Conventional robots, like giant industrial robots used in the car industry, are set to reach $14.9bn value this year, up from $12bn in 2018.

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Robotics play a huge role in the manufacturing landscape today. A growing number of businesses use manufacturing robots to automate repetitive tasks, reduce errors, and enable their employees to focus on innovation and efficiency, causing the entire sector’s impressive growth.

According to data presented by AksjeBloggen.com, the global market value of conventional and advanced robotics in the manufacturing industry is expected to continue rising and hit $18.6bn in 2021, a 40% increase in three years.

Market Value Jumped by $5.4B in Three Years

Robots have numerous roles in manufacturing. They are mainly used for high-volume, repetitive processes where their speed and accuracy offer tremendous advantages. Other manufacturing automation solutions include robots used to help people with more complex tasks, like lifting, holding, and moving heavy pieces.

Companies turn to robotics process automation to cut manufacturing costs, solve the shortage of skilled labor and keep their cost advantage in the market.

In 2018, the global market value of conventional and advanced robotics in the manufacturing industry amounted to $13.2bn, revealed the BCG survey. In 2019, this figure rose to $14.8bn and continued growing. Statistics show the market value of manufacturing robots hit $16.6bn in 2020. This figure is expected to jump by $2bn and hit $18.6bn in 2021.

Conventional robots, like giant industrial robots used in the car industry, are set to reach $14.9bn value this year, up from $12bn in 2018.

The market value of advanced manufacturing robots, which have a superior perception, adaptability, and mobility, tripled in the last three years and is expected to hit $3.7bn in 2021. Combined with big data analytics, advanced manufacturing robots allow companies to make intelligent decisions based on real-time data, which leads to lower costs and faster turnaround times.

The BCG survey also showed most manufacturers believe advanced robotic systems will have a massive role in the factory of the future and plan to increase their use. More than 70% of respondents defined robotics as a significant productivity driver in production and logistics.

European and Asian Companies Lead in the Use of Advanced Manufacturing Robots

Analyzed by regions, European and Asian companies lead in the use of advanced robots, while manufacturers from North America lag behind. However, the survey showed 80% of respondents from the US plan to implement advanced robotics in the next few years.

The survey also revealed that manufacturers in emerging markets, especially China and India, are more enthusiastic about using advanced robots than those in industrialized countries. These companies may be looking to automation as a way to overcome a skilled labor shortage and improve their ability to compete in international markets.

Germany had the largest robot density in the manufacturing industry among European countries, with 346 installations per 10,000 employees in 2019. Sweden, Denmark, and Italy followed with 277, 243, and 212 installations per 10,000 employees, respectively.

Statistics also show that companies in the transportation and logistics and technology sector lead in implementing advanced robotics, with 54% and 53% of manufacturers who already use such solutions. The automotive industry and consumer goods sector follow with 49% and 44% share, respectively.

Manufacturers in the engineered products, process, and health care industries lag behind, with 42%, 41%, and 30% of companies that use advanced manufacturing robots. However, around 85% of manufacturers in these sectors plan to start using advanced robotic systems by 2022.

Gurpreet Purewal, Associate Vice President, Business Development, iResearch Services, explores how organisations can overcome the challenges presented by AI in 2021.

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2020 has been a year of tumultuous change and 2021 isn’t set to slow down. Technology has been the saving grace of the waves of turbulence this year, and next year as the use of technology continues to boom, we will see new systems and processes emerge and others join forces to make a bigger impact. From assistive technology to biometrics, ‘agritech’ and the rise in self-driving vehicles, tech acceleration will be here to stay, with COVID-19 seemingly just the catalyst for what’s to come. Of course, the increased use of technology will also bring its challenges, from cybersecurity and white-collar crime to the need to instil trust in not just those investing in the technology, but those using it, and artificial intelligence (AI) will be at the heart of this. 

1. Instilling a longer-term vision 

New AI and automation innovations have led to additional challenges such as big data requirements for the value of these new technologies to be effectively shown. For future technology to learn from the challenges already faced, a comprehensive technology backbone needs to be built and businesses need to take stock and begin rolling out priority technologies that can be continuously deployed and developed. 

Furthermore, organisations must have a longer-term vision of implementation rather than the need for immediacy and short-term gains. Ultimately, these technologies aim to create more intelligence in the business to better serve their customers. As a result, new groups of business stakeholders will be created to implement change, including technologists, business strategists, product specialists and others to cohesively work through these challenges, but these groups will need to be carefully managed to ensure a consistent and coherent approach and long-term vision is achieved. 

2. Overcoming the data challenge

AI and automation continue to be at the forefront of business strategy. The biggest challenge, however, is that automation is still in its infancy, in the form of bots, which have limited capabilities without being layered with AI and machine learning. For these to work cohesively, businesses need huge pools of data. AI can only begin to understand trends and nuances by having this data to begin with, which is a real challenge. Only some of the largest organisations with huge data sets have been able to reap the rewards, so other smaller businesses will need to watch closely and learn from the bigger players in order to overcome the data challenge. 

3. Controlling compliance and governance

One of the critical challenges of increased AI adoption is technology governance. Businesses are acutely aware that these issues must be addressed but orchestrating such change can lead to huge costs, which can spiral out of control. For example, cloud governance should be high on the agenda; the cloud offers new architecture and platforms for business agility and innovation, but who has ownership once cloud infrastructures are implemented? What is added and what isn’t? 

AI and automation can make a huge difference to compliance, data quality and security. The rules of the compliance game are always changing, and technology should enable companies not just to comply with ever-evolving regulatory requirements, but to leverage their data and analytics across the business to show breadth and depth of insight and knowledge of the workings of their business, inside and out. 

In the past, companies struggled to get access and oversight over the right data across their business to comply with the vast quantities of MI needed for regulatory reporting. Now they are expected to not only collate the correct data but to be able to analyse it efficiently and effectively for regulatory reporting purposes and strategic business planning. There are no longer the time-honoured excuses of not having enough information, or data gaps from reliance on third parties, for example, so organisations need to ensure they are adhering to regulatory requirements in 2021.

4. Eliminating bias

AI governance is business-critical, not just for regulatory compliance and cybersecurity, but also in diversity and equity. There are fears that AI programming will lead to natural bias based on the type of programmer and the current datasets available and used. For example, most computer scientists are predominantly male and Caucasian, which can lead to conscious/unconscious bias, and datasets can be unrepresentative leading to discriminatory feedback loops.

Gender bias in AI programming has been a hot topic for some years and has come to the fore in 2020 again within wider conversations on diversity. By only having narrow representation within AI programmers, it will lead to their own bias being programmed into systems, which will have huge implications on how AI interprets data, not just now but far into the future. As a result, new roles will emerge to try and prevent these biases and build a more equitable future, alongside new regulations being driven by companies and specialist technology firms.

5. Balancing humans with AI

As AI and automation come into play, workforces fear employee levels will diminish, as roles become redundant. There is also inherent suspicion of AI among consumers and certain business sectors. But this fear is over-estimated, and, according to leading academics and business leaders, unfounded. While technology can take away specific jobs, it also creates them. In responding to change and uncertainty, technology can be a force for good and source of considerable opportunity, leading to, in the longer-term, more jobs for humans with specialist skillsets. 

Automation is an example of helping people to do their jobs better, speeding up business processes and taking care of the time-intensive, repetitive tasks that could be completed far quicker by using technology. There remain just as many tasks within the workforce and the wider economy that cannot be automated, where a human being is required.

Businesses need to review and put initiatives in place to upskill and augment workforces. Reflecting this, a survey on the future of work found that 67% of businesses plan to invest in robotic process automation, 68% in machine learning, and 80% investing in perhaps more mainstream business process management software. There is clearly an appetite to invest strongly in this technology, so organisations must work hard to achieve harmony between humans and technology to make the investment successful.

6. Putting customers first

There is growing recognition of the difference AI can make in providing better service and creating more meaningful interactions with customers. Another recent report examining empathy in AI saw 68% of survey respondents declare they trust a human more than AI to approve bank loans. Furthermore, 69% felt they were more likely to tell the truth to a human than AI, yet 48% of those surveyed see the potential for improved customer service and interactions with the use of AI technologies.

2020 has taught us about uncertainty and risk as a catalyst for digital disruption, technological innovation and more human interactions with colleagues and clients, despite face-to-face interaction no longer being an option. 2021 will see continued development across businesses to address the changing world of work and the evolving needs of customers and stakeholders in fast-moving, transitional markets. The firms that look forward, think fast and embrace agility of both technology and strategy, anticipating further challenges and opportunities through better take-up of technology, will reap the benefits.

With virtually all companies looking at AI, what are some of the key risks they need to consider before implementation?

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Today virtually all companies are forced to innovate and many are excited about AI. Yet since implementation cuts across organisational boundaries, shifting to an AI-driven strategy requires new thinking about managing risks, both internally and externally. This blog will cover “the seven sins of enterprise AI strategies”, which are governance issues at the board and executive levels that block companies from moving ahead with AI. by By Jeremy Barnes, Element AI

1- Disowning the AI strategy

This is probably the most important sin. In this case, a CEO and board will say that AI is a priority, but delegate it to a different department or an innovation lab. However, success is not based on whether or not a company uses an innovation lab—it’s whether they are truly invested in it. The bottom line is that the CEO and board need to actively lead an AI strategy.

2- Ignoring the unknowns

This happens when companies say they believe in AI, but don’t reach a level of proficiency where it’s possible to identify, characterise and model the threats that emerge with new advances. Even if it is decided not to go all-in on AI innovation, it’s still important that there is a hypothesis for how to address AI within a company and an early warning system so the decision can be re-evaluated early enough to act.  Being a fast follower requires as much organizational preparation and lead time as leadership.

3- Not enabling the culture

The ability to implement AI is about an experimentation mindset. That and an openness to failure need to be adopted across the company. Organisations need to keep in mind that AI doesn’t respect organisational boundaries. Most companies want high-impact, low-risk solutions that could simply lead to optimising, rather than advancing new value streams. It is hard to accept increased risk in exchange for impact but it will come as part of the continuous cultural enablement of an experimental mindset.

4- Starting with the solution

This is the most common sin. It’s important to be able to understand the specific problems you’re trying to solve, because AI is unlikely to be a solution for all of them, and especially not blindly implementing a horizontal AI platform. Have the conversation at board level to ensure that an overarching AI strategy, and not simply quick-fix solutions, is the priority.

5- Lose risk, keep reward

As mentioned in the third sin, it is natural for companies to want to implement AI without any risk. But there is no reward without risk. A vendor motivated to decrease risk will also decrease innovation and ultimately impact by making successes small and failures non-existent. AI creates differentiation only for companies that are willing to learn from both their successes and their failures. A company that doesn’t effectively balance risk in AI will ultimately increase its risk of disruption.

6- Vintage accounting

Attempting to fit AI into traditional financial governance structures causes problems. It doesn’t fit nicely into budget categories and it’s hard to value the output. The link between what you put in and what you get out can be less tangible or predictable, which often makes it harder to square with existing plans or structures. Model the rate of return on AI activities and all data-related activities. This demands that these activities affect profit (not just loss) and assets (not just liabilities).

7- Treating data as a commodity

The final sin concerns data and its treatment as a commodity. Data is fundamental to AI. If data is poorly handled, it can lead to negative impacts on decision-making. Data should be treated as an asset. The stronger, deeper and more accurate the dataset, the better models that you can train and more intelligent insights you can generate. But, at the same time, when personally identifiable information is stored about customers, it can be stolen, risking heavy penalties in some jurisdictions. You need to build towards data from a use case rather than invest blindly in data centralisation projects. So, now you know what not to do. Here are some of the simple things that you can do to move ahead. First, talk to your board about how long it will take to become an AI innovator, modelling it out, rather than simply discussing it conceptually.

Second, prepare for change and put in place monitoring. AI shifts all the time, so you’ll want to regularly check in to adjust and pivot your strategy. It’s important to develop a basic skill set so you can redo planning exercises with your board. Third, model out risks in both action and inaction. But don’t model them in a traditional approach, which is to push risk down to different business units and then compensate those units for reducing risk rather than managing trade-offs. Instead, view those trade-offs in terms of risks and rewards, and start to think about how you are accounting for the assets and liabilities of AI. Ultimately, you want to start to model what is the actual rate of return for all these activities that you are doing. Then benchmark it against what you see in other companies from across the industry, and that will give you a good picture of the current situation and where to go.

Understanding what it isn’t is just as important as understanding what it is, says Jim Logan who has nearly three decades of experience in financial services and technology…

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I’ve been working in the financial services space for close to thirty years now. I’ve seen many trends and technologies emerge. Some take hold, several are just a flash in the pan. Regardless of how long a concept sticks around, one thing remains: Terminology plays a material role in shaping perceptions. In a world where messaging tends to over complicate things, too many acronyms and too many buzzwords all work against what should be the primary objective: clearly illustrating value. I’ve found this to be equally true when it comes to artificial intelligence or ‘AI’.

Generally speaking, the word artificial doesn’t readily call to mind a positive image, does it? By definition, the word “artificial” has listed meanings of, “insincere or affected” and “made by humans as opposed to happening naturally.”  It is the second part of this definition I’d like to explore a bit further.

Artificial Intelligence is, in fact, created by humans. And it isn’t a new fad or concept. Many don’t realize that the term was first coined by John McCarthy, Ph.D. and Stanford computer and cognitive scientist, back in 1955.  AI has continued to evolve as a material concept, with practical applications across many industries, ever since.

For financial service professionals, particularly those of us involved with fighting financial crime and preventing money laundering, AI can have tremendous impact and practical application.  Before we dive a bit deeper, I feel it’s important to first understand what AI isn’t.

AI is not intended to simply be a digital worker, certainly not within financial services and fighting financial crime. Yes, AI can automate various functions. We’re all familiar with the concept of ‘bots’ and virtual assistants. However, those are rudimentary examples of robotic process automation. True AI is human led and a continuous, instantaneous learning process that drives tangible value. AI is not merely a play to cut costs or replace human capital. Rather, AI enhances the bottom line by keeping compliance staff costs flat in the immediate term and enables our human experts to more appropriately manage their time, by focusing talent on investigations that matter the most.

One of the most valuable aspects of AI, in the context of anti money laundering and compliance, is the speed by which it can be deployed. We’re talking about time to market and time to value in a matter of weeks. Not months, not multiple quarters – simply weeks. But I don’t mean a generic, black box concept. I’m specifically referring to a highly precise, tailored AI solution that has extensive proof points and, more importantly, far-reaching global regulatory approval.

AI shouldn’t simply be an extension of legacy rules-based routines, nor a way to further automate the process of scoring or risk weighted alert suppression. That simply dilutes the true value of AI, and does not maximize the cost and efficiency benefits.

The cost of compliance continues to grow at a staggering pace, particularly for financial institutions and insurance companies. Equally of concern, the impact of fines for non-compliance has also skyrocketed in the last decade. Specifically to the tune of $8.4 billion last year across North America alone.

What if you could literally solve every single name screen, sanction, and transaction alert? What if you could achieve this without sacrificing any aspect of control and security? What if you could increase the throughput, efficiency and accuracy of your compliance operations without adding a single dollar of staff expense to your budget?

Let’s stop talking in terms of what if and have a meaningful conversation regarding how. I’m helping clients achieve all of these measures today and that is from a perspective proven in production. Here at Silent Eight we’re a team founded by engineers and data scientists, solving real world challenges in the anti money laundering and financial compliance market.

Artificial Intelligence isn’t scary…it isn’t a black box…and it isn’t the futuristic world of tomorrow – it is the here and now, and it’s battle tried and tested.

Temenos, the banking software company, partners with Microsoft to offer AI-driven Financial Crime Mitigation solution to help banks combat surge cybercrime during Covid-19 outbreak.

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Temenos, the banking software company, announced today a joint effort with Microsoft to enable access to its AI-powered, Financial Crime Mitigation (FCM) SaaS solution to allow banks to protect both their customers and their organization from financial crime increase during the pandemic, particularly as banks have moved to remote working to protect their staff. Temenos AI-powered, Financial Crime Mitigation SaaS solution based on Microsoft’s fast, scalable and secure Azure cloud platform can be deployed within weeks. 

Temenos and Microsoft are opening up access to banks for a 14-day trial, available until 30 of June. As part of the collaboration with Microsoft, Temenos is offering system access and online tutorials for users to familiarize themselves with navigation of the system and learn how it can support them in a revised operating landscape. Temenos unveiled the open access initiative of its FCM software at its virtual event Temenos Community Forum Online, 29-30 April.

Temenos FCM provides enterprise-wide financial crime protection for a highly regulated and fast-changing environment. It allows banks’ operators to respond to alerts and collaborate with team members while working remotely. Throughout the Covid-19 crisis, Temenos customers from Tier 1 banks to regional banks and neobanks have continued to benefit from Temenos FCM’s comprehensive coverage regardless of the fact that their teams are working remotely.

Financial regulators worldwide and organizations such as the European Central Bank are warning that the Covid-19 pandemic may result in an increase in financial crime and other misconduct due to market disruptions, reduced staff, and other factors, as has been the case during past global crises. Opportunistic fraudsters and criminals are adapting their methods of targeting people and countries in distress as new threat vectors open up.

The Financial Actions Task Force (FATF), the global standard setter for combating money laundering and terrorism financing, warns businesses to remain vigilant for emerging money laundering and terrorist financing risks as criminals may seek to exploit gaps and weaknesses in Anti-Money Laundering/Combating the Financing of Terrorism (AML/CFT) systems under the assumption that resources are focused elsewhere. Fraudsters have already been very quick to adapt well-known fraud schemes to target individual citizens, businesses and public organizations. These include various types of adapted versions of telephone fraud schemes, supply scams and decontamination scams.

Jean-Michel Hilsenkopf, Chief Operating Officer, Temenos, said: We are proud to be able to offer our cloud-native and AI technology to support banks in the fight against financial crime, which has increased as a result of the pandemic. As a strategic global banking software partner of Microsoft, we are pleased to join efforts to deliver Temenos Financial Crime Mitigation as SaaS on Microsoft Azure’s resilient, secure and proven cloud platform. We are committed to providing robust and up-to-date sanction screening, AML, KYC and fraud management protection combined with powerful AI-driven transaction monitoring and sanction screening to help banks worldwide.”

Marianne Janik, Country General Manager, Microsoft Switzerland, said: “We have been pioneering with Temenos in the cloud for a decade. We are proud to join forces to help banks use the power of Temenos’ market-leading Financial Crime Mitigation solution based on our secure, scalable and resilient global Azure cloud platform to combat financial crime surge due to Covid-19.” 

More than 200 banks use Temenos FCM SaaS solution, which covers watch-list screening, anti-money laundering, fraud prevention – suspicious activity prevention – and KYC, delivering industry-leading levels of detection and false positives of under 2% vs industry average of 7% and above. Temenos FCM can be deployed as a standalone, or integrated into any banking or payments platform including cloud-native, cloud-agnostic Temenos Transact and Temenos Infinity. It provides unrivalled levels of detection and resilience against financial crime and Total Cost of Ownership (TCO) savings of more than 50%. Temenos FCM provides banks with the next generation of AI-driven FCM capabilities that can run on any public cloud, as a service or on premise.

The global developer of artificial intelligence solutions is releasing a free search platform to help clinical and scientific researchers find answers and patterns in research papers

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Information on COVID-19 is evolving fast and this AI-powered platform leverages a semantic search model that will allow users to quickly connect disparate information. The platform can execute searches based on specific inquiries, along with critical paragraphs copied from a relevant paper. Unlike keyword searches, the queries do not need to be specifically structured, and actually perform better in longer form. This initial version is configured to work with the COVID-19 Open Research Dataset (CORD-19) corpus. Element AI is looking for users and organizations from various groups to test the platform and suggest other data sets and features that could best fit their needs.

The group’s Element AI is looking to work with include:

Clinical researchers who need to incorporate many phenomena to make a rich model of the pandemic and its impacts.

Government, Public Safety and Public Health authorities looking to find best practices across different countries.


Pharmaceutical companies working on new therapies or vaccine trials, as well as identifying existing therapies that could provide immediate help.

-Scientific researchers and data scientists who are working on novel ways to connect research across the body of knowledge already available for COVID-19.

“Research data and reports are being published at an unprecedented pace as organizations scale up their efforts to respond to COVID-19. We want to contribute, and this free platform is our way to help the community locate and gather knowledge to find answers and patterns,” said Jean-François (JF) Gagné, CEO and Co-founder of Element AI. “We encourage the scientific and healthcare community to use this free platform and engage with our team to quickly ramp up and collaboratively meet the needs of the people working to slow down and contain COVID-19. We hope that their feedback and collaboration will help us quickly add features and datasets on top of what we already have made available” added Gagné.

The COVID-19 platform leverages technology from the Element AI Knowledge Scout product, which uses natural language techniques to tap into structured and unstructured sources of information. The first version will be progressively updated in coming weeks as additional datasets emerge. The site can be accessed at: https://www.elementai.com/covid-research.

Mauro Guillén Zandman, Professor of International Management, The Wharton School, University of Pennsylvania, USA Srikar Reddy, Managing Director and Chief…

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Mauro Guillén Zandman, Professor of International Management, The Wharton School, University of Pennsylvania, USA

Srikar Reddy, Managing Director and Chief Executive Officer, Sonata Software Limited and Sonata Information Technology Limited

Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading, and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI.  But the scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore. And it’s just the beginning. As AI applications require ever greater amounts of data to help machines learn and perform tasks hitherto reserved for humans, companies are facing increasing public scrutiny, at least in some parts of the world. Tesla and Uber have scaled down their efforts to develop autonomous vehicles in the wake of widely reported accidents. How do we ensure the ethical and responsible use of AI? How do we bring more awareness about such responsibility, in the absence of a global standard on AI?

The ethical standards for assessing AI and its associated technologies are still in their infancy. Companies need to initiate internal discussion as well as external debate with their key stakeholders about how to avoid being caught up in difficult situations.

Consider the difference between deontological and teleological ethical standards. The former focuses on the intention and the means, while the latter on the ends and outcomes. For instance, in the case of autonomous vehicles, the end of an error-free transportation system that is also efficient and friendly towards the environment might be enough to justify large-scale data collection about driving under different conditions and also, experimentation based on AI applications.

By contrast, clinical interventions and especially medical trials are hard to justify on teleological grounds. Given the horrific history of medical experimentation on unsuspecting human subjects, companies and AI researchers alike would be wise to employ a deontological approach that judges the ethics of their activities on the basis of the intention and the means rather than the ends.

Another useful yardstick is the so-called golden rule of ethics, which invites you to treat others in the way you would like to be treated. The difficulty in applying this principle to the burgeoning field of AI lies in the gulf separating the billions of people whose data are being accumulated and analyzed from the billions of potential beneficiaries. The data simply aggregates in ways that make the direct application of the golden rule largely irrelevant.

Consider one last set of ethical standards: cultural relativism versus universalism. The former invites us to evaluate practices through the lens of the values and norms of a given culture, while the latter urges everyone to live up to a mutually agreed standard. This comparison helps explain, for example, the current clash between the European conception of data privacy and the American one, which is shaping the global competitive landscape for companies such as Google and Facebook, among many others. Emerging markets such as China and India have for years proposed to let cultural relativism be the guiding principle, as they feel it gives them an edge, especially by avoiding unnecessary regulations that might slow their development as technological powerhouses.

Ethical standards are likely to become as important at shaping global competition as technological standards have been since the 1980s. Given the stakes and the thirst for data that AI involves, it will likely require companies to ask very tough questions as to every detail of what they do to get ahead. In the course of the work we are doing with our global clients, we are looking at the role of ethics in implementing AI. The way industry and society addresses these issues will be crucial to the adoption of AI in the digital world.

However, for AI to deliver on its promise, it will require predictability and trust. These two are interrelated. Predictable treatment of the complex issues that AI throws up, such as accountability and permitted uses of data, will encourage investment in and use of AI. Similarly, progress with AI requires consumers to trust the technology, its impact on them, and how it uses their data. Predictable and transparent treatment facilitates this trust.

Intelligent machines are enabling high-level cognitive processes such as thinking, perceiving, learning, problem-solving and decision-making. AI presents opportunities to complement and supplement human intelligence and enrich the way industry and governments operate.

However, the possibility of creating cognitive machines with AI raises multiple ethical issues that need careful consideration. What are the implications of a cognitive machine making independent decisions? Should it even be allowed? How do we hold them accountable for outcomes? Do we need to control, regulate and monitor their learning?

A robust legal framework will be needed to deal with those issues too complex or fast-changing to be addressed adequately by legislation. But the political and legal process alone will not be enough. For trust to flourish, an ethical code will be equally important.

The government should encourage discussion around the ethics of AI, and ensure all relevant parties are involved. Bringing together the private sector, consumer groups and academia would allow the development of an ethical code that keeps up with technological, social and political developments.

Government efforts should be collaborative with existing efforts to research and discuss ethics in AI. There are many such initiatives which could be encouraged, including at the Alan Turing Institute, the Leverhulme Centre for the Future of Intelligence, the World Economic Forum Centre for the Fourth Industrial Revolution, the Royal Society, and the Partnership on Artificial Intelligence to Benefit People and Society.

But these opportunities come with associated ethical challenges:

Decision-making and liability: As AI use increases, it will become more difficult to apportion responsibility for decisions. If mistakes are made which cause harm, who should bear the risk?

Transparency: When complex machine learning systems are used to make significant decisions, it may be difficult to unpick the causes behind a specific course of action. Clear explanations for machine reasoning are necessary to determine accountability.

Bias: Machine learning systems can entrench existing bias in decision-making systems. Care must be taken to ensure that AI evolves to be non-discriminatory.

Human values: Without programming, AI systems have no default values or “common sense”. The British Standards Institute BS 8611 standard on the “ethical design and application of robots and robotic systems” provides some useful guidance: “Robots should not be designed solely or primarily to kill or harm humans. Humans, not robots, are the responsible agents; it should be possible to find out who is responsible for any robot and its behaviour.”

Data protection and IP: The potential of AI is rooted in access to large data sets. What happens when an AI system is trained on one data set, then applies learnings to a new data set?

Responsible AI ensures attention to moral principles and values, to ensure that fundamental human ethics are not compromised. There have been several recent allegations of businesses exploiting AI unethically. However, Amazon, Google, Facebook, IBM and Microsoft have established a non-profit partnership to formulate best practices on artificial intelligence technologies, advance the public’s understanding, and to serve as a platform about artificial intelligence.

Peltarion, leading AI innovator and creator of an operational deep learning platform, today announced the findings of a survey of…

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Peltarion, leading AI innovator and creator of an operational deep learning platform, today announced the findings of a survey of AI decision-makers examining what they see as the impact of the skills shortage, and suggestions on how to overcome it. The research, ‘AI Decision-Makers Report: The human factor behind deep learning’, presents the findings of a survey of 350 IT leaders in the UK and Nordics with direct responsibility for shepherding AI at companies with more than 1,000 employees.

The report finds that many AI decision-makers are concerned about the business impact of the deep learning skills shortage. 84% of respondents said their company leaders worry about the business risks of not investing in deep learning, with 83% saying that a lack of deep learning skills is already impacting their ability to compete in the market. These companies are exclusively focusing on recruiting data scientists (71% of AI decision-makers are actively recruiting to plug the deep learning skills gap), and this is already impacting their ability to progress with AI projects:

  • Almost half (49%) say the skills shortage is causing delays to projects
  • 44% believe the need for specialist skills is a major barrier to further investment in deep learning
  • However, almost half (45%) say they are struggling to hire because they don’t have a mature AI program already in place

“This report shows that companies can’t afford to wait for data science talent to come to them to progress their AI projects. The fact is, many organisations are already starting to lose their competitive edge by waiting for specialised data scientists. The current approach, which relies on hiring an isolated team of data scientists to work on deep learning projects, is delaying projects and putting strain on the talent companies do have,” explains Luka Crnkovic-Friis, Co-Founder and CEO at Peltarion. “In order to solve the deep learning skills gap, we need to make use of transferrable talent that can be found right under companies’ noses. Deep learning will only reach its true potential if we get more people from different areas of the business using it, taking pressure off data scientists and allowing projects to progress.” 

Less than half (48%) of respondents said they currently employ data scientists who can create deep learning models, compared to 94% that have data scientists who can create other machine learning models. This shortage is having a direct impact on teams: 93% of AI decision-makers say their data scientists are over-worked to some extent because they believe there is no one else who can share the workload. However, with the right tools, others can make a serious impact on AI projects.

“Organisations need to move projects forward by bringing on existing domain experts and investing in tools that will help them input into AI projects. This will reduce the strain on data scientists and lower deep learning’s barrier to entry,” concludes Crnkovic-Friis. “We need to make deep learning more affordable and accessible to all by reducing its complexity. By operationalising deep learning to make it more scalable, affordable and understandable, organisations can put themselves on the fast track and use deep learning to optimise processes, create new products and add direct value to the business.” 

AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5…

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AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5 examples of how AI is revolutionising the retail experience Written by: Dale Benton

Marks and Spencer

In early 2019, M&S announced a new Technology Transformation Program, one that will allow M&S to become a digital-first business and deliver key improvements in customer experience. As part of this transformation, M&S has partnered with Microsoft to investigate and test the capabilities of technology and artificial intelligence in a retail environment. M&S will look to integrate machine learning, computer vision and AI across every endpoint – both in its stores and behind the scenes. Every surface, screen and scanner in its stores will create data – and enable employees to act upon it. Every M&S store worldwide will be able to track, manage and replenish stock levels in real time – and deal with unexpected events.

https://www.marksandspencer.com/
https://twitter.com/marksandspencer
https://www.facebook.com/MarksandSpencer

John Lewis/Waitrose

The John Lewis Partnership is currently partaking in a three-year trial, deploying robots to one of its farms, which grows produce for its Waitrose & Partners brand.  The robots, named Tom, Dick and Harry, are delivered in partnership with the Small Robot Company. Each will be equipped with a camera and AI technology to gather topographical data, while autonomously obtaining accurate, plant-by-plant data in order to enable higher farming efficiency.  The data will also be used to develop further machine learning capabilities. The trial will also provide the John Lewis Partnership’s Room Y innovation team with valuable insight to support innovation and inform how robotics and Artificial Intelligence (AI) could be used further in other areas of the business.

https://www.johnlewis.com/
https://twitter.com/JLandPartners
http://www.facebook.com/johnlewisretail

Walmart

One of the biggest retail companies in the world has been piloting and implementing artificial intelligence solutions across its stores for a number of years.  As part of a technology program, called Missed Scan Detection, Walmart has deployed AI-equipped cameras in more than 1,000 of its stores. These cameras, developed in part with Everseen, tracks and analyses activities at both self-checkout registers and those manned by Walmart employees. If an item isn’t scanned at checkout, the cameras will detect the and notific a checkout attendant of the problem. The AI technology allows Walmart to monitor its inventory product quantities, but also significantly reduce theft across its stores.

https://www.walmart.com/
https://www.facebook.com/walmart

Amazon

Amazon Go represents a whole n era of shipping. The concept is simple, walk into an Amazon Go store, pick up whatever you want and walk back out.  The idea is to create a “Just Walk Out” experience. Described as the “most advanced shopping technology”, customers simply download the Amazon Go app. Powerful machine learning and AI technology automatically detects when products are taken from or returned to the shelves, keeping track of them all in a virtual cart. Once customers leave, Amazon will collate all of the data and produce a receipt and charge the customer’s Amazon account.

amazon.co.uk

https://twitter.com/amazon
https://www.facebook.com/AmazonUK/

Morrisons

One of the UK’s largest food retailers with more than 120,000 colleagues in 494 stores serving over 11 million customers every week, Morrisons turned its attention to AI with JDA Software. Looking to vastly improve the customer experience, Morrisons looked at reducing queues at checkouts, and improving on-shelf availability. Morrisons invested in Blue Yonder – a Demand Forecast & Replenishment solution from JDA, which uses Artificial Intelligence (AI) technology to improve demand planning and reinvigorate replenishment based on customer behaviour in every store. Over a 12-month period, Morrisons was able to generate up to 30% reduction in shelf gaps and a 2-3 day reduction in stockholding in-store. AI technology has also enabled Morrisons to close the execution gap, optimizing availability while reducing wastage, enhancing shelf presentation and meeting stockholding targets.

groceries.morrisons.com
https://www.twitter.com/morrisons
http://www.facebook.com/Morrisons

By Craig Summers, Managing Director, Manhattan Associates Customer experience can be make or break for retailers. In fact, recent research…

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By Craig Summers, Managing Director, Manhattan Associates

Customer experience can be make or break for retailers. In fact, recent research shows that flawed customer experiences could be costing British retailers up to £102 billion in lost sales each year. This shouldn’t be news to retailers; the modern consumer demands a connected, consistent experience that is personalised to them, whether it’s online or instore. The same research found that running out of stock in-store was the biggest contributor to lost revenue, with 79 per cent of consumers saying they would not return to make a purchase if they found their desired item was out of stock. This frustration is only amplified if an out of stock product is marketed to the consumer. 

Personalisation isn’t anything new but if the basics aren’t right, retailers risk not delivering on customer experience. Many retailers still aren’t getting it right – and, explains Craig Summers, Managing Director, Manhattan Associates, inept personalisation is affecting the bottom line.

Misplaced Personalisation

The way in which retailers can engage with customers has changed radically over the past decade, from social media onwards. Add in the compelling appealing of Artificial Intelligence (AI) and the promise of incredibly accurate and timely promotional offers, and personalisation has become a foundation of any retail strategy. Yet while the marketing activity is becoming ever more sophisticated, personalisation cannot be delivered by marketing alone. 

Without integrating marketing activity to the core operation, retailers risk repelling rather than engaging customers. Product offers that are out of stock in the customer’s size. Promotions not on offer at the local store. Incentives to buy an item the customer has already purchased – not a problem for a standard food or household item, incredibly annoying if it’s an expensive mountain bike or cashmere jumper. Customers are becoming increasingly familiar with ostensibly personalised offers that fail to deliver a great experience.

What is the thinking behind a promotion that cannot be purchased by the customer? Why set such high expectations when they cannot be met? Enticing a customer to click through an emailed offer may be the measure of marketing success – but when that customer is unable to make a purchase because the desired item is not available in his or her size, that is at least one lost sale and a bottom line retail failure.

Complete Experience

Are retailers listening to what their customers want from personalisation? Great personalised offers will not deliver any value if they are not linked to the rest of the business. Smart technologies, such as AI, without any doubt have a role to play in delivering personalisation – but they are not the foundation. The foundation is getting the basics right. It is ensuring that when a customer wants to buy a product – online or instore – it is available. It is about providing Store Associates with the ability to track stock anywhere in the supply chain, reserve it for a customer to try on instore or have it sent direct to their destination of choice.  It is about combining stock availability information with customer insight to make intelligent suggestions, both instore and via marketing promotions. 

Bottom line success is, essentially, about the quality of the interaction. And that means considering not just the accuracy of the promotional offer but the complete customer experience. What is achievable today? What can be done well? If a product is being promoted to an individual, is it available in the right size? Is it available locally, or only in flagship outlets? It is these disconnected experiences that are fundamentally undermining customer experience and brand value.

Conclusion

The future of customer personalisation is incredibly exciting. AI promises the ability to predict a customer’s desires before the customer. Fabulous. But only fabulous if that product is available to buy, at a time and place to suit that individual. Right now personalisation is about the retailer; it is about being clever with promotions.  It needs to be about the customer; it needs to be about delivering the quality of experience that drives sales.

Retailers need to go back to basics: use technology to recreate the ‘corner shop model’ of the past, at scale. By creating a truly immersive experience for their customers, retailers can find a way to make personalisation profitable again.

The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with…

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The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with 133 million new jobs expected to be created globally.

In the UK alone, up to a third of jobs will be automated or likely to change as a result of the emergence of AI – impacting 10.5 million workers.

The findings come from a new report – Harnessing the Power of AI: The Demand for Future Skills – from global recruiter Robert Walters and market analysis experts Vacancy Soft.

Ollie Sexton, Principal at Robert Walters comments:

“As businesses become ever more reliant on AI, there is an increasing amount of pressure on the processes of data capture and integration. As a result, we have seen an unprecedented number of roles being created with data skill-set at their core.

“Our job force cannot afford to not get to grips with data and digitalisation. Since 2015 the volume of data created worldwide has more than doubled – increasing (on average) by 28% year-on-year.

“Now is the perfect time to start honing UK talent for the next generation of AI-influenced jobs. If you look at the statistics in this report we can see that demand is already rife, what we are at risk of is a shortage of talent and skills.”

Demand for Data Professionals

IT professionals dedicated to data management appear to be the fastest growing area within large or global entities, with volumes increasing ten-fold in three years – an increase in vacancies of 160% since 2015.

More generally speaking, data roles across the board have increased by 80% since 2015 – with key areas of growth including data scientists and engineers.

What has been the most interesting to see is the emergence of data scientist as a mainstream profession – with job vacancies increasing by a staggering 110% year-on-year. The same trend can be seen with data engineers, averaging 86% year-on-year job growth.

Professional Services Hiring Rapidly

The rise of cybercrime has resulted in professional services – particularly within banking and financial services – hiring aggressively for information security professionals since 2016, however since then volumes have held steady.

Within professional services, vacancies for data analysts (+19.5%), data manager (+64.2%), data scientist (+28.8), and data engineer (+62%) have all increased year-on-year.

Top Industries Investing in AI

  1. Agriculture
  2. Business Support
  3. Customer Experience
  4. Energy
  5. Healthcare
  6. Intellectual Property
  7. IT Service Management
  8. Manufacturing
  9. Technical Support
  10. Retail
  11. Software Development

Tom Chambers, Manager – Advanced Analytics and Engineering at Robert Walters comments:

“The uptake of AI across multiple industries is bringing about rapid change, but with that opportunity.

“Particularly, we are seeing retail, professional services and technology industries’ strive to develop digital products and services that are digitally engaging, secure and instantaneous for the customer – leading to huge waves of recruitment of professionals who are skilled in implementing, monitoring and gaining the desired output from facial recognition, check-out free retail and computer vision, among other automation technologies.

“Similarly, experimental AI is making huge breakthroughs in the healthcare industry, with the power to replace the need for human, expert diagnoses.

“What we are seeing is from those businesses that are prepared to invest heavily in AI and data analytics, is they are already outperforming their competitors – and so demand for talent in this area shows no signs of wavering.”

To download a copy of the report click here.

In a world awash with a seemingly never-ending list of technology buzzwords such as automation, machine learning and Artificial Intelligence…

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In a world awash with a seemingly never-ending list of technology buzzwords such as automation, machine learning and Artificial Intelligence (AI) to name a few, AI is one such technology that is moving away from simple hype and stepping closer to reality in procurement.

Here, CPOstrategy looks at 5 ways in which AI is being utilised in procurement…

This featured in the August issue of CPOstrategy – read now!

Efficiency and accuracy

Procurement, by its very nature, is tasked with handling huge quantities of spend and with spend comes spend data. Often described by leading CPOs as a repetitive task, understanding and sorting that spend data is now being achieved through the implementation of AI.

Through the use of AI, procurement teams can remove human error, increase efficiency and realise greater value from spend data.

Chatbots

One of the biggest ways in which AI is being implemented around the world is in the customer interaction space. In telcos, for example, customer support can now be handled via a highly developed AI chatbot that uses legacy data and context to provide real-time, and unique, solutions for customers.

In procurement, chatbots follow a similar path for both internal and external customers.  With tailored and context-aware interactions, chatbots create an omni-channel user experience for all stakeholders in the procurement ecosystem.

Supplier risk identification

Procurement and risk go hand in hand and one of the biggest risks is identifying and working with the right partner. Working in partnerships, which ultimately proves to be a failure, can be extremely costly and so AI is now being used to reduce the risk of failure.

Machine Learning technology, powered by AI, captures and analyses large quantities of supplier data, including their spend patterns and any contract issues that have emerged in previous partnerships, and creates a clearer picture of a supplier in order for the procurement teams to be able to identify whether this particular partner is right for them – without spending a penny.

Benchmarking efficiency

Benchmarking is key to any organisation’s ambition to measure and continuously improve its processes, procedures and policies. In procurement, organisations such as CIPS are used as examples of best practice in which procurement functions all over the world can benchmark against and identify any gaps.

Similar to supplier risk identification, AI can be implemented within ERP systems to analyse the entirety of data that passes through procurement and present this key data in easy to digest formats.

Examples include data classification, cluster analysis and semantic data management to help identify untapped potential or outliers in which procurement teams can improve their processes.

Purchase order processing/Approving purchasing

Procurement has evolved from its traditional role as simply managing spend into a strategic driver for a number of organisations all around the world.

As the role of the CPO has changed, technology such as AI has been implemented to free up their time from the menial tasks (such as PO processing and approving purchases), allowing them to spend more time in areas of growth. 

AI software can be used to automatically review POs and match them to Goods Receipt Notes as well as combining with Robotics Process Automation (RPA) to capture, match and approve purchases through the use of contextual data. This contextual data allows AI to identify and make decisions based on past behaviour.

Liked this? Listen to Natalia Graves, experienced Chief Procurement Officer, discusses the complexities of digital transformation in procurement!

By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company Now, more than ever, agility is the currency…

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By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company

Now, more than ever, agility is the currency of success. And while agility may be about responding intelligently to the changing nature of the marketplace, those responses must be rooted in a plan. Today, many organizations leverage newer technologies in the cloud for planning, having moved away from manual spreadsheets. And while the cloud offers greater collaboration and the ability to easily combine both historical and real-time data, it’s just the beginning. Digital transformation is changing and will continue to change the definition of best practice planning in organisations. As such, the next step for business planning revolves around two key areas—advancements in AI and machine learning, and increased automation.

The power of ‘what if’

What-if scenarios are already incredibly powerful for strategic decision-makers. Organisations can model different versions of the future based on historical information and predictive analytics before choosing the best path forward. Consolidating executional data within organisations is the first step in capitalising on future AI opportunities. However, there is a lot more to come. In fact, compared with what AI is going to make possible, scenario planning is still in its infancy.

Today’s scenario planning is a good proof of concept, but as long as humans are driving the creative process—it relies on people to ask the right questions of the right data—what-if planning is going to be constrained by available resources. The most advanced decision-making today is typically supported by a few best-estimate scenarios—maybe four or five at most. However, in truth, there are many more possible futures to potentially prepare for, and what looks like best practice now is going to seem vastly limited in scope before too long.

As the volume and variety of available data grows, and access to that data gets easier, AI and machine learning algorithms will make it possible to drill down, consolidate, and leverage incredibly granular information at the highest levels.

AI and machine learning use cases

To consider how these AI and machine learning algorithms will work, let’s look at a use case of a CEO aiming to achieve a 40 percent growth target over a two-year period and wants to model what that looks like to present at the annual executive offsite. AI and machine learning-enabled planning could help to quickly and automatically find the optimal growth path, while accommodating any conditions and assumptions on the fly.

Essentially, the planning system could measure historical performance and recommend a market segment mix strategy, along with the associated budget increases in the specific marketing and sales activities needed to support it. If they then decide they need to cap growth in sales to smaller businesses in order to also expand into enterprises and international markets—while also maintaining expenses at a certain increase—an alternative, optimised model could be quickly created without any manual lifting.

A future with machine learning

The future of business planning is not just about thinking bigger—it is about making better decisions and operationalising them faster. That’s where machine learning comes in. Increased automation, driven by algorithms, is going to blur the boundaries between planning, execution, and analysis until planning cycle times have all but evaporated.

Planners will be able to ask deep, complex strategy questions and see the results modelled in real time. As the data becomes more trusted, they will be able to make significant, informed, “just-in-time” decisions, confident in the patterns surfaced in the data. And as the line between planning and transactions systems begins to blur and disappear, plans will automatically cascade down to operational departments—even down to individual workflows—in real time.

‘Strategy’ will become the province of human-driven innovation while planning becomes an organic, ongoing exercise of continuous improvement inextricably linked to the transactional systems that execute plans.

Leading the change

Today finance acts as the central junction within business planning and is, therefore, a natural steward for change, helping normalise new habits and behaviours for the rest of the organisation. As such, there is a strong case to be made for finance teams to double down on their new position as stewards of change by acting as transformation leaders—both for existing processes, and for future, unknown developments.

Finance’s role will change significantly in order to leverage technology developments in the data-driven, AI future. Driving collaboration with business partners, breaking down data silos, and embracing new technologies and processes to keep pace with today’s rapidly changing business environment will be key. The result will be an augmented, intelligent planning process that delivers true business agility.

Everyone wants to implement Artificial Intelligence (AI) and Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7 trillion…

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Everyone wants to implement Artificial Intelligence (AI) and Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7 trillion in GDP by globally 2030, and as this market grows, AI and BI will shift from industry buzzwords, to key market differentiators, before eventually becoming the new normal in the corporate landscape.

Yet bringing AI and BI on board is a big leap if it’s your first major data project. Stibo Systems’ Claus Jensen, Head of Emerging Technology, comments on the role of MDM as a vital foundation to implement emerging data technology.

Most CEOs don’t trust their own data.*

Let that sink in for a moment.

Almost every business is looking to data solutions to fuel the next phase of growth and innovation. AI and BI are firmly on the agenda, yet a report by Forbes Insights and KPMG found 84% of CEOs are concerned with the quality of the data they’re basing their decisions on.

That’s a significant disconnect. Businesses at board level want to implement ‘next generation’ data projects, but don’t trust the data that will be fed into them. For CDOs and other data leads, this presents a difficult situation. They need to meet demand for cutting-edge data projects, knowing that there is a certain level of mistrust in the data at their disposal.

For many CDOs, that mistrust isn’t limited to the CEO. Think about the data you are currently processing: how confident are you that it’s being accurately sourced, entered, saved, stored, copied and presented? How well do you know that data journey once it leaves your sphere of control? Are you certain that a single source of truth is being maintained?

The data gold rush

It may only be major data breaches that make the headlines, but in the global gold rush for data, too many businesses fail to accurately extract, store and interpret data.

Mistakes are made at every stage in the process – in fact, so bad are we at processing data, a report by Royal Mail Data Services claims that around 6% of annual revenue is lost through poor quality data.

It’s equally bleak in the US, where Gartner’s Data Quality Market Survey puts the average cost to US business at $15 million per year.

Despite this, we’re rapidly moving the conversation from data capture to artificial intelligence (AI), business intelligence (BI) and connected devices (IoT) – and for good reason.

Putting aside the issue of bad data (we’ll come back to that), businesses now have access to more data than they can handle – according to SAS’ Business Intelligence and Analytics Capabilities Report, 60% of business leaders struggle to convert data into actionable insights, and 91% of companies feel that they are incapable to doing it quickly enough to make useful changes. 

Business Intelligence and Analytics Capabilities Report

In large businesses, where data streams are blended from many sources, machine learning can help data scientists monitor figures to flag outliers, irregularities and noteworthy patterns.

Once flagged, business leaders can use BI to bring those patterns to life, helping pave the way for the most appropriate, and profitable, action.

Stibo Systems’ Head of Emerging Technology, Claus Jensen, believes it’s only a matter of time before we see AI regularly used within business product features – with machine learning automating tasks thanks to effective data interpretation.

Jensen and his team are working at the forefront of data: building master data management solutions in conjunction with AI and BI. “We’re entering into a new era of data analytics,” says Jensen. “Data scientists aren’t going away, but they can do more and more high-level work as certain use cases are solved by AI.” 

One of these use cases is machine learning-based auto classification. “For retailers onboarding thousands and thousands of new products every month, it’s really time consuming for them to have the vendor categorise the product into the vendor taxonomy.

“Machine learning can automate this based on product description and image.”

Running before we can walk

As exciting as this sounds, businesses eager to install new uses for data often face significant challenges: their data isn’t watertight, or it’s siloed, often both.

In a piece penned for the Financial Times, Professor of Economics at Stanford Graduate School of Business, Paul Oyer, wrote: “Smart managers now know that algorithms are as good as the data you train them on.” In other words, AI (and analytics for that matter) can only ever be as good as the date you feed it.

Which brings us back to the question of trust. What needs to happen for CEOs to trust their own data?

While there’s no single answer to this question, a master data management (MDM) solution is a good place to start.

“You can think of MDM as the foundation, a layer, that provides a single source of the truth for data,” explains Jensen. “Analytics and machine learning is only useful if the data you’re working on is accurate. That’s where MDM comes in; it ensures information presented, and actions taken, are based on fact and reality.

“Otherwise, business analytics is just a nice and colourful way to look at bad data, and what’s the point in that?”

To find out more about how MDM can turn data into business value through actionable insights, forming a solid foundation to AI and BI, visit https://www.stibosystems.com/solution/embedded-analytics-platform.

In today’s market expectations are growing and the stakes are high, with one mistake potentially costing a retailer their reputation….

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In today’s market expectations are growing and the stakes are high, with one mistake potentially costing a retailer their reputation. Due to this level of risk, brands find reducing their hands on approach to processes difficult, but what they don’t realise is that technology such as Artificial Intelligence and Machine Learning could prove to be their hero, not their villain. Entrusting their data and brand values to such technologies may seem like a scary step, but as David Griffiths, Senior Product Marketing & Strategy Manager, Adjuno, discusses, it’s one that will free up retail teams to add value and cut costs.

In AI should we trust?

There are a great deal of obstacles to overcome when it comes to the stigma attached to AI. A key challenge facing the progression of this technology is that individuals simply do not trust it. The fear of the unknown is one concern that pops up most commonly, with people battling a perceived perception that those who use this technology will lack control.

But a new age of retail is approaching and there is now an even greater need for brands to define their processes in order to keep up. Consumers want to receive products that are of a high-quality and they want to receive them now. These expectations are taking us beyond the traditional methods of retailing and leading us into a world immersed in technology, a world that benefits from the helping hand of AI.

Informing key decisions

With AI, retailers will be able to gain valuable insights in warehouse management, logistics and supply chain management, and make more informed and proactive decisions. This technology makes it easier to analyse huge volumes of data in an efficient fashion, helping to detect patterns and providing an endless loop of forecasting. Using this knowledge to identify factors and issues impacting the performance of the supply chain, such as weather events, retailers will be able to take a forward-thinking approach to decision-making. An approach that will lead to reduced costs and delays. 

By extending human efficiency in terms of reach, quality and speed, this technology can also help to eliminate the more mundane and routine work that’s faced by employees across the retail spectrum. From tackling flow management by assessing key products to ensuring there is enough stock available to improving production planning, a more informed use of time will help equip brands to face every consumer request and demand.

This is particularly important for those brands whose product line extends further than apparel wear, and steps into the realm of hardware. With diversity comes a need for more proof points and in turn, an extended volume of data. Retailers will be battling to work across an even greater number of suppliers and distribution centres, and accommodating the expectations of a larger customer base. Considering this, it is fundamental that every last bit of data is refined and utilised to streamline processes. AI is providing retailers with a platform to do this, offering the potential for significant changes across the entire product journey.

A data conundrum  

The benefits of using AI to consolidate data are endless. Traditionally, teams have relied on spreadsheets to collate information, hindering their ability to forward plan. With AI this is no longer the case, a much more accurate picture of the hero products, sizes and colours likely to sell, can be achieved by looking at multiple scenarios in real time and pulling them together.

This doesn’t mean that AI will replace creative buying teams. AI doesn’t forecast trends, it can’t predict what consumers will be buying in 2020, it can only report on the product lines. It can however help buying teams assess partners, analyse stock patterns, track costs, enable capacity planning and help optimise shipments. This data is invaluable to teams, especially for any new buyers who may need extra guidance. 

Conclusion

AI is set to transform the retail scene as we know it. But in order to make implementation a success, there shouldn’t just be a focus on the evolution of data management, there must be an evolution of mindsets too. After all, if a retailer fails to jump on board with AI and embrace a new era of change, then their customers will be the ones who suffer.

Companies that use voice plus touch interactions with their products and services are actually seen as less trustworthy and less…

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Companies that use voice plus touch interactions with their products and services are actually seen as less trustworthy and less engaging by their users, according to new research from emlyon business school.

The research, conducted by Margherita Pagani, Director of the AIM Research Center on AI in Value Creation and Professor of Digital Marketing at emlyon business school, and colleagues from ESSCA School of Management and Florida State University, College of Business, analysed the impact and differences between ‘touch’ interaction and ‘touch and voice’ interaction on personal consumer engagement and brand trust.

The researchers created two separate experiments, focused on a utilitarian product and then a hedonic product, both of which had over 90 participants belonging to generation Y, which is commonly equipped with the latest smartphones and frequently use them for business interactions. For both experiments, participants had to interact with the brand using their smartphone including a phone call to the company to ask a specific set of questions.

One group was required to interact with the brand through the smartphone using a touch-only interaction, and the other used both touch and voice interaction – either Apple’s Siri or OK Google. After interacting with the company, participants were asked to rate their customer experience. The participant’s answers were then measured to evaluate personal engagement with the tasks, their level of trust with the brand and their privacy concerns.

The researchers found that participants who used the touch-only interaction experienced a much higher level of personal engagement with the brand compared to those who used the touch plus voice interaction.

Prof. Pagani says,

“Many companies have introduced new AI products that use voice-activation such as Amazon’s Alexa, Google’s Home Assistant or Apple’s Siri. These have been introduced in order to increase customer experiential engagement, stimulate the interaction and collect more data that allow to better personalise the experience through machine learning.  However, our study shows that in the initial phase of adoption, adding voice recognition actually has the opposite desired effect. Even if voice may be considered as a way to develop a much more natural interaction, the level of cognitive efforts required to the brain using two sensory modes (voice and touch) are higher. Therefore, consumers find it harder to completely engage with the product and can easily be distracted”.

The researchers also found that participants who used the touch-only interaction felt as though they had more control over the information they shared and therefore had greater confidence in the brand. Users stated that they found it much simpler to input information using only one sensory method; touch.

“The lack of familiarity with how these digital voice interactions actually work is likely to be the reason as to why consumers are less trusting of brands that use both touch and voice. Whilst the use of touch also garners much more control for a consumer, as opposed to voice”.   The study, published in the ‘Journal of Interactive Marketing’ is the first of its kind to explore the effects of new voice-based interface modes on marketing relationships. Whilst technology multiplies the way that consumers can interact with brands, this study shows that too much interaction can actually harm a company, and offers managers guidance on how to increase personal engagement and brand trust.

Welcome to the June issue of Interface Magazine! Read the latest issue now! This month’s cover features Gary Steen, TalkTalk’s…

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Welcome to the June issue of Interface Magazine!

Read the latest issue now!

This month’s cover features Gary Steen, TalkTalk’s Managing Director of Technology, Change, and Security, Gary Steen regarding the telco’s commitment to thinking, and acting, differently in a highly competitive marketplace…

TalkTalk is an established telecommunications company that fosters a youthful, pioneering spirit. “I like to think of TalkTalk as a mature start-up,” says Managing Director of Technology, Change and Security, Gary Steen. “We are mature in terms of being in the FTSE 250, with over four million customers, relying on our services every day through our essential, critical national infrastructure. But that said, I definitely think we start our day thinking as a start-up would. What can we do differently? How do we beat the competition? How do we attract great talent? We’ve got to come at this in a different way if we are going to succeed in the marketplace. We are mature, but we think like a start-up.”

Elsewhere we speak to Natalia Graves, VP Head of Procurement at Veeam Software who reveals the secrets to a successful procurement transformation. Graves was tasked with looking at the automating, simplifying, and accelerating of Veeam’s procurement and travel processes and systems around them, including evaluating and rolling out a company-wide source-to-pay platform. “It has been an incredible journey,” she tells us from her office in Boston, Massachusetts. We also feature exclusive interviews with PTI Consulting and cloud specialists CSI.

Plus, we reveal 5 of the biggest AI companies in fintech and list the best events and conferences around.

Enjoy the issue!

Kevin Davies

IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual…

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IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual agent in conversational AI by Everest Group.

Chetan Dube, CEO at IPsoft, commented: “With 1Bank we provide the most humanlike digital experience in the marketplace, built from the knowledge we’ve gained serving six of the world’s leading banks with conversational AI. We are giving banks the possibility of providing customers with their own personal banker around the clock.”

1Bank answers FAQs, but also resolves complex customer needs, by understanding customer intent. It can also switch context, mid-conversation. Its machine learning Learning (ML) abilities also mean that 1Bank can improve over time.

Some of the tasks 1Bank can carry out are:

  • advising on unpaid bills, proactively informing customers of an incoming bill and communicating any insufficient funds, making a money transfer and asking if the customer wants to set up payment for the bills when they are due.
  • recommending and setting up recurring payments, making payments from different accounts, opening and closing accounts.
  • helping customers locate transactions.
  • assisting with individual and potentially fraudulent charges on credit cards and disputing them, getting a new pin, getting a balance transfer or applying for a new credit card.
  • creating travel alerts after a customer made an airline purchase and proactively recommending the next step, such as, when traveling to exchange and withdrawing cash.

1Bank can integrate with existing tools and interfaces, and it can be added to existing applications to help customers quickly access the information and service they need. This includes mobile apps, desktop or kiosk apps, website modules, or within consumer chat applications, such as Facebook Messenger and Amazon Echo.

It is a measure of how much we take sophisticated technology for granted that the appearance of a pop-up chatbot…

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It is a measure of how much we take sophisticated technology for granted that the appearance of a pop-up chatbot screen, asking questions and providing sensible responses, is no longer considered remarkable.

Chatbots today inhabit websites, intranets, apps, and social media platforms, and have become so ubiquitous as to become almost invisible. Interacting with a text screen is a natural activity, and most users don’t seem to care much about whether the other side of the conversation is a human or a bundle of code.

From a corporate perspective, chatbots can be a win/win. Increasingly reliable in their responses and cheap to operate, they are available night and day and are instantly scalable. Whether your site or app has one visitor a day or thousands, the bot is always eager to help.

We’ve come a long way from the disastrous early attempts at providing AI assistance – remember Microsoft’s paperclip? – but what a good chatbot does today is much the same as that much-loathed animated character: identify what a user is trying to do, and offer appropriate help.

Artifical intelligence

You will have heard of the Turing Test, which held that if a computer could provide responses that were indistinguishable from those of a human, the machine had to be considered intelligent. Are we there yet?

Despite some well-publicised claims, the answer is still, probably, no. In 2014 a program called Eugene Goostman successfully tricked Turing Test judges into believing it was a 13-year-old Ukrainian boy. This controversial victory is still a failure, though, because nobody expects or wants to find an adolescent behind a real-world help screen.

More relevant is the Loebner Prize for the most convincing chatbot. This awards bronze medals each year to the best contenders, but has never made a silver (text) or gold (audio visual) award – the equivalent of a Turing Test pass.

A glance at the best entries from the 2018 competition shows why. Eleven bots were asked 20 questions, winning two points for a human-like answer and one for a plausible response. Out of a maximum of 40 points, the winner scored 27 and the lowest just 12.

Even simple questions can make the tech fall over. The winner, a chatbot called Tutor by Ron C Lee, answered “Do you know how to make toast?” with “No, we haven’t”.

Chatbot limitations

While there remain limits on what a chatbot can convincingly do, this need not be a problem if it is deployed in the right way. Recent research from Penn State University found that while many appreciate an apparently empathetic response from a bot, those who believe machines are actually capable of consciousness do not.

“The majority of people do not believe in machine emotion, so took expressions of empathy and sympathy as courtesies,” said researcher Bingjie Liu. “However, people who think it’s possible that machines could have emotions had negative reactions from the chatbots.”

The answer is only to use them for things they are good at, says James Williams, who leads the development of advanced chatbots with Nottingham-based software company MHR. While chatbots are now common in consumer interfaces, he notes, there is much potential in the enterprise space.

Business bots

When applied within the company’s flagship human resources (HR) software, Williams says the conversational interface is an excellent way to simplify common transactions. “You’ll hear us talk a lot about reducing friction,” he says, which means anything that slows down a routine interaction.

An example is an employee submitting an expenses claim, which MHR’s Talksuite does through an AI-driven chatbot. “Taking a picture of a receipt is a natural thing to do, and the AI will recognise the image, understanding the content as well as the context. Bots are really good for processes with lots of rules or lots of steps, and here it just asks a few questions and saves the employee a lot of hassle. Less friction.”

Knowing when not to deploy a bot can be just as valuable. Williams recounts one client which had deployed a complex chatbot for its newly joining employees, known in HR circles as the onboarding process. “The chatbot went through everything plus the kitchen sink, so the employee was there for 20 minutes or more being interrogated by a machine. It was just awful. A web-based form is a much better interface in this situation.”

His final advice is to consider the image the bot projects. “Any personality in a chatbot tends to come accidentally, unlike a website or an app. If you let software developers write the conversation, you might end up with a bot that’s actually a bit of a dick. People make judgements on things like language and punctuation. It’s fine to be personable and friendly, but it should be clear when the user is talking to a bot and when any transition to a human interaction takes place.”

Quest Solution Inc, provides supply chain and artificial intelligence (AI) based machine vision solutions. It has been awarded a project by…

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Quest Solution Inc, provides supply chain and artificial intelligence (AI) based machine vision solutions. It has been awarded a project by a leading supply chain and logistics provider in the US. The release doesn’t detail who the leading supply chain provider is, but it does reveal that the project is valued at around $US7 million.

A patent that will allow for a robot to live at your home and handle your deliveries has been filed by Amazon. The patent outlines plans for a robot that will completely transform last mile delivery capabilities, even potentially delivering packages in the early hours between 2am and 6am.

Back to AI, NFI Industries and Transplace are paying attention to this technology through partnerships with firms that add AI capabilities to transportation and distribution. Both companies have announced a partnership with Noodle.ai with the goal of enhancing logistics services and technology capabilities.

In a video interview with CNBC, Lance Fritz, the CEO of Union Pacific, is concerned that supply chain disruption won’t return to normal. He believes the biggest concern lies in trade and that the challenges with China should be resolved as soon as possible.

In an interview with Sky News, Peter Schwarzenbauer, BMW board member responsible for Mini and Rolls Royce, has said that the firm will need to think about moving production from the UK in the event of a no-deal Brexit. Remaining would be too costly for the organisation and some production would move to countries like Austria. Toyota shares similar concerns with Johan van Zyl, head of Toyota’s European operations, telling the BBC that Brexit hurdles would ‘undermine Toyota’s competitiveness’.

Blockchain remains an interesting solution for many in the supply chain and Blockchain Labs for Open Collaboration (BLOC) has recently started working with NYK, a Japanese shopping company, and BHP, a mining company, to establish a sustainable biofuel supply chain using BLOC’s blockchain fuel assurance platform.

Also in the news: HighJump, a global supply chain solutions provider, awarded five women in its Top Women Leaders in Supply Chain awards; Cryptobriefings Kiana Danial examines whether VeChain can deliver a supply chain solution; Apple releases a supply chain document that reveals how iPhone, airpods and other products are all zero waste; and SIGTTO GM, Andrew Clifton, looks to the LNG supply chain.