Accenture Labs Realizing the promise of ‘Intelligent Business Solutions’ through Open Source AI Implementing enterprise AI, as-a-service, through a flexible, modular, framework

Following some lengthy ‘winters’, the promise of Artificial Intelligence (AI) is now being fulfilled. We’re seeing strong and growing business interest in AI from companies throughout the Fortune 100 and beyond, with most having already initiated substantial investments in the technologies that underpin enterprise AI.

This groundswell in favor of enterprise AI coincides with technology breakthroughs in big data, language-, speech- and vision-processing and machine-learning algorithms (deep-learning neural networks) which, combined, are pushing

the boundaries of automation. Helping to build the momentum, related compute and storage capabilities have been surging in capacity and dropping in price.

AI automation technologies help enable the machine automation of cognitive steps typically handled by humans in connection with a business process. Examples include extracting information, summarizing data, and predicting outcomes based on past patterns. AI augmentation technologies, meanwhile, complement and extend human ability to perform complex tasks such as answering questions, or providing advice.

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Taken together, the adoption of these technologies by companies across all industries heralds the era of ‘intelligent business solutions’. As we explain in our 2016 Technology Vision, this means much more than a simple transfer of tasks from man to machine. The real power of intelligent automation is to fundamentally change traditional ways of operating and transform what people can achieve by weaving systems, data and people together.

Powered by AI, the increasing sophistication of automation and augmentation technologies is invigorating the workplace, changing the rules of what’s possible, and delivering decisive competitive advantage. With much of the most exciting and disruptive developments in these technologies now emerging from the open-source ecosystem, companies urgently need a flexible, efficient and secure way to implement best-of-breed AI solutions, whenever and wherever they will deliver maximum value to the business.

The immediate and most tangible outcome of these developments is the automation and augmentation of complex knowledge tasks within the enterprise, using AI software that can learn dynamically from data and interactions. The impact will be enormous: one report from the McKinsey Global Institute estimates that knowledge work automation will have between US$5.2-6.7 trillion in economic impact by 2025.² In another recent study,³ Accenture found that AI has the potential to double economic growth rates by 2035, changing the nature of work and boosting labor productivity by up to 40 percent. Indeed, far from creating a dehumanized future, our research affirmed that the real promise of AI is its power to augment what humans are great at and make them better at what they do.

We’re already seeing AI being embraced by companies in every industry. In Financial Services, for example, robo-personalized advisors, intelligent underwriting and automated agent-based banking services are already in action, and will soon be commonplace. Life Sciences and Healthcare companies are leveraging AI’s power in areas including drug discovery and pharmacovigilance. And in the Retail sector, companies now use intelligent recommendation systems and in-store experience enhancement.

Ubiquitous AI? It’s here already

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To arrive at these findings, the Accenture Institute for High Performance, in collaboration with Frontier Economics, modeled the impact of AI for 12 developed economies. We found that AI would deliver the greatest economic benefit to the United States, helping to increase its annual growth rate from 2.6 percent to 4.6 percent in 2035 (equivalent to an additional US$8.3 trillion in gross value added (GVA)).

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Companies are looking to AI to bring new skills and tools that help their people do new jobs and invent a new future for themselves and the organization. And there’s no shortage of commercial and Software-as-a-Service (SaaS)-based AI offerings available from leading vendors and startups. However, this multitude of options notwithstanding, AI adopters are being confronted with some tough decisions in their journeys toward enterprise AI. These include:

Pervasive adoption…but challenges remain

In many ways, these challenges flow from a marketplace dynamic that’s currently out of step with the breakneck pace of technology change in AI, as well as the spend limits within which IT departments must operate today.

CostVendor offerings, which are often limited in business application, can be costly to implement (proof of concept work for some of these applications can cost US$1 million+).

Data residence and securityCompanies may not be comfortable sharing sensitive data in public or third-party administered clouds (especially if financial and healthcare-related personally identifiable information is involved). This poses challenges for machine learning and data analytics.

Customizability and ease of use Because SaaS service end-points can often be opaque and relatively inflexible, it can be challenging to customize AI solutions/modules to business needs.

Vendor lock-in/technology obsolescenceAI is a rapidly maturing and fast-changing field, in which new, disruptive technologies are emerging almost every week; companies tied to a single vendor run the risk of technological obsolescence.

Future proofingThe latest AI technologies, such as deep learning, are growing in sophistication at a rapid pace: the evolution from conventional neural networks to word2Vec to deep recursive/recurrent/convolutional networks took place within just over ten years. Companies need to be consistently ahead of the curve to exploit the potential of best-of-breed technologies, and that’s not possible when they’re tied to a single vendor that may become legacy at any moment.

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Open source: a new world of opportunity for AI In the digital era, the open-source technology ecosystem is growing fast, encompassing solutions spanning cloud (OpenStack), mobility (OMA), analytics and, increasingly, AI. Highlighted in a recent article by InfoWorld,⁴ there are various drivers behind this trend. Uppermost amongst these is the new technology vendor mindset. Far from open-sourcing software only when it’s no longer viable for monetization, vendors, and startups in particular, often now view this as the preferred route for disseminating disruptive new technologies. The rapidly expanding ecosystems around Hadoop and Docker are prime examples.

Crucially too, there’s fast-growing recognition that the world of software development has changed forever. With GitHub established as the world’s largest open-source community (now hosting almost 30 million projects), corporates are showing more confidence in open-source than ever before. Of course, security issues remain, especially so in the wake of Heartbleed and Shellshock, two widely-reported bugs targeted at open source. But we’re seeing these concerns allayed by projects like OpenSSL, as well as by the ring-fencing approaches adopted by companies.

Also fueling the rise and rise of open-source is cloud’s penetration into this area. Corporate’s IT teams increasingly favor cloud-based applications over on-premise installations, a preference that was reflected in InfoWorld’s 2015 ‘Bossie’ awards where many of the top-rated open-source applications featured SaaS and hosted options.

Specific to AI, we’re seeing waves of open-source innovation, with new developments coming online at a rapid clip—particularly over the past year. A recent report on Linux.com put this into perspective, heralding the arrival of open-source AI applications that can run on affordable hardware and manage extremely demanding data processing and predictive tasks.⁵ Google, Facebook and IBM, amongst other tech leaders, are in the forefront of this innovation. Google, for instance, open-sourced a program that it spent years developing. The program, TensorFlow,⁶ supports Google’s own AI software, along with other analytics programs. IBM donated SystemML, its machine-learning program, to Apache Spark, an open-source cluster-computing framework.⁷ And Yahoo released CaffeOnSpark, its deep-learning software, for use on GitHub and Apache.⁸

These and other developments show how much activity there is in open-source AI. Why is this happening now? In large part because the developers of these technologies recognize that open-sourcing their software will drive creativity and enhancements through collaboration—at a far greater speed than would otherwise be possible. And it’s not just the tech giants that are powering developments in this field. The vibrant startup community is also playing its part. Accenture, for example, is actively engaged with the AI startup ecosystem through the firm’s Open Innovation program.

As part of this, we’re collaborating with Saffron Tech (now part of Intel) and Expert System – just two of the startup AI platforms to have emerged from this quarter; we also engage with many other startups in our drive to discover and harness the best of ‘next generation AI’ for our clients.

Building an architecture for AI advantageAll these developments underline just how much innovation is taking place in the open-source AI space (and how important it has become for companies to gain unfettered access to it). Disruptive new technologies are coming online all the time. And provided companies are geared up to take advantage of them, they can tap into solutions that are the polar opposite of inflexible and expensive: the defining characteristics of many commercial AI services.

AI need not have an opaque, monolithic façade. What’s needed is the ability to select and use the right set of open-source capabilities for the right opportunity, without being tied in to a single technology provider. To provide this ability, companies need to define architectures that are:

• flexible

• modular (with plug-and-play flexibility)

• service-oriented

• consistently best-in-class.

To make this happen at speed, what’s needed is a ‘living’ framework. Available as a service, for on-demand flexibility, this should combine the ‘latest and greatest’ open-source software with enterprise data and models to deliver modular, reusable, scalable and cost-effective AI automation and augmentation services. And it’s what Accenture is helping to provide through the Accenture AI Engine (AAIE).

An engine for flexible, cost-effective enterprise AIAccenture Artificial Intelligence Engine (AAIE) has been developed specifically to overcome the challenges companies encounter with vendor-specific AI options (costs of licensing and implementation, data residence and security, lack of customizability, vendor lock-in, and limited future-proofing). Far from tying users to a single AI software provider, AAIE has been developed to drive the continuous harvesting and reuse of best-of-breed components.

AAIE follows a transparent layered architecture whose core capabilities can be used to create recipes for the higher-level services, which can themselves be further trained with data and rules for a particular domain and purpose. The emphasis is on flexibility. Through an intuitive, graphical and interactive user interface, the engine gives data scientists and AI application builders enough transparency to choose the sets of components they need for a particular task. Reusable libraries and trained modules allow for easy reuse of functionality, including domain-specific data, models and algorithms (in areas such as pharmacovigilance, commercial lease management and incident and defect management). This is a big positive for AI and machine-learning applications because, at the moment, most applications are developed ground-up (with considerable cost and effort).

Typically closed domain and complex, enterprise AI applications give rise to challenges including:

• insufficient data for training

• lack of trained SMEs with data science abilities

• inadequate platform flexibility and capability

• time constraints.

AAIE addresses these issues head-on, providing:

• faster time-to-build through a rich technology and domain library of harvested components

• data science know-how through an intelligent user interface studio, which enables ‘drag and drop’ and auto-validation capabilities

• flexibility through:

> a scalable and modular micro-services-oriented architecture

> access to the best technology solutions through an open, agnostic innovation model

> ‘open access’ source code enabling higher customizability

> choice between cloud/on-premise deployment.

HfS on AAIE and Accenture’s Intelligent Automation initiative

“Accenture has an open, agnostic innovation model, which draws the best technology solutions from a broad ecosystem rather than fixing on any specific vendor platform. In our view, however, the crucial element in Accenture’s strategy is their artificial intelligence engine that provides an architecture abstraction layer for interacting with various autonomics services such as natural language processing and machine learning.”

Source: https://www.hfsresearch.com/sb/autonomics-are-accelerating-journey-intelligent-automation

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Flexible access to best-of-breed AI solutions: the benefitsThe Accenture Artificial Intelligence Engine is already in use and delivering business value to companies worldwide (including Accenture). Examples include:

Helping to reduce incident ticket triaging backlog for a large retailer by 20 percent.

Accelerating pharmacovigilance—helping to reduce human effort in one life sciences client pilot by over 25 percent.

Helping to deliver up to 40 percent automation benefit through lease abstraction for Accenture Credit Services, a full-service provider of residential mortgage transformation services.

Conducting large-scale sentiment analysis and aspect categorization for Accenture’s business to analyze employee survey data with unprecedented precision.

A platform for the future In the drive to implement enterprise AI, the extraordinary richness and choice of open-source software has been an extremely welcome and timely development for companies. Until now, however, companies have struggled with how best to ensure flexible access to the most up-to-date software, at the right time (at the right cost)—without being tied to a single vendor and running the risk of technical obsolescence. To deliver all these advantages and unleash the massive potential of AI innovation, companies need a modular, plug and play framework, available as-a-service and built on a robust foundational platform. The objective? On-demand, flexible consumption of best-of-breed AI services in a standardized way. Vital added value will come from access to composite or

aggregate services (for specific and/or complex AI tasks) along with functional/domain services with specific domain rules and data ontologies.

Research for our 2016 Technology Vision showed 70 percent of executives are making significantly more investments in AI now than they were just two or three years ago.⁹ And uptake will only escalate as new applications for these technologies are exploited enterprise-wide. Intelligent automation brings fundamental changes to how businesses and individuals work. And an intelligent platform for harnessing AI innovation is the essential engine for delivering the competitive advantage that these changes will provide.

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About Accenture

Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions—underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With approximately 375,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com.

About Accenture Labs

Accenture Labs invents the future for Accenture, our clients and the market. Focused on solving critical business problems with advanced technology, Accenture Labs brings fresh insights and innovations to our clients, helping them capitalize on dramatic changes in technology, business and society. Our dedicated team of technologists and researchers work with leaders across the company to invest in, incubate and deliver breakthrough ideas and solutions that help our clients create new sources of business advantage.

Accenture Labs is located in seven key research hubs around the world: Silicon Valley, CA; Sophia Antipolis, France; Arlington, Virginia; Beijing, China; Bangalore, India, Dublin, Ireland, and Tel Aviv, Israel. The Labs collaborates extensively with Accenture’s network of nearly 400 innovation centers, studios and centers of excellence located in 92 cities and 35 countries globally to deliver cutting-edge research, insights and solutions to clients where they operate and live.

Contact Us

Marc Carrel-Billiard Global Managing Director Accenture Technology R&D marc.carrel-billiard@accenture.com

Sanjay Podder Managing Director Accenture Lab Bangalore sanjay.podder@accenture.com

Shubhashis Sengupta Fellow, Accenture Labs

shubhashis.engupta@accenture.com

References1 www.accenture.com/gb-en/insight-intelligent-

automation-technology

2 www.mckinsey.com/business-functions/business-technology/our-insights/disruptive-technologies

3 ‘Why Artificial Intelligence is the Future of Growth,’ can be accessed at http://www.accenture.com/futureofAI

4 www.infoworld.com/article/2986769/open-source-tools/5-key-trends-in-open-source.html

5 www.linux.com/news/open-source-projects-are-transforming-machine-learning-and-ai

6 www.tensorflow.org

7 www.zdnet.com/article/ibms-systemml-machine-learning-system-becomes-apache-incubator-project/

8 http://venturebeat.com/2016/02/24/yahoo-open-sources-caffeonspark-deep-learning-framework- for-hadoop/

9 www.accenture.com/gb-en/insight-intelligent-automation-technology

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