While true artificial intelligence is some way off, businesses are taking advantage of intelligent automation, like machine learning, to improve business operations, drive innovation and improve the customer experience
The meaningful and transformational value created by the implementation of AI and automation means its integration is now a top priority for both boardrooms and governments across a variety of sectors.
AI and automation is changing the business environment across industries, delivering new opportunities through intelligent, automated products. Some companies are ahead of the curve, and others are stagnating in their adoption of the tech.
Board members and decision-makers are increasingly aware of the benefits of AI and automation, but the question should always remain: ‘Is it right for my business? How does it solve a problem?’.
With the general rise of this technology into business operations also comes challenges, dangers and potential risks to the human workforce. This feature will examine all these aspects and hope to give an overall look at AI and automation in the enterprise.
What is AI?
Artificial intelligence (AI) is effectively an umbrella term to describe processes of intelligent automation, like machine learning, natural language processing (NLP), cognitive computing and deep learning. True AI is when a computer or robot can think and act as a human brain would. In reality, these sentient, self-sufficient ‘beings’ — seen in films like Ex Machina or dare I say, The Terminator — are a long way off.
At the moment, companies are using autonomous processes to improve operations, and change the face of customer service (through, for example, AI-powered chatbots), while spurring innovation to new heights. AI is a set of algorithms that can solve a specific set of problems — and it works best with a large amount of quality big data.
Almost every industry will be impacted and transformed by ‘AI’ and automation in the next few years. Manufacturing — perhaps more so than others — is one industry currently seeing the benefits of implementing this technology in operations. Over the next five years, these smart factories — using tech like robots and automation — will act as the catalyst for a new global economy and herald in Industry 4.0.
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When it comes to manufacturing, artificial intelligence is beginning to and will “touch each and every stage of the supply chain, whether it be the logistics, manufacturing or maintenance of goods,” explains Graeme Wright, chief technology officer for manufacturing, utilities and services at Fujitsu. “As yield is of utmost importance when it comes to manufacturing, we will see many more applications of AI put in place to improve manufacturers output.”
Board members and decision-makers are now aware that traditional business models will simply not do in a continuously disrupted business environment. “Those in the c-suite must reengineer their operations in a way that embraces AI,” says Frank Palermo, global head, digital solutions, Virtusa. “A good starting point would be for organisations to automate much of their ‘drudge’ work, and focus instead on providing tailored, personalised products and services that satisfy customers and meet growing expectations.”
The benefits of AI: is it necessary?
Simply, AI and automation — like some other emerging technologies — will allow businesses to cut costs, boost productivity by freeing up workers from more mundane tasks, increase agility and flexibility, and spur innovation — all the buzzwords.
Indeed, when done right, implementing this technology will allow businesses “to grow revenues, product lines and offer differentiated customer experiences,” confirms Barry Matthews, head of UK, Ireland and Netherlands at ISG.
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AI and automation stands out from other technologies, in that it will help advance those other technologies into the mainstream business environment. For example, AI will advance IoT. It will be “indispensable in processing the large volumes of data that arrive from devices,” says Misha Bilenko, head of Machine Intelligence and Research at Yandex.
As more and more data is produced from technologies like the IoT and virtual (and augmented) reality devices, AI and automation will be essential in not only managing that data, but also in supporting the increased strain on business networks.
“With networks powered by AI and machine-learning, the workplace becomes less tangled up in complexity and repetition, and more agile as a result; something that is greatly needed as our business world becomes increasingly borderless and far more competitive,” explains David Goff, head of Enterprise Networks at Cisco UK & Ireland.
For the plethora of benefits to be realised, however, organisations and their decision-makers need to ensure that the technology solution is addressing a problem or gap. “Without a clear strategy, the return on investment is far less likely to be maximised,” clarifies Matthews.
Ultimately, this should be the message. A new technology(ies) — like AI and automation — can’t be implemented for the sake of it, because it’s ‘new and shiny’. Regardless of what technology it is, business leaders need to identify a problem that needs fixing or identify how to improve systems and practices, in order to justify integrating something into operations.
A 2018 survey by Spiceworks found that 50% of organisations have not implemented AI due to the lack of use cases in the workplace. And a further 29% noted that security and privacy concerns hold back AI adoption, with concerns around costs following close behind. From this perspective, the challenge of integrating this technology stems from a fear of the unknown, rather than anything practical.
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Indeed, at this stage there are not a huge number of companies utilising this technology, “because although the technology exists in consumer and niche cases, there are not yet easily implementable solutions built for the enterprise,” says Ian Aitchison, senior product director at Ivanti. “It’s at that early stage of maturity where consumer is actually leading ahead of the enterprise. As vendors bring prebuilt AI tech in as an advantage to their enterprise customers, the adoption becomes easier.”
If the leadership team of an organisation does decide to integrate AI and automation then there will be number of challenges to overcome; including control, a lack of skills and legacy systems.
• AI and control: Businesses must be able to control the scope of how it’s AI makes decisions, i.e. the ability to change from “opaque – where the decisions being made by the AI are not easily explainable – where they need to insist on it being transparent and able to explain its results,” explains Don Schuerman, CTO, Pegasystems. Following regulation, like the EU General Data Protection Regulation and the California Consumer Privacy Act, businesses now need to switch between transparent and opaque AI operating. How can you control the technology without a costly, major teardown? How can you make sure that you can trust your AI? It is important, therefore, “for companies to have the ability to control the level of transparency, such as using a “T-Switch”. If the T-Switch is toggled to the opaque setting, then anything goes. If the T-Switch is toggled to transparent, then the AI must provide information about its behaviour and anything opaque will be actively blocked from execution. Because we do not live in a world where “Either/Or” is a reality, in reality the T-Switch will be a slider ranging from 1 (very opaque) to 5 (completely transparent),” says Schuerman.
• Lack of skills and AI: The global digital skills crisis has been well documented, and any organisation looking to integrate AI and automation processes will need employees who can work with, and understand the technology. “Industry at large will need more data scientists who are also domain experts and understand the relevance of the training datasets as they conduct regression and optimise their ML algorithms. This will determine how smart the AI is,” explains Michael Segal, area vice president strategy, NETSCOUT. Finding the right people to integrate this technology, therefore, will be a significant challenge for organisations looking to benefit from AI. Solutions? Well this boils down to getting more students involved with STEM subjects at school, and tech industries providing easier routes into professions from college and university. The issue of diversity is also a factor, and if the technology industry and education systems can get more girls interested in tech then there will be a lot more digital talent to pick from.
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• AI and legacy systems: Many large enterprises are based on legacy systems, and integrating any new, emerging technology with these is a challenge. “Many businesses face the challenge of working out how automation fits in with existing systems and processes,” confirms Sharon Einstein, VP EMEA Robotic Automation and AI at NICE. This burden of legacy systems can prove a significant stumbling block, along with ever-increasing cost pressures. “Many enterprises face a major challenge in bridging the gap between expectations and reality,” explains Kalyan Kumar, corporate vice president and CTO IT Services at HCL Technologies.
AI in cyber security
Once integrated, AI — along with business best practice — has many applications. Improving an organisation’s cyber security posture is one of them.
“AI and ML are just tools, and it’s how you use the tools that matter,” says Etienne Greeff, CTO and founder of SecureData. “There’s certainly a role for ML and AI in cyber security; for example, they are very good at dealing with lots of information and trying to understand what is normal and what’s anomalous.”
He does argue, however, that “in cyber security and in application security, there’s actually no known application of AI. There’s no autonomous agent that automatically defines threats; that does not happen yet, and it’s not very close to happening.”
AI is not a silver bullet. But, as the use of the technology continues to grow, it should become part of a robust, defence-in-depth information security strategy that keeps data safe and focuses on risk management, rather than incident response.
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Will AI pose a risk to my job?
In 2017, Jim Yong Kim, the World Bank chief warned that the automation of millions of jobs was endangering the future hopes and ambitions of people. It is true that the more mundane, task-led jobs are being digitised. Retail giant, Ocado, for example has created a robot-operated warehouses, where the human element is — in-part — eliminated.
This trend will become more common and is part and parcel of any industrial revolution, where, in fact, more jobs are created. “Automation will to evolve the way we work. To avoid being displaced, workers will need to adapt themselves to focus on higher value tasks which might require retraining,” says Palermo.
In the UK, a Deloitte study of automation showed that whilst 800,000 low-skilled jobs have been eliminated, automation created 3.5 million new jobs. Those jobs paid on average nearly $13,000 more per year than the ones that were lost.
However, the idea of retraining and new roles is all well and good, but what if you are in the transport industry as a driver and your job is rendered obsolete with the advent of autonomous driving? Will millions of these people be expected to retrain in software engineering and then compete against those who have already already trained? It is an unrealistic proposition — the introduction of a universal basic income is increasingly likely.
So, while some jobs are certainly under threat — whether this is right or not falls under ethical AI discussions — the human element to the workforce is not, by in large, under threat. Technologies, like AI and automation, need to be overseen, programmed and analysed.
When more businesses eventually make the imminent jump to AI, how can they ensure AI-readiness? What do organisations need to do to make sure their systems are not overwhelmed?
First and foremost, businesses must make sure their data is AI ready. This requires a central data hub, which is capable of extracting and analysing data from multiple sources — AI solutions can then make effective use of this full picture to deliver customer engagement strategies, or whatever insight that needs to be garnered.
Building AI into the foundation of the enterprise is also key. To embrace the possibilities that AI is already offering and will offer in the future, enterprises need to go on a journey to rebuild and reimagine themselves on an AI-powered foundation, a collaboration between man and machine.
If this new AI-led business strategy is adopted in the right circumstances to solve the right problems, it will help businesses become better, faster and more agile than ever before — and allow them to overcome natural limitations.
C-suite priority? Everyone’s priority
This depends entirely on the type of business and what problems need solving. AI should not, and cannot, be used to solve every problem in an organisation.
However, under the right set of circumstances, the umbrella of AI can ease the increasing pressure on boardrooms while helping provide incredible value to customers. “The technology allows businesses to save money, make money and manage risk, three of the most important motivators for any boardroom,” says Colin Redbond, head of Technology Strategy, Blue Prism.
Ultimately, today, more and more businesses are grasping with the power of AI to meet the escalating expectations of consumers and stakeholders.
Outside of the private sector, the emphasis on AI is very much a priority for the UK government. This was demonstrated last year when the Department for Digital, Culture, Media and Sport (DCMS) announced that 50 leading technology companies and organisations have contributed to the development of an AI deal worth almost £1 billion, including almost £300 million of private sector investment into UK sector.
The announcement followed the launch of the government’s Industrial Strategy, within which AI was highlighted as one of the UK’s four ‘grand challenges’. MPs also released a report analysing the government’s AI strategy, stating that it needed to rethink funding, procurement and infrastructure.
Other endeavours taken by the government include the establishment of the Government Office for AI, the AI Council and the Centre for Data Ethics and Innovation.
Secretary of State for Digital, Culture, Media and Sport Matt Hancock said at the time: “The UK must be at the forefront of emerging technologies, pushing boundaries and harnessing innovation to change people’s lives for the better.
“Artificial Intelligence is at the centre of our plans to make the UK the best place in the world to start and grow a digital business. We have a great track record and are home to some of the world’s biggest names in AI like Deepmind, Swiftkey and Babylon, but there is so much more we can do.
“By boosting AI skills and data driven technologies we will make sure that we continue to build a Britain that is shaping the future.”
The meaningful and transformational value created by the implementation of AI and automation means its integration is now a top priority for both boardrooms and governments across a variety of sectors. However, how they can “keep jobs and retrain employees as more and more processes become automated,” will be crucial, explains Segal.
The main priority for business leaders, concludes Redbond, “must be to democratise automation strategies across their organisation. Giving business users the opportunity to control new digital operating systems will be the key to the technology being used to its full potential.”
This article has appeared in InformationAge.