfect emails can have a heavier weight, the other agents will read and email and the AI will suggest lesser CSRs the best reply based on the star CSR’s previous replies. The same will be true for chat, SMS, and the social media channels

Right now, every contact center CEO has a choice:

• Stay the course with the existing solution, improving efficiencies and balancing the increasing demands for as long as you can.

          OR

• Start the transformation journey, knowing that there’s a more sustainable, more value-generating way to offer Customer Care going forward.

As in voice recognition software or automated online assistants, developments in how machines process language, retrieve information and structure basic content mean that OmniChannel with AI can provide answers to employees or customers in natural language rather than in software code. This technology can help to conserve resources for large call centers and for customer interaction centers.
As OmniChannel with AI bring in more technologically-advanced solutions to businesses around the world, contact centers that adopt this automation process, whether in-house or outsourced, will cut costs, drive efficiency and improve quality.

The next big leap will come from technology — but not just any technology. Please feel free to contact Call Center Pros to leverage OmniChannel contact center workflow automation, analytics and A.I. across all of your contact center processes. Please contact us for any call center technology need or telecom challenge.

Thank you,

James Wilson, CEO
Call Center Pros
+1-404 936 4000
james@call-center-pros.com
http://www.linkedin.com/in/jameswwilson