If you analyze closely, artificial intelligence is everywhere around you. You carry it in your phone in the form of almost every social media app, Alexa, Siri, Google is AI-powered digital assistants, and high-end cars come with AI-enables self-parking systems. These are some of the many examples of how this futuristic technology is impacting our daily lives. 

Not just for entertainment, AI has uses in almost every industry to speed up common actions. In the on-going pandemic, AI has gone far as to even detect potential COVID-19 cases within a particular radius using bots. Let’s take a look at how artificial intelligence has affected every aspect of life, from leisure to work. 

Business Front 

At work, artificial intelligence has improved manpower morale and business efficiency. If a company is looking to hire employees, artificial intelligence programs help in identifying ideal profiles to create an interview pool. Business giant JP Morgan and fast-food company McDonald’s uses Pymetrics which is an AI-powered software that collects data to process potential candidates for company hiring. Pymetrics assess profiles on the basis of the person’s resume and objective behavioral data that is checked according to the requirements of the company. 

Social Networking 

Facebook is notoriously famous for its controversies. Artificial intelligence helped Facebook’s algorithm detect all types of content that violated its social hate policy. As a result of that AI-program, 97% of the hate-fueling content was removed without being reported by the users. Not just this, the ads you actively see on any social media platform are backed by AI and machine learning. 

Academics

Coronavirus drastically changed everyone’s daily routine. One of the changes was online school/college/university classes. This distance learning has put significant pressure on educational institutes to retain student attention during virtual tutoring. Affective Spotlight, a Microsoft tool, uses artificial intelligence to test the attention levels during the class. It analyses body movements, facial expressions, body language which is given numerical values for the participants. 

Healthcare 

Not swaying away from the topic of the on-going global pandemic, the healthcare sector had to make too many dynamic changes to serve better patient care. Many hospitals and clinics adopted telemedicine practices where they put AI-powered bots for first-level patient interaction (making appointments), providing initial diagnosis, reminding patients to take medicines on time, educating them about common tests and procedures, etc. AI is also helping in the vaccine drive by identifying asymptomatic COVID-19 patients and digitizing patient files for a better digital tracking system. 

Gambling 

Gambling involves taking big risks. Rdentify uses artificial intelligence to monitor live chat and identify problem gamblers. Its AI technology can monitor live chats and online gambling habits, providing players with a scoreboard of risk percentage for every game. The machine learning system then uses the risk percentage to intimate the player with a good time to leave the game and also highlights if a person is addicted to gambling. Rdentify has been adopted by many AAMS safe online casinos. This application’s scoring system is in sync with the operator’s CRM which helps in calculating the risks in real-time. Thanks to that, grave gambling risks will be reported immediately to customer support. To control the growing gambling issues in many parts of the world, Rdentify has partnered with AgeChecked, a verification and monitoring software. 

Artificial Intelligence makes our lives better in many ways, be it leisure or jobs. Artificial intelligence has a bright future in human life as scientists continue to work on this ever-developing technology. We are already seeing chatbots, virtual assistants, home robots making simple life decisions for us, be it telling Siri to make a grocery list to using Rdentify to stop gamblers from burning a hole in their wallets.

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AI and ML in intelligent analytics can drive the efficiency in business

Analytics has been changing the way organizations operate for a long while. Since more organizations are dominating their utilization of analytics, they are diving further into their data to build proficiency, acquire a more prominent upper hand, and lift their bottom lines significantly more.

Analytics powers your business, however, what amount of value would you say you are truly harnessing from your data?

Artificial intelligence and machine learning can help. Artificial intelligence is a collection of technologies that extract patterns and valuable insights from huge datasets, then making forecasts dependent on that data. Truth be told, AI exists today that can assist you with getting more value out of the data you as of now have, bind together that data, and make forecasts about customer behaviors based on it.

The adoption of AI has been driven not just by increased computational power and new algorithms yet additionally the growth of data now accessible. For intelligence analysts, that multiplication of data implies surefire data over-burden. Human analysts essentially can’t adapt to that much information. They need assistance.

Intelligence leaders realize that AI can assist to adapt to this data downpour yet they may likewise consider what sway AI will have on their work and staff. For example, Twitter utilizes machine learning and AI to assess tweets in real-time and score them utilizing different measurements to show tweets that can possibly drive the most engagement.

Google is researching virtually every part of machine learning and is making advancements in old-style algorithms and different applications like speech translation, prediction systems, natural language processing, and search ranking.

Artificial intelligence plays a significant part in assisting organizations with handling data without forfeiting accuracy or speed.

With digital transformation widely being embraced, the volume and size of data have expanded significantly. Also, dealing with such gigantic data isn’t simple. Artificial intelligence- fueled data-driven innovation can help organizations manage such data to guarantee importance, worth, security, and transparency. They can depend on AI data integration platforms to ingest, change, and use information easily and with accuracy. Such platforms give an end-to-end encrypted environment that protects information from undesirable infringing and breaches, and make them hard to work with.

Artificial intelligence and ML frameworks exist that utilize analytics data to assist you with foreseeing results and effective blueprints. Artificial intelligence- empowered frameworks can analyze information from many sources and deliver forecasts about what works and what doesn’t. It can likewise deeply jump into information about your customers and offer predictions about buyer inclinations, marketing and sales channels, and product development strategies.

Artificial intelligence/ML advances empower companies across various industries to harness value from customer information with no trouble. For instance, AI data integration solutions empower all business users to map information between various fields to make it simpler to incorporate the data into a unified database. Since these arrangements can be effortlessly utilized by non-technical users, IT people need not assume full responsibility. This leaves IT to zero in on other vital tasks.

These solutions use ML algorithms to provide predictions of data, which can additionally quicken the data transformation process. Since the decisions are taken utilizing algorithms, the chance of mistakes like missing qualities, deceptions, errors, and so on, reduce. Hence, companies can use AI/ML tools to change the manner in which they deliver customer value. They can plan and integrate data and keep up data integrity, improving decision-making and boosting growth.

The advantages of AI and ML, notwithstanding, can go a long way beyond time savings. All things considered, intelligence work is a never-ending process; there is consistently another difficulty that demands attention. So saving time with AI won’t decrease the staff or trim intelligence budgets. Or maybe, the more noteworthy value of AI comes from what may be named an “automation dividend”: the better ways experts can utilize their time after these advances reduce their workload.

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This intelligence report includes research based on current scenarios, historical records and future forecasts. In this research report, specific data on various aspects such as type, size, application and end user were checked. It offers a 360-degree overview of the competitive landscape of industries. The SWOT analysis was used to understand the strengths, weaknesses, opportunities and threats faced by companies. This helps companies understand the threats and challenges ahead. The Intelligent Apps market is growing steadily and the CAGR is expected to improve over the forecast period.

The intelligent apps use the functionalities of AI, cognitive computing, big data and analytics, and others to provide an advanced analytical output that can be utilized for different applications, such as prioritizing emails, security tooling, virtual personal assistants, virtual customer assistants, enterprise applications, and others.

Our report covers the critical market information considering the rapid progression & wide-ranging impacts of COVID-19 virus on the global economy, and help you understand which countries or business segments are likely to get most affected.

Few of the main competitors currently working are –

IBM, Apple Inc., Ayasdi AI LLC, BigML, Inc., Google, H2O.ai, Hewlett Packard Enterprise Development LP, Microsoft, Oracle, SAP SE

THE INSIGHT PARTNERS RESEARCH REPORT GUIDANCE

The report provides qualitative and quantitative trends of global Intelligent Apps across type, deployment, organization size, end-user, and geography.
The report starts with the key takeaways (chapter two), highlighting the key trends and outlook of the global Intelligent Apps .
Chapter three provides the research methodology of the study.
Chapter four further provides ecosystem analysis along with PEST analysis for each region.
Chapter five highlights the key industry dynamics in the Intelligent Apps , including factors that are driving the market, prevailing deterrent, potential opportunities as well as future trends. Impact analysis of these drivers and restraints is also covered in this section.
Chapter six discusses the global Intelligent Apps scenario, in terms of historical market revenues, and forecast till the year 2027.
Chapter seven to eleven discuss Intelligent Apps segments by type, deployment, organization size, end-user, and geography across North America, Europe, Asia-Pacific, Middle East and Africa, South and Central America. They cover market revenue forecast, and factors driving and governing growth.
Chapter twelve describes the industry landscape analysis. It provides detailed description of various business activities such as market initiatives, new developments, mergers and joint ventures globally along with a competitive landscape.
Chapter thirteen provides the detailed profiles of the key companies operating in the global Intelligent Apps . The companies have been profiled on the basis of their key facts, business description, products and services, financial overview, SWOT analysis, and key developments.
Chapter fourteen, i.e. the appendix is inclusive of a brief overview of the company, glossary of terms, contact information, and the disclaimer section.

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The world is becoming more automated – from collaborative robots through to computer programs which can sift through thousands of documents almost instantaneously, organisations can now save time and money in new ways. The technology can now be used for necessary but tedious, time-consuming tasks that would take humans much longer and be more prone to error. However, there are aspects of automation which are misunderstood and misrepresented – I talk here about artificial intelligence (AI), where the hype is spreading faster than the aptitude of the technology.

Subsets of AI – like machine learning or deep learning are often referred to as AI, when they are not. In fact, they are closer to intelligent automation than artificial intelligence. Intelligent automation (IA) can help organisations by using existing data and automating analysis based on that data, ultimately helping to improve operations and workflow, as well as reducing redundant responses. But neither technology is truly “intelligent” in the sense that they cannot think or act like humans. We are many years away from that.

Artificial Intelligence – will it reach its true potential?

Artificial intelligence is certainly a buzzword in security, yet many capabilities are misinterpreted, undefined or misunderstood. Misunderstanding the capabilities of AI will often lead to unrealistic expectations.

In data science, AI refers to a fully functional artificial brain that is self-aware, intelligent and that can learn, reason and understand. While advancements in what is referred to as AI technologies have come a long way and will continue to do so, the reality of AI, however, is very different from an intelligent computer that can learn and make decisions like a human.

In practice and as it relates to the physical security industry, AI is a technology that runs a series of algorithms, searches through large databases or does calculations swiftly to provide deeper insights. The results can help users make decisions more quickly and efficiently depending on the application. General examples of applications that fall under “AI” would be facial recognition, object detection or people counting.

However, the broad nature of the term means that often, expectations and hype exceed its capabilities, and causes disappointment. Currently, only its subsets are tangible, such as machine learning techniques that include neural networks and deep learning. For example, deep learning uses task-specific algorithms to help train a computer to properly classify inputs. To do this, programmers essentially teach a computer by inputting a very large amount of data with corresponding labels, improving the technology’s ability to recognise new inputs. In a real-life scenario, deep learning is being used very effectively in automatic number plate recognition (ANPR).

It is possible to train the system by feeding it raw images of number plates, alongside parameters for it to work within, so it knows it can only class said images with possible outputs. This then lets the system take an image of the back of a car it has not seen before and identify the characters on the plate, along with other useful information such as location, colour and model. For a human, this would be a tedious and time-consuming task – but it’s ideal for computers with minimal human supervision.

Another vertical where machine learning can offer significant value is retail, thanks to its ability to monitor and identify trends. For example, such technology can help stores determine retail conversion rates or the number of people visiting a location versus purchasing. A high-accuracy deep learning algorithm can track the number of visitors and combine this with sales data to present valuable information to a human operator.

While highly advantageous for well understood applications, current AI technology has its limitations. Specific use cases and algorithms can certainly help organisations achieve greater operational efficiency, but it cannot teach itself completely new tasks or automatically make sense of data that it hasn’t been first taught. In addition, it can be difficult for users to interpret how an AI technology, such as deep learning, came to a decision or output.

AI does not give meaning to something on its own, but it can allow users to make more knowledgeable decisions and perform tasks more efficiently. If investigators run a face captured on video through a facial recognition database, for example, it’s important to know that faces returned as matches are not guaranteed positive matches but rather meet the probability requirements previously programmed into the algorithm. In other words, it can be a very valuable and necessary start for users to eliminate some mundane legwork and facilitate more informed investigations or decisions, but should only ever be used as a tool to aid human decision-making.

This is well illustrated by Met Police trials of facial recognition where all potential matches are sent to a trained officer to perform a secondary verification. The trials take advantage of the fact the technology can look at many more faces over a much longer period of time without getting tired than any police officer. Yet there is an acceptance that the trained human must have the final say as they have wider decision-making capabilities than any machine on earth today.

Defining Intelligent Automation

Intelligent automation also allows users to eliminate the donkey work, helping them make quicker, more informed decisions. It can automate some of those decisions too, as it integrates both automation and data together to recommend an outcome. One way to look at IA is that it uses an organisation’s existing data from different technologies and enables large-scale analysis to automate operations and improve productivity.

In security, IA can amalgamate and automate differing datasets, such as thermometer readings, video, incidents, facial recognition, number plate recognition, map-based data and other records. This correlation means humans are then able to assess specific problems or situations, instead of being presented with calculations of different databases but no combined conclusion. Like most automation platforms, IA is at its most potent when deployed for specific solutions.

For businesses to get the most out of intelligent automation, the technology should have a clearly defined environment where the emphasis is on the human input with machines doing the heavy lifting and not on the machines making decisions. With IA, humans review and approve machine decisions to help better drive outcomes.

In terms of deployment, critical national infrastructure is a strong example. These buildings have multiple systems, including temperature sensors, airflow sensors and a centralised security system. Intelligent automation can be used to automatically pull video footage, send a map of where an incident is located, and sound an alarm in the event that both the temperature spikes greatly and the airflow sensor states “danger,” suggesting the possibility of a fire or chemical spill. The technology can help enable an efficient response by initiating a specific standard operating procedure (SOP) when necessary – such as unlocking specific doors, notifying management, etc.

Whilst generating time and financial savings, IA can help create a culture of innovation. Automating processes frees up employees, allowing them to concentrate on more interesting and stimulating skilled tasks – learning more and yielding more value in turn.

Accuracy needs to be the priority, not marketing

There’s no denying that using buzzwords like AI makes a product sound sexy – even if it’s not entirely accurate. From toothbrushes to security programs, we regularly see the term misused and it’s time for that to stop. There is great potential for automation to benefit businesses by making more of data – thereby uncovering insights and informing decisions which was previously not possible.

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As per UiPath, 2014 was the moment when robotic process automation began to be a noteworthy contender to business process outsourcing. A while later, it took just two additional years until it began to be institutionalized by business organizations. Where are we today? We are at a point where both adoption and scaling have progressed colossally, and RPA has arrived at new degrees of development, turning into an unquestionable requirement for organizations resolved to seek after a real competitive advantage.

To put it in an unexpected way, numerous companies have learned at this point productive approaches to beat the challenges of RPA implementation and deployment, and automation has evolved into a core technology. In spite of the fact that this can give useful assistance to white-collar workers who perform business processes, it’s something a long way from a human commitment in the workplace, representing a deterministic type of business process management.

In the past few years, we’ve seen the headway of rising innovations, for example, artificial intelligence (AI) and analytics. With deep learning, you can train a neural system to mirror the human cerebrum and with machine learning, you can analyze huge amounts of data to discover patterns that illuminate great decisions.

The solid progressions of these advances are inescapable and organizations need to harness them to improve their operations. These large shifts and developments are driving the digital “workforce” transformation. So as to receive the best reward, organizations must be aware of and plan for this change. However, likewise with any good thing, additionally there are challenges and purposes of disappointment involved.

Inability to automate end-to-end processes

For the more complex processes, RPA tools might be deficient for straightforwardly automating all the process steps. “Divide and conquer” is the prescribed approach to this. Upgrade these sophisticated tasks, break them into less complex parts, and start automation here. Also, try to use the joint work of RPA and other digital technologies like machine learning or optical character recognition. Remember, however, the additional costs required by this, so don’t go for end-to-end intelligent automation when cost-efficiency becomes questionable.

Security by design in IA

With any technology, security totally should be a primary need. For IA the greatest security issue regularly emerges at where human and machine connect. For instance, human error during an automated financial reporting process can bring about losing-man weeks, not days and a deferral on the reporting of the group’s finances. Other security issues that should be considered incorporate rebel get to; data loss; hacking; privilege abuse; vulnerabilities and malware, which all show the centrality of security to IA deployments.

However, similar to well backed up data, everything can’t be lost! Those hoping to deploy IA should notice security conventions like encrypting information and different layers of verification, alongside decreasing access rights and requiring human approval on specific procedures.

Selection of Appropriate Process

Not all procedures are appropriate for automation. You should identify processes with clear processing instructions (format driven), in view of institutionalized and prescient principles. Procedures that require a high level of manual info, structured and repetitive input involve exercises that are increasingly susceptible to human error; this is the reason they are additionally good candidates for automation.

Another plan to be considered in the selection procedure is that the more steady a business task, the more smooth and viable (and along these lines cost-effective) its automated version. Relatedly, processes with measurable savings will make it simpler to assess realistically the impact of RPA on your company.

The need of Appropriate Skills

Soft skills are a significant establishment to build upon. For instance, we should consider problem-solving: It’s true that IA can tackle a few issues that people can’t. In any case, when issues aren’t completely characterized, people can utilize their reasoning aptitudes to make sense of a solution that machines wouldn’t have the option to discover.

Besides, the ability to team up and conceptualize to make new thoughts is a component that can boost business processes and optimize performances and it has nothing to do with robots. These are human abilities and represent the additional worth that automation essentially can’t offer. Therefore, organizations should concentrate throughout the following decade on the way that, in spite of the fact that machines will bit by bit become all the more powerful, people will really be even more important since innovation will go about as an integrator and not as a substitution for abilities required.

The jobs of the future will require a reskilling of the human workforce. The response to digital disruption lies in our ability to apply humankind to the new challenges that emerge. IA and AI have just been shown to have significant and expansive advantages and, while it is troublesome if not impossible to foresee with complete certainty where the innovation will go next, what is increasingly sure is that its utilization will just multiply.

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Amid accelerated innovation in fields like artificial intelligence, IoT, and data analytics, the smart cities movement has picked up momentum in recent years. With 68 per cent of the global population set to live in urban areas by 2050, according to United Nations estimates, the push to make the world’s cities more connected, efficient, and citizen-friendly comes at an opportune time.

Big data lies at the heart of smart city innovation. Drawing on data from connected devices, public agencies, private citizens, and more, cities will be able to optimise their operations and effectively manage change as more and more people call them home.

The payoff will be significant. The McKinsey Global Institute calculates that smart city technologies have the potential to boost key urban quality-of-life indicators by 10 to 30 per cent. While the benefits of smart cities will be far-reaching, three areas in particular – traffic management, public safety, and infrastructure maintenance – attest to why data-driven innovation is crucial to the future of urban life.

Intelligent traffic management

• What are Smart Cities? Everything you need to know

Talk of transportation in the smart city often centres on the distant prospect of autonomous vehicles, but cities are already harnessing data to deliver big improvements in urban mobility. This trend will only accelerate over the coming years, with revenue from traffic-focused smart city technologies more than doubling from $2 billion in 2019 to $4.4 billion in 2023.

Data collected by IoT sensors and CCTV cameras, for instance, can be utilised to help city planners address bottlenecks, make traffic flow more efficient, and reduce congestion. Citizens also benefit from open data: With real-time access to traffic information, for instance, commuters can better plan their journeys and avoid congestion. 

Traffic management platforms already exist that combine data from public agencies, connected cars, camera feeds, IoT sensors, mobility apps, and many other sources – just one example of how the intelligent use of big data can not only enable better traffic management, but also save lives.

From cameras and sensors that can monitor parking availability, to traffic lights fed data in real time to facilitate more efficient traffic flow, big data is fuelling a new wave of transportation innovation – and while such solutions are a boon to city officials and municipal planners, the end users – ordinary citizens – are the biggest beneficiaries.

Promoting public safety

• What makes smart cities, smart?

Public safety is on track to be a major growth market for smart cities, with forecasters calling for the market to reach $295.98 billion by 2023.

What does public safety technology look like in practice? It’s the use of GPS data to find missing persons. It’s the collection of data from IoT devices and sensors to feed vital information to emergency responders for more accurate dispatching. It’s the deployment of AR-equipped drones that overlay critical information for emergency responders, allowing for more effective search-and-rescue operations during major events like the California wildfires.

It’s also the use of data-driven policing – through heat maps, gunshot detection technology, smart cameras, and more. Such use cases have sparked fears of Big Brother’s encroachment, and while it will be crucial to balance civil liberties and public safety, smart city planners can’t afford to lose sight of the promise of big data for public safety. McKinsey projects that smart city public safety solutions can reduce fatalities by up to 10 per cent, crime incidences by up to 40 per cent, and emergency response times by up to 35 per cent.

Managing smart cities’ infrastructure

• How smart cities can underpin our drive to a sustainable world

Population growth and climate change will both pose significant, overlapping challenges to cities’ infrastructure over the coming decades – but big data can help city planners adapt.

Monitoring hard-to-reach areas, drones can generate rich, actionable data to guide decisions on infrastructure repair and maintenance, thereby preventing potentially deadly structural deficiencies from going unaddressed. We have all seen the catastrophic damage that can be caused by an oversight of dam maintenance — vulnerabilities that will more likely be caught with the use of smart data to monitor and check for anomalies.

Beyond damage prevention, the use of data not only keep structures physically sound, but also keeps cities’ arteries pumping. Supply chains depend on smooth roads, sound bridges and well-functioning trains — without them, economies go into freefall. Utilising data, governments can ensure these lifelines’ long-term integrity.

Data on resource availability, supply and demand, and climate will enable smarter electric grid management, as IoT devices feed operators information in real time to drive more intelligent decisions about supply management during both peak and off-peak hours.

For municipalities aspiring to be truly smart cities, having a robust data strategy in place is paramount. The data they use to streamline and optimise their operations must be rich and abundant – which is why city planners must take a holistic approach to data collection and sharing. This will entail public-private collaboration and continued investments in analytics capabilities. When cities commit to this approach, they’ll be significantly smarter – and their citizens will be much better-off.

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Microsoft reported stronger results for its fourth quarter (ended June 30) that it said were once again helped by continued growth in its Azure cloud computing business.

Revenue in Microsoft’s overall Intelligent Cloud business grew 19% from a year earlier, to $11.4 billion, the company said July 18. Within that business, server products and cloud services revenue increased 22%, “driven by Azure revenue growth of 64%,” it said in an earnings news release.

But the cloud continued to drive growth across other areas of Microsoft’s business also. For example, revenue in Productivity and Business Processes increased 14% to $11 billion. Within that segment: Office Commercial products and cloud services revenue grew 14%, driven by Office 365 Commercial revenue growth of 31%; Office Consumer products and cloud services revenue increased 6% as Office 365 Consumer subscribers increased to 34.8 million; and Dynamics products and cloud services revenue jumped 12%, driven by Dynamics 365 revenue growth of 45%, Microsoft said.

Revenue in the More Personal Computing business segment inched up 4% to $11.3 billion, with standouts in that segment including a 13% jump in Windows Commercial products and cloud services revenue. The one weak area was gaming, where revenue fell 10%, with Xbox software and services revenue dipping 3%.

Total Microsoft Q4 revenue grew 12% from a year earlier, to $33.7 billion, while profit increased to $13.2 billion ($1.71 a share) from $8.9 billion ($1.14 a share).

One positive sign for Microsoft is that “customer commitment to our cloud platform continues to grow,” CFO Amy Hood told analysts on the company’s earnings call. During the fiscal year that just ended, “we closed a record number of multi-million dollar commercial cloud agreements, with material growth in the number of $10 million plus Azure agreements,” she said.

Microsoft CEO Satya Nadella used the call to highlight Azure’s strength, along with several new cloud advancements on multiple fronts, including artificial intelligence (AI).

“Azure is the only cloud that extends to the edge – spanning identity, management, security and infrastructure,” he said. This year, Microsoft introduced new cloud-to-edge services and devices – including Azure Data Box Edge, Azure Stack HCI and Azure Kinect – that he noted bring “the full power of Azure to where data is generated.” While Azure Sphere is a “first-of-a-kind edge solution to secure the more than nine billion MCU-powered endpoints coming online each year,” Internet of Things (IoT) Plug and Play “seamlessly connects IoT devices to the cloud without having to write a single line of embedded code,” he said.

Calling Azure the “most open cloud,” he pointed out that, in Q4, Microsoft “expanded our partnerships” with Oracle, Red Hat and VMware “to make the technologies and tools customers already have first-class on Azure.”

Azure, meanwhile, is “the only cloud with limitless data and analytics capabilities across the customers’ entire data estate,” he said, adding: “The variety, velocity and volume of data is increasing, and we are bringing hyper-scale capabilities to relational database services with Azure SQL Database. New analytics support in Cosmos DB enables customers to build and manage analytics workloads that run real-time over globally distributed data. And we offer the most comprehensive cloud analytics – from Azure Data Factory to Azure SQL Data Warehouse to Power BI.”

The “quintessential characteristic for any application being built in 2019 and beyond will be AI,” he went on to predict, telling analysts: “We are democratizing AI infrastructure, tools and services with Azure Cognitive Services – the most comprehensive portfolio of AI tools – so developers can embed the ability to see, hear, respond, translate, reason and more into their applications. And this quarter we introduced new speech-to-text, search, vision and decision capabilities. New updates to Azure ML streamline the building, training and deployment of machine learning models – bringing a no-code approach to machine learning.”

Microsoft’s “differentiated approach – from developer tools and infrastructure to data and analytics to AI – is driving growth,” he said, boasting “the world’s leading companies trust Azure for their mission-critical workloads, including more than 95 percent of the Fortune 500.” On that front, AT&T recently selected Microsoft’s cloud in “one of our largest cloud commitments to-date,” he noted.

Moving up the stack to business process, he also noted that Microsoft’s Dynamics 365 “uniquely enables any organization to create digital feedback loops that take data from one system and use it to optimize the outcomes of another, enabling any business to become an AI-first business.” The company recently introduced Dynamics 365 AI, a new class of AI applications, he pointed out.

Microsoft is “infusing AI across Microsoft 365 to enable new automation, prediction, translation and insights capabilities,” he went on to say. For example, with Workplace Analytics and Microsoft Search, “we take your relationships, schedules and activities and distill insights and knowledge – to help people work smarter, not longer.”

Microsoft is also “investing in cybersecurity to protect customers in today’s ‘zero trust’ environment,” he said, adding: “Microsoft is the only company that offers end-to-end security – spanning identity, device endpoints, information, cloud applications as well as infrastructure. It starts with Azure Active Directory and builds with three new services we introduced this year”: Microsoft Threat Protection, Azure Sentinel and Azure Confidential Computing.

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LONDON and AUSTIN,  Texas, July 15, 2019 /PRNewswire/ — Blue Prism (AIM: PRSM), a leader in Robotic Process Automation (RPA), today announced plans to deliver leading-edge intelligent automation solutions and cognitive services on Microsoft Azure.

The solutions will enable an on-demand Digital Workforce and contribute to Blue Prism’s goal of delivering its connected-RPA platform on Azure, allowing customers to accelerate automation projects, increase productivity, and improve customer experiences without needing capital investment in infrastructure.

“Microsoft’s vision for AI and our Azure AI platform centers on combining the power of the Azure cloud with decades of breakthrough research to deliver more innovation, flexibility and operational agility for our customers,” says Lance Olson, Director of Program Management, Applied AI, Microsoft. “Using Azure AI services like Form Recognizer, we look forward to working with Blue Prism to build intelligent applications that extract insights in forms, receipts, and other content to drive better business decisions.”

The collaboration will integrate Blue Prism’s automation capabilities with Azure AI technologies including Azure Cognitive Services and Azure Machine Learning as well as offering access to Microsoft Office 365. It’s also possible to leverage technologies like Microsoft Flow, Logic Apps, PowerApps and Power BI with Blue Prism, and the plan is to invest in these and other areas more deeply in the near future.

This news falls on the heels of the announcement of a free, fully functional enterprise trial of Blue Prism on the Microsoft Azure Marketplace. This offering gives new users access to Blue Prism’s award-winning RPA software on Azure through a quick and easy deployment process – no additional components needed. Blue Prism plans to reveal similar capabilities with other leading cloud vendors shortly.

“Partnering with Microsoft gives Blue Prism a roadmap for evolving Digital Worker capabilities with broader AI and cognitive capabilities,” says Dave Moss, CTO and co-founder at Blue Prism. “We want to drive consumption and adoption of connected-RPA with all users by providing a full range of intelligent automation possibilities. The future of work lies with those who embrace a Digital Workforce and empower people to do the things only people can do.”

Blue Prism also announced its plans to acquire Thoughtonomy, a provider of  a fully-integrated SaaS platform on Microsoft Azure. These announcements strengthen the company’s track record of success with customers looking to deploy RPA solutions on Microsoft Azure.

For more information and to sign up for the Blue Prism trial, visit: Blue Prism Robotic Process Automation on the Azure Marketplace. Blue Prism is at Microsoft Inspire this week, so feel free to drop by booth #1611 to find out more about running RPA in the cloud.

About Blue Prism
In this digital era where start-ups are constantly disrupting markets, only the most agile and innovative enterprises survive and thrive. At Blue Prism, we pioneered Robotic Process Automation (RPA), emerging as the trusted and secure intelligent automation choice for the Fortune 500 and the public sector. Now we bring you connected-RPA supported by the Digital Exchange (DX) app store—marrying internal entrepreneurship with the power of crowdsourced innovation.

Blue Prism’s connected-RPA can automate and perform mission critical processes, allowing your people the freedom to focus on more creative, meaningful work. More than 1,300 global customers leverage Blue Prism’s digital workforce, empowering their people to automate billions of transactions while returning hundreds of millions of hours of work back to the business. Visit www.blueprism.com to learn more about Blue Prism (AIM: PRSM).

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