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AI for a new decade



AI for a new decade

By Dr Iain Brown, Head of Data Science, SAS UK & Ireland

There is no doubt that AI is up-and-coming. But, as ‘Back to the Future’ proves, only a handful of futuristic predictions end up becoming reality. It’s best to speak to experts on where AI might be heading in the coming years. Today’s innovative business leaders are the ones implementing and experimenting with AI, and they are our best bet for understanding its future.

Last year, I was invited to speak at the Boardroom Club. Among this network of C-suite executives, business leaders and technical experts, predictions were shared on how businesses can extract value from AI in the future. From growing the economy to increasing corporate responsibility and transforming employment, AI could cause seismic changes over the coming decade. To find true value, businesses must keep ahead of the curve.

AI must move out of the lab for the economy to bounce back

The clear consensus was that it has been a challenging year for business. With global economic growth hovering around 3%, 2019 has seen the slowest rate of expansion since the Great Recession. Many factors are weighing on the global economy, but one of the most persistent challenges felt by participants has been flat lining productivity – the value of output created by workers.

It’s premature to claim that AI will ride to the rescue, but it will make a big difference. When it comes to improving productivity, the most significant development over the next few years will be the operationalisation of AI in organisations. This means the innovations, new algorithms and technologies developed in R&D will finally be rolled out and integrated into the rest of the organisation.

These deployments will happen quickly and at scale. They’ll give swathes of employee’s access to AI tools that will liberate them from mundane, repetitive tasks and give them invaluable insight into the challenges and opportunities of the organisation. Staff will be empowered with better decision making and added time to focus on innovation and creating strategic value for their company, boosting productivity and profits. PWC expects this uplift will add $15tn to global GDP by 2030.

Yet where will the catalyst for this transformation come from? In recent years, organisations have invested in teams of data scientists and other innovators to brainstorm digital transformation and AI projects. However, while these new entrants bring creativity and expertise, they often lack the business context to actually translate their ideas into reality.

This has contributed to the dreaded ‘last mile’ of analytical implementations, where according to IDC only 35% of organisations say analytical models are fully deployed in production. An innovative AI project is all well and good, but what value does it generate for the business and where should it be deployed? This is a question most have so far failed to answer.

Building the bridge between an interesting idea and a practical project that generates real value has long been a struggle. However, this will change as more innovative ideas are generated from the business side of the organisation. Ordinary employees will be encouraged and rewarded to propose applications for AI that will optimise and transform how they work. Technology will also play its part to make things easier, thanks to the speed and flexibility offered through cloud deployments and containerisation.

Pressure is famously applied downwards, and inevitably it will be the C-Suite that fuels much of this transformation. Executives are waking up to the value of AI and, crucially, are seeing their rivals achieve success with the technology. That’s why operationalisation will accelerate and reach fever pitch before the close of the decade.

Tightening regulation creates an opportunity for better AI ethics

Unconscious bias is a growing concern as AI takes on more and more of our most important decisions. Given time and attention, we’ll begin to see its causes – which primarily boil down to unrepresentative data sets and data quality issues – addressed.

It’s understandable, however, to be skeptical of major improvements in the short term. One participant argued that AI would fail to be unbiased much in the way human decisions display prejudice and lead to unfair outcomes. This would be the case so long as AI relies on ungoverned big data – that is, the historical data humans already use to make decisions today without the appropriate governance controls in place. In this environment, the same imperfect insights would lead to the same unbalanced outcomes.

I’m more optimistic, however. The change won’t necessarily come from how AI uses data, but rather the attitude of businesses towards the data they hold. The old maxim ‘garbage in, garbage out’ still definitely applies to AI. Yet, we shouldn’t underestimate the will of organisations to improve, take notice and take responsibility for their systems.

I think we’re bound to see greater corporate responsibility towards the technology in the years to come. This will partly be due to tightening regulation. In the same way the Cambridge Analytica scandal shone a light on data privacy and abuse, we’re likely to see a similar watershed case of AI gone wrong.

Updated AI ethics guidelines are to be presented to the European Commission in early 2020, but these will only be the thin edge of a wedge as governments legislate to place boundaries on the technology. Companies will need to adapt or pay the price.

However, the biggest driver of all will be consumer demand. Some may think there’s no economic incentive to building ethical AI, but I think they underestimate how powerful a competitive advantage ethics can be. Business is founded on trust, and consumers are far more likely to share their data if they feel your AI system will keep it safe.

Organisations will take responsibility in many different ways. To avoid negative outcomes, AI creators will start introducing controls to ensure their creations can’t use data in an unethical manner. Greater emphasis will be placed on the training of AI systems and the process for reviewing decisions will be strengthened. On the end user side, companies will also begin holding decision makers to account for the choices they make based on AI-generated insight.

The job market and role responsibilities will be transformed by AI

Discussions on AI will inevitably turn to automation and job losses. Yet all too often the potential of AI to actually create new roles is overlooked. Technological advances have rendered many professions redundant down through history, but equally they have given birth to entire new industries and fields of employment. After all, how many app developers did you know 10 years ago?

In fact, 59% of the Boardroom Club believed AI would increase employment levels, not decrease them. Despite some fears, I think what we’ll see in the next few years is a jobs migration rather than a jobs reduction. Organisations will move into new areas, aided by the latest AI technologies, opening new opportunities for their staff.

It’s difficult to predict what these new roles will look like, though we’re already seeing signs of the shape they could take. Skills in AI development will be highly prized as organisations constantly seek to compete and innovate their models and algorithms. Equally, as organisations take greater responsibility for their data and AI systems, more validation and screening focused roles will emerge. Humans still trump AI when it comes to the understanding of context, so they’re ideally placed to review AI decisions to make sure they are fair and accurate.

Of course, as with every technology revolution there will be an adjustment period. Some workers dependent on low-skilled, manual and repetitive work will no doubt see employment opportunities diminish as robotic process automation solutions are rolled out. That’s why it’s so important for reskilling and education to begin as early as possible. I’m confident both government and businesses will continue to invest in British workers and explore new ways of training them up to participate in the future economy.

AI is bringing significant changes to businesses right now. As the rate of change increases over this decade, we will start to feel AI’s effects as new insight is discovered and jobs move away from repetitive tasks. Inevitably, there will be changes that not even the business leaders of the Boardroom Club can detect. Organisations must stay open-minded in the face of oncoming transformation; though tricky at first, integrating AI harmoniously into processes could be the key to smashing business goals this decade.


Study: 1 in 10 fintechs’ main priority for 2021 is survival



Study: 1 in 10 fintechs’ main priority for 2021 is survival 1
  • FinTech Connect reveals that many fintechs simply want to survive the next year
  • 44% of fintechs are focused on optimising business processes to improve efficiencies
  • Over a third said they had launched new services addressing new demands

FinTech Connect, the trade show that connects the global fintech ecosystem, today revealed the priority for one in ten fintech firms over the next year is survival. The findings from FinTech Connect’s FinTech State of Play Benchmarking Report, which is based on a survey of 144 fintech professionals, explores the biggest industry issues of 2020 and looks forward to what 2021 has in store.

Impact of Covid-19

As remote working and living remains a priority to keep customers safe, fintechs have adapted their offerings. Although a number of other sectors including hospitality and travel have suffered as a result of the Coronavirus pandemic, fintechs remain confident that business will survive and even thrive.

  • 40% said Covid-19 had accelerated their digital transformation model
  • 36% said they had launched new services addressing new demand
  • 34% said their growth had accelerated as a result of the pandemic
  • 65% said that the remote working had driven innovation

The Wake of Wirecard

Despite the Wirecard scandal prompting industry soul searching and a review of regulation and governance practices, 83% of fintechs said the collapse had no impact on their own business. However, when fintechs are asked about the wider impact on the industry:

  • 59% said it will result in overcorrection from regulatory bodies
  • 42% said it will result in declining trust from customers
  • 25% said it will lead to declining investment into the sector

Brexit Uncertainty

Despite the uncertainty caused by Brexit, fintechs remain confident in their ability to manage Brexit:

  • 40% of respondents believe London will remain the European capital of fintech after Brexit
  • 30% of fintechs admit they haven’t made significant headway preparing for Brexit

“The spread of COVID-19 has brought the sector’s profitability and long-term business model sustainability into sharp focus—to a point where I believe the path to profitable scale for challenger banks has been structurally altered. But it is not at all to write off the sector,” said Abhijit Akerkar, Non-Executive Director, TBC Bank Group PLC. “Challenger banks have several long-term advantages—they are native to the digital arena, with more efficient cost structures, organizational agility, and, most importantly, higher customer loyalty. These advantages will help challenger banks weather the storm.”

“Whether we look forwards or backwards, Covid-19 is defining a new status-quo for the industry. From regulation to innovation to funding and culture, it is impossible to step out of the shadow cast by the pandemic,” Laurence Coldicott, Content Director, FinTech Connect “In response, fintech’s are prioritising digital transformation to meet customers where they are, and improving operational processes to ensure they are as efficient as possible.”

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How to Build an AI Strategy that Works



How to Build an AI Strategy that Works 2

By Michael Chalmers, MD EMEA at Contino

Six steps to boosting digital transformation through AI

In the age of artificial intelligence, the way we interact with brands and go about our work and daily lives has changed. No longer blithe buzzwords, AI tools and algorithms are solving real business problems, streamlining operations, boosting productivity, improving customer experience, and creating opportunities for advantage in a competitive marketplace.

However, many businesses struggle to unlock the full benefits that come with its adoption across the whole organisation. Making the most of AI requires a strategic focus, alignment with the specific operating model of the business, and a plan to implement it in a way that delivers real value.

Not all AI strategies are equal. To be successful, businesses need to set out how the technology will achieve objectives and identify the specific assets and case uses that will set them apart from competitors. The process of creating and delivering a successful AI strategy includes the following six essential elements that will help to bake in business success.

  1. Start with your vision and objective

One slip-up companies often make when developing an AI strategy is a failure to match the vision to the execution. Almost inevitably, this results in disjointed and complicated AI programmes that can take years to consolidate. Choosing an AI solution based on defined business objectives established at the start of a project reduces the risk of delay and failure.

As with any project or initiative, it’s crucial to align your corporate strategy with measurable goals and objectives to guide your AI deployment. Once a strategy is set and proven, its much quicker and easier to roll it out across divisions and product teams, maximising its benefits.

  1. Build a multi-disciplinary team 

AI is not an island. Multi-disciplinary teams are best placed to assess how the AI strategy can optimally serve their individual needs. Insights and inputs from web design, R&D and engineering will together ensure your plan hits objectives for key internal stakeholders.

It’s also important to recognise that with the best will and effort, the strategy might not be the perfect one first time around. Being prepared to iterate and flex the approach is a significant success factor. By fostering a culture of experimentation, your team will locate the right AI assets to form your unique competitive edge.

  1. Be selective about the problems you fix first

Selecting ‘lighthouse’ projects based on their overall goals and importance, size, likely duration, and data quality allow you to demonstrate the tangible benefits in a relatively short space of time. Not all problems can be fixed by AI, of course. But by identifying and addressing issues quickly and effectively, you can create beacons of AI capability that inspire others across the organisation.

Lighthouse projects should aim to be delivered in under eight weeks, instead of eight months. They will provide an immediate and tangible benefit for the business and your customers to be replicated elsewhere. These small wins sow the seeds of transformation that swell from the ground up, empowering small teams to grow in competency, autonomy and relatedness.

  1. Put the customer first, and measure accordingly

Customer-centricity is one of the most popular topics among today’s business leaders. Traditionally, businesses were much more product-centric than customer-centric. Somebody built products and then customers were found. Now, the customer is, and should be, at the heart of everything businesses do.

By taking a customer-centric approach, you will find that business drivers determine many technology decisions.  When creating your AI strategy, create customer centric KPIs that align with the overall corporate objectives and continually measure product execution backwards through the value chain.

  1. Share skills and expertise at scale through an ‘AI community of practice’

The journey to business-wide AI adoption is iterative and continuous. Upon successful completion of a product, the team should evolve into what’s known as an ‘AI community of practice’, which will foster AI innovation and upskill future AI teams.

In the world of rapid AI product iterations, best practices and automation are more relevant than ever. Data science is about repeatable experimentation and measured results. Suppose your AI processes can’t be repeated, and production is being done manually. In that case, data science has been reduced to a data hobby.

  1. Don’t fear failure: deploying AI is a continuous journey 

The formula for successful enterprise-wide AI adoption is nurture the idea, plan, prove, improve and then scale. Mistakes will be made, and lessons learned. This is a completely normal – and valuable – part of the process.

Lighthouse projects need to be proven to work, processes need to be streamlined and teams need to upskill. Businesses need a culture of learning and continuous improvement with people at the centre, through shorter cycles, to drive real transformation.

An experimental culture and continuous improvement, through shorter cycles, can drive real transformation. A successful AI strategy acts as a continually evolving roadmap across the different business functions (people, processes and technology) to ensure your chosen solutions are working towards your business objectives. In short, let your business goals guide your AI transformation, not the other way around.

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Iron Mountain releases 7-steps to ensure digitisation delivers long-term benefits



Iron Mountain releases 7-steps to ensure digitisation delivers long-term benefits 3

Iron Mountain has released practical guidance to help businesses future-proof their digital journeys. The guidance is part of new research that found that 57% of European enterprise plan to revert new digital processes back to manual solutions post-pandemic.

The research revealed that 93% of respondents have accelerated digitisation during COVID-19 and 86% believe this gives them a competitive edge. However, the majority (57%) fear these changes will be short-lived and their companies will revert to original means of access post-pandemic.

“With 80% still reliant on physical data to do their job, now is a critical time to implement more robust, digital methods of accessing physical storage,” said Stuart Bernard, VP of Digital Solutions at Iron Mountain. “Doing so can enhance efficiency and deliver ROI by unlocking new value in stored data through the use of technology to mine, review and extract insight.”

Why revert?

When COVID-19 hit, companies had to think fast and adapt. Digital solutions were often taken as off-the-shelf, quick fixes – rarely the most economical or effective. But they are delivering benefits – those surveyed reported productivity gains (27%), saving time (20%), enhancing data quality (13%) and cutting costs (12%).

So what now?

The Iron Mountain study includes guidance for how to turn quick-fixes into sustained, long-term solutions. The seven-steps are designed to help businesses future-proof their digital journeys and maximize value from physical storage:

1)     Gather insights: The COVID-19 pandemic allowed organisations to test and learn. Companies should ensure these insights are fed into developing more robust solutions.

2)     Use governance as intelligence: Information governance and compliance are fundamental to data handling. But frameworks aren’t just a set of rules, they hold valuable insights that can be turned into actionable intelligence. Explore your framework to extract learnings.

3)     Understand your risk profile: A key early step is to analyse where you are most vulnerable. With data in motion and people working remotely, which records are at risk? What could be moved into the cloud? Are your vendors resilient?

4)     Focus where you will achieve greatest impact: To prioritise successfully, you need to know where you will achieve the largest impact. This involves looking beyond initial set-up costs towards the holistic benefits of digitisation, including reducing time spent on manual scanning, and the risk of compliance violations.

5)     Reach out and collaborate: We are all in this together. Your IT, security, compliance and facility management teams are all facing the same challenges. Ensure you collaborate across functions to develop robust, integrated solutions.

6)     Find a provider who can relate to your digital journey: For companies that still rely heavily on analogue solutions, digitisation can be daunting and risky. It pays to find a vendor who has been on the same journey, understands your paper processes and can guide you through the digital world.

7)     Prioritise and evolve communication and training programmes: To reap the full rewards from any digitisation initiative, thorough and continuous communication and training is critical. Encouragingly, our survey found that 81% of data handlers have received training to work digitally which is an excellent step in the right direction, but consider teams beyond data handling to truly succeed.

The research was commissioned by Iron Mountain in collaboration with Censuswide. It surveyed 1,000 data handlers among the EMEA region. It found that the departments that have digitised more due to COVID-19 include IT support (40%), customer relationship management (36%), and team resource planning (34%).

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