Category: Disruptive Technology

16 Mar 2019
3 Things That Will Help You Leverage AI

3 Things That Will Help You Leverage AI

If artificial intelligence isn’t at the top of your priority list, it should be. Deloitte’s “Tech Trends 2019: Beyond the digital frontier” report shows AI topping the list of tech trends that CIOs are eager to invest in. Deloitte predicts that the next two years will see a growing number of companies transition certain functions, such as insurance claim processing, to fully autonomous operations backed by AI.

Terms like “cognitive technologies” and “machine learning” have become buzzwords, but these trends will strengthen–particularly as these systems begin to harness the scads of data available from which they can extract insights.

But AI’s promise is more general than just data mining. Lu Zhang, founder and managing partner at Fusion Fund, describes the technology as applicable to a broad swath of commerce: “AI’s application space has developed. The AI market has great potential across various industry verticals such as manufacturing, retail, healthcare, agriculture, and education.”

Even with this potential of AI for business, many business leaders feel held back from taking the actions necessary to implement it at their companies. So let’s take a look at some things you can do now to overcome those barriers.

1. Get your C-suite on board the AI train.

Any change is hard to create when the top of the organization is not fully on board. IDC found that 49 percent of enterprises surveyed cited problems related to stakeholders’ reluctance to buy in as a barrier to AI adoption. The first step in setting up AI at your company is to make sure the members of the C-suite understand the value–particularly in the long term–that AI can bring.

The evidence is out there: An Accenture report predicts that AI could increase productivity by up to 40 percent by 2035. And when dealing with data, AI really shines, enabling exciting new opportunities to discover valuable business insights. For instance, a McKinsey Global Institute analysis found when AI combines demographic and past transaction data with information gleaned from social media monitoring, the resulting personalized product recommendations can lead to a doubling of the sales conversion rate.

Aside from providing your company’s leadership team industry data proving AI’s worth, it’s imperative to also show them evidence of the value for your business specifically. You can do this by implementing a small AI project, such as using a chatbot to help answer customer questions online. After seeing the success of one AI use case, your C-suite is more likely to be ready for further AI-driven digital transformations.

2. Pack quality data onto the train’s cargo car.

Of course, AI can only create value from data if you have data–and not just any data, but good data. Despite the world generating incomprehensible volumes of data every minute, 23 percent of respondents to a Vanson Bourne/Teradata survey of senior IT and business leaders reported that C-suite executives aren’t using data to inform their decisions. Data has to be relevant to a company’s business model, and sometimes the systems are not in place to capture the data business leaders need.

Nor is it just a matter of access to relevant data; data quality is critical as well. Data that contains many factual errors or omissions need to be cleaned before it can be fed to AI algorithms so that the insights derived from the data set reflect reality and not just data noise. To prepare your data beforehand, have your team scan it for missing or incomplete records, empty cells and misplaced characters, and data that’s entered in a different format from everything else–any or all of which could throw off your algorithms. There are machine learning platforms with tools to help your team with this task, such as the DataRobot platform, which uses tools like Trifacta to facilitate the data prep process.

3. Hire–or train–the right crew members.

Finally, make sure your team members have what it takes to launch your new AI initiative and keep it aligned with best practices. You’ll want to put together a team that includes roles such as a systems architect, data engineer and/or data scientist, and a business analyst, among possible others. The team should be focused on creating scalable solutions that take advantage of the latest approaches in the fields of machine learning, deep learning, big data, SQL and NoSQL databases, and other areas of active development.

Not that assembling such a team will be easy: the Vanson Bourne/Teradata survey found about a third of respondents cited talent as the bottleneck to advancing their AI plans. That isn’t surprising given there may be only about 3,000 AI professionals who are actively seeking jobs–against about 10,000 available jobs in this country alone.

11 Mar 2019

5 Reasons Why Innovation Fails

Today, many corporations are doing startup calls, setting up company accelerators, intrapreneurship programs, tech scouting, hackathons and datathons. However, despite these efforts, many companies still do not achieve innovation results.

Getting innovation results is somewhat difficult, but keeping innovation going is even harder. Regular innovation depends on many factors such as the top management’s leadership, the corporate culture, the available resources and the methodology.

After years of conversations with professionals in multiple sectors (e.g. construction, energy, manufacturing…) we have identified some tips to overcome factors that either kill or slow down innovation in companies.

1. Your organization is not ready to innovate

If you want your company to change the way it works, you need to help the organization get their buy-in. First of all, the management of the company needs to play an active role in communication and change management.

Get people excited. Show your company vision and goals. Explain how the world is changing and how technology entry barriers are getting lower. Encourage the organization to work together and build knowledge networks to scale up your innovation capabilities.

Make a plan. Create a roadmap to articulate the change process. There are many ways to do open innovation, prioritize the ones that can create networks of expertise as quickly as possible. Translate the global goals into actionable ones for the business areas. Invest: You will not get sustainable results without resources.

2. You only bet on one type of innovation

If you only bet on disruptive innovation, you are taking the risk of getting no results, even in the long term. If you decide to only go for incremental innovation, you can end up being the king of a big obsolete business, like Kodak.

A good option would be to create an innovation portfolio that aims for short and long-term goals. A balanced portfolio enables a regular delivery of business results while also betting on long-term or disruptive innovation.

Regular successful milestones are also important and create trustworthiness. Therefore, do not forget about incremental innovation, which is fundamental to stay competitive and also builds adaptability.

3. Business innovation results are not set, measured and shared

Two years ago, I met a recently-appointed corporate innovation director in the metals sector. I asked about their innovation goals but he said that, before setting the goals, he needed to hire people and create an innovation team. I thought that it would be more convenient to set some goals first and then hire the right people to achieve them.

Last year, an energy company’s innovation area answered the same question with the number of newly published patents. My thoughts were that patents cannot be considered as an innovation result, at least not until they bring some income.

Whenever I try to find innovation rankings online, I always find that most reports use R&D investment to show innovation performance. Actually, this indicator is rather an input and not an appropriate innovation output. At the end of the day, I wonder how companies get better results or improve their innovation without setting clear goals.

4. Innovation happens ‘by accident’

Business results, like the income from new products or services, must grow as the innovation becomes more mature within the company. However, these results should also be somewhat predictable and not accidental.

I learned from the founder of a consulting company how some companies innovate by accident and, for it to happen again, you have to keep pushing them constantly. The reason for this situation is a poor innovation management.

In order to achieve an efficient innovation, you need to actively manage it. This management is responsible for setting up a process, devote resources and monitor results. Progressive companies combine a solid innovation management, which brings recurrence, with effective execution capabilities, which bring results.

5. Innovation is only focused on continuous improvement

The higher the business results, the better the innovation. Therefore, the most innovative companies are using technology innovation not to improve their internal efficiency (e.g. reducing their production cost) but to generate more income, (e.g. through diversification).

For instance, a steel manufacturer may be considered innovative so long as it reduces the energy use or improves manufacturing traceability through IoT. However, its innovation level will dramatically increase so long as it uses IoT to generate more income (for instance, by introducing a new service to sensorize inventories of steel coils at customer sites).

Continuous improvement does not make a company innovative per se. What really makes the difference is the ability to change the products and create new operations or services.


09 Mar 2019
How AI And Machine Learning Helps In Up Skilling To Better Career Opportunities

How AI And Machine Learning Helps In Up Skilling To Better Career Opportunities

The AI market is expected to grow from $21.46 Bn to $190.61 Bn between 2018 and 2025

AI will create nearly 2.3 million jobs by next year

Mathematical and programming skills are central to acquiring competency in this field


There are at least two clear trends that show a demand-supply mismatch in tech jobs in cutting-edge IT fields such as Artificial Intelligence and Machine Learning. One is via industry predictions that estimate growth in the AI market from $21.46 Bn to $190.61 Bn between 2018 and 2025.

Year on year growth is projected to be an impressive 36.62% during the same period. The second trend is more subtle. Big Indian IT firms in the US are reportedly ‘hoarding’ employees in these two fields as they foresee a shortage of skilled experts. They also fear a corresponding rise in the cost of hiring employees for tech contracts they have bagged for the future.

How Are AI & Machine Learning Being Used In Industry?

Unlike the exaggerated robots of the 2001 Steven Spielberg movie of the same name, Artificial Intelligence (AI) in reality is tamer. AI is understood to mean ways of making computers, computer-controlled robots or program think intelligently mimicking the manner in which humans think intelligently.

A computer program with AI can use can solve generic problems it is programmed to instead of just specific ones. They can accommodate new modifications to input without breaking structure. Traditional programmers would have to sort, sift and debug thousands of lines of code to make modifications.

AI finds applications in strategy games such as chess or poker where advance moves are determined by heuristic logic, natural language processing, virtual assistant technology, image and speech recognition and automated robotics.

General AI systems which can solve any given problem are rare. Insurance and banking organizations regularly use AI to monitor fraud. Marketers use AI every time you shop online to gather your browsing habits and predict what you are most likely to buy. They will then advertise those products through pop-ups and logos. Self-driving cars, auto-pilot modes and smart homes using sensors all rely on AI and affect daily lives of consumers.

There is also a difference between AI and Machine Learning (ML) although a number of articles on the web club them together or use them interchangeably. “ML is the study of computer algorithms that improve automatically through experience” according to Tom Mitchell of Carnegie Mellon University. It is simply one of the ways we use to achieve AI or something closer.

Read more:

05 Mar 2019
Healthcare AI to Play Major Role in $15.7 Trillion Economic Boost

Healthcare AI to Play Major Role in $15.7 Trillion Economic Boost

Artificial intelligence in healthcare is expected to play a significant role in bringing a $15.7 trillion boost to the global economy.

Advances in artificial intelligence within the healthcare industry will contribute significantly to the $15.7 trillion economic boost related to machine learning, according to a new report from PwC.

The firm anticipates a 14.5 percent increase in North America’s GDP by 2030, driven largely by AI’s ability to reduce waste, and support better decision-making.

Gains in productivity are expected to contribute $6.6 trillion to the overall total.

“From the personal assistants in our mobile phones, to the profiling, customization, and cyber protection that lie behind more and more of our commercial interactions, AI touches almost every aspect of our lives. And it’s only just getting started,” the report said.

“AI is set to be the key source of transformation, disruption and competitive advantage in today’s fast changing economy.”

The healthcare industry topped PwC’s list of industries ripe for significant disruption, sharing the number one spot with the automotive sector.

Every part of healthcare is lining up for change: providers, pharma and life sciences, payers, and consumers should all prepare for deeper integration of artificial intelligence into their processes and experiences.

“AI-powered diagnostics use the patient’s unique history as a baseline against which small deviations flag a possible health condition in need of further investigation and treatment,” the report explains.

“AI is initially likely to be adopted as an aid, rather than replacement, for human physicians. It will augment physicians’ diagnoses, but in the process also provide valuable insights for the AI to learn continuously and improve.”

Despite the huge potential, however, healthcare is likely to see slower adoption than many other industries.

Sectors such as retail, logistics, and financial services may have more immediately obvious opportunities for automation, and are more likely than healthcare to see widespread adoption within the next one to three years.

While 54 percent of retail organizations are expected to reach AI maturity by 2022, just 37 percent of healthcare entities are likely to do the same.

The financial services sector is anticipated to be fully AI-driven within the next seven years, but 40 percent of healthcare stakeholders will still need to work on infusing artificial intelligence into their operations during that time period.

Healthcare faces different challenges than these other industries, including strict privacy and security regulations and a deeply-rooted legacy technology environment, both of which make it difficult for organizations to apply machine learning techniques to their data assets.

In addition, healthcare organizations tend to face pushback from employees when introducing changes to workflows, especially when those changes raise the fear of job loss or alterations to the patient-provider relationship.

Read more:

02 Mar 2019
Addressing the promises and challenges of AI

Addressing the promises and challenges of AI

A three-day celebration event this week for the MIT Stephen A. Schwarzman College of Computing put focus on the Institute’s new role in helping society navigate a promising yet challenging future for artificial intelligence (AI), as it seeps into nearly all aspects of society.

On Thursday, the final day of the event, a series of talks and panel discussions by researchers and industry experts conveyed enthusiasm for AI-enabled advances in many global sectors, but emphasized concerns — on topics such as data privacy, job automation, and personal and social issues — that accompany the computing revolution.

Kicking off the day’s events, MIT President Rafael Reif said the MIT Schwarzman College of Computing will train students in an interdisciplinary approach to AI. It will also train them to take a step back and weigh potential downsides of AI, which is poised to disrupt “every sector of our society.”

“Everyone knows pushing the limits of new technologies can be so thrilling that it’s hard to think about consequences and how [AI] too might be misused,” Reif said. “It is time to educate a new generation of technologists in the public interest, and I’m optimistic that the MIT Schwarzman College [of Computing] is the right place for that job.”

In opening remarks, Massachusetts Governor Charlie Baker gave MIT “enormous credit” for focusing its research and education on the positive and negative impact of AI. “Having a place like MIT … think about the whole picture in respect to what this is going to mean for individuals, businesses, governments, and society is a gift,” he said.

Personal and industrial AI

In a panel discussion titled, “Computing the Future: Setting New Directions,” MIT alumnus Drew Houston ’05, co-founder of Dropbox, described an idyllic future where by 2030 AI could take over many tedious professional tasks, freeing humans to be more creative and productive.

Workers today, Houston said, spend more than 60 percent of their working lives organizing emails, coordinating schedules, and planning various aspects of their job. As computers start refining skills — such as analyzing and answering queries in natural language, and understanding very complex systems — each of us may soon have AI-based assistants that can handle many of those mundane tasks, he said.

“We’re on the eve of a new generation of our partnership with machines … where machines will take a lot of the busy work so people can … spend our working days on the subset of our work that’s really fulfilling and meaningful,” Houston said. “My hope is that, in 2030, we’ll look back on now as the beginning of a revolution that freed our minds the way the industrial revolution freed our hands. My last hope is that … the new [MIT Schwarzman College of Computing] is the place where that revolution is born.”

Speaking with reporters before the panel discussion “Computing for the Marketplace: Entrepreneurship and AI,” Eric Schmidt, former executive chairman of Alphabet and a visiting innovation fellow at MIT, also spoke of a coming age of AI assistants. Smart teddy bears could help children learn language, virtual assistants could plan people’s days, and personal robots could ensure the elderly take medication on schedule. “This model of an assistant … is at the basis of the vision of how people will see a difference in our lives every day,” Schmidt said.

He noted many emerging AI-based research and business opportunities, including analyzing patient data to predict risk of diseases, discovering new compounds for drug discovery, and predicting regions where wind farms produce the most power, which is critical for obtaining clean-energy funding. “MIT is at the forefront of every single example that I just gave,” Schmidt said.

When asked by panel moderator Katie Rae, executive director of The Engine, what she thinks is the most significant aspect of AI in industry, iRobot co-founder Helen Greiner cited supply chain automation. Robots could, for instance, package goods more quickly and efficiently, and driverless delivery trucks could soon deliver those packages, she said: “Logistics in general will be changed” in the coming years.

Read more:

28 Feb 2019
Wealth Managers Need to Re-Think Digitalization

Wealth Managers Need to Re-Think Digitalization

Wealth management clients are not as tech-phobic as the average wealth manager believes, concluded a study conducted by Scorpio Partnership and sponsored by FactSet.

When the research and analysis firm surveyed 877 UK- and US-based wealth management clients, it found that the appetite for digital interactions ran a spectrum.

The average survey respondent was 37 and described themselves as an early adopter or a technology follower and mirrored the technology adoption traits of millennials.

They’re interested in the digital experience, but it has to be engaging and worth something and not a reheated version of what they can get from a different channel, according to Tasha Vashisht, a senior manager at Scorpio Partners during a recent webinar.

“However, they have high expectations,” she said. “They probably will wait and try again if they have a disappointing experience.”

She cited an unnamed wealth manager that posted amount updates online and saw little uptake since the wealth manager already provided the same information through an already existing channel.

“Clients need a reason to log on,” said Vashisht.

Unfortunately for wealth managers, there is no “one size fits all” approach for which content will draw clients online.

What attracts the mass affluent client may not be the same that would attract ultra-high net worth clients.

The most straightforward approach to deploying a digitalization strategy is to ask clients what they want, she said.

Each wealth manager needs a custom digitalization strategy, which could be segmented into onboarding, execution/trade, and dealing with client requests.

One area that wealth managers could stress online is investor education, according to Vashisht. Ultra-high net worth holdings and financial sophistication do not always go hand in hand.

“Year-on-year clients are struggling with basic vocabulary like ‘volatility’ and where risk sits in their portfolios,” she said.

There is no one way to implement a digital strategy, “but as long as you have the internal focus, you are fine,” added fellow presenter Philipp Zerhusen, director of market development at FactSet.

Such programs do not need massive budgets and firms can turn to third-party providers, he added.


27 Feb 2019
Making the Northern digital economy a reality

Making the Northern digital economy a reality

When it comes to the digital economy, politicians and businesses cannot afford to prioritise some parts of the North, and ignore others.

While the politics are always complicated, the case for the Northern Powerhouse has never been clearer. Supporting growth beyond the South East by focusing on infrastructure development, local decision-making and skills not only offers huge economic potential but is the right thing to do in an economy – and a society – that has been too focused around a few London postcodes.

But there’s a real risk that, without a careful approach, we’ll replicate those old mistakes, with those areas well-placed for investment continuing to grow, while others are left behind and out of scope for the new economy.

When it comes to the digital economy – a business environment in which tech and digitally focused companies are able to grow – this risk is even higher. There’s a perception that “tech” jobs exist only for London millennials who never set foot outside of Shoreditch, or for academics moving between London, Oxford and Cambridge. These images could hardly be further from stereotypes of “left behind” towns in Lancashire, Yorkshire and Cheshire.

But why should good, future-proofed jobs be the preserve of the South? Northern communities deserve to benefit from new opportunities created through massive investment and government support. The country’s creative industries sector is growing twice as fast as the economy as a whole, and employment in digital businesses rose by 13.2 per cent from 2014 – 2017. The jobs which are being created are well-paid, with roles requiring tech skills having higher than average salaries.

There’s a risk of speaking about the “North” as if it’s one entity, whereas the truth is more complicated. There have been some big changes in urban areas such as Manchester and Leeds, where digital hubs have been established and where there are good prospects for future growth.

Read more: 

20 Feb 2019

Invest In Disruptive Innovation For Big Potential Returns

Invest in disruptive innovation to pursue potent potential profits, seasoned Wall Street money manager Hilary Kramer advised in a recent interview.

The easy money already has been made in the so-called FAANG stocks of Facebook (NASDAQ:FB), (NASDAQ:AMZN), Apple Inc. (NASDAQ:AAPL), Netflix, Inc. (NASDAQ:NFLX) and Google’s parent Alphabet (NASDAQ:GOOGL), Kramer told me at the latest MoneyShow in Orlando, Florida. However, less-established companies than those could become the next disruptive innovation leaders, she added.

“We’re not just following the momentum like everyone else,” Kramer told me. “We are really finding tomorrow’s and next year’s momentum.”

Key indicators that Kramer said she and her team track involve changes in macrotrends, legislation, geopolitics and consumer tastes to find the next places to invest in disruptive innovation. For example, Kramer said her recommendations typically skip over the social media companies that rose sharply in the past and instead include promising “Baby Boomer” stocks, such as one that provides knee and joint replacements.

Invest in Disruptive Innovation Through Undervalued, Growing Companies

Kramer, a Wall Street professional who leads the GameChangersValue AuthorityTurbo Trader and Inner Circle advisory services for individual investors, added that her analysis involves identifying the most undervalued companies that have the greatest growth quarter-over-quarter on the top- and bottom-lines. The process led her to recommend London-based multinational medical equipment manufacturing company Smith & Nephew plc (NYSE:SNN) for those seeking to invest in disruptive innovation for orthopedics reconstruction, advanced management of hard-to-heel wounds, minimally invasive joint surgery and products to repair broken bones.

Read more:

16 Feb 2019
Is Your Company Ready For AI?

Is Your Company Ready For AI?

One of the biggest mistakes IT champions make when pitching trendy transformative technologies like AI, blockchain or quantum computing to executives is not doing their homework. They often fail to identify the problem they are trying to solve, determine whether it’s worth solving and understand whether their company is positioned to solve it.

This article proposes a simple four-part framework your champions can use to assess AI readiness so stakeholders can get a good early read on the likelihood of success before you invest too much time and effort in AI initiatives. To animate this framework, I will apply it to a real-world scenario — assessing organizational readiness for AIOps, or the application of AI technology to transform IT operations.

The framework comprises four elements.

    1. Problem Identification

How often does someone in a spirited meeting regarding a business or IT challenge say, “Wait a minute. What problem are we trying to solve?” Often, in-depth discussions about solutions become disconnected with problems, a major reason why technology initiatives often get shot down during executive review. It’s critically important that your champion is clear about the problem AI solves and its underlying cause.

To demonstrate to stakeholders that the problems an AIOps initiative will address are understood, champions should answer assessment questions like these:

  • Are there one or more key metrics or KPIs (e.g., downtime, service levels) that AIOps can directly impact?
  • Can identified IT operations problems and their causes be clearly linked to AIOps capabilities and benefits?
  • Is it clear that your current technology or process can’t be easily tweaked to solve the same problems?

Of course, you can generalize these questions and apply them to any IT initiative, not just AIOps.

  1. Strategy Alignment

Champions and sponsors of IT initiatives must establish a clear connection between chain-of-command priorities and technology solution investments. Without this, they are unlikely to gain executive traction for their initiatives. In the case of AIOps, it’s important to tightly link potential benefits with key priorities of the CIO and VP of operations such as reducing business disruptions or freeing up resources for strategic projects like cloud migrations or M&A.

Champions can establish strategic alignment by answering these assessment questions:

  • Are IT operations processes, challenges, pain points or use cases mapped to AIOps solution capabilities?
  • How are IT operations performance outcomes — such as fewer major incidents or faster mean time to resolution (MTTR) for incidents — linked to these process challenges?
  • How are key executive-level priorities, strategic objectives or key business initiatives linked to anticipated IT operations performance benefits?
  1. Business Case Viability

This focuses simply on determining whether a reasonable business case can be made. At a minimum, this means that your champion has identified key business drivers and is comfortable that the business value can be quantified in a relatively straightforward manner. A high-level business case with ballpark estimates of the potential value would be even better. A formal business case and ROI analysis that includes more validated estimates (e.g., after a PoC evaluation) comes later.

In the case of AIOps, this translates into validating that process improvements can be measured and quantified into financial benefits. This includes identifying major process areas where this can be reliably estimated like cost savings from automation, downtime reduction and savings from reducing the mean time to resolution for incidents.

While some technology initiatives are justified based on soft/qualitative benefits alone, you should assume that you’ll need harder justification to win executive approval for a program like AIOps.

Here are example assessment questions:

  • Can we reliably baseline current process performance and metrics such as team utilization and effort taken to complete specific tasks?
  • Do we know what success looks like and how to measure it? For example, can we strategically align and roll up measurable benefits such as cost savings, downtime reduction and process cycle time savings?
  • Will we be able to estimate process performance and metric improvement (e.g., effort saved due to automation, downtime reduction) based on a proof of concept or other approaches?

Read more:

13 Feb 2019
Intelligent Automation Is Equipped To Level The Playing Field For Emerging Economies

Intelligent Automation Is Equipped To Level The Playing Field For Emerging Economies

With so much uncertainty building in markets around the world, even described as market mayhem by CNN recently, we can expect 2019 to be the year that artificial intelligence (AI) and automation hit the mainstream. After years, even decades, of future-gazing and speculation about what life would be like in a world where we co-exist with AI or robots, it seems the pressures on enterprises and governments are becoming so acute that they increasingly feel compelled to change the way they deliver products and services.

One aspect that has remained largely under the radar but, from my perspective, is one of the most exciting developments we’ve seen over the past 12 months has been the way in which AI and automation are now being deployed within emerging economies, and how this can change future global trade patterns. I’ve seen strategic steps in AI adoption being taken by international businesses looking to expand into new markets more efficiently as well as local organizations looking to scale up operations in an agile, rapid and cost-effective way that allows them to compete in the global market.

It’s a trend that I expect to accelerate significantly this year, and will no doubt absorb hours of debate at upcoming gatherings of global influencers. It points to the huge potential that these technologies present for emerging economies and for businesses of all sizes operating within these countries.

Redefining Resourcing And The Intelligent Automation Opportunity 

There is a growing acceptance that AI and robotics, far from being a threat to today’s global workforce, can actually be complementary to organizations’ workers and society more generally. Most leaders’ interest lies in a more rounded evaluation of how we can navigate the change in employment structures all over the world to deliver widespread benefit. And what early adopters have already learned is that automation is no longer just about cost or resource efficiency; instead, digital labor is a strategic asset, a game-changer within businesses.

One new and particularly interesting dynamic of intelligent automation (IA) is the way in which it can deliver specific advantages to emerging economies and the businesses that operate within them. My company, which offers an IA platform solution, works with more than 200 clients across 29 countries in five continents, so we have gained a clear picture of how it is delivering value in different ways across regions and economies.

Read more: