AI in Your EnterpriseNikita Dhami Head of AI & Machine Learning, Analytics A/NZ

October 2019

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AI explained The term ‘Artificial Intelligence’ (AI) was coined by John McCarthy in 1956 and has had a complicated journey to arrive at its current status as an independent branch of study and innovation. The concepts associated with AI today were developed by personalities as diverse as the sci-fi novelist Isaac Asimov, mathematician Alan Turing, cognitive scientist Marvin Minsky, and many others. Punctuating the path to the present were advancements like the SHRDLU natural language parser developed by Terry Winograd at MIT, the pioneering Shakey the Robot by SRI International, and clinical expert system MYCIN by Edward Shortliffe at Stanford University, among others. While all these milestones were major developments, AI nevertheless was for long stuck in still-not-quite-there territory until IBM’s Deep Blue created a sensation by beating chess superstar Garry Kasparov in 1997. This event marked a steep upward turning point in AI’s trajectory.

Conceptually, AI has been around for decades. So, what’s changed since the 1950s? Why now? What seems to have made the crucial difference is that, until recently, we didn’t have the necessary computing power or storage to make AI practical. And yes, artificial general intelligence, vividly foretold by the likes of Iron Man’s JARVIS, isn’t around yet, with robust debate around whether it is tantalisingly near or forever unreachable. But narrower, focused applications of AI are now well within our grasp and are rapidly proliferating. This sure-footed progress over the past decade means that AI no longer needs to be, say, a phase in the third year of your organisation’s strategic digital transformation.

This sure-footed progress over the past decade means that AI no longer needs to be, say, a phase in the third year of your organisation’s strategic digital transformation. Yes, AI is a big thing for enterprises. It’s a nimble partner now, not the awkward beast of old. The key is to have a plan, start small, and scale fast.

In the present stage of its accelerating evolution, AI is all about learning algorithms: supervised, unsupervised, reinforcement, and deep learning. In supervised learning, regression algorithms and their kin are commonly deployed, along with decision trees and random forests. Supervised learning employs historical or training data to help make predictions. Unsupervised learning lets AI machines learn from raw, unlabeled data, often unstructured, using methods such as cluster analysis. These techniques help with tasks such as customer segmentation or sentiment analysis. These techniques help with tasks such as customer segmentation.

Methods like Q-learning often support reinforcement learning to calculate expected future rewards for each action. A rather famous example involves the AI program created by Google DeepMind to play Atari games. Over time, the algorithm understands the implications of its actions better via a ‘reward signal,’ and improves itself. This makes reinforcement learning very similar to how humans learn. Deep learning, based on artificial neural networks, has in recent years become quite a buzzword because of its widespread conspicuous applications. These include autonomous vehicles, fraud detection, image and speech recognition, and language translation, among others. Deep learning algorithms’ performance and ability to solve complex problems increase with data availability – not infrequently to the stunning standards of science fiction, justifying the buzz.

Everything we’ve discussed so far has focused on the ‘what.’ However, AI-driven cognitive solutions are all about the ‘why.’ The problem will drive the algorithm and, therefore, the solution. But that doesn’t diminish the importance of data quantity and quality, which plays a very significant role, as always.

Table of contents

AI explained 2

Current trends 3

AI today 4

Challenges 4

AI applications 5

Building a case for AI investment 6

AI Centre of Excellence (CoE) 7

How can DXC help? 8

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Unquestionably, organisations that use information effectively have a significant competitive advantage over those that don’t. And it’s all about focusing on growth, and not just cost savings. By concentrating on only furthering automation to reduce costs, we miss hidden opportunities for greater personalisation and market differentiation. While the old cliché about not using technology for technology’s sake holds true as ever, in the age of wow-factor AI it can be hard to resist. But even for organisations that yield to this temptation, visionary CEOs and CIOs can steer their AI adoption well to avoid their competitors leaving them behind.

While research indicates that not all AI investments achieve the desired returns, many companies know that AI is a source of untapped opportunity. So how can you exploit the opportunities, manage the risk and, most importantly, maximise returns? It all starts with the right team, a few experiments, a single win. As we said early on: think big, start small, scale fast. Can AI help segment and profile your end-users? Can it help understand what their needs will be, and when and how to best communicate with them? Can it help reduce risk exposure while doing all that? Think about your value chain and how you make decisions today. AI can help monitor and predict patient outcomes; it can predict optimal asset maintenance or factory failure; it can forecast demand or automate pricing; optimise customer experience; identify fraud – the applications are multifarious and traverse industries.

Enterprise-wide success is driven by revenue generation, business-wide alignment, and operationalisation of AI across every part of an organisation. After several decades of progress, AI is now set to become a significant source of value and competitive advantage for many businesses.

Current trendsThe story that AI market trends narrate is becoming interesting and exciting. Decision-makers across spectrum have recognised that AI can use their vast quantities of data to optimise decision-making and increase efficiencies. However, adoption is not yet pervasive. Current research indicates that most CEOs are planning organic growth initiatives, with the majority of business decision – seeing AI as pivotal to the future success of their organisation. Many also believe that the steady rise of AI is inevitable and favourable for business prospects. AI can provide the most efficient way for an organisation to grow organically by effectively predicting the best way to sell, while also enhancing the entire value chain.

Organisations now seek broad-ranging advice from how to start with AI to operationalising algorithms right across the enterprise. Many are keen to realise the value of existing investments in analytics resources and AI, or to prove the value of additional investment in their digital or data environments. Success requires integration with legacy systems, ensuring data is kept secure, and running algorithms at scale. AI presents exciting opportunities right now with the right conditions in place. But you may well ask: can it be done at scale? There is a need to automate, scale, and operationalise existing siloed analytics capabilities. However, this doesn’t necessarily require an all-in approach. You can get started with a small scope and move through various business innovations, adding complexity as needed. Critical to success is engagement with the business and speedy evidence of progress. Solutions can be delivered through careful selection from a network of pluggable, replaceable parts from the best vendors. Engaging the broader workforce in AI experiments can also help the organisation to move effectively from idea to enterprise-scale AI innovation.

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AI todayWhile AI has been around and on the verge of breaking into the big league for many years, has only recently been elevated to its current star status. This is largely because the infrastructure necessary for success is now available and cost-effective. AI today benefits from the increased availability of computing power, access to and low cost of storage, and cheaper and faster communications that allow it to be practical and deployable across industries and lines of business.

Since 2012, giant leaps have been made in the quality and innovation of everyday technologies that enable today’s AI. It is now being integrated and embedded into systems and applications across many different areas of our lives, bringing AI closer to ubiquity and the term itself into everyday usage. However, despite the enterprise-level interest in AI and its potential to fundamentally change the dynamics of business today, most AI implementations are as yet only at very early stages.

In more recent years, speech and facial-recognition software have become progressively convincing and accurate, and daily interaction with these innovations is commonplace via social media, CCTV video processing, and border control, amongst others. Solutions like chatbots and voice-recognition systems have been widely deployed and are easily accessible to any organisation, no matter where you might be in your journey from a data perspective. These technologies have the potential to make significantly greater impact in other sectors, such as medicine, where they can assist in diagnoses, some surgeries, and other life-saving tasks.

Challenges Business and IT executives wanting to deploy AI into the enterprise to create an innovation strategy and build a high-performing AI culture do face challenges. Companies that clear these early hurdles to align their business to AI will outperform businesses that narrow their focus on the technological aspect of AI.

As DXC research shows, current problems mainly relate to moving the organisation beyond AI proofs-of-concept to measurable business improvements, with many companies reporting at least a few challenges on the path to adoption. Of these firms, 40% believe that time to implement and integrate with existing systems is a significant barrier to adoption, with a similar percentage also saying that they lack clear understanding of the benefits and intended use of AI. These problems stem from or are related to the integration of AI with legacy systems, keeping data secure, running algorithms at enterprise scale, and taking advantage of the entire AI vendor ecosystem.

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In addition to the technology required to make AI a reality, organisations must also have the skillset to execute the dream and the mindset to accept and make decisions based on AI-driven insights. In particular, the business users leveraging the insights need to spend time understanding how AI works and how it can best be applied to enable growth. This requires a significant cultural change that is often overlooked but cannot be underestimated and stepped around.

In a recent global executive study on AI conducted by MIT Sloan Management Review, the findings – based on a survey of more than 2,500 executives and 17 interviews with leading experts – found that companies successfully capturing real value from AI exhibit a distinct set of behaviours. These companies:

• Integrate their AI strategies with their overall business strategy.

• Take on large, often risky, AI efforts that prioritise revenue growth over cost reduction.

• Align the production of AI with the consumption of AI. This is done through the alignment of business owners, process owners, and AI expertise to ensure that they adopt AI solutions effectively and pervasively.

• Unify their AI initiatives with their greater business transformation efforts.

• Invest in AI talent, data, and process change in addition to (and often more so than) AI technology. They recognise that AI is not all about technology.

Currently, only 10% of companies with AI solutions believe they are seeing the full expected benefit. However, for the rest, this may be a mindset issue where the measurement of success is often based on old ways of thinking. For example, AI allows decisions to be made in a very targeted manner – say by generating 50 very high-quality leads as opposed to 500 low-quality inquiries that might produce the same result. This may be difficult to reconcile for many, but the adjustment is important so time and effort should be spent realigning existing mindset to the AI reality. For example, a transformed Microsoft reported a 60% increase in sales-qualified leads that have been scored through machine learning, and they are continuing to see improvements in AI technology like bots at a steady clip.

The net benefit of the above suggested success behaviours, and the underlying commitments they demand, is to address and resolve difficulties generating value with AI, manage unavoidable competitive and implementation risks, and effectively exploit AI-related opportunities.

AI applicationsAI can make significant contributions to an organisation’s growth and is doing so in many cases across a range of different industries around the world. It provides the ability to learn from data, improve decision making, and automate complex parts of a business. The goal is to eventually create artificial general intelligence, where a machine will be able to answer questions or pose new questions without having to be taught. While artificial general intelligence is still beyond the horizon, current real-life applications and benefits are broad, and can include:

• Customer loyalty and intimacy improvements through automatic recognition of preferences or predictions on churn or retention.

• Profitable growth by optimising up-sell and cross-sell opportunities at every interaction, or proactively identifying new revenue opportunities, including AI-driven design of new products and services.

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• Service quality improvements through the proactive notification of potential issues to users, or by enhancing collaboration and improving knowledge bases that dynamically adjust in response to the interaction.

• Market share increases with more consistent identification of high value, profitable prospects, and continuous improvements to predictions through self-learning from campaign feedback and changing customer behaviour.

• Behaviour monitoring (of applications, infrastructure, and users) to predict probable future outcomes or analyse past performance issues or root causes of problems.

• Automation of manual, repeatable tasks across the organisation, such as regular workflows, to make them smarter and to evolve based on specific outcomes.

DXC has several clients across a broad range of industries already achieving real business value and innovation from AI across the above applications. One standout example is the implementation of a large-scale prescriptive analytics solution to help a manufacturing plant reduce total cost to serve customer demand by up to 5%. The solution produced:

• A reduction in total cost to serve by up to 5%

• An optimised, restructured supply chain network for plan/product/customer location mapping.

• Cost optimisation and optimal plant network planning projections for 5- and 10-year horizons.

Building a case for AI investmentAccording to a recent Gartner survey, 37% of organisations are still looking to define their AI strategies, while 35% are struggling to identify suitable use cases. While a vast majority of executives believe that technologies such as AI are essential to better business decisions, this sanguine result isn’t a given, just as innovation won’t happen automatically with data and analytics.

Companies need the ability to experiment with AI, deploy trials into the enterprise, and prevent disruption from competitors. Once a company understands AI and its potential applications, it needs to think about making a business case for a pilot. Focus on a business scenario or problem that can use AI as part of the overall solution. Try to link this to a broader business strategy for greater impact.

DXC recommends a think big, start small, scale fast approach. Experiment with use cases that prove value and avoid disruption. Then, when you’re ready to extend your capabilities to an enterprise-wide strategy, you will need to embed new thinking by addressing the critical culture change required. This means organisations making specific attempts to improve the culture of their decision making. This is crucial, as the ability to adopt AI will rely on executives and front-line workers embracing these new ways of working. Be sure to factor this culture change process into your business case.

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AI Centre of Excellence (CoE)The idea of establishing a CoE in AI is not particularly radical, but it does depend on the organisation’s size and appetite for risk. DXC believes that building a CoE and aligning it to business will derive more value than treating AI as just a part of IT. For many large enterprises, a CoE might be both practical and necessary to manage the infrastructure and data consistency. Smaller enterprises could establish a centralised point or small team to manage the process. Starting small and scaling up is both viable and achievable but will still require the same level of understanding and commitment.

One of the critical components of AI – machine learning – is derived from statistical regression. This raises the issue of whether an AI CoE should be combined with the analytics line of business (LoB). If advanced analytics is entrenched in the analytics groups of the enterprise today, then it makes sense to upskill employees to absorb AI. Regardless of whether you establish a CoE rooted in an analytics LoB or otherwise, it can and should pursue a variety of activities. It might surprise you to learn that some of these are not algorithmically inclined but concern an end-to-end data-to-insights journey that includes:

• Starting with a cohesive, understood, and accepted vision for AI. Always build with use-cases that consider business value – from cost to potential revenue as well as impact on people.

• Knowing that AI must augment and support – remember, we are not at general AI level yet – so use-cases must be practical but with a think big, start small, scale fast approach.

• Aligning to a target technical state to support AI development with standard AI utilities – standardised architecture to manage AI deployment and orchestration.

• As with all significant change management, identifying your change agents, and establishing a network of interest to keep them apprised of significant developments. These agents must be present across all levels of the organisation as AI champions.

• Giving them the success stories! Use success stories as a way to generate interest, excitement, and an appetite for growth in AI activity. The change agents or AI champions play a vital role here, so it’s crucial to engage them in the communications management process as with any program management methodology.

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About DXC TechnologyDXC Technology, the world’s leading independent, end-to-end IT services company, manages and modernizes mission-critical systems, integrating them with new digital solutions to produce better business outcomes. The company’s global reach and talent, innovation platforms, technology independence and extensive partner network enable more than 6,000 private- and public-sector clients in 70 countries to thrive on change. For more information, visit www.dxc.technology.

© 2019 DXC Technology Company. All rights reserved. DG_2454a-20. October 2019

How can DXC help?AI offers a significant positive impact on organisations if implemented well. CIOs and other business leaders should find the critical business points where human interaction or expertise adds value then, consider how AI might support those efforts to create even more value. If you identify and assess which business outcomes would benefit most from AI, you’ll have a better starting point for the most beneficial impact. Second, evaluate how AI might help achieve those outcomes.

When planning AI, be sure to include technology experts, as well as data and analytics people and LoB experts. Decide on high impact outcomes, but consider pursuing softer or smaller payoffs where the risk is low but the opportunity for lessons learned is present. By starting small and taking an agile, incremental approach, you’ll be able to run experiments more easily, discover what works, and make rapid changes to improve results.

For further advice or support in advancing AI within your organisation, DXC can provide the ability to fund and implement projects in small increments. Project scope is flexible with an explicit intent to experiment, learn, and pivot. Instead of expensive rip-and-replace, we always try to first extend any existing investment in technology, applications, and expertise.

DXC AI consultants advise, support, and manage our clients’ transformations based on their professional and financial goals, current readiness, culture, operational practices, and compliance requirements. Let DXC help you unlock the insights needed to implement and operationalise AI successfully.

Don’t be disrupted – be the disruptor. Let us help you innovate and transform to differentiate with speed and quality.

That’s DXC. That’s Digital Delivered.

Nikita Dhami, Head of AI & Machine Learning

Nikita Dhami is a highly experienced, reliable, and efficient leader of dynamic teams, including running marketing automation and AI functions, operating in fast-paced environments. She has a background in leading and implementing end-to-end analytic solutions for businesses, which in turn, deepened her understanding of and thought leadership on data, AI and machine learning, and marketing applications.

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