The Road to Enterprise AI

2084

Shopping for Enterprise AI

Automation through AI

Introducing RAGE-AITM

Two Key RAGE-AITM Innovations

The Significance of Context, Language, Reasoning

AI Survey Results AI in the Enterprise

From the Gartner Files: Predicts 2017: Artificial Intelligence About RAGE Frameworks

The Road to Enterprise AI

2084

Gartner makes the following observations in its “Predicts 2017: Artificial Intelligence” research note.

1. By 2019, more than 10% of IT hires in customer service will mostly write scripts for bot interactions.

2. Through 2020, organizations using cognitive ergonomics and system design in new artificial intelligence projects will achieve long-term success four times more often than others.

3. By 2020, 20% of companies will dedicate workers to monitor and guide neural networks.

4. By 2019, startups will overtake Amazon, Google, IBM and Microsoft in driving the artificial intelligence economy with disruptive business solutions.

5. By 2019, artificial intelligence platform services will cannibalize revenues for 30% of market-leading companies.

In the same research note Gartner also provides a set of very helpful guidelines around navigating these early days of the resurgence of AI, and

Issue 2

driving change strategically and methodically, outside of the hype zone.

Wisely, Gartner’s predictions go out only three years.

On 25-Jan, 2017, sixty eight years after it was published, George Orwell’s dystopian 1984 became the number one best-selling book on Amazon.

Orwell’s Big Brother, omnipresent government surveillance, Ministry of Truth that dispenses lies or “alternative facts”, society at perpetual war, however dark, can still be conceptually grasped, even if not understood. Extrapolating current reality to paint a picture of the future so far

The Road to Enterprise AI

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away cannot possibly be easy, but may not have seemed impossible at the time. Couple that with great creativity and outstanding storytelling, and you had a bestseller then and now again, more than half a century later albeit a few decades behind schedule. Importantly, Orwell did not need to involve technology to paint the 1984 he imagined and did not have to deal with the exponential nature of the expansion and influence of technology.

Fast forward to today and imagine writing the sequel “2084”. Even the bravest of authors and the farthest seeing crystal ball gazers would admit that is simply not possible. The forces that our runaway technological evolution will unleash by 2084 are way beyond the predictive powers of any budding Orwell, Huxley or Nostradamus today.

That however is not holding back the hype machinery. Futurists, analysts and even scientists in small numbers, are all contributing. Anything is plausible in our post science fiction world. Celebrity entrepreneurs are taking pot shots with under-researched personal views, such as declaring with conviction that we may all be living in a massive computer simulation! But even as the fine line between Artificial Intelligence and the Real Intelligence of sentient beings continues to get blurred, how imminent is this brave new world and what should we do in the meantime?

For those of us in the business of Business Technology, we will have to learn to do our thing against the backdrop of the hype and outrageous predictions and yes, some remarkable technology breakthroughs. How does one, distill fact from everything else, identify and apply emerging technology to run our Enterprises more effectively and efficiently?

In this article we will explore a simple and effective approach to shopping for Enterprise AI in this tricky emerging market, by first zeroing in on the right questions. This will help us arrive at a set of principles and guidelines to map the Road to Enterprise AI for the next 5-10 years. Guidelines that we believe will become the basis of most Enterprise AI choices and selection during this period. We will also introduce a solution built on these principles that is already deployed in several Fortune 100 firms today.

Shopping for Enterprise AI

What are the characteristics of Business Technology and AI we need to run the Enterprise today?

Our observations over the past couple of years, which is also backed by several industry watchers and analysts, is that Business and Technology leaders tend to ask questions along the following lines, as they explore the market to seek out AI driven competitive advantages for their companies:

• How do I sift through all the emerging AI technologies and the obtuse jargon of AI? How do I understand my options?

• What AI technology should I bet on?

• Who does AI for the Enterprise? I read about self-driving cars, computers beating Gary Kasparov, and see Bob Dylan vouching for IBM’s Watson’s ability to create music, but I don’t need that to manage internal operations of my firm or design and manage customer experience.

• Everyone else seems to be deploying AI. Am I getting left behind?

All of these questions are AI centric. It presupposes the answer. Artificial Intelligence, the ultimate magic bullet of machine learning, deep learning, NLP, NLG, etc. The proverbial hammer looking for a nail. These, dear Business Leader, are “wrong questions”. You are suffering from a case of AI and Machine Learning bandwagon effect. And you are not alone.

The right question to ask, it seems to us, is the one you have always asked, “How do I apply technology to solve my business problem quickly, economically and permanently [in a manner that is responsive to future change]?”

Try the following 10 questions. They are designed to help organize our technology shopping approach, protect us from hype, and at the same time leverage the best that technology has to offer today.

1. What is the business opportunity or problem I am trying to address, or how do I discover the real problem or opportunity I should be going after?

…Growth, cost, compliance, customer satisfaction, responsiveness?

2. What is the business process transformation that will solve my business problem?

…Automation of knowledge work, straight through processing, automation of high variability processes, legacy modernization, others?

“How do I apply technology to

solve my business problem quickly,

economically and permanently (in a manner that is

responsive to future change)?”

3

3. Is there any emerging technology out there today that has solved similar problems?

…Is it proven, is it deployed, who are the clients, what are they saying? What are analysts saying?

4. Can my problem be solved completely / fundamentally?

…I am not interested in temporary, partial, band aid solutions like Robotic Process Automation (RPA) or side-stepping the core process challenges by simply outsourcing my problem

5. Can my problem be solved within 90 days, so I can realize outcomes quickly?

…I am looking for true agility not IT development philosophies like Agile. In my experience Agile and ideas like fail-fast take the exact same time as traditional methods to get to ultimate business outcome.

6. How do I build a perpetually flexible and extensible solution that is responsive and can adapt rapidly to change?

….I don’t want to revisit this again and again as soon as something changes. Rather, I would like to build further, go deeper, broader or do things differently. I want to start small and extend, expand and iterate through new use cases at my pace. All without much fuss.

7. I need complete auditability. A traceable solution.

….Black boxes are not acceptable in my business. I need a full audit trail.

8. The solution needs to fit in seamlessly, non-intrusively, into my technology environment.

…. I need to preserve/leverage the investments I have already made.

9. Can a single technology platform solve my problem end to end?

…Don’t push the burden of completing incomplete solutions on me. I don’t want to have to choose from an inventory of systems and deal with integration problems. I want everything required to solve my business problem, like workflow systems, ETL,

analytics systems, NLP, machine learning, etc. in the same platform?

10. How should I apply new and emerging technologies, like Machine Learning and AI?

Explicit AI questions are at the end of the list. AI is an enabler not the destination. “AI enabled Automation” or “Automation through AI” puts this is perspective, i.e. the fundamental goal is still intelligent automation.

Automation through AI

Everything that can change will: decisions, organization structures, regulations, business requirements, technology standards, data standards, IT project priorities, internal systems, external systems. These are the new table stakes.

And complexity will continue to increase. And the rate of change will continue to increase.

So whatever approach we adopt going forward has to start with the idea of Flexibility or Responsiveness. The flip side of the Flexibility coin is Speed…slow flexibility is the same as unresponsiveness. We need both Speed and Flexibility.

The primary driver of inflexibility and sluggishness in the world of Business Technology today is in the very method we use to solve automation problems. The software development lifecycle [SDLC] that we have had to adopt since the beginning, still stands in between business need and realized solutions, separating these events by 12-24 months and delivering rigid inflexible solutions. There are many levels of translation from idea to solution, multiple hand-offs to different specialists, including programmers, elaborate testing at every step. And the whole cycle has to be repeated with every requirement change or fix, making the process fundamentally slow and inflexible.

The antidote for this decades-long, fundamental and industry wide shortcoming is a model-driven automation framework where enterprise applications can be assembled near real time, where the distance from idea to realization is a one-step journey, no translations, no programming. No new code required or generated. All applications, big or small, complex or simple, that is built on a fully model driven platform will add no lines of code. All business logic resides as metadata.

“The flip side of the Flexibility

coin is Speed…slow flexibility is the same as

unresponsiveness. We need both

Speed and Flexibility.”

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The next task would be to ensure this model-driven enterprise application development platform, is broad enough to address and contain most process automation activities from data ingestion, processing, output generation and distribution, managing work orchestration and distribution, etc. And assembling a process automation solution should be a simple visual modeling exercise, requiring no specialized skills, to ensure easy and fast development and rapid implementation of changes.

Finally, we need to integrate AI and machine learning capability, and deep learning technology that is fully traceable. If we could pull all of this into a single platform we would have a very strong affirmative response to every question on the shopping list above.

Such a platform would be ideally positioned to enable Automation-through-AI, Analysis-and-Transaction Processing, Learning-and-Execution, Work-and-Workflow, Speed-and-Flexibility, all on the same platform.

Introducing RAGE-AITM

RAGE-AITM, from RAGE Frameworks, was designed and built on the principles above. It enables real time AI powered Enterprise Application development. It is designed to solve broad end-to-end mission critical business problems quickly and flexibly. This is accomplished via a set of twenty building blocks or engines that intelligently automate all/most business processes in their entirety. Solving a process automation problem in its entirety is the key. Solving a part of it would be very sub-optimal, as stepping out of the solution assembly framework would involve programming and the traditional SDLC and the speed and flexibility advantages of a no-code model driven paradigm would be lost.

The RAGE-AITM platform is a pioneering platform, driving the emergence of intelligent businesses. It is business process centric from the ground up. The platform fosters a business process oriented thinking. Because processes can be institutionalized in and executed by intelligent machines, it makes the whole debate around functional vs process oriented organizational design somewhat moot. You can be both.

FIGURE 1 RAGE-AI Platform

Source: RAGE Frameworks

“The RAGE-AITM platform

is a pioneering platform, driving

the emergence of intelligent businesses.”

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Intelligent machines will be the process glue in the organization.

Two Key RAGE-AITM Innovations

The RAGE-AITM platform embodies two key technology innovations – zero-code, model-driven software development using highly abstract components, and traceable machine learning.

Zero-code Model-driven Flexible Software Development:

Using RAGE-AITM, the entire software application development lifecycle can be reduced to a modeling exercise using an extensive array of abstract components. The platform has 20 abstract components that together enable the rapid, data driven implementation of end-to-end business processes including complex, knowledge intensive tasks. Entirely new applications can be assembled rapidly and modified at will2.

Using the platform, we can also create flexible software frameworks. These frameworks are a complete skeleton of a certain set of business processes and can be configured at any level of granularity to suit a specific enterprise. For example, the commercial lending framework modeled on top of RAGE-AITM is one such framework. It provides a default set of business processes out of the box but enables every aspect of those business processes to be configured by clients without any programming. Zero-code, model-driven software development is a key innovation which will unleash a significant number of application level innovation in the years to come. The benefits accrue from the model driven architecture that such platforms will rely on.

Traceable Machine Learning

There have been huge strides in the use of AI to analyze the vast amounts of digital information that has become available today. The excitement here has to do with the ability to analyze large data sets and let the machines discover patterns in the entire population instead of making arbitrary assumptions about how the data might behave in the real world. In addition to analyzing complete data sets instead of small samples of data, the ability to analyze unstructured text, offers an exciting opportunity for enterprises to become ‘intelligent’. They can systematically automate knowledge-intensive processes like ‘competitive

intelligence’ and integrate the insights with the rest of the business in a systematic and meaningful way.

Most work on Big Data analytics today however relies on computational statistical methods and produces ‘black box’ solutions. While such approaches are likely to suffice for homogeneous data sets, for many real world issues, we need to be able to trace the reasoning that machines find and deploy. So executives can understand ‘why’ and ‘how’. Black boxes won’t suffice. RAGE AITM has pioneered deep learning technology based on linguistics and is attempting to elevate natural language processing to natural language understanding.

Intelligent machines built on RAGE-AI™ perform two functions: provide insight to humans to enable the optimal design of intelligent business processes through continuous analysis of vast amounts of data, and automatically execute business transactions without human assistance based on an automated business process designed by humans. Using AI methods, machines will acquire knowledge from continuous analyses of vast amounts of data. Such knowledge and insight will be provided to humans to create/refine their designs.

Several industry-first Intelligent Machines or solutions built on RAGE-AITM have been deployed in Banking, Consulting, CPG, Manufacturing, High Tech and Insurance industries.

The tag line for RAGE Frameworks Inc., “It’s Possible”, attempts to say in a couple of words, what has taken years of continuous innovation to produce.

The Significance of Context, Language, Reasoning

Today, artificial intelligence (AI) is rapidly emerging out of R&D labs and into the mainstream. Smart technologies are changing every aspect of our lives, from the way we work, to health care, education, travel, and transportation. One example: the self-driving cars produced by Google and Tesla. There are also many successful applications in the computer vision space.

But what about the non-vision applications of AI: that is, areas including non-spatial data – most importantly, text and numbers? Because of AI’s revolutionary potential, its applications in

“In addition to analyzing

complete data sets instead of small samples

of data, the ability to analyze

unstructured text, offers an exciting

opportunity for enterprises

to become ‘intelligent’.”

6

FIGURE 2 AI in the Enterprise

non-vision problems have attracted tremendous interest. There have also been attempts to replicate what worked with spatial data and apply it to text (and numbers)… a blind rush of computational, statistically based approaches to process natural language. Such approaches attempt to turn text into data and then look for deep patterns in that data.

AI technologies must overcome three challenges to be successful in the non-vision world (and perhaps even in the vision world): language, context, and reasoning.

A recent MIT Technology Review article, “AI’s Language Problem,” eloquently points out the first challenge. Today’s AI technologies, including those IBM Watson and Google Alpha Go, struggle to process language the way that humans do. That’s because the large majority of the current implementations approach text as data, not as language. They apply the same techniques that worked on spatial data to text.

The second challenge—understanding context—is related to the language problem, but is sufficiently significant that it should be thought of as an independent issue. Natural language text needs to be processed in the right context. The right context can only be developed if the technology focuses on the language structure, not just on the words in the text, as most current technologies seem to be doing, according to a 2014 article in IEEE Computational Intelligence Magazine. Then there’s the third challenge: the traceability of reasoning that the solution deploys to reach its conclusion.

Various technologies are attempting to address all three challenges today. Several successful enterprise AI solutions deal with language, context, and reasoning transparency effectively.

To address the language challenge in AI, we have to understand the language by using its linguistic structure and the principles we have learned to express our thoughts. A deep understanding of the linguistics structure in text would involve applying several principles from computational linguistics to decompose the text back into the concepts and verbiage used to connect them in the text in context. This is essentially reverse-engineering the text back to its fundamental ideas to understand how those ideas were connected together to form sentences and paragraphs.

RAGE-AITM applies deep linguistic learning and natural language understanding to interpret text in its context and keeping the reasoning visible at all times. With the adoption of deep linguistic learning, we can maintain full and complete visibility to the reasoning.

AI Survey Results

RAGE conducted a survey with a targeted group to get to the heart of what senior leaders would value most in the AI solutions that they are looking for. The survey included 132 senior business executives, 67% of whom were C-level execs, including CEOs, COOs, CIOs, and CTOs. 80% of the respondents were from companies with greater than $1 billion in annual revenues.

The survey topic was “Can artificial intelligence deliver for today’s enterprise”.

The response to the question “What capabilities are important to the AI solutions you would invest in?” are shown below in Fig 2. Not surprisingly

Source: RAGE Frameworks

“AI technologies must overcome

three challenges to be successful

in the non-vision world (and

perhaps even in the vision world):

language, context, and reasoning.”

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FIGURE 3 RAGE Intelligent Machines

Source: RAGE Frameworks

“RAGE-AI applies deep linguistic

learning and natural language

understanding to interpret text

in its context and keeping the

reasoning visible at all times.”

Context, Language and Reasoning, bubbled up to the top.

AI in the Enterprise

RAGE has a set of widely deployed and ready-to-deploy intelligent machines built on RAGE-AITM, providing Automation through AI in the Banking, Consulting, CPG, Manufacturing, High Tech and Insurance industries. See Fig 3. In addition, a broad range of custom solutions have also been developed on RAGE-AITM.

As an example, RAGE’s Wealth Management solution, LiveWealthTM, is currently being used by 3 of the top 5 global wire-houses for servicing their high net worth and institutional clients. This Intelligent Machine has three core modules. One that aggregates data for assets held away with hundreds of custodians, automatically dealing differences in form and format in which the data is sent, often on paper. The second is a performance analytics and flexible reporting module. The third module, Active Advising, has five intelligent agents that support the advisor and enable them to be more effective at their job. One of these intelligent agents for example continuously monitors portfolio performance and alerts the advisor on specific actions.

The AI shopping list and RAGE-AITM

1. What is the business opportunity or problem I am trying to address, or how do I discover the real problem or opportunity I should be going after?

…Run AI enabled rapid diagnostics to zero in on areas of biggest opportunity, where they are not already obvious.

2. What is the business process transformation that will solve my business problem?

….Once you have a response to question #1, run further analysis on the process, using RAGE-AITM to determine how best to solve the process problem

3. Is there any emerging technology out there today that has solved similar problems?

….Yes. With broad deployment dealing with leading edge AI challenges.

4. Can my problem be solved completely / fundamentally?

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….Yes. AI driven intelligent and deep automation, work and workflow automation.

5. Can my problem be solved within 90 days, so I can realize outcomes quickly?

….Yes. Enterprise scale mission critical solutions have been built on RAGE-AITM and deployed in less than 90 days.

6. How do I build a perpetually flexible solution that is responsive and that can adapt rapidly to external and internal changes?

….All solutions built on RAGE-AITM are fundamentally flexible. All process configuration and behavior are stored as data, easy to set up and easy to change, at any time.

7. I need complete auditability. A traceable solution.

….All analysis RAGE-AITM is fully traceable to source and all solutions come with a complete audit trail.

8. The solution needs to fit in seamlessly, non-intrusively, into my Technology environment… …RAGE-AITM solutions can be introduced non-intrusively. For a legacy modernization exercise for example, RAGE-AITM solutions can be introduced in the cloud, or inside the client’s firewall, while retaining all systems of record, which can be gradually integrated into and/or replaced by the RAGE solution at the pace desired by the client.

9. Can a single technology platform solve my problem end to end?

….Yes. The twenty engines of RAGE-AITM is broad enough to address most business process automation problems.

10. How should I apply new and emerging technologies, like Machine Learning and AI?

….RAGE-AITM has industry leading AI technology built in. It addresses most of the challenges of prevalent AI technologies today in the context of business processes.

The technology, the approach and the proof points above are a good representation of the state-of-the-art “AI in the Enterprise”

While perhaps less exciting than drones delivering Amazon packages and driverless cars delivering take-out, the state-of-the-art AI in the Enterprise is now enabling us to automate things that were previously written off as automation proof.

So if one were to write a futuristic book today, not as a sequel to Orwell’s 1984, but as a tentative peek into the future of Business Technology, and if it were titled 2025, then we could hazard a guess that it will include several of the elements described here.

By 2025 the execution of processes and enterprises would be largely automated. They would be run on AI technology that simulates the nuances of human behavior [the sledgehammer of statistical algorithms would be relegated to lesser problems], that can understand language and context, a framework that can solve big problems in their entirety. As several Fortune 100 companies using RAGE-AITM are already experiencing.

Source: RAGE Frameworks

“RAGE-AITM has industry leading

AI technology built in. It

addresses most of the challenges

of prevalent AI technologies today in the

context of business

processes.“

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Predicts 2017: Artificial Intelligence

Artificial intelligence is changing the way in which organizations innovate and communicate their processes, products and services. Practical strategies for employing AI and choosing the right vendors are available to data and analytics leaders right now.

Key Findings

• Chatbots driven by artificial intelligence (AI) will play important roles in interactions with consumers, within the enterprise, and in business-to-business situations.

• Smart machines need to be properly set up, maintained and continuously governed if they are to be of maximum benefit to the enterprise.

• Smaller “boutique” vendors are offering chatbots targeted at specific industries and can perform niche tasks that the big-name players — like Amazon, Google, IBM and Microsoft — are not equipped to provide.

• Large AI vendors must adjust their strategies to compete with the smaller, more-nimble competitors that are threatening to dominate the market.

Recommendations

Application leaders, data and analytics leaders and strategists should:

• Find workers who excel at internal communications and articulating processes to lead bot scripting and development.

• Seek out proposals from smaller AI vendors for specific project needs.

• Establish skills programs for developers in algorithm testing, content acquisition and data employment in artificial intelligence projects.

Strategic Planning Assumptions

By 2019, more than 10% of IT hires in customer service will mostly write scripts for bot interactions.

From the Gartner Files:

Through 2020, organizations using cognitive ergonomics and system design in new artificial intelligence projects will achieve long-term success four times more often than others.

By 2020, 20% of companies will dedicate workers to monitor and guide neural networks.

By 2019, startups will overtake Amazon, Google, IBM and Microsoft in driving the artificial intelligence economy with disruptive business solutions.

By 2019, artificial intelligence platform services will cannibalize revenues for 30% of market-leading companies.

Analysis

What You Need to KnowThis year’s Predicts are gathered under the topic of “artificial intelligence.” While we continue to use the term and notion of “smart machines,” vendors and end-user organizations are familiar with the term “artificial intelligence,” so we will be increasingly applying that term in this research area.

Smart machines won’t run themselves, no matter what the movies and TV have shown since Fritz Lang’s Metropolis. AI continues to drive change in how businesses and governments interact with customers and constituents. And our 2017 predictions show that the humans — as is always the case in computing change — are the pivot on which AI can turn.

Organizations must plan through their adoption of AI because such fundamental changes always require agility.

Organizations must concentrate on:

• Identifying key roles, the workers that will fill them and the metrics they will have to meet.

• Considering smaller, disruptive vendors and service providers.

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• Implementing suitable methodologies for execution.

• Planning tactical and strategic targets for projects.

• Recognizing that AI may threaten core assumptions about revenue sources and volume.

Excitement about artificial intelligence is growing rapidly. Gartner analysts’ conversations with clients have grown in number exponentially in the last two years. As can be seen in Table 1, there were 14 inquiries with Gartner clients that used the term “Artificial Intelligence in 2014.” In 2015, there were 89, an increase of more than 500%. As of the end of the third quarter of 2016, Gartner had fielded more than 290 queries about artificial intelligence since January, representing a greater than 200% increase only partway through the year.

Implementing chatbots, for example, is a business imperative for organizations with extensive customer service needs. Chatbots will soon deliver customer satisfaction at significantly lower cost than human customer service agents. But building and maintaining the neural networks required will take dedicated workers with specific skills.

Chatbots are growing in popularity because AI systems can manage customer and worker progress through a decision tree more effectively than human direction can. The next generation of customer interaction chatbots will cause less customer frustration. For example, machine learning could effectively employ useful contextual data to skip a concern several levels in the service hierarchy more reliably than a worker could.

Chatbots are just one category of AI applications that will drive disruption in many businesses. To make AI opportunities a large-scale proposition rather than isolated curiosities, companies will need to invest in AI programs.

Agile enterprises will seek out AI vendors with the best solutions. Large vendors like Google and Amazon dominate, but smaller vendors offering more-targeted AI products are on the rise. Enterprises should make sure they seek out these smaller vendors when searching for AI solutions.

Employing AI offers enterprises the opportunity to give customers an improved experience at every point of interaction, but without human governance, the opportunity will be squandered.

Source: Gartner (November 2016)

FIGURE 1 Inquiries to Gartner About Artificial Intelligence (AI)

2,500

0

2,000

1,500

1,000

500

2014 2015 2016

Record Count

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Strategic Planning AssumptionsStrategic Planning Assumption: By 2019, more than 10% of IT hires in customer service will mostly write scripts for bot interactions.

Analysis by: Martin Reynolds

Key Findings:

• Chatbots generate the best returns when backed by well-scripted decision trees written by dedicated staff.

• The best scripters understand the business and how customers interact with it.

• Programmers are not the best choice to design customer service interfaces.

Market Implications:

Chatbots hold the potential — through natural-language processing — to make the customer experience easier, faster and more satisfying while furthering business goals. Chatbots have access to available knowledge about the customer and how similar customers responded. But they need a great “navigator” to deliver the best results.

The fundamental technology that underpins chatbots is not changing. It is a decision tree that leads the customer to the right result. The tree structure gives good answers with correct grammar. One AI transformation is the introduction of highly reliable speech to text. The text tags result in better navigation of the tree. Every enterprise has the opportunity to improve its business incrementally by tapping into the potential of chatbots. For the enterprise, investing now to make such communication models work effectively will pay dividends for years.

The key is hiring the right people to shape the knowledge tree to customer needs, and identifying the customer keywords that drive the correct moves within the tree. Programmers are typically not well suited to this task, as it requires both a customer service touch, and in-depth knowledge of the business.

Instead, dedicated bot scripters are the answer. Call center workers often have the needed skills to be effective bot scripters. They understand the business as well as how customers think when shopping for a product, making them effective at choosing keywords and linking nodes in the right way.

These bot scripters will also be able to handle exceptions (where the bot cannot identify the correct next step and has to hand the task to a human operator).

As chatbots get better at helping customers, more calls will be handled automatically — and with higher satisfaction. Customer service representatives will have time to deal with tougher questions… and the tougher customers.

Over time, AI may lead us to new ways to support customers. However, the decision tree, because of its high-quality results when it successfully completes an interaction, will underpin bot services for some time to come.

By way of evidence, we have companies such as IPsoft, Nuance and x.ai building bot systems that use the “human augmented” approach. Sometimes, bot agents open up with the question “tell me, in a few words, what you want to do.” This opening allows a smarter bot to start interacting with a customer at a deeper location in the tree.

It is also worth noting that, in China, WeChat public account chatbots are becoming the new face of commerce. It is critically important to build a customer-facing bot strategy now.

Recommendations:

• Hire dedicated people to write bot scripts.

• Interview the best employees in your call center as potential bot scripters.

• Recognize that good quality chatbots may significantly improve some informational aspects of operations.

Strategic Planning Assumption: Through 2020, organizations using cognitive ergonomics and system design in new artificial intelligence projects will achieve long-term success four times more often than others.

Analysis by: Kenneth F. Brant

Key Findings:

• Gartner’s inquiries indicate that organizations are not approaching AI and smart machines holistically or as systems with implementation challenges different from traditional IT.

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• AI and smart machines are in the early stages of innovation and commercialization; vendors report they are subject to much greater “churn” in the marketplace than traditional IT offerings and that end-user IT organizations struggle with new system requirements.

• Organizations investigating AI and smart machines have focused prematurely on evaluating technology categories and/or vendor evaluation selection before acquiring the skills and methodologies for successful implementation.

• Success in defining the “art of the possible” and in tech-focused proofs of concept (POCs) for AI and smart machines is often followed by difficulties in scaling and maintaining system performance.

Market Implications:

CIOs must evolve their workforce — and strategic partnerships with service providers — if they are to achieve sustainable change in the disruptive era of smart machines. CIOs must reallocate budgets and resources while acquiring new competencies within their organizations.

Deploying smart machines means that CIOs must take into account several factors that affect the enterprise’s ability to both prove and sustain value in smart machines:

• Cognitive Ergonomics: As defined by the International Ergonomics Association, cognitive ergonomics is concerned with “mental processes, such as perception, memory, reasoning, and motor response, as they affect interactions among humans and other elements of a system.” This is a subset of the larger field of human factors and ergonomics.

Part of the human factors field, employing cognitive ergonomics ensures that the mix of smart machines and human workers is effective and augments the intelligence of both in problem solving and decision making.

• System Design for Maintainability: System design is the transformation of an idea into a system that meets the designer’s requirements and the end user’s needs. Maintainability is the degree to which the design can be easily and economically repaired.

This concept is important with respect to the ease of adding and updating content in smart machines, the ease of tuning the various elements of smart machines (such as the optimum combination of content, algorithm(s) and user interface), the ease of appending, reformatting and expanding content, plus the ease of retraining the neural network.

Recommendations:

CIOs and enterprise architects must:

• Balance their organization’s current focus on the “what” and the “who” of artificial intelligence to include more attention to the “why” and the “how” of implementations in order to drive long-term value through human factors and design principles.

• Reskill their organizations to include holistic, related competencies in content acquisition, preparation, updates, algorithm testing, training and retraining of neural networks, design for human acceptance and effectiveness, and design for high-reliability systems.

• Conduct POCs with the actual professionals who will use the smart machine in order to gain insights into the human factors that will drive usage and effective outcomes. The success of these POCs should not be based on the conclusions of data scientists in a laboratory environment.

• Build the proven benefits of cognitive ergonomics and design for maintainability observed in human-centered POCs into their change management programs for smart machines.

Strategic Planning Assumption: By 2020, 20% of companies will dedicate workers to monitor and guide neural networks.

Analysis by: Magnus Revang

Key Findings:

• Many enterprises view smart machines as magical investments that need neither tending nor updating once they are deployed.

• While many enterprises have data scientists doing dedicated work on advanced analytics

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and machine learning in other parts of the company, there is little collaboration with IT and these systems are traditionally maintained outside of the IT application portfolio.

• Many CIOs have not prioritized the skills needed to implement, maintain and update neural networks, leading them to either play catch-up or cede that responsibility to other parts of the enterprise.

• Much of the hype around smart machines focuses on the relatively few places they are employed in public and fails to consider the thousands of places artificial intelligence is already used in the background systems of enterprises.

Market Implications:

The assumption that smart machines with neural networks can be deployed as a finished project — with no consideration to the challenges of continuously maintaining and monitoring the implementation — will lead to failure for many enterprises. In reality, neural networks need to be retrained constantly as data is collected. No longer can models stand the test of time for two or three years like they did in the 1980s. Today’s models are only good until new information becomes available.

An example of the retraining needed to keep neural networks working at their best can be seen in the way modern automobiles adapt to their drivers. A trip to the mechanic that results in an adjustment to the gearbox causes the car’s “brain” to restart the process of learning the driver’s habits. It’s the same for any neural network that receives new data.

CIOs need to understand that workers with new skill and a new way of looking at problems are needed for successful retraining of smart machines. Developers, with their typical binary approach (it works or doesn’t) have a difficult time working with neural networks. People with different backgrounds — in design, data science and logic, for example — have the mindset to work with neural networks.

Separation of responsibilities for neural networks will be split among different parts of the enterprise, making it essential for CIOs to remain relevant by ensuring IT owns the platform that is running the

neural network. Instituting a robust governance structure establishes how responsibility is divided among areas of the organization.

Smart machines hold the potential to further business goals through the use of neural networks, which are becoming more adept at creating models based on datasets than are their human counterparts. At the same time, effective use of neural networks requires a change in thinking and redeployment of IT staff. Resources currently used for development will be needed to feed the endless retraining loop that allows neural networks to maintain their value to the enterprise.

Recommendations:

• Completing POC exercises is imperative for CIOs to show the value in the smart-machine era.

• CIOs must look outside of programmers to hire data scientists and other staff members with the skills to create and maintain neural networks.

• CIOs need to make the business case for neural networks to ensure the project is properly funded.

Strategic Planning Assumption: By 2019, startups will overtake Amazon, Google, IBM and Microsoft in driving innovation in the AI economy with disruptive business solutions.

Analysis by: Jim Hare

Key Findings:

• Every software application (and business process) is likely to become more intelligent as more vendors embed AI capabilities into their solutions.

• CIOs must find the AI vendors that offer domain-specific solutions that best align with their businesses to help automate and improve decision making. The number of startups offering AI solutions is accelerating. Research firm CB Insights reported 140 AI startups in the first quarter of 2016, up from 70 in all of 2011. Other sources suggest there are now 2,000 to 3,000 AI-related firms (see “Entering the Smart-Machine Age”).

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• Many startups are led by employees who often previously worked on AI projects at big vendors like Amazon, Google, IBM and Microsoft.

Market Implications:

The explosion in the number of vendors offering AI and machine-learning solutions has created a gold rush with small startups threatening to grab most of the nuggets from the big vendors that get most of the hype. The robustness of the AI economy is evident in the $1.5 billion in equity capital raised by 200 startups, as reported by CB Insights. These startups are focused on building businesses to solve difficult real-world problems with AI at the center.

Employees who learned the basics, and even built the AI technologies at the big vendors, have moved on to start their own firms. These small vendors offer products tailored to specific industries or lines of work. Of course, many of these startups will likely be acquired or try to stay independent to become giants themselves. According to CB Insights, over 140 private companies working to advance AI capabilities have been acquired since 2011, with more than 40 acquisitions taking place in 2016.

While many startups are focusing on industries ripe for disruption like healthcare — for example, to help discover drugs and offering care — any industry that collects data that can be analyzed can benefit from the power of AI. The use of AI in retail, financial services, investment advising and call centers, among others, has proven its ability to help companies make smarter decisions in less time with more context, and to get to the next best action decision quicker than possible by a human alone.

For example, in healthcare, “computer assisted diagnosis” has been used to review the early mammography scans of women who later developed breast cancer, and the computer spotted 52% of the cancers as much as a year before the women were officially diagnosed. Many online news publishers like AP, Fox, and Yahoo use AI to write simple stories like financial summaries, sports recaps and fantasy sports reports. AI isn’t yet writing in-depth investigative articles, but it has no problem with basic reports that don’t require a lot of synthesis.

With data collection rising, the places where humans can match the ability of machines to make real-time decisions will be precious few. Solutions to help lines of business (such as sales

and marketing departments) to improve decision making are also popular. Smart machines take data and send salespeople off to focus on the most promising opportunities. CRM vendor Salesforce has acquired a number of smaller AI-savvy companies to take advantage of the possibility of more-effectively supporting its clients.

The proliferation of industry- and domain-specific applications from specialist firms is an opportunity for enterprises to use packaged “smart analytics” solutions to leverage increasing levels of data and more-sophisticated analytics to improve and augment their decision-making processes — without the need to hire teams of data scientists. Still, end-user organizations should be mindful of avoiding the risk of standardizing on just one or two AI specialists given the acquisition euphoria underway and how fast the technologies are evolving.

Recommendations:

• CIOs should analyze key business processes to locate the areas where AI could be applied to augment and improve human decision making, especially in underserved areas of the organization that lack access to analytics.

• Data and analytics leaders need to understand both the benefits and limits of AI. The goal is to find the right way to blend humans and machines. For example, AI capabilities such as deep neural networks (DNNs) can perceive patterns that humans can’t detect. They can also “program” a model to classify from large bodies of data and, under certain circumstances, be more effective and efficient than other approaches. But if the available data falls below a threshold, no amount of artificial intelligence can solve the problem.

• CIOs should investigate and evaluate packaged AI solutions in the market before considering building a custom AI solution from scratch. There are new startups emerging almost every day focused on solving different business problems. Packaged AI solutions can usually be deployed faster and require less technical resources to support.

Strategic Planning Assumption: By 2019, artificial intelligence platform services will cannibalize revenues for 30% of market leading companies.

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Analysis by: Frances Karamouzis

Key Findings:

• From 2010 through 2015, funding in the AI sector has multiplied nearly sevenfold.

• Since 2000, 52% of large global companies have gone bankrupt, have been acquired or have ceased to exist.

• Many company failures have resulted from companies that did not see the disruptive forces in their respective industries coming, and/or failed to adapt and shift once a big disruption was upon them.

Market Implications:

There is a series of studies that all reach similar conclusions — market-leading companies that get disrupted or disintermediated face their demise. A landmark study showcased that, over the last 35 years, the rate of failure for leading global companies is accelerating.1 The same study found that more than half of these companies have gone bankrupt, have been acquired or have ceased to exist.

A study by Standard & Poor’s found that the average life span of a company listed in the financial company’s index of leading U.S. companies has decreased by more than 50 years in the last century, from 67 years in the 1920s to 15 years today, according to Professor Richard Foster of Yale.2

John Chambers, former CEO of Cisco, was quoted as saying “More than one-third of businesses today will not survive the next 10 years. Companies should not miss the market transition or business model nor underestimate your competitor of the future — not your competitor of the past.”3

One of the leading causes of disruption is a variety of artificial intelligence technologies. Some do not fit the exact definition of AI, but employ various technologies such as deep neural networks, machine learning or some type of cognition-based functionality. In fact, the investment community has recognized this and responded with record level investment. AI startups have raised an aggregate $967 million in funding since 2010, with investments going to companies in 13 countries and 10 industry categories,

including business intelligence, e-commerce, and healthcare.4 Above and beyond these investments, Baidu, the Chinese internet search giant, has created a $200 million venture capital unit to invest in artificial intelligence projects.5

These venture capital investments into startups and R&D initiatives within existing firms will certainly lead to a huge proliferation of new business ideas that will cause significant disruption to conventional products and services offered by the current market leaders. These new AI platform services will cannibalize revenues of the largest companies as they will be focused on tightly coupling products and services together to create a customer experience that permeates for a longer time, and is defined by new and different commercial structures.

The next big shift is convergence of technology products and services to create next-generation service offerings that will include AI platforms. More specifically, Gartner defines these next-generation service offerings as “intelligent automation” services that use one or more AI technologies (such as a cognitive-computing technology platform) as the basis of an offering’s core value proposition.

These AI platform services offerings include AI (or AI-related) technologies as part of their core platform or underpinning. Such inclusion often requires R&D investment to achieve an aggregated (tightly bundled) solution that ensures predictable, reliable outcomes. This often results in a relatively large portion of value being derived from IP, accelerators and verticalization, as opposed to pure labor or licensing.

This notion of combining technology and services marks the key shift from labor-driven to IP-driven offerings, and results in different commercial terms, which are based on outcomes. In this structure, commercial terms are directly tethered to distinctive business results (outcomes in the form of completed transactions or fully enabled processes) where payment is triggered by delivery of the AI platform service. This is juxtaposed against more-conventional commercial terms, which are defined by licensing of software or services that are priced by labor hour, which places the focus on effort (input) rather than output.

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Recommendations:

CTOs and CIOs should:

• Take a strong lead in your industry by developing multidisciplinary, cross-functional teams in order to track, adopt and execute POCs for AI platform services.

• Focus on multiple ideation workshops per year and many POCs to ensure you are part of the successful companies that will engage in disrupting others rather than being disrupted.

• Be dynamic and vigilant about iteratively exploring the changing landscape of market offerings, provider investments, competitor adoption, and related regulatory, legal, ethical and societal shifts.

• Ensure this is an enterprisewide initiative with CEO sponsorship and funding. There will be lots of small passionate teams across the organization that are exploring and trying new options. It’s important that everyone is connected, coordinating and sharing. While each team may be going at a different cadence and velocity, there should be periodic knowledge sharing and IP exchange.

A Look BackIn response to your requests, we are taking a look back at some key predictions from previous years. We have intentionally selected predictions from opposite ends of the scale — one where we were wholly or largely on target, as well as one we missed.

On Target: 2013 Prediction — By 2016, Microsoft will offer virtual personal assistants in Microsoft Office 365.

Analysis By: Whit Andrews

We predicted in 2013 that by 2016 Microsoft would offer virtual personal assistants (VPAs) in Microsoft Office 365. We were correct, in that the Cortana (which had not yet been announced or described at the time) now integrates to Office 365. The product is not exclusive to or embedded in Office 365, but it is key to Microsoft’s strategy

and is a prominent aspect of the Windows 10 operating system, as well as being part of a range of consumer applications. Cortana was announced in 2014, and its integration to Office 365 was announced and previewed in 2015.

Missed: 2012 Prediction — By year-end 2017, 25% of workers will engage search in business applications through natural expression at least five times daily.

Analysis By: Whit Andrews

We predicted in 2012 that by year-end 2017, 25% of workers would engage search in business applications through natural expression at least five times daily. At the time, Siri’s effectiveness was improving and IBM Watson was impressing organizations with its effective deconstructions of natural expression in popular demonstrations. Google Now also grew in prominence as Android incorporated it more broadly. However, APIs to the VPAs did not emerge as swiftly as we expected them to do (more have become available in the recent past, but notably, they are more accessible from consumer-only specialists such as Facebook, Alexa and WhatsApp). And only two in five of the references for the 2015 Enterprise Search Magic Quadrant indicated that their search vendor supports natural-language question answering. We still expect this model to become stronger, largely through improved “autosuggest,” as APIs continue to mature.

Evidence1 “Startup Survival, Failure and Growth,” Kauffman Report.

2 “Can a Company Live Forever,” BBC News.

3 “Retiring Cisco CEO Delivers Dire Prediction: 40% of Companies Will Be Dead in 10 Years,” Business Insider UK.

4 “Baidu Launches $200 Million Venture Capital Unit Focused on Artificial Intelligence,” South China Morning Post.

5 “Deep Interest in AI: New High in Deals to Artificial Intelligence Startups In Q415,” CB Insights.

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About RAGE Frameworks

RAGE Frameworks Inc. is a leader in knowledge-based automation technology and services providing AI for the Enterprise. RAGE-AI™ is a no-code patented platform for end-to-end automation of knowledge-based processes. RAGE-AI™ is currently used by some of the largest banks, manufacturers, consulting companies, high tech firms, and logistics companies. Headquartered in Dedham, Massachusetts with global operations centers in Pune and Belgaum, India, RAGE offers unprecedented speed, flexibility and insight in solving today’s most complex, critical business problems. Visit us at www.rageframeworks.com

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