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The ability to use artificial intelligence effectively is also a large concern for IT decision makers.

T leaders face a range of challenges, but data security ranks as the no. 1 concern, according to a Tuesday survey from Adobe.

Among the 1,000 US IT decision makers polled, 47% put data security at the top of their list of priorities. Respondents pointed to such challenges as making sure that data is secure, that customers can act upon it, and that it can easily flow through the myriad systems that organizations have in place.

While organizations are focusing on data security, other data management issues remain. Grappling with the huge avalanche of data available in their organizations naturally seems like a top challenge. But another aspect of data management scored higher as a challenge among the IT leaders polled.

“Surprisingly, having too much data is one of the least-cited challenges; actually using the data effectively is a much more important issue,” Ronell Hugh, head of product strategy and marketing for Adobe Experience Platform, said in a press release.

A full 63% of those surveyed said they’ve been successful at integrating different data sources, 59% said they’ve been able to clean their data to reduce inaccuracies, and 59% said they’re able to use the data effectively to personalize it for their customers.

Artificial intelligence (AI) and machine learning were next on the list. Almost 90% of those polled said they envision an increase in the use of AI and machine learning in their future. But 40% cited the ability to implement AI as a top concern. The survey revealed a disconnect in AI between IT teams and the business side at many organizations, with only around half of the IT leaders surveyed expressing positive feedback about the effectiveness of their current AI and machine learning solutions.

Improving the customer experience

AI and machine learning were cited by 41% of those polled as providing the most value in improving the customer experience. Other technologies seen as improving CX were the Internet of Things (IoT), voice, immersive technologies including virtual and augmented realities, and chatbot technology.

Among many organizations, IT teams and the business side seem to be working together as true partners. Some 53% of the IT leaders surveyed said they collaborate with their customers on both the vision and implementation of technology. Of the rest, 27% said the business side envisions technology solutions and works with IT to implement them, while 18% said that IT decides on the right technologies with input from the business side.

Overall, 95% of the IT leaders expressed optimism about their ability to positively affect their customers, and 69% said they believe they have the right tools and systems to create personalized experiences for their customers.

“IT decision makers have a firm seat at the table,” Hugh said in the release. “They feel that they are a strategic partner to other lines of the business, and most often they feel their department is organized in a way that enables their teams to support improved customer experiences.”

Source: https://www.techrepublic.com/article/why-data-security-is-now-a-top-concern-for-it-leaders/

The automotive industry is one of the most high-tech industries in the world – so a headline finding in a report published this week was, on the face of it, somewhat surprising.

Capgemini’s report – Accelerating Automotive’s AI Transformation – found that during 2018, the number of companies in the industry deploying AI “at scale” grew only marginally by 3%.

This reflected that just 10% of respondents surveyed said that their organizations were deploying AI-driven initiatives across the entirety of its operations “with full scope and scale,” during 2018, compared to 7% in 2017.

The relatively slow pace of growth is evidence that “the industry has not made significant progress in AI-driven transformation since 2017”, the report concludes – a surprising finding given the scale of investment and enthusiasm shown by industry leaders.

I spoke to one of the report’s authors, Capgemini’s Ingo Finck, who told me “To an extent, I did find this surprising, because from the discussions we’ve been having with these companies we see that the vast majority – more than 80% – mention AI in their core strategy.

“It’s clearly a strategic factor for them, so yes … we were surprised by the relatively slow growth rate.”

Before we start delving into the possible reasons for this slow uptake, it’s worth noting that there is a key geographic variation: In China, the number of automotive companies working at scale with AI almost doubled, from 5% to 9%.

This is explained to some extent by the comparatively “open” approach taken by China’s AI giants, such as Baidu’s development of the open source Apollo platform. This has involved it partnering with over 130 other businesses and organizations.

Finck explains that the slow growth demonstrated in other regions could be down to the fact that organizations are taking a more mature approach to AI deployment. This might mean they are moving away from “try everything and see what works” methodologies, towards focusing on proven use cases that can then be scaled.

Another disparity is apparent when we consider the sizes of the businesses that are reporting growth in AI deployments.

“We can see that the smaller companies are struggling more with AI – whereas with larger companies [with revenue of $10 billion plus] the adoption rate is higher.

“The way we interpret this is that the complexities in small companies are almost the same as they are in large companies – many of the difficulties in applying AI are the same across small and large organizations.”

In fact, there’s a clear correlation, as would be expected, between the amount of money invested and the scale of an organization’s AI deployments. This is clearly a limiting factor for smaller players in the industry.

Of those that have successfully deployed at scale, 80% have done so by spending more than $200 million on AI. Of those that judge themselves not to have successfully deployed at scale, just 20% have spent that amount.

While self-driving, autonomous cars are often talked about as the “headline” use case for AI in automotive, today’s reality is that cognitive learning algorithms are mainly being used to increase efficiency and add value to processes revolving around traditional, manually-driven vehicles.

Significant AI deployments highlighted by the report, mostly at larger OEM organizations, include:

• Prototyping – General Motors uses machine learning in their product design operations.
• Modeling and simulation – as used by Continental to gather 5,000 miles of virtual vehicle test data per hour.
• Sales and marketing – Volkswagen uses machine learning to predict sales of 250 car models across 120 countries, using economic, political and meteorological data.
• Quality control – Audi uses computer vision-equipped cameras to detect tiny cracks in sheet metal used in its manufacturing processes, which would not be visible to human eyes.

These companies fall into a category that Capgemini defines as “scale champions” – they have successfully deployed AI at scale, and all tend to display a number of characteristics – a focus on high benefit use cases, good AI governance, significant levels of investment and, importantly, show a willingness to “upskill” employees.

“We’ve learned that AI is most effective when it comes as a human/machine combination,” Finck tells me.

“In the same way that you improve your AI capabilities, you also have to upskill and educate your staff. That’s more than just training or hiring a few more data scientists. It’s about educating the rest of the organization – the casual user of AI.”

All of these challenges go some way to explaining the slower than may have been expected adoption of AI across the industry. One thing Finck is certain of, and which is borne out by the report’s broader findings, is that AI has a key role to play in the industry’s future.

He says “I think companies understand that it’s far more than just a ‘plug-in’ technology – it’s a core technology that they have to adopt – like the engine, or information technology. The challenge is embracing this technology across not just the product, but also the service, and the organization.”

Capgemini’s full report, Accelerating Automotive’s AI

Source: https://www.forbes.com/sites/bernardmarr/2019/03/27/artificial-intelligence-in-automotive-industry-surprisingly-slow-uptake-and-missed-opportunities/#35e9b60b7707

CFOs are shifting their priorities from cutting costs to rapidly investing in technology and data.

Significant percentages of senior financial executives currently implement technologies such as advanced analytics (38 percent) and machine learning (30 percent), while many plan to dedicate additional resources to frontier technologies within two years, including:

• Artificial intelligence, or AI (41 percent)
• Drones and robots (30 percent)
• Blockchain (40 percent)
• Robotic process automation, or RPA (41 percent).

In addition, financial leaders plan to speed up their efforts to implement several popular technologies within the next two years, including optical character recognition (45 percent) and broader distributed ledger technology (44 percent), according to Grant Thornton’s 2019 CFO Survey, conducted in partnership with CFO Research.

Further, year-over-year comparisons show that CFOs are seeing dramatic changes in technology adoption at their companies:

• Forty-two percent reported their finance functions regularly make use of advanced and automation technologies in corporate development and strategic planning, compared to 18 percent in Grant Thornton’s 2018 CFO Survey;
• Forty percent reported their finance function already implements advanced technologies and automation technologies in risk management, compared to 20 percent in 2018;
• Thirty percent use machine learning, compared to 8 percent in 2018; and
• Twenty-five percent use AI, compared to 7 percent in 2018.

“Financial leaders must embrace and adapt to new technologies to ensure their organizations operate efficiently,” said Srikant Sastry, national managing principal of Advisory Services at Grant Thornton.

“The speed with which CFOs are investing in IT shows a clear vision of the digital transformation they want to see at their companies. But, advanced technologies like AI, RPA, drones, and robotics require CFOs to focus on specific use cases, workforce preparation, and measurements for these technologies to facilitate and maximize a timely return on investment.”

CFO role continues to evolve

CFOs reported that they are well-positioned to collaborate with IT and ensure that digital investments fit into their organization’s innovation strategies: Nearly 91 percent of respondents agree or strongly agree it is the CFO’s job to ensure their companies fully realize the benefits of technology investments.

That said, almost the exact same amount of respondents (92 percent) believe the finance function of the future must do a better job of leveraging both technology and people.

According to the “All systems go: CFOs lead the way to a digital world” survey, CFOs see themselves as part of a cross-functional, collaborative team.

Of the nearly 400 financial leaders surveyed, 95 percent of respondents said their company’s CFO is a key stakeholder of enterprise transformation planning; while 94 percent reported their company’s CFO actively supports an innovation culture; and 90 percent agreed the company’s CFO actively shares insights about how to run a lean, efficient function with their peers in business units.

Challenges ahead: Skill and collaboration

As CFOs speed toward transformative technology, 60 percent believed the finance function must provide advanced analytical support. The most important skill sets senior financial executives want to develop in the finance function are:

• Data analytics (55 percent),
• Business strategy (40 percent),
• Operations management (35 percent), and
• Technology acquisition (33 percent).

Moreover, the survey showed that financial leaders want to recruit and retain employees who possess traditional financial expertise, while also showing an eagerness to learn new technologies and process design.

“Over the next two years, talent and skills will be one of the top three challenges the IT function faces as it seeks enterprise growth, along with system complexity and business integration,” says Chris Stephenson, Business Consulting principal at Grant Thornton.

“As the finance function of the future takes shape, it will demand new skill sets. Leaders will need to move full speed ahead to invest in the right technology and people to transform their businesses and, ultimately, guide strategic decision making across their organizations.”

Stephenson concludes that this transformation will not end with recruiting and developing employees to have the right set of skills – or merely implementing the latest technologies. “Working more closely with the IT organization can help the CFO rethink end-to-end finance processes. CFOs and CIOs must closely collaborate on digital transformation to remain competitive,” he says.

Source: https://www.helpnetsecurity.com/2019/03/26/cio-cfo-collaboration/

In a news environment dominated by political and economic shifts, the devastating effects of cyber crime and data breaches go largely unreported. Yet, for small to medium-sized businesses (SMEs) in South Africa, which arguably form the backbone of a teetering economy, cyber crime and data theft are undermining growth and sustainability.

While reliable statistics are nearly impossible to obtain, it is clear that SMEs are falling victim to hackers on a weekly basis. Why? Business leaders have clearly not stayed abreast of a cyber risk landscape that has changed dramatically over the past decade. Not only have the nature and scope of the risks evolved, but so too have the tools and strategies required to mitigate them.

For SMEs, it is critical to understand this evolution and to bring the organisation in line with current (and future) trends, says Colin Thornton, MD of Turrito Networks.

A simple question of software

When looking back 10 to 15 years ago, cyber security for SMEs generally equated to a decision around which anti-virus software to choose. As long as the anti-virus was reputable, and kept up to date, business leaders could tick the cyber security box. In most cases, this box was probably located towards the very bottom of the business agenda… and budget.

As businesses began to embrace more seamless and accessible Internet connectivity, the cyber threat level began to rise. Employees gained access to social media platforms, and started to harness these platforms for business as well as personal communications. The use of e-mail within organisations became paramount to productivity, along with other emerging connectivity tools such as Skype.

New barriers needed

With employees becoming increasingly ‘plugged in’, hackers and fraudsters soon identified newbie Internet users as soft targets for online scams and phishing. Along with the unwanted tides of advertising and ‘junk’ mail, these scams prompted businesses to install spam filters along with the trusted anti-virus software.

In addition to targeted attacks, business leaders very soon had to contend with the peril of dodgy Web sites. As employees delighted in newfound connectivity and Web surfing freedom, they blundered their way onto infected sites and unsecured environments. This forced businesses to invest in firewalls as well as more sophisticated anti-virus software.

Mobility, endpoint security emerges

Over the past several years, smartphones, laptops and other mobile devices have become integral to modern working environments. Particularly for SMEs and start-ups, being able to work and communicate remotely has proven critical to survival. Yet, with the proliferation of mobile devices that connect into sensitive business platforms and environments, the cyber risk naturally intensifies. Another way of thinking about it is that as ‘entry points’ to business information and systems multiply, so too do the risks.

To combat this heightened risk environment and the emergence of mobile working solutions, businesses embraced the concept of ‘endpoint security’. In techie terms, endpoint security is simply a security approach that focuses on ‘locking down’ endpoints (think individual computers, phones, tablets and other network-enabled devices) in order to keep networks (businesses) safe.

Along with the focus on endpoint security, the heightened risk environment also sparked off interest in penetration testing and vulnerability assessments. Penetration tests are designed and intended to exploit weaknesses within IT networks and thus to determine the degree to which hackers can gain unauthorised access to business information and assets. These tests can be manual or automated and are performed by IT security professionals. Even as recently as five years ago, regular tests such as these were considered a nice-to-have but were fairly far down the IT task list. Nowadays, they should be near the top.

The devil is in the data…

While the reality of hyper connectivity has deepened the complexity of cyber risks, this connectivity is also fuelling the creation of data. As we have seen in recent years, data has been likened to the new ‘oil’ of the global economy, and as such, it has become an asset that needs vigilant and well-structured protection. Notably, in the World Economic Forum’s Global Risks Report 2019, “massive data fraud” was ranked the number four global risk facing organisations of all kinds.

For SMEs, which are just as likely to fall victim to data breaches as their larger counterparts, data security now demands a full strategy of its own. To begin with, SMEs have to harness tools such as SharePoint to implement secure document management. Such tools ensure that employees not only store data in a safe and organised way, but that they can also collaborate on files and safely share information with outside parties. Moreover, with legislation such as the Protection of Personal Information Act 4 of 2013 (POPI) and the General Data Protection Regulation (GDPR) coming into effect, businesses of all sizes will have to enforce strict data governance frameworks, or else risk falling afoul of the law.

Moving beyond silos

When assessing the current cyber risk environment for SMEs, perhaps the most significant element of the digital evolution is that cyber security has become everyone’s responsibility: it does not simply fall to the business owner, the tech guy, the manager, etc… every employee has become a target, and similarly, everyone has a role to play. This means education and internal training has to be the first and most fundamental part of any cyber security strategy. Today, businesses are still allowing staff to save sensitive data on their own laptops, memory sticks and consumer cloud platforms like Dropbox, for example, which immediately places the business at risk of a data breach.

As the risks continue to change and evolve, so too do business owners and employees have to elevate their own awareness and online behaviours. Digital transformation continues to propel business and innovation forward, but the dark side of cyber security is casting a formidable shadow.

Source: https://www.itweb.co.za/content/mYZRX79PzD9vOgA8

About the author

Colin Thornton founded Dial a Nerd in 1998 as a consumer IT support company, and in 2002, the business-focused division was founded. Supporting SMEs is now its primary focus. In 2015, his company merged with Turrito Networks, which provides niche Internet services outside of the local network. These two companies have created an end-to-end IT and communication solution for SMEs, from supplying a laptop right through to designing and delivering a fibre-connected geo-redundant hybrid-cloud solution. This type of end-to-end service was typically only possible for enterprise customers, but now SMEs, mid-market organisations, homes and schools can benefit too, for a fraction of the cost. Thornton has subsequently become the Managing Director of Turrito.

In this article, Sascha Giese examines how Hybrid IT and cloud computing will be AI’s biggest asset as the government embraces digital transformation

The term artificial intelligence (AI) is familiar to most of us. Looking beyond the realms of science fiction, however, AI has the potential to have a positive impact in a government context, because the take-up of AI will more than likely be the result of the adoption of hybrid and cloud computing.

AI is highly effective but historically has had a long adoption timeframe, similar to other excellent technologies waiting for the perfect use case within day-to-day IT environments. But many believe that’s about to change. Public sector investment in AI is expected to rise rapidly in the coming years. According to the SolarWinds U.K. Public Sector IT Trends Report 2018, over half of the surveyed public sector IT pros predict that AI will be among the biggest technology priorities in three to five years.

Here in the U.K.,

emerging technologies are already being used to improve services. In a recent article, Oliver Dowden, minister for implementation in the Cabinet Office, shared the example of Her Majesty’s Revenue and Customs (HMRC), which has used Robotic Process Automation (RPA) technology.

According to Dowden, in the example of employer registration end-to-end processing, up to 85% of applications are automatically processed, thanks to the deployment of 12,500 robots and the automation of 56 processes across multiple lines of business. The technology validates data from online applications and provides a unique reference number to new employers to enable them to begin employing staff.

Signs point to hybrid IT and cloud adoption as primary factors in the rise of AI adoption. In fact, the two can have a synergistic relationship, as AI will enhance the capabilities provided through a hybrid IT or cloud environment.

One of the great advantages of the cloud is its ability to serve as a platform for public sector IT pros to acquire and use technologies as a service, versus buying them outright. Applications, storage, infrastructure—all of these are now available as a service.

AI is no different. Each major cloud provider offers its own machine learning services (MLaaS) platform, which allows third-party AI application developers to build their smart applications on each of these cloud platforms. With the availability of AI platforms, comes the opportunity to “let someone else” handle the intricacies of creating AI applications, which may lead to a wide variety of new AI-based applications.

Two further advantages of cloud that present an environment ripe for AI are worth mentioning:

1. Abundant computing and storage capacity to access vast amounts of data

Abundant capacity means applications have the room to use as much computing power as necessary to accomplish highly complex computing algorithms, and storage to access vast amounts of data so applications have the information necessary to use those complex algorithms to deliver far more “intelligent” information.

The network making its transition to “Software Defined Everything” can allow AI to use additional resources when necessary and return that capacity when it’s finished with complex issues. Templates, policies, and dynamic scaling are designed to make this more than possible. It even becomes simple.

2. AI’s role in managing this highly intelligent environment

Take the Internet of Things (IoT), for example.

AI has the potential to allow for a dramatically enhanced ability to manage elements that, to date, have been difficult to manage or even track. Taking that scenario even further, the intelligence and data analytics behind AI may also provide the ability to implement far more broad-reaching automation.

With the automation of hybrid IT and cloud, comes greater efficiency and more opportunity for innovation. I’d call that a win-win.

Source: https://www.openaccessgovernment.org/ais-biggest-asset/61029/

While some farmers are using IOT to save time and money, it’s not always a straightforward process.

Lack of data and power connectivity on farms is an obvious challenge. Another hurdle is the need to buy and install multiple IOT solutions for similar tasks, says Srini Chandrasekar, Director, Azure Global Engineering at Microsoft.

“A specific IOT provider may give you hardware with sensors for measuring soil moisture, a solution in the cloud and a mobile app,” Chandrasekar says.

“But if you want to measure nitrogen and PH levels, you go to a different provider, they give you their own sensors and application and the data sits on their site.”

“It’s very hard for farmers, even if they work with a system integrator, to build solutions that are customised for their needs.”

In Chandrasekar’s view, the problem is the result of hardware and software vendors forming one-on-one partnerships to offer bundled solutions.

Those bundles solutions may not be interoperable with other solutions, and might not provide access to advanced AI and data analytics services.

A common platform

To solve this problem, Microsoft is encouraging hardware and software vendors to enable their products to work with its IOT platform. That platform includes Microsoft Azure and a range of Azure services, including the Project FarmBeats initiative.

A common platform should improve interoperability, allowing ISVs and system integrators to build IOT solutions faster and more affordably, says Chandrasekar.

Hardware and software vendors will also benefit by leaving the task of maintaining the cloud stack and AI services to Microsoft.

“There are a lot of solutions out there that don’t use the Azure IOT Hub functionality for management, so they try and write their own management stack,” says Chandrasekar. “These are complex systems. Maintaining and investing in them gets to be hard for small companies over time.”

Microsoft is also creating templates, which it calls solution accelerators, to make it easier for end-user organisations to build IOT solutions.

Plenty of companies have demonstrated the benefits of using Microsoft’s IOT stack. They include Australian agtech provider The Yield, which worked with Microsoft, Intel and Bosch several years ago to build a system the oyster farming industry uses to monitor water quality.

Building an ecosystem

Microsoft is also hoping hardware and software vendors see that building on its platform will expose them to a wide array of complimentary products.

That includes a marketplace of AI models and additional data – for example, Microsoft will encourage providers of weather and geospatial information to offer that data via Azure.

“Our goal is to make it easy to aggregate all this data from the field, and weather and geospatial data, and use AI models and other technology to analyse it,” Chandrasekar explains.

Chandrasekar wants to enable an ecosystem of Project FarmBeats-enabled products that farmers can buy off the shelf and connect to solve problems.

“We are not an agtech solution provider. We are trying to enable agtech solution providers to be successful,” Chandrasekar says.

“You will see a lot more public information and partnerships with Project FarmBeats towards the later part of this year.”

Source: https://www.iothub.com.au/news/how-microsoft-plans-to-connect-more-farmers-to-ai-522116