Month: October 2019

19 Oct 2019
6 pillars of AI

6 pillars of AI

The application of artificial intelligence (AI) methods, technology, and solutions represent a fundamental shift in how people interact with information, and a huge opportunity for government agencies to improve outcomes.

However, a common misconception is that AI is “plug-and-play.” Perhaps because of this, according to McKinsey research, only 8% of companies use practices that enable effective adoption of AI.

For this reason, here is a test for AI readiness that we call the “6 pillars of AI.”

These pillars make certain that a finished AI product provides:

  • The right solutions for its users, and
  • Enduring value to the organization as a whole.

AI is most effective when an organization has centered all functions around using it. Project-based AI has its place, but the more that an organization shifts from seeing AI as a tool to seeing it as a broad methodology, the more the promises of AI will be realized.

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Therefore, in every AI project we recommend the use of these 6 Pillars of AI to ensure that the solutions developed and the transformations made achieve broad organizational goals and bring lasting value to the organization.

6 pillars of successful AI adoption and projects

1.  AI is only of value if it improves something

Is AI the best approach to your problem, given the desired outcome and required investment?

Currently AI is a hot topic in government IT. It’s exciting, seen as forward-thinking, and generally looks bright and shiny. This causes organizations to get into trouble because deep analysis isn’t made on how AI will bring broad, long-lasting value.

The first question organizations should ask is:

  1. What do you want to accomplish, and how do you imagine AI can help you reach that goal?

The second question organizations should ask is:

  1. Based on that goal, are the costs of implementing AI acceptable? Is the business impact worth the cost to the business?

The cost of AI is so much more than its sticker price; truly adopting AI in order to realize its full potential requires transforming an organization’s culture, vision and strategy. Such broad transformation is not easy nor cheap, and thus needs to be taken into consideration when developing an AI strategy or planning an AI acquisition.

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2. AI is only of value if it augments human function

Is the process of using AI easier than the old way of doing things?

Though AI is supposed to make jobs easier and more efficient, an organization should carefully evaluate exactly how the proposed solution will do this. A valuable AI solution should decrease human dependencies.

In other words, if it takes your people longer to work with the AI solution, rather than to do the process “manually,” then the AI is not actually decreasing human dependency and is therefore not doing what it should fundamentally do.

If your AI solution is failing here, before concluding that the AI solution itself is the problem, your organization should evaluate the solution, including asking the following questions:

  • How accurate is our data?
  • How efficient is our data infrastructure?
  • Are we using the right AI model?
  • Is our team adopting and implementing the solution as intended?

This evaluation might uncover that your solution is valuable and will decrease human dependencies, if only your data were better, your infrastructure was efficient, or your team fully adopts it as intended.

3. AI is a human multiplier, not a replacement

Does the AI solution remove repetitive subsets of jobs, enabling your people to be more efficient, productive, and able to focus on higher value tasks?

While AI that has the intelligence to operate independent of human input is theoretically possible, the vast majority of companies that could effectively use AI today will use it in such a manner that still depends on people to guide its use and make decisions.

Don’t imagine that an AI solution will be able to replace a person or a team in your organization; instead, an effective solution should turn your people into “super humans” — enabling them to process, for example, twice as much input as before.

4. Data is the foundation of all AI operations

Has your leadership developed an infrastructure strategy that will enable AI success?

It hardly needs to be said that for a machine to learn, it must have data from which to learn — the more, the better. An organization’s AI solution is only as good as the quantity and quality of the data it’s built upon.

To store the necessary data, such organizations must have large computational power, access to data science expertise, and data sets on which to train models.

5. Data strategy is critical to AI benefits

What is the quality of your data?

Data infrastructure alone is not enough for AI to be effective, much less to produce desired benefits for an organization.

Data strategy overall involves creating processes to collect records (particularly results and outcomes), which in turn produce data. Data is the required input for machine learning, and the desired outcome of AI—predictive/explanatory models—are dependent on effective ML.

Once data has been gathered, it’s vital for the data strategy to outline how that data can be verified, cleaned, and structured so that the data is accurate and usable.

The principle “garbage in, garbage out” succinctly describes the importance of a well-formed data strategy and its place as one of our pillars of AI.

6. AI must produce a usable output

Did the AI model produce a usable output, and if so, is that output valuable for the organization?

Finally, the proposed (or developed!) AI solution must lead to a result that brings value — to the specific project in which it is implemented, and ultimately to the whole organization.

If, from the start, the effectiveness and scope of a proposed AI solution will be siloed or limited by an organization’s structure or culture, the value of that solution should be questioned. This doesn’t mean there isn’t value in small projects with quick turnaround and limited results — only that a successful AI output or even model is not the same as AI supporting a business process.

Together, these 6 pillars of AI facilitate agencies and contractors in making certain that:

  • AI is the right methodology for the problems being faced
  • The AI solutions developed are an effective approach to those problems
  • The output from AI will ultimately lead to long-term value for the organization


17 Oct 2019
Artificial Intelligence Could Be a $14 Trillion Boon to the Global Economy—If It Can Overcome These Obstacles

Artificial Intelligence Could Be a $14 Trillion Boon to the Global Economy—If It Can Overcome These Obstacles

Global growth is stalling. Trade wars are hammering manufacturers, from Shanghai to Stuttgart to Seattle. But, awful as today’s economic outlook appears, Industry 4.0 is alive and well, its most ardent backers say.

Industry 4.0 is the catch-all term for the implementation by businesses of big data, improved robotics and artificial intelligence systems. And it’s still expected to be a major driver in global growth over the next decade, and beyond. Yes, even in manufacturing.

By 2035, this A.I.-powered push will provide a $14 trillion boost to the global economy, consulting giant Accenture predicts.

That’s the assessment of Marc Carrel-Billiard, global senior managing director at Accenture Labs, who rattled off these numbers during his keynote presentation at World Summit A.I. in Amsterdam on Wednesday. By way of example, he cited research that traced the progress in one growing area of A.I.-powered automation: call centers. Five years ago, A.I. bots could successfully resolve one out every ten customer phone calls. Today, he said, it’s 60%.

Moreover, he predicted, this push to automate will not be the jobs-killer the more bearish economists out there fear.

But before technologists take a victory lap, there’s a caveat.

They’re not a threat to jobs, he says, “because these systems are not very intelligent.” AI—and its many iterations: machine learning, natural language processing, machine vision, image- and voice-recognition—is well adapted at highly specialized tasks. It does a decent job telling you what the weather will be tomorrow, or ordering movie tickets or helping you find the fastest route home during the evening commute. All manner of businesses are using A.I. increasingly on the enterprise level to make sense of the vast flows of structured and unstructured data they collect to root out inefficiencies, and save costs.

But, as Carrel-Billiard notes, A.I. still has a blind spot. It’s trained to interpret certain data sets, not infer meaning or context from a complicated world. A.I. is a specialist, not a generalist, he says. And therefore, much work is needed to make these systems truly intelligent.

Gary Marcus, professor of psychology and neural science at New York University and author of Rebooting AI, is even more frank in his assessment. He calls deep learning— the subset of A.I. that can make sense of huge amounts of data with little to no oversight from human minders—a misnomer. It’s good at narrowly focused tasks, but he questions its much-ballyhooed potential to, for example, revolutionize transportation (self-driving cars) and medicine (analyzing huge volumes of MRI scans for signs of cancerous growths). “Deep learning is no substitute for deep understanding,” he says.

“The number of radiologists who’ve been replaced by deep-learning systems?” he asks. “Zero.”

Carrel-Billiard, for one, believes that in order for A.I. systems to be truly effective they need to be designed to be accountable, transparent and free of bias—not just super-fast task rabbits. Only then will such systems reach their full potential.

On day one of the World Summit A.I., much of the early discussion was about the need to build so-called ethical A.I. systems. Marcus and Carrel-Billiard, among others, challenged the development community to build A.I. systems that are accountable, transparent and free of bias.

Unless it’s responsible, Carrel-Billiard says, “nobody will trust it, and nobody will use it.”


16 Oct 2019
Why Most Companies Are Failing at Artificial Intelligence: Eye on A.I.

Why Most Companies Are Failing at Artificial Intelligence: Eye on A.I.

Most companies that say they’re using artificial intelligence have yet to gain any value from their A.I. investments. 

A survey from MIT Sloan Management Review and Boston Consulting Group released Tuesday found that companies that view A.I. as merely a “technology thing,” akin to a product rather than a business overhaul, fail to gain financial results. The survey’s authors defined the “value” of an A.I. project as lifting sales, reducing costs, or creating a new product.

The survey, based on responses from nearly 2,500 executives, found that seven out of ten companies report little to no impact from their A.I. projects so far. Overall, 40% of the surveyed companies that have made “significant investments” in A.I. have yet to report any business gains.

There is a clear difference in the A.I. strategies between the “winners” and “losers,” according to Boston Consulting Group managing director Shervin Khodabandeh. For instance, companies that are getting some value from their investments view A.I. as a way to upend and change current business practices likes sales, rather than simply buying an A.I. tool from a vendor, he said.

Also, at the most successful companies, business leaders oversee A.I. initiatives. These executives, who control budgeting and resources, then build a group of data scientists and key personnel from departments like sales or marketing to oversee the A.I. project to completion.

This process is markedly different than the traditional technology approach at most businesses, in which CIOs decide which data-crunching projects to pursue. The downside to this CIO-driven tactic, Khodabandeh said, is that the A.I. projects become isolated and neglected by the overall executive team. 

The report confirms the findings of other recent surveys about A.I. and business that show companies struggle with their data-crunching initiatives. A KPMG survey earlier this year found that most executives believe it will take many years before their A.I. projects create a “significant return on investment.” 

Beyond the latest survey, Khodabandeh said companies that are successful in using A.I. often create their own mini-IT departments, built specifically for A.I. projects. Doing so allows the companies to brainstorm a specific business process they want to improve, like forecasting which products to sell, and then letting their data scientists pick and choose the A.I. technologies to do the job.

“He or she starts with something like, ‘I want my marketer to do their business differently,’” Khodabandeh said about how business-side executives should approach A.I.. “They don’t say, ‘I need reinforcement learning.’”


15 Oct 2019
Seven steps for legacy companies to drive disruptive innovation

Seven steps for legacy companies to drive disruptive innovation

To successfully bring ground-breaking products to market, companies should explore looking beyond their core offerings and establish the innovation function as a separate entity independent of the core business. To successfully design and launch disruptive products, organisations should consider taking the following steps.

Enterprises often stand in their own way by stalling disruptive innovations or ideas out of sheer loyalty to their core businesses. Inflexible organisational structures and legacy operating models often create blind spots, blocking out the sight of potential market threats. They are tentative about whether products or services built with new technology can produce the growth rates needed to satisfy shareholders.

Today, business and technology leaders in every industry recognise that best management practices and a healthy R&D budget are no longer the only ingredients to compete against volatile market forces. To successfully bring ground-breaking products to market, companies should explore looking beyond their core offerings and establish the innovation function as a separate entity independent of the core business.

To successfully design and launch disruptive products, organisations should consider taking the following steps.

1, Reframe the problem question in terms of customer experience.

Finding an opportunity in a problem often starts with reframing the situation from a customer’s perspective. Slowing revenue is often a critical indicator of a changing market. A natural company tendency is to ask: ‘How do we improve sales?’ Analysing the situation from a customers’ perspective could potentially lead companies to ask: ‘What is causing customers to leave their current provider?’

Companies need a repeatable approach to identifying innovations with high revenue potential. Combining big data with field research on behaviours and emotions to gain a deep understanding of customers’ motivations and desires is an efficient way to achieve this.

2, Adopt ethnography to mobilise ‘thick data’ for understanding customers.

Thick data is data generated by ethnographers, anthropologists and other behavioural scientists who study human behaviour and its underlying motivations. In placing customers at the centre of business decision-making, companies must combine data science with human science to gain a more nuanced understanding of their potential behaviours. Thick data eliminates the guesswork on customer attitudes. Combining big data and business acumen with field research on customers’ emotions helps the company assess the growth potential of the new product/service and how customers and their ecosystems use the products. This approach leads to insights about new business models, the messages that catch customers’ attention, and the new market segments waiting to be tapped.

3, Design a consistent user experience for customers across all channels.

Today, customers expect consistent experience across all digital touchpoints ? web, mobile and other new channels enabled by voice-activated assistants. Businesses need to apply the findings from the ethnographic study to design a customer experience model bearing in mind the challenges they want to solve and the events that trigger the customers’ use of the product. The experience delivered should be predicated upon the behavioural patterns and aspirations identified.

4, Excite your CXOs, business sponsors and investors with a strong business case.

It is imperative that companies conduct market research to precisely define their markets and businesses. Evaluating the following parameters and making adequate provisions to address them would help them build a strong case to win venture investors’ mindshare.

  • Potential market size of the innovation
  • Other players in the ecosystem, their market-share and activities
  • Data-backed case studies to prove that the product addresses a revenue generating market.
  • Estimated time on resource planning and training to operate the new service.
  • An incisive plan to bring the product/service to the market.

5, Keep the implementation simple, flexible and adaptable.

Businesses should be prepared for constant change and innovation should be a continuous process. Today, a fully agile organisation has the wherewithal to roll out multiple product releases in a day and commit changes to production in less than an hour. Traditional product development release cycles take 18-24 months, which is far too slow compared to the agile development tools and methodologies that start-ups use. As changes are required on short notice and at higher frequency, building an agile organisation can help respond faster to the changing market dynamics with shorter product development lifecycles. This helps rapid decision-making and brings innovation to market quickly.

6, Consider a dedicated marketing and sales organisation as you launch the product.

Usually companies, when launching new products or services in the market, tend to insert them into their current sales and marketing organisations. Rather than integrating them with the current operating model, companies should consider creating a dedicated marketing team for the purpose. These products or services should be brought to the market independently and steered with dedicated go-to-market strategies tailored primarily for the new market requirements.

7, Turn operations and maintenance into an asset for continuous innovation.

With a micro-services architecture that focusses on building single-function modules and agile processes in place, operations teams can monitor service uptake in real time, detecting shifting market conditions in days instead of months. The innovation team should be empowered and channelised to focus exclusively on new product development.

Looking ahead: Foster a culture that supports innovation and accelerates change

Innovation should not be treated as a one-time phenomenon; it should be at the core of any business. Taking their cue from Silicon Valley, many companies have tried forming their own incubators. The idea is to bring together diverse teams to brainstorm and prototype several new products to sell them through existing sales and marketing channels. An increasingly popular option is to collaborate on digital product innovation with an experienced partner that has a start-up culture and scalable organisational structure. IDC Worldwide reports that business leaders and directors will increase spending on digital strategy and agency services, from $24 billion in 2019 to $38.8 billion in 2022. By working with a partner, businesses can circumvent a host of organisational barriers to innovation, such as politics, bureaucracy, and entrenched processes.

The new digital era calls for a different mind-set and for many companies, the practical approach to innovation is to engage an experienced partner with the ability to quickly ramp up a bespoke, seasoned team that can combine data science, human science and business acumen to conceive, build and bring new digital products to market at scale.


14 Oct 2019
The Coming AI Spring

The Coming AI Spring


Artificial intelligence can generate tremendous value for us all, if policymakers and businesses act swiftly and smartly to capture its full benefits and mitigate the inevitable risks. The long-awaited “AI spring” may finally be arriving, but we will need to be prepared to manage its onset with care.

LONDON – Artificial intelligence (AI) is all around us, generating excitement about how it could increase prosperity and transform our lives in multiple ways. Yet the technology is also likely to be disruptive. Policymakers and businesses must therefore try to capture the full value of what AI has to offer, while avoiding the downside risks.

The idea of AI has been around for more than a half-century, and we have lived through previous periods of excitement followed by long stretches of disappointment – “AI winters” – when the tech didn’t live up to the hype. But recent progress in AI algorithms and techniques, combined with a massive increase in computing power and an explosion in the amount of available data, has driven significant and tangible advances, promising to generate value for individuals, businesses, and society as a whole.

Companies are already applying AI techniques in sales and marketing to make personalized product recommendations to individual customers. And in manufacturing, AI is improving predictive maintenance by applying “deep learning” to high volumes of data from sensors. By deploying algorithms to detect anomalies, firms can reduce the downtime of machinery and equipment, from jet engines to assembly lines. Our research has highlighted hundreds of such business cases, which together have the potential to create between $3.5 trillion and $5.8 trillion in value per year.

AI also can contribute to economic growth by augmenting and substituting labor and capital inputs, spurring innovation, and boosting wealth creation and reinvestment. (AI will also create some negative externalities and transition costs, but these will be outweighed by its benefits.)

We estimate that AI and analytics could add as much as $13 trillion to total output by 2030, increasing the annual rate of global GDP growth by more than one percentage point. Furthermore, our research suggests that AI will have the greatest benefits if it focuses on innovation-led growth, and if its diffusion is accompanied by proactive management measures – in particular, retraining workers to give them the skills they will need to thrive in the new era.

As AI contributes to faster GDP growth, social welfare is also likely to increase. We estimate that AI and related technologies could improve welfare by 0.5-1% per year between now and 2030. That would be similar to the social impact of previous waves of technology adoption, including the information and communications technology revolution.

AI could help to improve many aspects of wellbeing, from job security and material living standards to education and environmental sustainability. Its biggest positive contribution to welfare may come in the areas of health and longevity: AI-driven drug discovery is several times faster than that based on conventional research. And AI-based traffic management can reduce the negative impact of air pollution on health by 3-15%.

One of the most exciting aspects of AI is its potential to help address a wide range of social challenges. Although the technology is not a panacea, it could potentially help the world to meet all 17 of the United Nations Sustainable Development Goals. AI applications that are currently being field-tested include efforts to assist with disaster-relief efforts, track smugglers (including human traffickers), and help blind people navigate their surroundings. And an AI disease-detection system can identify skin cancer as well as or even better than professional dermatologists can.

For all its potential, however, AI also poses substantial challenges that need to be addressed. The technologies themselves are still in the early stages of development, and more breakthroughs are needed to make them widely applicable. And there are considerable problems of data availability, which in turn affect the quality of AI models.

One critical area of concern is the impact of AI and automation on work. Overall, we expect that there will be enough work for everyone, and that more jobs will be gained than lost as a result of the new technologies. But policymakers will need to manage significant transitions and challenges arising from AI adoption at national, regional, and local levels.

In the fastest automation-adoption scenario, up to 375 million workers worldwide will need to switch occupational categories by 2030, while some 75 million will be affected in a midpoint scenario. The nature of almost all jobs will change, as people interact more closely with smart machines in the workplace. That will require new skills, presenting companies and policymakers with the major challenge of training and retraining the workforce at scale. And as demand for high-skill jobs grows, low-skill workers could be left behind, resulting in increased wage and income inequality.

The diffusion of AI will also raise difficult ethical questions. Some of these will relate to the use and potential misuse of the technology in areas ranging from surveillance and military applications to social media and politics. Algorithms and the data used to train them may introduce new biases, or perpetuate and institutionalize existing types. Other critical concerns include data privacy and the use of personal information, cybersecurity, and “deep fakes” that could be used to manipulate election results or perpetrate large-scale fraud.

Despite these challenges, AI can generate tremendous value for us all, if policymakers and businesses act swiftly and smartly to capture its full benefits and mitigate the inevitable risks. The long-awaited “AI spring” may finally be arriving, but we will need to be prepared to manage its onset with care.


13 Oct 2019
AI (Artificial Intelligence): What’s The Next Frontier For Healthcare?

AI (Artificial Intelligence): What’s The Next Frontier For Healthcare?

Perhaps one of the biggest opportunities for AI (Artificial Intelligence) is the healthcare industry. According to ReportLinker, spending on this category is forecasted to jump from $2.1 billion to $36.1 billion by 2025. This is a hefty 50.2% compound annual growth rate (CAGR).

So then what are some of the trends that look most interesting within healthcare AI? Well, to answer this question, I reached out to a variety of experts in the space.

One of the key trends is the use of health AI to spur the transition of medicine from reactive to proactive care. Machine learning-based applications will preempt and prevent disease on a more personal level, rather than merely reacting to symptoms. Providers and payers will be better positioned to care for their patients’ needs with the tools to delay or prevent the onset of life-threatening conditions. Ultimately, patients will benefit from timely and personalized treatment to improve outcomes and potentially increase survival rates.

In the next five years, consumers will gain more access to their health information than ever before via mobile electronic medical records (EMR) and health wearables. AI will facilitate turning this mountain of data into actionable health-related insights, promoting personalized health and optimizing care. This will empower patients to take the driving wheel of their own health, promote better patient-provider communication and facilitate high-end healthcare to under-privileged geographies.

Today, there are millions of physiologic parameters which are extracted from a patient. I believe the next mega trend will be harnessing this AI-driven “Smart Data” to accurately predict and avoid adverse events for patients. The aggregate of this data will be used to formulate predictive analytics to be used across diverse patient populations across the continuum of care, which will provide truly personalized medicine.

Andrea Fiumicelli, who is the vice president and general manager of Healthcare and Life Sciences at DXC Technology:

Ultimately, AI and data analytics could prove to be the catalyst in addressing some of today’s most difficult-to-treat health conditions. By combining genomics with individual patient data from electronic health records and real-world evidence on patient behavior culled from wearables, social media and elsewhere, health care providers can harness the power of precision medicine to determine the most effective approaches for specific patients.

This brings tremendous potential to treating complex conditions such as depression. AI can offer insights into a wealth of data to determine the likelihood of depression—based on the patient’s age, gender, comorbidities, genomics, life style, environment, etc.—and can provide information about potential reactions before they occur, thus enabling clinicians to provide more effective treatment sooner.

One key advance to consider is the use of carefully curated datasets to form Synthetic Control Arms as a replacement for placebo in clinical trials. Recruiting patients for randomized control trials can be challenging, particularly in small patient populations. From the patient perspective, while an investigational drug can offer hope via a new treatment option, the possibility of being in a control arm can be a disincentive. Additionally, if patients discover they are in a control arm, they may drop out or elect to receive therapies outside of the trial protocol, threatening the validity and completion of the entire trial.

However, thanks to advances in advanced analytics and the vast amount of data available in life sciences today, we believe there is a real opportunity to transform the clinical trial process. By leveraging patient-level data from historical clinical trials from Medidata’s expansive clinical trial dataset, we can create a synthetic control arm (SCA) that precisely mimics the results of a traditional randomized control. In fact, in a recent non-small cell lung cancer case study, Medidata together with Friends of Cancer Research was successful in replicating the overall survival of the target randomized control with SCA. This is a game-changing effort that will enhance the clinical trial experience for patients and propel next generation therapies through clinical development.


12 Oct 2019
Demystifying Artificial Intelligence in the Corporation

Demystifying Artificial Intelligence in the Corporation

Artificial Intelligence (AI) is top of mind for leading corporations these days – 96.4% of top executives reported earlier this year that AI was the number one disruptive technology that they were investing in, up from 68.9% just two years ago. In addition, 80% of these executives identified AI as the most impactful disruptive technology, up from 46.6% two years earlier.

Yet, for many organizations, Artificial Intelligence remains a mystery. For specialists, AI implies a very specific connotation in terms of intelligence demonstrated by machines, in contrast to the more common usage of AI which encompasses all varieties of machine assisted learning, most notably machine learning, deep learning, and natural language. For the sake of this discussion, we will assume the broadest definition of AI. As corporations struggle to understand the applications and benefits that AI can deliver for their organizations, firms have established AI Centers of Excellence, AI Labs, and other sandboxes for piloting AI capabilities tied to business use cases.

So, how can we demystify AI to deliver measurable business value? Rob Thomas, a senior IBM executive who is General Manager of IBM Data and AI, which includes Watson, has a few ideas. He has outlined his framework for AI adoption in a newly published report from O’Reilly Media, “The AI Ladder”. In the report foreword, O’Reilly Media Founder and CEO Tim O’Reilly observes, “Everyone is talking about ‘AI’ these days, but most companies have no real idea of how to put it to use in their own business”. O’Reilly emphasizes the cultural change in mindset that accompanies adoption of any new technology approach, noting that “each new technology revolution breeds new business leaders” who let go of old assumptions about the way things work. O’Reilly notes, “only later do companies realize how much they might need to change their business model to truly make use of new capabilities”. IBM’s Thomas observes, “AI is not about executing a single business project – it’s about changing an entire business culture. It’s about creating a culture of iteration, experimentation”.

The report employs the metaphor of the “AI Ladder” to describe a series of stages that a firm must pass through to become AI-enabled and realize measurable business value. The concept of the AI Ladder is premised on the notion that organizations require a prescriptive approach to understand where they are in their AI maturity. By diagnosing and understanding the stage of maturity of each organization, a firm can then employ the AI Ladder as a framework for outlining the steps and capabilities that will guide the organization toward realization of the benefits that result from machine and human augmentation. The basic tenets of the AI Ladder can be summarized as:

  1. Start with the business problem that you are attempting to address
  2. Understand your data requirements – these are the foundation for AI success
  3. Develop the right skills to leverage AI capabilities
  4. Focus on algorithmic trust and data integrity to ensure credibility
  5. Recognize the need for cultural and business model change.

A central message of The AI Ladder is that AI success depends upon data, and effective data management provides the fundamental building block for AI enablement. It was only with the advent of Big Data in recent years, that proliferating sources and volumes of data could be combined with massive computing power. This has helped AI emerge from decades of nascent experimentation, as a foundation of data collection, organization, and analysis provides the foundation upon which AI capabilities and algorithms are deployed. The AI Ladder describes the challenges that corporation face in organizing their data to enable AI when confronted by issues including lack of data, too much data, and lack of quality data. It’s long been understood that organizations typically invest 80% of their efforts in preparing their data so that it can be used productively. Thomas echoes the critical linkage between successful data management and effective AI enablement, noting “the vast majority of AI failures are due to failures in data preparation and organization”.

New generations of tools and technologies are helping reduce the amount of time that corporations now spend on data preparation, with the result that firms can focus efforts on analysis and business results. Companies are moving to modern data architecture approaches that are enabling simpler data collection and access. The advent of Cloud Computing has accelerated the adoption of AI through the migration of data assets to the Cloud. By renting rather than purchasing data storage capabilities, corporations are transforming the economics of data management, and realizing increases in speed and efficiency. They are no longer responsible for managing data access and security, no longer required to make large capital investments in computer hardware purchases, only pay for and access their data as they need it, and as a result, accelerate adoption of Big Data and AI.

So, what does this all mean for business? A 2017 study by PwC, reported that “Global GDP will be 14% higher in 2030 as a result of AI – the equivalent of an additional $15.7 trillion. This makes it the biggest commercial opportunity in today’s fast changing economy”. A 2017 Gartner study argues that Artificial Intelligence will create more jobs than it eliminates, and that by 2021, “AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity”. IBM’s Thomas suggests that although today there is only a 4%-8% adoption rate of AI within the corporate world, within the next 18-24 months we are likely to see this rate explode t0 80%-90%.   This would suggest that AI is rapidly reaching the moment of critical mass that will lead to business adoption and business value. IBM’s Thomas concludes, “AI is at execution state”. Have you launched your AI Center of Excellence? It appears that the time to act is now.


09 Oct 2019
Dubai Economic Department Joins Blockchain Business Registry

Dubai Economic Department Joins Blockchain Business Registry

In a recent development, the economic department of Dubai has joined the Unified Business Registry Platform (UBRP) built on the Blockchain platform as a Service (BPaaS).

The main goal of developing this platform is to improve the business infrastructure in Dubai. It will help license issuers in managing trade licenses and corporate registries. The Dubai government is planning to host 40 government entities on UBRP. 

The CEO of Dubai Economy’s Corporate Support Services, Abdullah Hassan, said that the UBRP is an innovative platform to uplift the Dubai Economy in its digital world. They are happy and proud to be the first blockchain-powered government in the world. According to the UAE vision 2021, the Dubai economy is looking for innovative solutions to streamline the future government. 

According to Abdullah, the BPaaS by Dubai Pulse will develop a regulation model to improve the businesses in Dubai. It will make the state a better place for citizens and companies. The two protocols of Dubai Pulse are Hyperledger Fabric and Ethereum enterprise clients. The platform provides many features to adapt new entities with their capabilities on external infrastructure. 

The CEO of Smart Dubai Government Establishment, Wesam Lootah, said that the Dubai Pulse platform is successful enough to change the capabilities of the government entities and organizations. Many government bodies are enjoying the benefits from the UBRP platform, and they are more close to the smart and digital Dubai. 

This platform provides various features like compatibility among all the protocols of blockchain, privacy & security of the organization, and Smart Contract repositories & service on demand. It helps people to accept the blockchain technology and amplify the adaptability through hybrid architecture in less ownership cost. 

This platform leads to the overall development of the Dubai Economy with its features to adapt to the blockchain. It will make people and organizations of Dubai happy and digitally smart.


08 Oct 2019


Disruptive innovation of today is going to transform society tomorrow. From the rise of smart voice assistants to venturing into the age of 5G technology, this generation has seen it all. Coming out of the conventional ways and moving forward towards innovative future with determination, has made its footprint in the current market. With the emergence of digital transformation, more and more businesses are adopting disruptive technologies to drive innovation.

In the race of being relevant with the present, digital platforms, in both theoretical and practical manner, have satisfied the definition of disruptive innovation by creating a new market, causing disruption in the existing market, adding potential value to the network and uprooting prevailing well-established markets.

For businesses who adapt to disruptive innovation in their workplace, have to go through a lot of plots and procedures to finally hit the market. The journey includes innovation (doing same things better than before) to bring in new ideas and introducing new things and ultimately reaching to a disruptive phase where they become confident enough to make existing operations obsolete and consume whole new and fresh ecosystem to the business.

Emanation of technologies including Robotics, RPA, Autonomous vehicles, Internet of things, Artificial intelligence, Space Colonization, Machine Learning, Deep Learning, Big Data, 3D Printing, High-speed travels, Blockchain and Cryptocurrency technology, Virtual Reality, has augmented the open-ended process of potential transformation.

The most recent and apt example we can quote here is of the new launch by tech giant Apple. Other than Netflix, Apple’s online streaming media is creating a buzz nowadays. Additionally, the company’s venture into the commerce industry with the introduction of Apple Card (Apple Credit Card), which surely comes forward as post-iPhone strategy, narrates all about the disruptive intervention of Apple into a non-tech industry to drive digital innovation.

As we can see, our regular periodic is enveloped in the layers of varied disruptive innovations which we didn’t realize entering in our lives. Well, we can merely predict how our future filled with technologies would look like whose optimistic propelling possibilities seem to be endless. Although many have asserted that this disruption can act as a destructive catalyst in certain ways for future society but it would be really unfair to land up with any negative conclusion without assessing the binary ends of it. Moreover, it depends on the users and society as a whole to sculpt the ultimate outcomes of these highly innovative and disruptive technologies.


07 Oct 2019
The Upside of AI Accessibility Now and in the Future

The Upside of AI Accessibility Now and in the Future

AI is often misunderstood by those who lack education or training in the technology. This interview with Max Versace, CEO and co-founder, Neurala offers some new insights into the technology.

AI is certainly on the rise, despite some of the concerns some have expressed about it leading to doomsday scenarios or a devastating loss of jobs, as described in AI Attitudes: What the Experts Consider of Concern. There is no cause for concern insists Max Versace, CEO and co-founder of Neurala, who explains the benefits that AI offers now and in the future for improving workflows. 

Is fear of AI holding businesses back from reaping its benefits now?

Fortunately not. In the 1970s, when computers were introduced in enterprises, there was an equally brief and illogical fear that they would take over from humans. The reality is that they augmented us, and the same will be true for AI, despite a few malicious attention-seeking critics spreading fears otherwise.

When it comes to AI and enterprises, many have already begun to implement AI as a part of their business and digital transformation strategy. In fact, about 80% of organizations are already using AI in some form. However, an overwhelming 91% of companies foresee significant barriers to AI adoption, such as a lack of IT infrastructure and a shortage of AI experts to guide the transition.

So, the problem is not fear of AI, rather a lack of education, training and tools preventing businesses from fully realizing the benefits of AI. That’s why there is a need to democratize AI across industries, so that anyone – not just engineers and data scientists – can build AI.

Think of it this way – we need a WordPress for AI: something that allows anyone to build AI, in that same way WordPress made website development accessible for all, without needing extensive expertise.

Only then will organizations be able to truly capitalize on the benefits of implementing AI into their workflows.

Is the fear grounded in actual threats or just the fear of the unknown due to the lack of transparency that makes AI appear to be a black box? Do you see that changing?

If you look at who is warning us about impending AI apocalypses, they are invariably AI outsiders that do not understand much about AI. As someone who has worked in the field both in academia and industry for a couple of decades, I see AI as a fantastic tool that can help make machines less dumb and more useful for us – augment productivity and help us re-allocate our time to more creative and critical tasks.


Also, as I mentioned earlier, there is a lack of accessibility and training around AI which has likely resulted in a lack of understanding. I think that if we can find a way to truly democratize AI, it will go a long way in terms of changing people’s opinions. 

Take the manufacturing industry for example – there is a lot of fear that AI, robotics and automation will take people’s jobs. But this is not the case.

In fact, AI has the ability to augment the workforce, providing new opportunities.

For industrial product managers, one of the biggest challenges is quality control. Product managers struggle to inspect each individual product and component (no human can inspect hundreds of products coming out of an industrial machine every second!), while also meeting deadlines for massive orders.


Moreover, humans are notoriously bad at visual inspection tasks, so why not delegate these tasks to AI? This will free up time for workers who can now focus on other tasks rather than spending time doing visual quality control.

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