As we began the new decade, technology is changing by leaps and bounds. The initial predictions for 2020 point to a serious integration of AI and human experience to study how Intelligent Automation technologies can be used to augment an enterprise experience.

Here are the top Artificial Intelligence (AI) trends that will change the decade:

The new decade will witness massive investments by global technology giants into AI technologies. In 2020, many factories of AI models and data will emerge helping AI technology and associated commercial solutions on a large-scale facilitating the enterprise. For instance, AI solutions in the customer service industry find its use cases in e-commerce, education, finance and related industries on a large scale.

Digital IQ will rise in this decade. Digital Intelligence is defined as the measurement of how organizations understand its business processes and the content and data within them from a variety of critical perspectives.

Digital Intelligence solutions assist enterprises by optimizing automation initiatives and complementing platforms like business process management and robotic process automation. 2020 will witness more and more enterprises adopting digital intelligence technologies into their digital transformation initiatives.

Deep learning is imperative to the development threshold of AI technology improving the quality and efficiency of AI applications. In 2020 and beyond, deep learning will be applied across multi-industries at a scale to accelerate transformation, upgrading and implement innovation.

According to the IDC (International Data Corporation) research, digital workers like software robots and cognitive bots will witness a growth of over 50% by 2022. Enterprises will welcome many digital robots willing to take up rule-based tasks in the office. Employees across geographies will collaborate with digital workers working alongside them in the future.

Individual technology systems like ERP, CRM, CMS, EHR, etc provides visibility into the processes controlled by their platform. To gain visibility, organizations will need to leverage Process Intelligence technologies which provide an accurate, comprehensive and real-time view of all processes across multiple functionalities, departments, personnel, functions across different locations.

In 2020 and beyond, AI will not only benefit the user experience but will be increasingly adopted by business users across geographies. Enterprises will leverage the internal marketplaces of robots and other easy-to-use automation tools available to across technical proficiencies. These new platforms will play a pivotal role in improving how employees get work done to improve customer experiences better than the competition.

Enabling cognitive automation will require new tools built for the task. AI-enabled Process and Content Intelligence technologies will provide digital workers with the skills and understanding necessary to deal with natural language, reasoning, and judgment, establishing context, providing data-driven insights.

The normalcy of AI in the workplace will also be the reason we see more human interaction with AI.

With the successful demonstration of quantum hegemony, quantum computing will usher in a new round of explosive growth in 2020. In terms of quantum hardware, the performance of programmable medium-sized noisy quantum devices will be further improved and have the ability of error correction. Quantum algorithms with certain practical value will be able to run on them, and the application of quantum artificial intelligence will be greatly developed.

In terms of quantum software, high-quality quantum computing platforms and software will emerge and be deeply integrated with AI and cloud computing technologies. Besides, with the emergence of the quantum computing industry chain, quantum computing will surely garner more attention in more application fields.

Organizations big and small will now invest in systems and methods to collect and record all the data they can, in a bid to improve their business process and functionalities.

The rapid growth in data, the reduced cost in storage, and the ease to access the data have shown incredible growth from the last decade. Data is driving the improvement of the customer experience, advancing analytics capabilities, allowing businesses to harness real value from intelligent automation, and enabling machine learning and AI that is driven by data.

Artificial intelligence can reshape and redefine the way we work and live. The growing trend we expect to see, and more is the integration of AI-enabled solutions in the workplace. These tools will help create better outcomes, ensuring enterprises are achieving their goals in a timely and efficient fashion setting new user experiences. When thinking about the needs of the hybrid workforce, leaders need to decide if simple task-based automation tools are the answer to their problems, or if they will require a mix of AI and other transformative technologies to achieve the next-gen intelligent and cognitive automation.

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Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

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Top Artificial Intelligence Trends that will Change the Decade - Analytics Insight

The automotive industry is a key driver and user of artificial intelligence (AI), which is fast percolating to different work areas. Now German major Durr has developed Advanced Analytics, the first market-ready AI application for paint shops.

This intelligent solution, which combines the latest IT technology and mechanical engineering expertise, identifies sources of defects and determines optimal maintenance schedules. It also tracks previously unknown correlations and uses the knowledge to adapt the algorithm to the plant using the principle of self-learning. Advanced Analytics is the latest module from the DXQanalyze product series. First practical applications are showing that the software from Durr optimises plant availability and the surface quality of painted bodies.

Why do body parts exhibit the same defects with an unusually high frequency? When is the latest that a mixer in the robot can be replaced without causing machine stoppage? Precise answers are important for sustainable economic success.Because every defect or every unnecessary maintenance procedure that can be avoided saves money or improves the product quality. Before there were very few precise conclusions that would enable the early detection of quality defects or failures.And if there were, they were generally based on a painstaking manual data evaluation or trial-and-error attempts. Artificial intelligence (AI) makes this much more accurate and automatic, explains Gerhard Alonso Garcia, Vice President MES & Control Systems at Drr.

The new self-learning Advanced Analytics plant and process monitoring system adds toDXQanalyze. The digital product series from Durr already included the Data Acquisition modules for acquiring production data, Visual Analytics for visualising it, and Streaming Analytics. The latter lets plant operators analyse in close to real time whether there are deviations from previously defined rules or target values in production using a low-code platform.

Durr'sAI application Advanced Analytics identifies sources of defects and determines optimal maintenance schedules.

AI application with its own memoryWhat makes Advanced Analytics special is that the module combines large quantities of data including historical data with machine learning. In the figurative sense, this means that the self-learning AI application has a memory. This means that it can use information from the past to both recognise complex correlations in large quantities of data and predict an event in the future with a high degree of accuracy based on the current condition of a machine. There are multiple applications for this in paint shops, whether at component, process, or plant level.

Durr software reduces plant downtimes through predictive maintenance,repair information.

Reducing plant downtime with predictive maintenanceWhen it comes to components, Advanced Analytics reduces downtimes through predictive maintenance and repair information, for example by predicting the remaining service life of a mixer. If the component is replaced too early, it increases the spare part costs and repair overhead unnecessarily, while leaving it too long to replace a component can result in quality problems during coating and machine stoppages. Advanced Analytics starts by learning the wear indicators and the temporal pattern of the wear using high-frequency robot data. Since the data is continuously recorded and monitored, the machine learning module individually recognizes aging trends for the respective component based on actual use and calculates the optimum replacement time.

Machine learning simulates continuous temperature curvesAdvanced Analytics improves quality at process level by identifying anomalies, for example by simulating the heat-up curve in the oven. Up to now, manufacturers only had data determined by sensors during measurement runs. However, the heat-up curves that are of vital importance for surface quality of the car bodies vary since the oven ages during the intervals between the measurement runs. This wear causes fluctuating ambient conditions, for example, in the strength of the air flow.

These days, thousands of bodies are produced without us knowing the temperatures to which the individual bodies were heated. Using machine learning, our Advanced Analytics module simulates how the temperature varies under different conditions. This gives our customers a permanent proof of quality for each individual body and lets them identify anomalies, says Gerhard Alonso Garcia.

Higher first-run rate increases equipment effectivenessAt plant level, theDXQplant.analytics software is used with the Advanced Analytics module to increase overall equipment effectiveness. The artificial intelligence tracks system defects such as recurring quality defects in model types, specific colours, or on individual body parts. This permits conclusions about which step in the production process is responsible for the deviations. Such defect and cause correlations make it possible to increase the first-run rate by allowing intervention at a very early stage.

Plant and digital expertise expertly combinedDeveloping AI-capable data models is a very complex process. Machine learning does not work by feeding unspecified amounts of data into a 'smart' algorithm, which then spits out an intelligent result. Instead, relevant (sensor) signals must be collected, carefully selected, and supplemented with structured additional information from production.With Advanced Analytics, Durr has developed a piece of software that supports different use scenarios, provides a runtime environment for machine learning models, and initiates model training. The challenge was there was no generally valid machine learning model and no suitable run-time environment we could have used.To be able to use AI at plant level, we combined our knowledge of mechanical and plant engineering with the knowledge of our experts from the Digital Factory. This resulted in the first AI solution for paint shops, explains Gerhard Alonso Garcia.

With Artificial Intelligence, systematic errors in the painting process can be detected, thus OEE can be increased by allowing intervention at a very early stage.

Interdisciplinary knowledge pays dividendsAdvanced Analytics was developed by an interdisciplinary team of data scientists, computer scientists, and process experts. Durr also entered into cooperation partnerships with several leading automotive manufacturers. This allowed the developers to access real-life production data and beta site environments in production for different application cases. First the algorithms were trained in the lab using a large number of test cases. Next, the algorithms continued learning on-site in real-life operation and autonomously adapted to environment and use conditions. The beta phase was recently successfully completed and showed the potential of AI.

Durr's India connectThe Durr Group has had a direct representation in India since 1997, and Schenck RoTec has since 1986. The Durr Group currently employs around 590 staff there, offering the entire portfolio including sales and service: Durr India, based in Chennai, offers painting, application, final assembly and energy efficiency technology products as well as air pollution control, noise abatement systems and coating systems for battery electrodes.

Since April 2015, Durr India has also been offering on-the-job as well as classroom training in paint and application systems to customers at its new training center.Schenck RoTec India, in Noida, is responsible for balancing technology as well as for testing and filling technology. The HOMAG Group produces machinery and equipment for the woodworking industry. It has a presence in Bangalore, where it operates a production site and sales and service company HOMAG India.

The Drr Group is one of the world's leading mechanical and plant engineering firms with extensive expertise in automation and digitalization/Industry 4.0. Its products, systems and services enable highly efficient manufacturing processes in different industries. The Durr Group supplies sectors like the automotive industry, mechanical engineering, chemical, pharmaceutical and woodworking industries. It generated sales of 3.92 billion euros in 2019. The company has around 16,500 employees and 112 business locations in 34 countries. The Group operates in the market with the brands Drr, Schenck and HOMAG and with five divisions:

Paint and Final Assembly Systems:paint shops as well as final assembly, testing and filling technology for the automotive industry

Application Technology:robot technologies for the automated application of paint, sealants and adhesives

Clean Technology Systems:air pollution control, noise abatement systems and coating systems for battery electrodes

Measuring and Process Systems:balancing equipment and diagnostic technology

Woodworking Machinery and Systems:machinery and equipment for the woodworking industry

Also read:Durr Systems wins German Innovation Award for robot painting system

/news-international/durr-brings-artificial-intelligence-to-the-paint-shop-56448 Durr brings artificial intelligence to the paint shop Durr brings artificial intelligence to the paint shop https://www.autocarpro.in/Utils/ImageResizer.ashx?n=http://img.haymarketsac.in/autocarpro/cc8e20a9-9966-44d0-bbfb-4cae9eefcc8e.jpg

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Durr brings artificial intelligence to the paint shop - Autocar Professional

Post the 60-day lockdown, as the Civil Aviation Minister, Hardeep Singh Puri issued guidelines to commence domestic flight operation with 1/3rd capacity from May 25, 2020, Bangalore International Airport (BAIL) has also decided to resume their domestic travel operations with a contactless journey from pre-entry of the airport to security check to the boarding of the flight.

The BAIL press release has stated that The technology will continue to enable a seamless airport journey, with greater emphasis on health and safety. Starting from their pre-entry process, which includes e-boarding pass and thermal screening of people to facial recognition system at the check-in process and self-service kiosk, BAIL has been relying on new-age technologies to transform the whole process of travelling.

Alongside, Hyderabads Rajiv Gandhi International Airport has also planned to resume its operations with contactless flying for their passengers. GHIAL has deployed thermal cameras for monitoring travellers along with Automatic Information Management System, a virtual help desk for guiding travellers with their problems, which in turn, omits any contact with travellers.

The COVID-19 created a halt for the aviation industry, with an 80% drop in the global flight activity at the end of April. Therefore, the aviation companies are strategizing differently to revamp their entire business process with digital technologies like AI, ML and RPA. In fact, from passenger identification and baggage screening to customer support and predictive maintenance, airports and airline companies can heavily rely on artificial intelligence to augment the industry work process.

AI has been a critical technology in transforming the operations of the travel industry amid this crisis. Not only has the technology been used to automate the travellers checking processes with minimum contact but also collect flight data for optimising rout and weather forecasting. Alongside, artificial intelligence has also been used to create virtual assistants for customer queries, enhanced logistics operation, facial recognition system replacing biometrics for security checks and self-service kiosks equipped with augmented reality. Airline companies are also involving artificial intelligence to improve their air safety; read here, how.

In fact, according to a recent survey, 97.2% of the aviation companies are working towards deploying big data, and artificial intelligence, with 76.5% of the firms are leveraging the value of collected data and empowering AI for cognitive learning initiatives. These numbers alone show that amid the crisis, airline companies and airports are rethinking technology to keep up their relevance.

Futurist Rohit Talwar of Fast Future said to the media that the majority of the aviation firms would give more attention to digital transformation, there could be radically different business models with a greater focus on technology and automation, designed for the era we are in. These advancements are aiming towards minimising the impact of this pandemic on their travellers experience.

These firms are majorly utilising AI, ML and robotics to keep their finances stable amid this crisis. One of the main areas where airline service providers are implementing artificial intelligence is the customer service aspect, which provides great potential for leveraging new technologies. Along with AI-based chatbots, firms are also giving airport security and aircraft monitoring with artificial intelligence.

Apart from these, aircraft manufacturers like Airbus have been using cloud-based systems for data collection and storing, and analyse the same to enhance the reliability of aircraft maintenance. Also, airlines and airports have deployed robotics technology to onboard passengers. Case in point: Bangalore International Airport, similar to Incheon, has been planning to use a humanoid robot, developed by a Bangalore-based startup Sirena Technologies, for assisting travellers with their boarding. Also, AirIndia, the countrys leading airline, has been using Taxibot on their A320 aircraft a robot-used aircraft tractor for their passenger boarding.

Many airlines and airport authorities are also partnering with the government to provide seamless travel for their passengers. In a recent development, the Ministry of Civil Aviation has also launched a connected application DigiYatra, which will process information through facial recognition at checkpoints, provide digital guidance systems, offer interactive kiosks and augmented reality apps for travellers.

Globally, airports in Singapore and Hong Kong are relying on thermal screening for monitoring passengers and robots to sanitise the airport. Also, Airlines, like Etihad, has been working on developing interactive automated kiosks that are an all-in-one system to check travellers temperature and heart rate before issuing their tickets and can process the massive volume of biometric data. Jorg Oppermann, Vice President Hub and Midfield Operations, Etihad Airways stated to the media that, We are testing this technology because we believe it will not only help in the current COVID-19 outbreak but also in future, with assessing a passengers suitability to travel, and thus minimising disruptions. Even, Los Angeles airport has stated using an advanced biometric self-boarding solution to help passengers travel securely with no contact.

Security is a prime concern for airports, and therefore it is imperative for the authorities to have a proper check of documents and identification of the passengers travelling. AI-enabled systems and tools equipped with facial recognition technology can help airport authorities identify passengers by using the data and matching the same with their passport photos. For instance, one of the American Airlines, Delta Airlines have installed cameras and deployed facial recognition technology to identify their passengers while checking in.

Alongside, airport authorities can also use the advanced technology in their security scanners to detect potential threats at significant and popular airports of the world. Many airlines have also deployed this technology in their mobile apps and automated the whole boarding process to provide a better travel experience to their customers amid their crisis. Technology like artificial intelligence and machine learning would also help in speeding up the process of attending customers, which in turn will help the officials in a longer run.

Picture Credit: Los Angeles Times

Along with identifying travellers and checking their documents, it is also imperative for airport authorities to review and screen the luggage of the travellers in order to detect any potential threats. With traditional methods, the luggage screening process could be tedious. However, with AI-based systems, security officials can quickly identify threatful and illegal items in travellers luggage in a much-simplified manner. These systems help in automated screening, which can detect potential threats in the luggage through X-rays and computed tomography.

In recent news, in an effort to enhance security, Pune Airport, in India, has deployed a smart luggage screening system enabled with AI technology, designed to automatically detect dangerous objects and other potential threats in travellers luggage, and alert operators in real time. According to Ajay Kumar, the Director of Pune Airport, This AI software technology can automatically detect various objects and other threats from the x-ray images produced during the screening of baggage and alert operatives. By exploring the potential of AI in luggage screening, the aviation industry can enhance its operations.

Pune Airport

One of the significant areas for the aviation industry to deploy artificial intelligence is to create a better travel customer experience for their passengers and customers. Not only it reduces employee costs but also speeds up the process with AI-powered chatbots as virtual assistants. According to a recent report, it is expected that by the end of this year, 4.72 billion passengers would be travelling via air, and that brings the necessity of having an efficient system that can handle such an increasing demand among travellers. Considering virtual assistants are cost-effective for airlines, it could be a preferred choice in the industry.

Companies are using AI-powered chatbots to provide flight-related information to their customers and customised attention to each traveller with their queries, which in turn help in saving several human hours doing mundane tasks. Many airlines also build their bots on popular social media apps Icelandair, a flag carrier airline of Iceland, has created a Facebook Messenger bot to communicate with its customers 24/7. These chatbots provide enhanced customer experience to the airline companies, and therefore are massively on the rise. In fact, according to a SITA report, 68% of airline companies and 42% of airports have implemented AI-powered chatbots to provide necessary information to their customers.

Maintenance of the aircraft in order to have glitch-free flying needs constant check of the machine. With AI-enabled tools, it is now easy for the airline authorities to predict potential failures of the aircraft, which not only saves a lot of money and time but also saves human lives. For this, the AI-based system collects a massive amount of real-time data and analyses the same to predict a systematic approach for the maintenance of the aircraft. This, in turn, reduces the failures of the plane, which could lead to crashes.

Flights need to travel for long durations, and therefore it is imperative to timely perform predictive maintenance to detect if any part is broken and need to be repaired. The technology uses NLP to scan the data and predict failures and recommend solutions for the same. Apart from maintenance, artificial intelligence can also support in-flight management, aircraft monitoring and other critical operations in the aircraft. In fact, IBMs Watson commercial for aviation stated that the technology could also help ground staff with repairing and maintenance of the aircraft.

Although the penetration of artificial intelligence in the aviation industry isnt a novel discovery, however, the pandemic outbreak has forced the industry to rethink their strategies to continue their business post COVID world. Therefore, to stand out in this continuously evolving technology market, airline companies and airports need to rely on automating their processes with the help of artificial intelligence to provide an enhanced experience to their customers.

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How Artificial Intelligence Can Help Aviation Industry With Contactless Flying Amid The Crisis - Analytics India Magazine

Artificial intelligence (AI) is clearly a growing force in the technology industry. AI is taking center stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing. New products are being embedded with virtual assistants, while chatbots are answering customer questions on everything from your online office supplier's site to your web hosting service provider's support page. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrating AI as an intelligence layer across their entire tech stack. Yes, AI is definitely having its moment.

This isn't the AI that pop culture has conditioned us to expect; it's not sentient robots or Skynet, or even Tony Stark's Jarvis assistant. This AI plateau is happening under the surface, making our existing tech smarter and unlocking the power of all the data that enterprises collect. What that means: Widespread advancement in machine learning (ML), computer vision, deep learning, and natural language processing (NLP) have made it easier than ever to bake an AI algorithm layer into your software or cloud platform.

For businesses, practical AI applications can manifest in all sorts of ways depending on your organizational needs and the business intelligence (BI) insights derived from the data you collect. Enterprises can employ AI for everything from mining social data to driving engagement in customer relationship management (CRM) to optimizing logistics and efficiency when it comes to tracking and managing assets.

ML is playing a key role in the development of AI, noted Luke Tang, General Manager of TechCode's Global AI+ Accelerator program, which incubates AI startups and helps companies incorporate AI on top of their existing products and services.

"Right now, AI is being driven by all the recent progress in ML. There's no one single breakthrough you can point to, but the business value we can extract from ML now is off the charts," Tang said. "From the enterprise point of view, what's happening right now could disrupt some core corporate business processes around coordination and control: scheduling, resource allocation and reporting." Here we provide tips from some experts to explain the steps businesses can take to integrate AI in your organization and to ensure your implementation is a success.

Take the time to become familiar with what modern AI can do. The TechCode Accelerator offers its startups a wide array of resources through its partnerships with organizations such as Stanford University and corporations in the AI space. You should also take advantage of the wealth of online information and resources available to familiarize yourself with the basic concepts of AI. Tang recommends some of the remote workshops and online courses offered by organizations such as Udacity as easy ways to get started with AI and to increase your knowledge of areas such as ML and predictive analytics within your organization.

The following are a number of online resources (free and paid) that you can use to get started:

Once you're up to speed on the basics, the next step for any business is to begin exploring different ideas. Think about how you can add AI capabilities to your existing products and services. More importantly, your company should have in mind specific use cases in which AI could solve business problems or provide demonstrable value.

"When we're working with a company, we start with an overview of its key tech programs and problems. We want to be able to show it how natural language processing, image recognition, ML, etc. fit into those products, usually with a workshop of some sort with the management of the company," Tang explained. "The specifics always vary by industry. For example, if the company does video surveillance, it can capture a lot of value by adding ML to that process."

Next, you need to assess the potential business and financial value of the various possible AI implementations you've identified. It's easy to get lost in "pie in the sky" AI discussions, but Tang stressed the importance of tying your initiatives directly to business value.

"To prioritize, look at the dimensions of potential and feasibility and put them into a 2x2 matrix," Tang said. "This should help you prioritize based on near-term visibility and know what the financial value is for the company. For this step, you usually need ownership and recognition from managers and top-level executives."

There's a stark difference between what you want to accomplish and what you have the organizational ability to actually achieve within a given time frame. Tang said a business should know what it's capable of and what it's not from a tech and business process perspective before launching into a full-blown AI implementation.

"Sometimes this can take a long time to do," Tang said. "Addressing your internal capability gap means identifying what you need to acquire and any processes that need to be internally evolved before you get going. Depending on the business, there may be existing projects or teams that can help do this organically for certain business units."

Once your business is ready from an organizational and tech standpoint, then it's time to start building and integrating. Tang said the most important factors here are to start small, have project goals in mind, and, most importantly, be aware of what you know and what you don't know about AI. This is where bringing in outside experts or AI consultants can be invaluable.

"You don't need a lot of time for a first project; usually for a pilot project, 2-3 months is a good range," Tang said. "You want to bring internal and external people together in a small team, maybe 4-5 people, and that tighter time frame will keep the team focused on straightforward goals. After the pilot is completed, you should be able to decide what the longer-term, more elaborate project will be and whether the value proposition makes sense for your business. It's also important that expertise from both sidesthe people who know about the business and the people who know about AIis merged on your pilot project team."

Tang noted that, before implementing ML into your business, you need to clean your data to make it ready to avoid a "garbage in, garbage out" scenario. "Internal corporate data is typically spread out in multiple data silos of different legacy systems, and may even be in the hands of different business groups with different priorities," Tang said. "Therefore, a very important step toward obtaining high-quality data is to form a cross-[business unit] taskforce, integrate different data sets together, and sort out inconsistencies so that the data is accurate and rich, with all the right dimensions required for ML."

Begin applying AI to a small sample of your data rather than taking on too much too soon. "Start simple, use AI incrementally to prove value, collect feedback, and then expand accordingly," said Aaron Brauser, Vice President of Solutions Management at M*Modal, which offers natural language understanding (NLU) tech for health care organizations as well as an AI platform that integrates with electronic medical records (EMRs).

A specific type of data could be information on certain medical specialties. "Be selective in what the AI will be reading," said Dr. Gilan El Saadawi, Chief Medical Information Officer (CMIO) at M*Modal. "For example, pick a certain problem you want to solve, focus the AI on it, and give it a specific question to answer and not throw all the data at it."

After you ramp up from a small sample of data, you'll need to consider the storage requirements to implement an AI solution, according to Philip Pokorny, Chief Technical Officer (CTO) at Penguin Computing, a company that offers high-performance computing (HPC), AI, and ML solutions.

"Improving algorithms is important to reaching research results. But without huge volumes of data to help build more accurate models, AI systems cannot improve enough to achieve your computing objectives," Pokorny wrote in a white paper entitled, "Critical Decisions: A Guide to Building the Complete Artificial Intelligence Solution Without Regrets." "That's why inclusion of fast, optimized storage should be considered at the start of AI system design."

In addition, you should optimize AI storage for data ingest, workflow, and modeling, he suggested. "Taking the time to review your options can have a huge, positive impact to how the system runs once its online," Pokorny added.

With the additional insight and automation provided by AI, workers have a tool to make AI a part of their daily routine rather than something that replaces it, according to Dominic Wellington, Global IT Evangelist at Moogsoft, a provider of AI for IT operations (AIOps). "Some employees may be wary of technology that can affect their job, so introducing the solution as a way to augment their daily tasks is important," Wellington explained.

He added that companies should be transparent on how the tech works to resolve issues in a workflow. "This gives employees an 'under the hood' experience so that they can clearly visualize how AI augments their role rather than eliminating it," he said.

When you're building an AI system, it requires a combination of meeting the needs of the tech as well as the research project, Pokorny explained. "The overarching consideration, even before starting to design an AI system, is that you should build the system with balance," Pokorny said. "This may sound obvious but, too often, AI systems are designed around specific aspects of how the team envisions achieving its research goals, without understanding the requirements and limitations of the hardware and software that would support the research. The result is a less-than-optimal, even dysfunctional, system that fails to achieve the desired goals."

To achieve this balance, companies need to build in sufficient bandwidth for storage, the graphics processing unit (GPU), and networking. Security is an oft-overlooked component as well. AI by its nature requires access to broad swaths of data to do its job. Make sure that you understand what kinds of data will be involved with the project and that your usual security safeguards -- encryption, virtual private networks (VPN), and anti-malware -- may not be enough.

"Similarly, you have to balance how the overall budget is spent to achieve research with the need to protect against power failure and other scenarios through redundancies," Pokorny said. "You may also need to build in flexibility to allow repurposing of hardware as user requirements change."

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