The automation of work has already begun, but according to a new whitepaper, not all employees have to be worried about losing their jobs to machines.
While the Internet has allowed for the rise of employees collaborating remotely; developments in artificial intelligence have also prompted more automation at the workplace.
How will these trends shake up the workplace in 2017? Here are four key factors to look out for, as based on a new whitepaper by Compass Offices.
Automation will take over, but employees might keep their jobs
According to employment portal Glassdoor, the ongoing fear that automation will render many people jobless appears to be unlikely.
It found that the roles most likely to be affected by automation will be routine jobs that do not require much creative judgement or flexibility.
For example, these would be jobs that involve answering generic emails or scheduling meetings. To counter this, workers need to develop skills that are complementary to technological advances, instead of working on the same tasks that machines will one day be able to automate.
This is currently happening in Singapore where Robotic Process Automation (RPA) – the practice of using software to automate structured workflow – is being implemented at the enterprise level. While humans will still be needed, the incoming trend is that business tasks will become increasingly codified.
HR will transform with the help of big data
While this point has been widely discussed, it still bears repeating.
HR will transform into “people science,” due to the rise of big data. Thanks to all the data gleaned from staff and customer base, businesses will be able to make better decisions, have faster turnaround times on projects and cater better to customers.
These could include being able to track an employee’s progression stages throughout a company, from onboarding to annual review. Being able to gather real-time feedback from staff is also another way to foster and retain happy workers.
Employee well-being will become a priority
Improving the well-being of employees has been increasing in importance over the past few years.
Studies show that cultivating good company culture, developing an attractive work environment and investing in the professional future of your employees leads to more productivity, engagement and retention.
The Compass Index, an annual survey of 1,200 workers across Asia Pacific, showed that 65.3% of workers in China consider “career development” their key motivator. This indicates that upward mobility is one of their top priorities.
In Hong Kong, where a mix of C-suites and managers were surveyed, “work environment” was considered by 29% the most important element at work, while “career development” came in at 26%.
Workers will look beyond compensation for satisfaction
Despite compensation being important to surveyed workers, high pay doesn’t move the meter much in terms of employee satisfaction. In its employer review survey, Glassdoor notes that “culture and values” was the number one important factor for respondents.
Today’s workers are looking for more than just a good paycheck; they want a place of work that resonates with their values.
But compensation still carries weight, as it is an indicator of opportunity for upward mobility at a company.
In fact, the Compass Index reveals that workers are optimistic about getting a pay raise this year. Respondents in the Philippines (65.6%), Hong Kong (43%), Singapore (38.8%) and Australia (34.4%) all replied hopefully when prompted for their thoughts on increased compensation this year.
The use of robotics and artificial intelligence in businesses is on the rise, but there are still significant challenges for organisations adopting the technologies. Two executives from global IT consulting and outsourcing group Capgemini spoke to IoT Hub about how best to meet these challenges and why the returns make the effort worthwhile.
“The amount of data that’s available now in places like social media and enterprises means it is becoming for efficient for machines to make decisions rather than humans, taking the human bias out of it and making decisions objectively,” said Saugata Ghosh, senior manager of digital services at Capgemini.
This trend, together with the maturity of robotic process automation (RPA) technologies over the last three to five years, has contributed to the growth in adoption of robotics and AI, Ghosh said.
“If you look at the spectrum of robotic automation, at one end you have simple rules-based automation where the economics of those are such that they are quite easy to implement and have strong returns on investment,” he explained.
“At the other end, towards the cognitive and artificial intelligence side, you’re also seeing accelerated maturity, with things such as driverless vehicles making it possible to automate tasks that we wouldn’t have previously thought of automating a few years ago.”
Ghosh is also observing convergence between both ends of the automation spectrum.
“In real life, many processes have an element of both. For example, in the case of email feedback analysis, the interpretation of the body of the email is within the realms of cognitive or pattern recognition, while the processing of the email once it has been analysed could be rules-based,” he said.
Ghosh has noticed a trend in the motivations of deploying RPA technologies from that of cost-saving to improved accuracy and customer experience.
“Initially, everybody was after headcount reduction. Most people are telling us now that their focus is on reducing errors, improving compliance, or improving the customer experience,” he explained.
“We’re certainly seeing maturity in this area and the focus has shifted from the tactical to more strategic and sustainable objectives.”
Hilda Carmichael, director of digital program delivery for digital services at Capgemini, added: “The ambition particularly around more traditional finance, HR and IT functions is to have better business partnering capabilities by eliminating more of those manual tasks, freeing capacity to properly engage with customers instead of being distracted with repeated, administrative tasks.”
How to meet the challenges
Despite the benefits that automation technologies can provide, Ghosh said that there are a number of challenges that businesses face when adopting AI.
“All organisations recognise the potential for RPA to significantly transform their business, but they have questions as to how they get started,” he said.
“These organisations may also have a good sense of what it takes financially to do a pilot or a proof-of-concept, but are aware that just because the entry barrier to adoption is low, they must also prevent uncontrolled proliferation of these technologies across the enterprise.”
“It all comes down to scope,” Carmichael added. “Companies need to pick a candidate set of processes by which they have a span of control that they can deploy initially.”
“Processes that cut across multiple functions within an organisation will require a greater set of engaged stakeholders.
“So start small, start with a number of high-volume, manual, repetitive set of processes that’s within your span of control, and go away and prototype that.”
Carmichael also said that business units should work together to build the business case and realise the potential of RPA.
“It doesn’t matter who leads the charge, whether it’s the business or IT, but there has to be a partnering component to it,” she explained.
“The business needs to determine and help codify the business rules, and IT needs to determine the infrastructure and scalability of the solution.”
Image Credit: Thinkstock
Robots are no longer a Sci-Fi dream, and are well on their way to establishing a reality that we all fantasized about. Artificial intelligence coupled with advancing technology has ensured that the next generation robots are brought to the aid of the service sector. The bots are finally demonstrating a remarkable ability to perform hard, dangerous, or menial jobs. These include tasks such as moving around heavy objects, providing customer assistance, aiding disabled people & patients, or even for security purposes in the defense sector. The service robotics industry is touted to soon take over from the human work in the next few years. This is due to its increasing adoption by diverse verticals for domestic as well as commercial purposes.
Warming up these cold machines, the populace is slowly accepting that robots make lives much easier and fruitful by saving them a lot of time. The exciting possibilities of employing the use of these intelligent machines in different applications has promised great potential for the service robotics market. Experts at Allied Market Research have observed that professional service robots contribute to a greater share in total market revenues, as compared to personal service ones. The industry promises endless prospects for tapping the potential of these smart bots.
Agro-robots: Revolutionizing agricultural techniques
The humble farmer who usually brings the grain to our table will soon be a state-of-the-art intelligent machine. Agriculture industry is set to witness a major revamp as increasing number of farm jobs can be accomplished by using robots. The bots will soon replace human labor force in agriculture, and eliminate the high costs associated with employing people to sow and harvest crops. The rising demand for essential food crops can be met with the deployment of robots in the various farm activities and minimize the time taken by each task.
Robotic engineers and researchers are coming up with new innovations to program robots for working in the agriculture sector. For instance, researchers from UK’s Harper Adams University have attempted to grow and harvest a complete hectare of cereal crops through intelligent machines. The project is entitled as Hands Free Hectare, and is based on the idea of precision farming. It aims to completely do away with the need for human workforce on the field. The project is one of the most ambitious projects, as it had been led in collaboration with precision farming specialist, Precision Decisions.
“We believe there is now no technological barrier to automated field agriculture. This project gives us the opportunity to prove this,” says Kit Franklin, one of the Hands Free Hectare researchers at Harper Adams University. Similar such researches are aimed at developing autonomous agriculture technologies that can introduce driverless tractors, crop irrigators and harvesting machine.
Humanoid robots to make banking fun & interactive
Banks, usually considered as uninteresting and serious places are now ready for some introduction of fun elements. Developers have come up with robots that are aimed at making banking a more interactive and personal experience. To see a cute robot, walk up and communicate politely with you when you enter a bank would certainly add to the “cool” factor. Alderan Robotics and SoftBank has teamed together and designed a humanoid robot, Pepper, which has the ability to read emotions. The artificial intelligence-backed robot was announced in 2014 and has already found place in major stores and banks across Japan and Europe. Asian countries, such as Taiwan have been quick to adopt these bots in their workplaces. Taiwan’s biggest insurer, Cathay Life Insurance, introduced its first mini ‘Pepper’ robot in its branch. The intelligent humanoid machines greet customers as they walk in, read their facial expressions and body language, and interact accordingly. They have made for a more engaging and fun experience, and are expert marketing tools. Pepper robots also provide information on financial products and even guide customers through the bank to intended departments.
“Pepper’s job is to greet customers and introduce products to make the wait for services less boring,” said Rachel Wang, the insurer’s executive vice president. Apart from providing ease of service and serving different functions, such robots are extremely effective in ensuring that customers are impressed by the organization’s marketing skills and stay loyal to the brand.
Intelligent machines to be the future of healthcare
It is extremely vital that the most intelligent machines created by mankind should also serve for human wellbeing. Healthcare is another area where robots can help revolutionize the practices in the industry. Engineers and medical researchers together have been continually developing robots that can cater to the needs of the patient and decrease the recovery time. Nano robots are the new rage among surgeons, where these tiny engineered devices are inserted into the body, and programmed to tackle cell damage and repair tissues within the body. This can potentially alter the way medicine is combined with technology for advanced healing techniques.
Moreover, robots are also being used by surgeons for aiding in complex surgeries and treatments. Along with these, the cold machines are also being made more humane and gentle, to help patients recover in hospitals. Elderlies and physically challenged patients are also reaping the benefits of having a human robot at their disposal, which can provide assistance to perform simple tasks, as well as remind them about medication. The robotic revolution is expected to be of great support to the healthcare industry, as it can realize tasks that are menial, intricate, or even potentially dangerous.
With the robotics revolution taking the world by storm, people across various verticals are warming up to the idea of these cold machines. Millennials are fascinated as well as amazed at the wide gamut of operations a robot is capable of achieving. Emerging nations are investing their resources to bring life to machines and create a task force of robots that can do menial, odd, or boring jobs that would make human beings more employable in other deserving areas. Robots are achieving more complicated levels of functionality and thus proving more effective than a human labor force. With a more futuristic outlook being adopted, the service robotics market can be counted on for meeting the demands of a fast-paced dynamic world.
The exciting change is applying deep learning and high-performance computing to achieve greater automation and accuracy in the interaction between computers and people
Oliver Schabenberger, SAS
“Cognitive computing is disruptive, combining technologies such as natural language processing, image processing, text mining and machine learning to augment human intelligence,” says Oliver Schabenberger, chief technology officer at SAS.
SAS has supported cognitive technologies in analytics for decades, he states. “The exciting change is applying deep learning and high-performance computing to achieve greater automation and accuracy in the interaction between computers and people.”
The goal is to extend human intelligence and apply it to solve complex problems using big data and analytics, says Schabenberger, at the inaugural Analytics Experience 2016.
Cognitive computing helps people and machines interact in natural ways, he states. “The computer makes sense of the world around us, it senses, reads, listens and sees. It provides feedback and results by speaking or writing in natural language and directing our actions.”
He says cognitive capabilities based on deep learning and artificial intelligence will be embedded in SAS solutions built on the SAS Viya platform.
The latest SAS additions to the portfolio of analytics that contribute to cognitive computing are SAS Visual Data Mining and Machine Learning and SAS Visual Investigator.
Cognitive services include question-answer systems that drive analytics, make recommendations, or learn from user responses. Customers also will have access to cognitive analytics, image processing, and deep learning in the open SAS Viya platform, enabling them to build cognitive solutions.
These include unparalleled natural language processing and open, deep learning API (application programming interface) libraries sitting on top of advanced analytics.
Combined, they help developers create cognitive computing systems and apply them to high volumes of fast-moving data from text and images, and soon from audio and video too.
360 degree customer view
SAS also announces the release of Customer Intelligence 360, which solves a critical problem of marketers – a fragmented understanding of customer across all channels.
“SAS Customer Intelligence 360 channels the power of data scientists to the digital marketers,” says Wilson Raj, global director of customer Intelligence at SAS. “With modern analytics approaches, such as machine learning, marketers can easily combine insights from existing and emerging channels to steer marketing decisions that are truly customer-centric across their entire organisation.”
When all data is easily accessible, well governed and up-to-date, intelligence analysts can stay ahead of issues
Brooke Fortson, SAS
SAS also announces the launch of a cloud-ready investigation and alert management product that will allow analysts to quickly gain a complete view of people, relationships, networks, patterns, events, trends and anomalies across all available data.
SAS Visual Investigator can help investigators look for insider threats, disease outbreaks, loan risks, drug trafficking, fraud or other emerging issues.
“Complex and disparate data can really slow down investigators,” says Brooke Fortson, product marketing manager for data science and emerging technologies at SAS.
“SAS Visual Investigator alleviates these challenges by bringing together data and exposing patterns of interest. The visual and interactive interface lets users import data, perform point-and-click exploratory analysis, and access third-party systems. When all data is easily accessible, well governed and up-to-date, intelligence analysts can stay ahead of issues.”
Major organisations are increasingly turning to Robotic Process Automation (RPA) to improve operational efficiency, productivity, quality and customer satisfaction.
ANZ, for instance, is experimenting with RPA offshore in India, the Philippines and China to manage routine tasks and allow human workers to refocus on new areas.
Similarly, Westpac is trialling artificial intelligence in its digital banking services to allow its customers to have basic questions about their finances answered by machines. According to the Accenture Technology Vision 2016, 54 per cent of Australian organisations have reported cost savings of 15 per cent or more from automating systems and processes over the past two years.
Though more and more organisations are turning to RPA, few have experience in adopting large scale, organisation-wide RPA capabilities. Because of this lack of understanding, Australian organisations attempting to implement large scale RPA transformation are vulnerable to costly mistakes.
There are six major mistakes that organisations can make when trying their hand at large scale automation programs. Organisations need to be wary of these mistakes and prepared with the right strategies to overcome them.
1. Thinking that robots are the whole solution
Few processes can be automated using only an RPA tool alone. Organisations often need to consider multiple tools and techniques, such as ‘mini bots’, natural language processing, data analytics, process re-engineering, mashups and more.
One of the most important and complex areas of implementing an automation program is solution design, which involves figuring out which combination of capabilities to apply to processes in order to create optimal efficiency.
Software robots should be introduced as part of a strategy of incremental investment in automation, analytics and artificial intelligence that will underpin transformation, modernisation and innovation in operations for the next decade and beyond. Focusing only on short-term cost reductions will not result in the full benefits of automation.
2. Introducing RPA without the support of IT
Because RPA tools require no integration to legacy applications and can be installed on any desktop, there is a tendency to perceive RPA does not need significant involvement from an organisation’s IT team. However, it is essential to ensure RPA systems are part of IT’s overall strategy in terms of security, reliability, scalability, continuity and fault tolerance. A lack of collaboration with IT can lead to costly and time consuming internal wrangling, as there is a risk that RPA pilots might not integrate with existing business process management systems developed in house.
Organisations should create an Automation Centre of Excellence, which is responsible for automation governance, idea generation, skill development, process assessment and organisation wide support, in order to ensure the changes fit within the overall IT structure and strategy and achieve best practice.
3. Running before learning to walk
Getting started in RPA is actually relatively simple for organisations. By testing and learning about RPA with one robot in a sandbox (or testing) environment, organisations are able to gain experience and insights without the risk of negative, organisation-wide impacts. While results from pilot programs, tests and reviews demonstrate the effectiveness of RPA for organisations, they need to be mindful of assuming they can run before they walk. While implementing one robot is relatively easy, implementing hundreds of robots across diverse processes – and integrating automation across the organisation – is much more difficult.
To make the process as seamless as possible, organisations should first consider consulting the wider business to allow different areas across the business to present ideas to be built into the program. A strong infrastructure support network is needed, complete with a virtual environment, server hosting and management, product installation and service capabilities to support large-scale rollout.
4. Letting everyone do their own
Because RPA is relatively inexpensive, easy to use and applicable across various contexts, larger organisations often let various departments organise their own RPA capabilities. However, when this happens, solutions can overlap and a random mix of tools and techniques that hamper future scaling can develop. This can also lead key risk practices to be inconsistently applied – or missed entirely – including business continuity planning, formal maintenance schedules, system documentation, IT security protocols, robot inventories and measures to preserve human process knowledge.
RPA at scale is best achieved within a common environment using common security, risk and quality standards under centralised control and governance procedures.
5. Thinking robots are ‘set and forget’
Robots should be thought of as true virtual workers that require continuous management and maintenance. When rules and procedures are updated, a change strategy should be applied to ensure virtual workers are kept up to date.
Leading organisations are implementing comprehensive governance frameworks to manage organisational change, update processes and manage service demand fluctuations.
6. Putting people strategy later
RPA has a range of positive implications for employees. Because the kinds of tasks being automated with RPA are mundane and repetitive, teams are able to focus more of their time on tasks of higher value that offer greater satisfaction. All RPA plans need to ensure their technology and people strategies are on the same page.
Failing to do this will at best cause delays in training, redeployments and team development – and at worst, lead to unrest in employees that feel uncertain about their future.
Image Credit: IMDb
IDC is forecasting big growth for cognitive computing and AI in the next 5 years. This infographic shows the growth, industries, and use-cases for these technologies.
What used to be science fiction is now an accepted path for IT. Multiple IT analyst firms are predicting that artificial intelligence technologies will become important components in future IT organizations. Indeed, at the Gartner Symposium, AI was simply another accepted factor in almost every system that will be created in the next decade.
That predicted acceptance is also reflected in new market forecast from research firm IDC. IDC’s Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide, which offers a full market forecast over the forecast period of 2016 to 2020, with expected compound annual growth of 55.1%
“Software developers and end user organizations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process,” said David Schubmehl, research director for cognitive systems and content analytics at IDC in a prepared statement.
“Recent announcements by several large technology vendors and the booming venture capital market for AI startups illustrate the need for organizations to be planning and undertaking strategies that incorporate these wide-ranging technologies,” he added.
But it’s not just about startups. Enterprises will play a big part, too, or risk being subsumed by digital disruptors, according to IDC.
“Identifying, understanding, and acting on the use cases, technologies and growth opportunities for cognitive/AI systems will be a differentiating factor for most enterprises and the digital disruption caused by these technologies will be significant.”
IDC says that enterprises across a broad range of industries will be able to enable cognitive systems and AI by applying algorithms and rules-based logic to data flows.
One word sums up this year in enterprise tech: clarity.
We learned that the emerging ecosystem of containers, microservices, cloud scalability, devops, application monitoring, and streaming analytics is not a fad. It’s the future, already powering Silicon Valley’s and Seattle’s most advanced tech companies. Throw in machine learning and IoT and you have a comprehensive framework for the next phase of enterprise IT, with continuous improvement as its founding principle.
At the same time, we became more aware of the widening gulf between this new world and most existing enterprise IT operations. That’s why the hoary phrase “digital transformation” refuses to die — the leap from legacy to modernity requires profound, multiphase, across-the-board change.
But what about next year? Well, when you know where today’s enterprise tech stands, it’s easier to look ahead. In that spirit, I offer my nine enterprise tech trends for the coming year and beyond (with no repeats from previous years!). Let’s start with the most obvious:
1. Advanced collaboration
After years of “business social networking” failures, Slack and its ballooning ecosystem have established chat-based collaboration as a first-order business application. Competitors abound, of course, from HipChat to Flock, and everyone wonders whether Microsoft Teams will be able to beat Slack at its own game — particularly since Teams comes free with Office 365.
But if you ask me, it’s odd that simple chat-based collaboration has taken off, because the chat room metaphor has been around since IRC. Developers have engaged in a deeper form of collaboration from the time Linus Torvalds introduced Git as a way to organize revisions to the Linux kernel, with GitHub, Bitbucket, and GitLab offering today’s most popular Git implementations. Jon Udell and others have suggested that GitHub could provide the basis of all sorts of collaborations beyond code.
More exciting, though, is the notion that machine learning might enable collaborative platforms to gather people, resources, and data in an organization to form workgroups on the fly, which is an idea that Zorawar Biri Singh put forth in a recent InfoWorld interview. Silo-busting collaboration is the key to digital transformation, so machine intelligence to enable that seems like a prime opportunity in this space for years to come. Flock already shows flashes of it with its “magic search” feature.
2. Deep learning
AI and its subset machine learning owe much of their resurgence to the cloud’s ability to serve up gobs of compute, memory, and data, on which algorithms can gorge themselves and produce useful results quickly. That goes double for deep learning, a compute-intensive variety of machine learning that employs multiple layers of neural networks operating on the same problem at the same time for tasks ranging from image recognition to fraud detection to predictive analytics.
All the major clouds give customers the ability to crank up the horsepower required (including GPU processing) for deep learning, with Google’s TensorFlow in the lead, which is available both as a service on Google Cloud Platform and as an open source project. Over time, IBM’s Watson has gained deep learning abilities as well, now accessible to developers in the Bluemix cloud. New offerings from Microsoft Azure (Microsoft Cognizant Toolkit) and AWS (the MXNet framework plus the new Rekognition, Polly, and Lex services) help make this the hottest space around.
3. The incredible SQL comeback
For a few years it seemed like all we did was talk about NoSQL databases like MongoDB or Cassandra. The flexible data modeling and scale-out advantages of these sizzling new solutions were stunning. But guess what? SQL has learned to scale out, too — that is, with products such as ClustrixDB, DeepSQL, MemSQL, and VoltDB, you can simply add commodity nodes rather than bulking up a database server. Plus, such cloud database-as-a-service offerings as Amazon Aurora and Google Cloud SQL make the scale-out problem moot.
At the same time, NoSQL databases are bending over backward to offer SQL interoperability. The fact is, if you have a lot of data then you want to be able to analyze it, and the popular analytics tools (not to mention their users) still demand SQL. NoSQL in its crazy varieties still offers tremendous potential, but SQL shows no sign of fading. Everyone predicts some grand unification of SQL and NoSQL. No one knows what practical form that will take.
4. The triumph of Kubernetes
We know what the future of applications looks like: microservices running in Docker containers on scalable cloud infrastructure. But when you break monolithic applications into microservices, you have a problem: You need to manage and orchestrate them. A few solutions have emerged to meet the challenge, including Apache Mesos, Docker Swarm, and Google Kubernetes.
It’s pretty clear at this point that Kubernetes has won, at least for now. Why shouldn’t it? After all, no company has had more experience running containers in production at scale than Google, using an internal system known as Borg, from which Kubernetes was derived. All the major clouds support Kubernetes, with CoreOS and Red Hat leading Kubernetes providers for both on-premises and cloud implementations. Add to those Heptio, a new startup formed by ex-Googler Craig McLuckie, co-founder of the Kubernetes project.
Kubernetes triumph may be short-lived, though, in part because we’re at such an early stage with containers. At the latest AWS re:Invent conference, for example, CTO Werner Vogels announced a slew of new container management and orchestration tools. Google will stick with Kubernetes for obvious reasons, but the cloud is where the action is and this contest is far from over. It’s just beginning.
5. Serverless computing
When you’re a developer, worrying about infrastructure, even the virtual kind, is a drag when you just want to concentrate on application logic and UI design. Serverless computing platforms take the industry’s long history of piling abstraction on top of abstraction to the next level so that such lowly concerns become a thing of the past. The serverless model also encourages developers to grab functions from a library and string them together, minimizing the amount of original code that needs to be written.
AWS Lambda is the best-known example of serverless computing, but other clouds have followed suit. Microsoft has Azure Functions and Google offers Cloud Functions. The startup Iron.io, which develops software for microservices workload management, also provides a serverless computing platform.
6. Custom cloud processors
Did you know that Amazon has a subsidiary that designs its own ARM processors for servers? Better known is Google’s foray into co-processing — the Tensor Processing Unit specifically designed to accelerate machine learning. Plus, Microsoft has added FPGAs to its data centers to optimize particular applications such as machine learning and plans to offer tools to enable Azure customers to program FPGAs as well. At Amazon re:Invent last week, AWS introduced its own FPGAs in the form of a new F1 instance type for EC2.
7. IoT interoperability
The established messaging protocol for IoT has long been MQTT, whose compact and efficient nature lends itself to low-power, relatively dumb devices. In May 2016, Google’s Nest subsidiary open sourced Thread, a mesh networking protocol that enables devices with more processing power to maintain peer-to-peer connections without relying on a hub.
The most interesting developments have emerged at the application layer. In October, the AllSeen Alliance merged with the Open Connectivity Foundation, which effectively unified the IoT software frameworks AllJoyn and IoTivity into a single open source project. More dramatically, at the Amazon re:Invent conference last week, AWS CEO Andy Jassy announced AWS Greengrass, a software core (and SDK) designed to run on IoT devices, enabling those devices to run AWS Lambda functions and connect securely to the AWS IoT platform. All the major public clouds now have IoT platforms, which are crucial to IoT progress, so you can expect Microsoft Azure, Google Cloud Platform, and IBM Cloud to deliver their own Greengrass-like offerings in 2017.
8. Hardware as a service
This one is kind of a sleeper. IDC predicts that in 2017, 10 percent of enterprises will begin exploring PC-as-a-service agreements with vendors. Reportedly, HP and Lenovo already have such rental programs in place. On the server side, Dell, HP, and Lenovo will begin offering Microsoft-managed servers preloaded with Azure Stack on a subscription basis. Oracle has the on-premises version of Oracle Cloud, dubbed Oracle Cloud Machine, that is offers via a “cloud-oriented subscription model.” Is this the end of capital investment in IT as we know it?
9. Python, Python, Python
OK, this one is a little silly. But each year, the ranks of Python programmers grow, with Python occupying the No. 4 position among all languages in the Tiobe Index. Python’s clean, English-like syntax has helped make it the most recommended first programming language.
People use it for everything, but in particular it has gained traction among data scientists. Moreover, Python has become the preferred language of devops engineers who write code to automate operations, and Python frameworks and IDEs continue to blossom. How devoted is the Python crowd? Here’s a clue: Python 3.6 was released on Christmas Day.