Despite teething problems, artificial intelligence (AI) has become mainstream. In fact, it is more than mainstream. It’s inevitable. That is to say, no matter how enterprises set up their technology infrastructure, it seems unlikely they will remain competitive without AI. A recent report, IBM’s 2021 Global AI Adoption Index, underlines this.

Based on a survey of 5,501 businesses globally, the report shows that one-third of companies are currently using AI in some way, while 43% are exploring it. However, problems remain. While recent advances are making AI more accessible than ever, the survey found that a lack of AI skills and increasing data complexity are top challenges. There are five take-aways from the research:

• Business adoption of AI was basically flat, but companies are planning significant investment.
• COVID-19 accelerated how businesses are using automation.
• Trustworthy and explainable AI is critical to business.
• The ability to access data anywhere is key for increasing AI adoption.
• Natural language processing is at the forefront of recent adoption.

A large majority of investments continue to be focused on the three key capabilities that define AI for business: automating IT and processes, building trust in AI outcomes, and understanding the language of business, the research showed.

Artificial Intelligence in the Mainstream

Other research indicates just how far AI has come in the past few years. Just this week, research conducted by San Mateo, Calif.-based Freshworks looked at the state of IT service management (ITSM) and IT operations management (ITOM) and found that AI technology has hit the mainstream, with 93 percent of businesses currently exploring or deploying some level of AI in ITSM.

That research showed that most organizations expect AI to be deeply integrated within ITSM tools instead of it being an add-on that requires additional effort to engage employees. Among the findings are:

• Virtually all IT managers (93 percent) are currently exploring or deploying some level of AI technology for ITSM/ITOM modernization.
• Nearly 70 percent of IT managers say AI is either critical or very important for upgrading and modernizing service desk capabilities.
• Among six AI use cases, ITSM chatbots are the clear leader in planned or actual AI deployments.

In terms of what organizations hope to achieve, the principal objectives are:

1. Speed of implementation (40 percent).
2. Integration with legacy systems (40 percent).
3. The overall cost of implementation (38 percent).
4. Training AI bots to return the most accurate response (39 percent).

AI Is Already Baked Into Business

Not only has the use of AI in the enterprise reached the mainstream, it also seems that more enterprises are starting to depend on it. Wayne Butterfield, director of ISG Automation, a unit of Stamford, Conn.-based technology research and advisory firm ISG, said it should be no surprise to see organizations adopting or experimenting with AI.

With so many AI components, ranging from machine learning to natural language processing, and lots of AI use cases in between, it’s likely that actual AI usage is even higher than current surveys suggest. AI, in one or more forms, is already built into many of the widely used enterprise platforms, Butterfield said. He cited the example of chatbots using natural language processing in platform tools like SAP, Salesforce and Workday as examples.

“Natural language processing, machine vision and machine learning are just a few of the ways AI is so prevalent in the enterprise today, and why AI will continue to be important moving forward across all industries,” Butterfield said. “Reading and responding to emails, conversing on WhatsApp, extracting a clause from a contract or even assisting in the picking of ripe fruit are all examples of AI currently in use across the enterprise.”

The Role of AIOps in AI Deployment

At the heart of any AI deployment is artificial intelligence for IT operations (AIOps). AIOps uses big data, analytics and machine learning to enhance IT operations and is inevitable for forward-thing organizations. Most modern enterprise IT environments consist of a complex mix of on-premise and cloud environments which run a wide variety of dynamic workloads that require frequent reconfiguration and scaling up and down, said Atul Varshneya, vice president for AI at Santa Clara, Calif.-based Tavant.

These applications and other IT systems generate a massive amount of data, and the data volume keeps increasing as IT environments evolve. Applying analytics and machine learning can help companies extract information from this data to make smarter decisions. For example, enhanced visibility into performance and dependencies across all environments can provide insight into significant events related to slow-downs or outages and automatically alert IT teams about problems and their root causes.

“Through intelligence enabled by analytics and ML techniques, rich information is extracted from the data generated by applications, and systems including various monitoring mechanisms,” Varshneya said. This results in several benefits:

• Proactive management of potential issues: AIOps can predict problems well in advance, enabling IT personnel to resolve them proactively.
• Faster resolution of identified issues: AIOps can also provide rich information about problems through the explainability of its prediction to help identifying root causes and get to resolutions faster.
• Efficient IT operations: With theses capabilities, alerts that require an urgent response can be reduced significantly, leading to more uptime and overall higher performing IT operations.

4 Steps to Make the Most of Artificial Intelligence

So how should enterprises proceed? There are four things enterprises need to consider on their way to AI adoption, said Sam Babic, senior vice president and CIO at Westlake, Ohio-based Hyland.

1. Start small, build momentum: Look for a high value, low complexity problem to solve or decision to make with AI as a starting point. This is also true when tackling projects at the organizational level. Demonstrate small, tangible wins to gain momentum for AI initiatives and then build momentum from there.
2. Create an AI/data center of excellence: In the formative stages of AI adoption, it is good to set up an AI center of excellence where subject matter experts either report directly or through the dotted line. This center of excellence provides focus and dedication to the topic and allows a centralized approach to patterns and practices derived through learning. Likewise, the purchase of tools and other decisions can be consolidated. As it grows, the center can expand into a community of practice with stakeholders throughout the organization.
3. Understand the outcomes you want: Oftentimes, organizations focus on understanding the opportunities AI can unlock and then map them to organizational goals. Instead, start with the organizational goal first and then map how AI can help. This seems like a nuance, but the latter approach enables the organization to more quickly focus on requirements necessary to accomplish the goal vs. getting lost in a sea of possibilities.
4. Be careful of bias: “Garbage in, garbage out” is a long-standing term that is even more important when leveraging AI. Operating with bad data, whether it is stale, incorrect or skewed will yield bad decisions. Training a machine learning algorithm is like training a child. Teach them bad habits and they will execute those bad habits. Closely analyze and clean data to ensure human bias is removed from training.

Proof of Value Replacing Proof of Concept

Artificial intelligence is more than just a nice addition to the technology stack, it’s essential if companies want to survive, said Bruce Orcutt, vice president of product marketing at Milpitas, Calif.-based ABBYY. And it’s getting more sophisticated.

“COVID was definitely an accelerant but also the developer skills shortage is a contributing factor,” he said.

Orcutt pointed to the example of document processing. AI technologies like optical character recognition and machine learning have been used to intelligently capture documents and send content to enterprise applications for years, but they required significant training and IT resources from IT. Now, more advanced AI makes that same legacy document processing technology easy for business analysts to use in the form of cloud-based, no-code platforms that can process any type of content they are working with.

“For AI to see rapid adoption, it needs to be user friendly, deployed quickly and return value immediately,” Orcutt said. “The days of ‘proof of concept’ are gone, enterprise leaders want proof of value now.”

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Chief Technology Officer at Unit4, overseeing development of intelligent software for service organizations.

There’s a lot of hype around machine learning, but what does it really mean in the context of enterprise software? How does it work, where is it adding business value today, and what should we expect from it in the future?

Let’s start with some definitions. Artificial intelligence (AI) is an umbrella term that includes machine learning (ML), deep learning and cognitive learning. The part most relevant to enterprise software is ML, which in this context is the ability to create automation through AI algorithms.

A lot of what ML does is really just statistical analysis: crunching numbers, measuring parameters, identifying patterns and projecting future outcomes based on past results. You don’t actually need fancy ML algorithms to do this; you can do it with standard logical programming.

The degree to which the ML itself improves business outcomes is currently marginal. The accuracy of a financial forecast, for example, is sensitive to far greater factors than whether the algorithm can refine itself slightly over time. If you haven’t got harmonized, accurate and complete data to start with, simply applying ML to it isn’t in itself going to result in better business decisions.

Realizing the Growth Potential of AIArtificial Intelligence Is Learning To Categorize And Talk About Art

A Solution Looking For A Problem?

In terms of Gartner’s hype cycle, ML is currently at the peak of inflated expectations. You cannot simply throw ML at a bucket of big data and expect it to magically come up with a perfect business plan.

As so often in business, you shouldn’t start with the technology itself. Before you think about where to apply ML, you need to step back and ask: What is it we’re trying to achieve?

Look for points in your business processes where some sort of judgment or prediction is required and where any small improvement in accuracy would have a disproportionate benefit to the business. These are the potential use cases for ML. Otherwise, ML is at risk of becoming a solution looking for a problem.

For example, if you apply ML instead of conventional statistics — and you have good underlying data — you should be able to continuously enhance the accuracy of the predictions to improve, say, operational efficiency and customer experience.

Where Is ML Adding Value Today?

ML is currently being used to good effect in enterprise software to automate routine business processes.

Receipt recognition: In this use case, an ML algorithm examines a scan of a receipt and deduces what type of receipt it is, then automatically matches it against an expense record in the ledger.

Smart invoice processing: Here, the ML algorithm examines a scanned paper invoice or electronic invoice and identifies the key elements: invoice number, customer number, amount, payment terms and line items, then matches them against the relevant purchase order or delivery note.

Time sheet completion: Typically, there are around five dimensions to completing a time sheet — project, task, level of resource, type of work and time spent — all of which, until now, had to be input manually. An ML algorithm can auto-populate them based on previous patterns. This can free up a lot of time for people and make work easier for them.

The Human Intelligence In AI

A great deal of human intelligence is required to get AI to work. To get predictable, reliable results, you have to decide the use case and make sure the data itself is of a high enough quality before setting the algorithm a task. Then you have to train it.

In its simplest form, training an algorithm involves a person checking the results and providing feedback on their accuracy. This is called supervised learning.

The human mind is by far the best pattern-matching machine in the universe. The average 2-year-old can probably correctly identify a cat after it’s seen two or three, while an ML algorithm might need to see 2,000 before it can be sure. But, once trained, ML excels at dealing with huge volumes of data and processing it very quickly, while never getting bored performing repetitive, tedious tasks day in, day out.

What Can We Expect Next?

Based on my experience, typically less than 20% of business processes are automated in enterprise software. I believe that in as little as two to three years we could see up to 80% of routine business processes automated by ML.

The current frontier is about how we interact with software, and there’s an ongoing paradigm shift around user experience. As in the book, The Best Interface is No Interface, which calls for an end to “screen-based solutions,” software should be able to recognize human speech through natural language recognition; ML is now making this a reality.

The next big leap forward will be to eliminate humans from some business processes entirely. In a workflow, at the point where a person has to approve something, an ML algorithm will examine an approver’s behavior and learn what falls within the usual tolerances. By copying the person’s judgments, ML can carry out the work itself and simply inform the user when it’s done.

Focus On What Matters

The promise of ML in enterprise software is to make it pervasive but not visible. Will it put us out of our jobs? No, but it will allow us to offload the hum-drum, low-value tasks and focus on what really matters — adding value to the business.

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Microsoft claims its new PyTorch Enterprise on Microsoft Azure is the first offering from a cloud platform to provide enterprise support for PyTorch, the popular open source deep learning framework.

Along with that enterprise support, it comes with prioritized troubleshooting and also integrates with other Azure solutions, such as Azure Machine Learning.

PyTorch is an open source machine learning/deep learning framework based on the Torch library, used for applications such as computer vision and natural language processing, driven by Facebook’s AI Research lab, according to Wikipedia.

Regular Visual Studio Magazine readers will know that it’s also a favorite tool of our own data science guru, Dr. James McCaffrey of Microsoft Research, who authors regular hands-on PyTorch-based tutorials for our Data Science Lab.

The new offering comes after Microsoft teamed up with Facebook to become a founding member of the PyTorch Enterprise Support Program, which helps service providers develop and offer tailored enterprise-grade support to their customers.

The enterprise support initiative was reportedly sparked by feedback from customers, who found it easy to get started with PyTorch but not so easy to implement complicated real-world enterprise production initiatives. Thus Microsoft will provide commercial support for the public PyTorch codebase. “Each release will be supported for as long as it is current,” Microsoft said in a recent blog post. “In addition, one PyTorch release will be selected for LTS every year. Such releases will be supported for two years, enabling a stable production experience without frequent major upgrade investment.”

Supported configurations include:

• PyTorch: version 1.8.1 and up.
• Libraries: torch, torchaudio, torchvision, torchtext, onnxruntime, and torch-tb-profiler.
• Python: version 3.6 and up.
• NVIDIA CUDA: versions 10.2 and 11.1.
• Operating systems: Windows 10, Debian 9, Debian 10, Ubuntu 16.04.7 LTS, and Ubuntu 18.04.5 LTS (x86_64 architecture only).

It does not, however, support C++ or Java interfaces, or PyTorch libraries and features marked “experimental and subject to change” including TorchServe and Pipeline Parallelism.

To be eligible at no additional cost, enterprise customers must join Microsoft’s Premier or Unified support programs. For such organizations, as part of the aforementioned prioritized troubleshooting, “The dedicated PyTorch team in Azure will prioritize, develop, and deliver hotfixes to customers as needed. These hotfixes will get tested and will be included in future PyTorch releases. In addition, Microsoft will extensively test PyTorch releases for performance regressions with continuous integration and realistic, demanding workloads from internal Microsoft applications.”

Microsoft is heavily invested in the PyTorch ecosystem, noting that the company’s data scientists like Dr. McCaffrey use PyTorch as the primary framework to develop models to enhance Office 365, Bing, Xbox and other offerings. That investment includes projects such as PyTorch Profiler, ONNX Runtime on PyTorch, PyTorch on Windows, DeepSpeed and more.

For now, PyTorch Enterprise is available on Azure Machine Learning and Data Science Virtual Machines (DSVM), coming soon to Azure Synapse Analytics.

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How artificial intelligence in data analytics can help visualise business data?

In an ultra fast-paced digital world, businesses of all sizes produce huge amounts of data that are challenging to keep up with. Such data carries much promise when it comes to analyzing them. Recent technological advances have changed how enterprise analytics perform. There are still some challenges to using data and analytics in many aspects of an organization. However, when using artificial intelligence in data analytics, businesses can produce outcomes far beyond what they can do manually, both in terms of speed and accuracy.

Analytical approaches comprising predictive models have now begun to shift merely to descriptive approaches, which is already beneficial for many users and continues to be valuable. Descriptive analytics has evolved much, making greater use of visual analytics. Despite this, making use of data and analytics to interpret and envisage significant phenomena in businesses is difficult.

Predictive models capitalize on past data and a reasonable amount of expertise to create and predict outcomes. However, the use of past data here limits how and when they can be deployed. Existing data analytics approaches have historically been a bit narrow. They are focused on particular functions or units, even though many business problems and issues cut across functions and units.

Data Analytics Influenced by Artificial Intelligence

Powered by automation and artificial intelligence, the next-generation of enterprise analytics is emerging. Apart from this, the innovation relies on connections across existing information systems and role-based assumptions about what decisions will be made on data and analytics. AI-enhanced software has the potential to assess data from any source and deliver meaningful insights. It can analyze customer data that can be particularly revelatory and disrupt product development while improving team performance and enabling businesses to know what works and what doesn’t.

Artificial intelligence typically refers to the field of data science. It leverages advanced algorithms to power computers to learn on their own. By integrating AI into their data analytics processes, businesses can be able to automatically clean, evaluate, explain and visualize their data.

In an article, Tom Davenport and Joey Fitts wrote that automation in analytics, often called “smart data discovery” or “augmented analytics”, is reducing the reliance on human expertise and judgment by automatically pointing out relationships and patterns in data. The systems, in some cases, even recommend what the user should do to address the situation identified in the automated analysis. Together these capabilities can transform how people analyse and consume data.

Artificial intelligence and automation have made significant advancements in data analytics that were inconceivable a few years ago. Enterprises these days are realizing the benefits of these technologies and using them to examine their data to derive fine-grained insights. AI is now creating new methods for data analysis. Historically, data engineers or analysts have had to use a query or SQL when it comes to analysing data. However, as the significance of data continues to grow, multiple ways to excerpt insights have emerged. Artificial intelligence emerges as a crucial technology, becoming the next step to query or SQL.

Earlier, data and analytics have been discrete resources that needed to be fused to accomplish value. This also required extensive knowledge of what type of data was apt for analysis within an organization. Most data analysts lacked such knowledge in a broader context. However, AI-powered analytics can increasingly provide context. Many key vendors are already using these capabilities in their transactional systems offerings, such as ERP and CRM.

In conclusion, this is just the beginning of data analytics powered by artificial intelligence. As the advances in this technology will continue to evolve, the potential of AI-driven data analytics tools will be striking.

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Today, the role of artificial intelligence in an enterprise has become so important that it has touched every facet of business. Its role will become more critical in the years to come.

For the purposes of this article, let us define A.I. as follows. Human intelligence is learnt from experience. Machines so far have been primarily used to follow instructions, i.e. programmed, hence machines have provided automation based on rules.

A.I. is not programmed to follow rules, it is like human intelligence, learns from “experience”, i.e. data.

A.I. application in businesses today can be divided into 5 key areas:

1. A.I. in data cleansing and streamlining.

2. A.I. in BI—i.e. AI to replace business analysts in preliminary analysis on dashboards.

3. A.I. in cognitive intelligence such as voice recognition, video analytics, face recognition.

4. A.I. in natural interaction – chat bots, NLP, Natural-Language Generation (NLG).

5. A.I. in expert systems—learning from myriad data sets and crystallizing an insight or action. This can be applied in classification of future unknowns e.g. fraud prevention, preventive maintenance. This can be applied also in forecasting quantities e.g. demand forecasting, supply shortage prediction. And can be applied in real time dynamic operations e.g. self-driving cars, dynamic digital marketing.

A.I. in data cleansing

There are A.I. applications today that can weed out errors in master data. In fact, a business was able to reduce their data errors in incoming data by 94% through A.I. application in data error correction. Humans used to correct such errors before and the correction was based on knowledge about the product, data on similar products and so on. If a product is a liquid and the units of measure are missing, the human planner used to correct that gap looking at the product being replaced by this new product or other liquid products in the same price range and so on, or the image of the product itself. AI now does the same and has been seen to be able to correct 94% of data errors without human help.

A.I. in BI

We know when reports and dashboards are delivered, junior analysts typically do a pre-analysis to ascertain exceptions, and explanations for those exceptions. These analysts circle these exceptions and comment on the components contributing to the exception. They also may mark something as high priority if an urgent action is needed. This saves the decision maker’s time and presents them with distilled information. Today, A.I. is able to do this – augmenting junior analysts using anomaly detection and auto-drill down, pattern recognition and clustering.

A.I. in cognitive intelligence

The usual examples people think of when talking of AI applications is in cognitive intelligence. As an example, in this pandemic, cognitive systems are tracking masks wearing compliance in closed spaces, distancing norms compliance in factories and warehouses. Cognitive intelligence is also used in less known forms, there is a recent MIT report on an AI model that can detect Covid-19 infection from your cough, being incorporated into an FDA approved cell phone app.

A.I. in natural interaction

Chat bots are here to stay. If we haven’t interacted with one yet, we are probably not clicking on that chat button on most online shopping sites. It is interesting that A.I. learning is not being achieved in most chat bots from chat histories of those businesses alone, but using innovative data sources such as Q&A dialogues in published interviews, consumer panel discussions, even published plays, and so on.

The way personalisation is achieved is even more interesting. If A.I. was simply replacing a human agent the chat would begin with “how may I help you?”. However, A.I. is much more than that. There are call intent prediction models which learn from the behavior of customers who had a similar interaction, purchase history and profile. Gleaning from data, what did such cohorts mostly call about when they did call? So, the conversation starts with “Are you calling about your oximeter ordered yesterday?”

A.I. in expert systems

This is the area impacted most post pandemic. For example, demand forecasting of CPG products using traditional techniques became impossible. However, applying A.I. made a difference.

For example, loyalty card identifiers in transaction data helped A.I. learn that there are some products which saw an upswing in sales that would continue (because the same shoppers were re-purchasing high quantities). Products like soap, sanitizers, home cleaners, etc. were identified for continued upswing from such analysis. Other items that did see an upswing but were unlikely to sustain (because the same shopper was not repurchasing high quantities) were items like paper towels, pet food, baby food, etc. This kind of ability in the first two months of dynamic demand helped many firms to plan better for the supply and allocation across locations.

With digital interactions rising, application of A.I. for a cookie less world has led to Federated learning of cohorts (FLOCs). FLOCs use models learning from customer cohort behavior through distributed data, without transferring data to a central server, thus protecting privacy.

What has been true of times of upheaval, applies to this one as well. It is a time of opportunity for those who leverage available tools including A.I. to transform their business to gain competitive advantage. Others who delay, cannot afford to miss this revolution. They will be late and may struggle, if they sustain at all.

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The term “digital transformation” has become so ubiquitous that it can mean almost any change from a manual process to an electronic one. But why do we have to think of change in terms of digital transformation? Few would argue that replacing an inefficient manual task with automation is a “transformation.” However, I think of change in terms of innovation, in terms of altering how we do something or the way we behave—in terms of disrupting an ecosystem. Innovation isn’t just automating processes that already exist, but rather applying technology to solve a problem in a different way.

In my view, one of the best ways organizations can approach a given problem space is by leveraging the myriad of data they collect every day. Data analytics comes to mind, of course — crunching a sea of data to find correlations and insights we can use to make a process better. How then do we decide what to do with those insights? You develop and train machine learning (ML) models to make more accurate, unbiased decisions based on the available data. Then you apply artificial intelligence (AI) to suggest the best way to act on those decisions to improve the chance of a successful outcome.

Using AI/ML for innovating the customer experience

One of the most visible targets of transformation initiatives is to improve the customer experience. The internet has removed geographical distance as a barrier between you and your competitors, so a company’s online presence is more important than ever. That’s why everyone is rushing to provide ever more-engaging online experiences, to hold a prospective customer’s interest.

Numerous companies provide website plugins to track a visitor’s clicks and actions, analyze them to intuit intention, and determine, for example, what content, advertisement, or offer to display next. Going beyond that, today’s most successful e-commerce sites also use AI/ML to personalize each shopper’s experience, like the order and presentation that will most likely result in another click or a purchase.

AI can anticipate with near certainty—based on past and present action, search patterns, profiles, external demographics and more—what a customer wants to see now and will do next. If successful, your website visitors will come to feel at-home, excited, and perhaps even brand loyalists. They’ll buy more and return more often.

But digital innovation shouldn’t stop with customer experience

There is nothing wrong with applying analytics, AI, and ML to create a more innovative and engaging customer experience. Not doing so can put you behind your competition. It’s all about building customer loyalty and boosting revenue.  

No matter how important customer experience is, however, it is a mistake to believe it is the only operational area that can (and should) be transformed using technologies like these. After all, today’s enterprise amasses data about more than just customers and orders. Your company, product, and delivery must broadly innovate — and all these happen on the backend. The efficiency of your internal operations — your support team, supply chain, production, inventory, quality control, human resources, and so on — can all benefit from applying AI and ML technologies. Consider just a few of many possible examples.

• Motivating a remote workforce – With so many teams working remotely, first-hand observation of employee engagement is next to impossible today. AI can analyze which applications employees use most, possibly even judging their levels of efficiency or frustration. Organizations can understand how happy, motivated, and engaged teams are so they can maintain or increase efficiency and productivity.
• Refining a business model and marketing – Beyond mere numbers, AI can analyze which products in your online portfolio work best and for which shoppers. Yes, this can help you shape the online customer experience. But it also lets you adapt which products you choose to keep or eliminate from your lineup (your business model) and adapt your offers based on observed customers’ choices or preferences (your marketing strategy).
• Protecting intellectual property – Organizations can even protect their patents, intellectual property, and product uniqueness by using AI, ML rules, and image recognition to smartly crawl the web to identify look-alike products and would-be theft.

The possibilities for internal process improvement across the enterprise are endless.

AI/ML isn’t just for large technology companies

In short, companies should apply AI/ML innovation to their operational processes as much as they do to the customer experience. Artificial intelligence isn’t just for technology companies, nor is it for analyzing and solving only technology problems. Organizations can use it to better understand their customers. They can use it to automate inefficient internal processes. They can leverage it for improving online security, boosting employee engagement, and reducing theft and risk.

Why aren’t more companies using AI? Frankly, they do not know how to start. They know they need to use it, but they don’t know where to “plug it in” to their systems first. Or they think they have to hire a team of AI engineers and build their solution from scratch.

Today AI is available for any company to use and benefit from, even smaller companies, without the need for a team of experts. There are many commercial apps and solutions in the marketplace that readily adapt to an organization’s existing processes. Some are even SaaS- and cloud-based solutions, meaning they do not require a big infrastructure investment to get started. The important thing to know is that any company can start small and scale up their AI solution in their own time — but getting started is the only way to stay competitive.

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Today, Amazon  announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas.

While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL.

By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood.

“This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases,” Wood told TechCrunch.

For starters, Wood says developers can take advantage of Aurora, the company’s MySQL (and Postgres)-compatible database to build a simple SQL query into an application, which will automatically pull the data into the application and run whatever machine learning model the developer associates with it.

The second piece involves Athena, the company’s serverless query service. As with Aurora, developers can write a SQL query — in this case, against any data store — and based on a machine learning model they choose, return a set of data for use in an application.

The final piece is QuickSight, which is Amazon’s data visualization tool. Using one of the other tools to return some set of data, developers can use that data to create visualizations based on it inside whatever application they are creating.

“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we’re announcing today help to make ML more usable and accessible to database developers and business analysts. Now anyone who can write SQL can make — and importantly use — predictions in their applications without any custom code,” Amazon’s Matt Asay wrote in a blog post announcing these new capabilities.

Asay added that this approach is far easier than what developers had to do in the past to achieve this. “There is often a large amount of fiddly, manual work required to take these predictions and make them part of a broader application, process or analytics dashboard,” he wrote.

As an example, Wood offers a lead-scoring model you might use to pick the most likely sales targets to convert. “Today, in order to do lead scoring you have to go off and wire up all these pieces together in order to be able to get the predictions into the application,” he said. With this new capability, you can get there much faster.

“Now, as a developer I can just say that I have this lead scoring model which is deployed in SageMaker, and all I have to do is write literally one SQL statement that I do all day long into Aurora, and I can start getting back that lead scoring information. And then I just display it in my application and away I go,” Wood explained.

As for the machine learning models, these can come pre-built from Amazon, be developed by an in-house data science team or purchased in a machine learning model marketplace on Amazon, says Wood.

Today’s announcements from Amazon are designed to simplify machine learning and data access, and reduce the amount of coding to get from query to answer faster.

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Businesses today continue to be bombarded by an increasing number of cyberthreats, as hackers become adept at identifying and exploiting vulnerabilities in security systems. A survey by the World Economic Forum ranked data theft and large-scale cyberattacks 4th and 5th in a list of the biggest risks facing our world. With cybercrime regularly hitting the headlines, regulators are implementing new security guidelines and costly fines for violations. Adding to the pressure are consumers who are increasingly prepared to abandon business with a company if they’ve been hit by a data breach. Businesses can’t afford to turn a blind eye to cybersecurity, which has now become a top priority for enterprises.

Attack vs defence: where things stand

The growth of IoT over the last decade has meant that thousands, if not millions, of devices are now contributing to network traffic, and all are potential entry points for attackers. With Gartner predicting that there will be 20.4 billion connected devices by 2020, the potential for unprecedented exposure is only going to continue. Furthermore, the more devices on a network, the more data security analysts have to wade through, making identifying potential threats harder than ever – especially when reports suggest that UK businesses faced a cyberattack every 50 seconds in the second quarter of 2019. While we’re seeing increased awareness around the threat IoT devices can pose, worryingly, cyberattacks on IoT devices have already increased by 300 per cent in 2019.

Compounding the vulnerabilities IoT devices can bring to networks is the nature of cybercriminals, who are constantly evolving their attacks which are becoming increasingly targeted and sophisticated. Furthermore, they’re also collaborating in marketplace environments, sharing tips and advice on how to launch attacks that will cause the most damage.

Most enterprises still rely on traditional approaches to network security to defend against threats. This approach relies on feeding historical data – i.e anomalous activity that was suspicious or malicious – into a learning algorithm so the system knows what to look out for in the future. This enables the system to flag suspicious activity that corresponds to historical data to security teams, and prevent such attacks slipping through the net.

However, this approach is no longer adequate in today’s evolving threat landscape, because it hinders an organisation’s ability to investigate activity that hasn’t been seen before, causing them to miss new attacks. Furthermore, behaviour that is deemed “normal” or “good” within an organisation is constantly evolving, and businesses have to be able to adapt in real time. This legacy approach to network monitoring also places additional stress and burden on security analysts, who don’t have the capacity to sift through the vast amounts of data collected by businesses and identify threats.  It’s no surprise that 56 per cent of senior executives think their cybersecurity analysts are overwhelmed by the sheer volume of data points they need to analyse to detect and prevent threats.

The result? Businesses that can’t identify new and sophisticated attacks, and attackers who are spending an average of 6 months within a network. Clearly, when it comes to enterprise anomaly detection, a change is needed.

Advanced detection: Deep learning & network monitoring

Deep learning powered network monitoring represents a solution to the problem. Increasingly seen as the next generation technology in network monitoring, deep learning is driven by unsupervised algorithms that continuously analyse an organisation’s regular behaviour in order to identify abnormalities. The algorithm is instructed to survey its own infrastructure and proactively search out and unearth the unknown, rather than the known “bad”. This allows businesses to detect unseen threats and take a proactive approach to cybersecurity.

Another advantage of deep learning algorithms is that they have the capability to sift through millions of pieces of data simultaneously in near real-time. The ability to identify anomalous patterns in vast data sets means deep learning network monitoring can perform a level of analysis that’s impossible for humans alone to replicate.

Empowered by deep learning tools, analysts are able to focus on the most rewarding part of their job: the investigation and detection of complex malicious activities. By accelerating access to the information, teams can collaborate and focus on understanding the root cause and the total extent of campaigns against organisations. As a result, security teams’ efficiency is boosted, stress is reduced, cybersecurity analysts’ work is highly valued and the overall organisation security is strengthened.

Businesses can no longer rely on traditional network monitoring methods that provide an inherently binary view of cybersecurity that focuses on good vs. bad behaviour. The volume of data collected by businesses is growing exponentially, and at the same time, cyberthreats are becoming increasingly sophisticated. Add in the fact that cybersecurity teams are under increasing pressure to do more with less and it’s easy to see why enterprises have historically been on the back foot.

Ultimately, deep learning transforms network security from a passive system that is fed seen behaviour, to an active solution that can detect threats in real-time and uncover things not seen before.

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In recent years, UK businesses have invested billions in large scale digital transformation initiatives with a view to becoming more automated, efficient and agile. But in reality, not all of these digital bets have paid off. According to one study, 1 in 4 UK businesses have had a digital transformation project fail. As executives look to the future, many are asking themselves how they can realise the operational gains of Artificial Intelligence (AI) and Machine Learning (ML) within their organisation to deliver smarter, more successful outcomes.

Enter the autonomous network: a fusion of machine and human intelligence that will revolutionise decision making, drive a new wave of opportunities and pave the way for the autonomous enterprises of the future. Leading companies are eager to embrace deeper human-machine integration, but what will this new trajectory look like? And how will it impact the workforce?

Powered by machines – but driven by humans

Businesses already leverage sophisticated automation technology that streamlines and speeds up workflows and decision making processes, but it isn’t yet intelligent. For example, automation allows data centre professionals to run the data centre on an app on their phone from anywhere at any time. Fast forward to the autonomous enterprise, however, and many of these processes could be trusted entirely to machines in real time, powered by an agile, reliable and resilient network.

Just as autonomous vehicles can be taught to drive, autonomous networks can be taught to silently manage, optimise, and secure themselves. By augmenting current automated systems with AI and ML, the self-healing, self-driving networks of tomorrow will operate with minimal human input.

Eventually, autonomous networks will function with the agility of a biological neural network, resolving security issues before they become apparent to humans, training themselves to proactively seek next-generation outcomes and enabling lightning-fast information flow.

Of course, these tremendously exciting benefits also tend to raise concerns of a “workerless future”, predicated upon the unstoppable rise of automation. In truth, while machines will become an increasingly common feature in the workplace, they stand to complement human capabilities. Just as AI-driven accounting software has made finance teams more capable – rather than obsolete – autonomous networks will enhance every aspect of an organisation, from incident response times to customer experiences. Put simply, no matter how rapidly AI advances, the path to full autonomy will ultimately be paved by human intelligence.

Prioritise skills in preparation for full autonomy

One should think of the autonomous enterprise as a vision, rather than a series of ‘plug in’ technologies. To join the dots between machines, leadership, company culture and frontline staff, it’s imperative that businesses adopt a multi-faceted approach. Technology alone will not be able to create the autonomous enterprise of the future. Key to this will be upskilling human capital at every level.

But, as any CTO can attest, augmenting technology with talent can prove challenging. While the majority of organisations are fully invested in AI-driven modernisation programmes, many are being held back by a looming skills crisis. According to one recent study, more than half of UK and US organisations (51 per cent) don’t have the necessary AI skills in-house to breathe life into their strategies. What’s more, the skills gap is considerably more acute in the UK: 73 per cent of businesses are struggling to hire the necessary talent compared to 41 per cent in the US.

This is particularly worrying for organisations’ data centres – the lifeblood of any modern enterprise. Disruption from AI, the cloud and security has already had a significant impact on data centre staff. In fact, a report from Uptime Institute reveals that many data centre workers simply don’t have the skills needed to modernise the data centre. AI and ML is expected to mitigate some of the effects of the skills gap by eliminating many mundane manual tasks that cause time-consuming bottlenecks, but it’s by no means a silver bullet.

A smooth transition to the autonomous enterprise will require employees to be agile and change their skills. Training should focus on skills development for existing professionals first and foremost. They should be given the opportunity to learn new tools (i.e. software, automation, performance management) to keep up with the speed of technological innovation. Organisations should also aim to recruit IT professionals with specialised knowledge of AI and automation. These workers may not automatically consider data centre jobs, but if businesses can create additional incentives, those skills could greatly augment current teams.

Whether upskilling current employees or tapping into today’s decentralised, cross-border talent network, enriching your organisation’s human capabilities will be key to unlocking the potential of the autonomous enterprise of the future. It won’t be easy but with the right mix of human ability, advanced network infrastructure and solutions, as well as policy based processes, organisations can prepare for the era of the autonomous enterprise today – and realise their vision tomorrow.

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Big data and analytics are topics firmly embedded in our business dialogue. The amount of data we’re now generating is astonishing. Cisco predicts that annual global IP traffic will reach 3.3 ZB per year by 2021 and that the number of devices connected to IP networks will be more than three times the global population by 2021, while  Gartner predicts $2.5M per minute in IoT spending and 1M new IoT devices will be sold every hour by 2021. It’s testament to the speed with which digital connectivity is changing the lives of people all over the world.

Data has also evolved dramatically in recent years, in type, volume, and velocity – with its rapid evolution attributed to the widespread digitisation of business processes globally. Data has become the new business currency and its further rapid increase will be key to the transformation and growth of enterprises globally, and the advancement of employees, ‘the digital natives’.

The Cisco Global Cloud Index points to the Cloud as the top driver as exponential data centre growth with cloud centre traffic quadrupling in the next five years. Data generated by IoT applications (such as connected homes, smart cities and healthcare) will be 600ZB (zettabytes) per year by 2020, 39 times higher than current data centre traffic which is 15.3 ZB.

Big Data therefore has a far-reaching impact and meaning. But how do we understand it and its benefits, along with analytics on the journey to Digital Transformation? Understanding the value of Data is key to the successful implementation of operational strategies that facilitate agile and effective business growth.

Big data means better business 

Data is an enabler of future strategies and immediate change, thanks to the power of predictive analytics and advanced data science. Correctly harnessing data can help to achieve better, fact-based decision-making and improve the overall customer experience. By using new Big Data technologies, organisations can answer questions in seconds rather than days, and in days rather than months. This acceleration allows businesses to enable the type of quick reactions to key business questions and challenges that can build competitive advantage and improve performance, and provide answers for complex problems or questions that have resisted analysis.

Big Data and analytics are becoming closely intertwined and need to work together to deliver the promised results of Big Data. Traditionally, Data management and analytics have resided in different parts of the organisation. Breaking down organisational boundaries and creating better integration between the IT and business departments is a critical step on the road to successful transformation.

There is also a widespread realisation of the need for better Business Analytics (BI).Business Analytics are the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. The key is integrating Big Data with traditional Business Analytics to create a data ecosystem that allows the business to generate new insights while executing on what it already knows.

Keep learning. Skills are everything.

Proficiency with data mining and visualisation tools ranks as one of the most important skills in determining project success.

All organisations need to consistently develop new data mining skills to fully realise the business potential. A key trend in big data is machine learning. Big data experts who can harness machine learning technology to build and train predictive analytic apps such as classification, recommendation, and personalisation systems are in high demand. Statistical and Quantitative Analysis, which aims to understand or predict behaviour or events through the use of mathematical measurements and calculations, statistical modelling and research, is also imperative to accomplishment. Other key data mining techniques that are employed industry wide include:

• Association –  one of the best-known data mining techniques. With association, a pattern is discovered based on a relationship between items in the same transaction.
• Classification is a classic data mining technique based on machine learning.
• Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique.
• Prediction is one of a data mining techniques that discovers the relationship between independent variables and relationship between dependent and independent variables.
• Sequential patterns analysis seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period.
• Decision tree technique; the root of the decision tree is a simple question or condition that has multiple answers.

Educate your stakeholders 

All stakeholders need to be educated and made aware of Data’s value and understand that it’s essential to business continuity and growth. But they may feel overwhelmed (and under informed) to the power and complexity of the data if it is not properly communicated and presented. Regular meetings, ideally face to face will enforce the importance of the issue and the need for their buy-in.

Deliver Digital Ready networks – it makes financial sense 

All today’s businesses must, via Network Function Virtualisation (decreasing the amount of proprietary hardware needed to launch and operate network services), and Software Defined Networking (that allows updates to be made in real time or as the business demands, in just a few clicks) deliver Digital Ready networks to gain competitive advantage.

The increased simplicity and reduced costs associated with deploying and maintaining a more digital-ready network are core benefits and therefore should be employed as a necessity to improve and enhance business efficiency.

Automation is a high priority 

Automation is a high priority in accelerating Digital Transformation, allowing organisations to optimise their existing processes. Automation technology is IT system and process agnostic, allowing businesses to build on their systems within the existing IT environment.

In order to create a transformative environment and improve speed and quality of delivery, organisations need to integrate automation into their existing processes to increase the ability to frequently release high-quality products –  and to enable revenue and profit growth.

Automation also improves operational efficiency and allows employees to focus on more rewarding tasks. With automation, cost-effective solutions are enabled for repetitive, rules-based tasks. In addition, the prospect of human error is eliminated, delivering outcomes that are 100% accurate. By automating tasks, companies can significantly reduce the overall process cycle.

The road towards digital transformation is a business critical one. Organisations embarking on this journey need to consider how each aspect of their business can be optimised to fulfil new digital objectives and new growth potential.  Big data and analytics play a pivotal role in digital transformation, enabling organisations to optimise their existing processes and stay ahead of the competition.

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