The potential is huge: the generative AI market is poised to grow to $1.3 trillion over the next 10 years from a market size of just $40 billion in 2022, according to a new report by Bloomberg Intelligence.

Getting AI right relies on quality data—particularly unstructured data. AI success depends upon the appropriate curation and management of this file and object data, which makes up at least 80 percent of all data in the world. This article identifies the challenges of those efforts and offers 10 tips for addressing them.

Managing Unstructured Data and ROT

Unstructured data, given its volume and the many different types of files and formats it comprises—from documents and images to sensor and instrument data, video, and more—is vexing to manage. Often distributed across multiple storage systems in the increasingly hybrid, multi-cloud enterprise, it is hard to search, segment, and move around as needed.

Due to its growth, unstructured data is expensive to store and backup. In fact, a majority (68 percent) of enterprise organizations surveyed in 2022 are spending 30 percent or more of their IT budgets on storage. These issues are made worse in data-intensive industries as copies of redundant, obsolete, and trivial (ROT) data are rarely deleted by researchers and other teams when projects are completed.

Managing unstructured data for AI requires new solutions and tactics, including a data-centric approach to guide cost-effective storage and data mobility decisions across vendors and clouds.

There’s also a growing need to ensure that the right data sets are leveraged. New research from Stanford found that the performance of large language models (LLMs) “substantially decreases as the input context grows longer, even for explicitly long-context models.” In other words, curating the right data sets may be more important than large data sets, depending on the project.

10 Tips for Managing Unstructured Data in Generative AI

Generative AI solutions, guidelines, and practices are changing daily. But establishing a foundation for intelligent unstructured data management can help organizations flex and shift through this transformative era. Here are some tactics to consider.

Start with visibility

Data indexing is a powerful way to categorize all of the unstructured data across the enterprise and make it searchable by key metadata (data on your data) such as file size, file extension, date of file creation, and date of last access. Visibility is foundational for right-placing data to meet changing business needs for archiving, analytics, compliance and so on.

Understand key data characteristics

When laying a foundation for AI, more information is better. The more information you have on your data, the better prepared you’ll be to deliver it to AI and ML tools at the right time—and the better prepared you’ll be to ensure you have the right storage infrastructure for these new use cases. At a minimum, you’ll need to understand data volumes and growth rates, storage costs, top data types and sizes, departmental data usage statistics, and “hot” or active versus “cold” or rarely-accessed data.

Tag and segment data

Once you have a base level of understanding about your data assets, you can enrich them with metadata for additional search capabilities. For instance, you may want to search for files containing personally identifiable information (PII) or customer data, intellectual property (IP) data, experiment name, or instrument ID. Those files could be segmented for compliant storage or to feed into an analytics platform.

Collaborate with departments

With so many use cases across organizations today for AI and other research, central IT and department IT liaisons need to work together to design data management strategies. This ensures that users have fast access to their most important data but can also access older data archived to low-cost storage when they need it.

Be selective with training data

Don’t give an AI tool more data than is needed to run a query. This reduces leakage and security risks to organizational data and it may also improve the chance of highly-relevant and accurate outcomes.

Segregate sensitive and proprietary data

Security was the top concern for generative AI in a recent Salesforce survey of IT leaders. By moving sensitive corporate data– such as IP, PII, and customer data–into a private, secure domain, you can ensure that employees won’t be able to send it to AI tools. Some organizations are creating their own private LLMs to circumvent this issue altogether, even though this can be expensive and requires specialized skills and infrastructure.

Work closely with vendors

Data provenance and transparency around the training data used in an AI application are critical—data sources in generative AI applications can be obscure, inaccurate, libelous, and unethical, and can contain PII. Non-AI applications are also now incorporating LLMs into their platforms. Find out how vendors are protecting your organization from the various risks of AI with your data and any external data within its LLM. Get clear on who’s liable for what when something goes awry. Ask for transparency in data sources from the vendor’s LLM.

Create an AI governance plan

If you work in a regulated industry, you’ll need to demonstrate that your organization is complying with data usage. A healthcare organization, for instance, would need to verify that no patient PII data has been leaked to an AI solution per HIPAA rules. An AI governance framework should cover privacy, data protection, ethics and more. Create a task force spanning security, legal, HR, data science, and IT leaders. Data management solutions help by providing a means to track and monitor what data moves to AI tools and by whom.

Audit data use in AI

Related to the above, if you choose to share corporate data with a general LLM such as ChatGPT or Bard, it’s important to track the inputs and outputs and who commissioned the project in the event there are issues later. Problems can include inaccurate or erroneous results from bad data, copyright lawsuits from derivative works, or privacy and security violations. Keep in mind that LLMs not only potentially expose your company’s data to the world but the data of other organizations—and your organization could be liable for the exposure or misuse of any third-party data discovered in a derivative work.

Choose the right tools

When your results must be factually accurate and objective, some generative AI tools may not be the best fit. Consider the recent revelations that ChatGPT’s latest version is generating significantly less accurate and lower quality responses. Machine learning systems may be better when your task requires a deterministic outcome.

Bottom Line

Despite the many concerns with AI—and especially generative AI—the groundswell of adoption is on the near horizon. A survey by Upwork found that 62 percent of midsize companies and 41 percent of large companies are leveraging generative AI technology. Another study found that 72 percent of Fortune 500 leaders said their companies will incorporate generative AI within the next three years to improve employee productivity.

No matter where your organization is on the adoption curve, AI will impact your employees, customers, and product lines sooner rather than later. Be prepared by taking a proactive data management approach that encompasses visibility, analytics, segmentation, and governance to your organization can reap the benefits of AI without bringing the house down.

Krishna Subramanian is COO and President of Komprise.

Experts predict 175 zettabytes of data worldwide within two years, much of it coming from IoT (Internet of Things) devices. Companies of all sizes should expect significant troves of data, most of it unstructured and not necessarily compatible with system of record (SOR) databases that have long driven mission-critical enterprise systems like enterprise resource planning (ERP).

Even unstructured data should be subject to many of the same rules that govern structured SOR data. For example, unstructured data must be secured with the highest levels of data integrity and reliability if the business is to depend on it. It must also meet regulatory and internal governance standards, and it must be able to move freely among systems and applications on clouds, internal data repositories, and mobile storage.

To keep pace with the enormous demands of managing voluminous high velocity and variegated data day-in and day-out, software-based tools and automation must be incorporated into data management practices. Newer automation technologies like data observability will only grow in importance, especially as user citizen development and localized data use expand.

All of these forces require careful consideration as enterprise IT builds its data management roadmap. Accordingly, here are seven emergent data management trends in 2023.

Hybrid End-to-End Data Management Frameworks

Enterprises can expect huge amounts of structured and unstructured data coming in from a wide range of sources, including outside cloud providers; IoT devices, robots, drones, RF readers, and MRI or CNC machines; internal SOR systems; and remote users working on smart phones and notepads. All of this data might be committed to long- or short- term storage in the on-premise data center, in a cloud, or on a mobile or distributed server platform. In some cases, real-time data may need to be monitored and/or accessed as it streams in real time.

In this hybrid environment, the data, its uses, and its users are diverse—data managers will need data management and security software that can span all of these hybrid activities and uses so data can be safely and securely transported and stored point to point.

IBM is a leader in the data management framework space, but SAP, Tibco, Talend, Oracle, and others also offer end-to end data fabric management solutions. A second aspect of data management is being able to secure data, no matter where it is sent from or where it resides—end-to-end security mesh software from vendors such as Fortinet, Palo Alto Networks, and Crowdstrike can meet this need.

The Consolidation of Data Observability Tools

Because many applications now use multiple cloud and on-premises platforms to access and process data, observability—the ability to track data and events across multiple platform and system barriers with software—is a key focus for enterprises looking to monitor end-to-end movements of data and applications. The issue with most organizations that are using observability tools today is that they are using too many different tools to effect end-to-end data and application visibility across platforms.

Vendors like Middleware and Datadog recognize this and are focused on delivering integrated, “single pane of glass” observability tool sets. These tools enable enterprises to reduce the number of different observability tools they use into a single toolset that’s able to monitor data and event movements across multiple cloud and on premises systems and platforms.

Master Data Management for Legacy Systems

As businesses move forward with new technologies, they face the challenge of figuring out what to do with older ones. But some of those continue to provide value as legacy systems—systems that are outdated or that continue to run mission-critical functions vital to the enterprise.

Some of these legacy systems—for example, enterprise resource planning (ERP) systems like SAP or Oracle—offer comprehensive, integrated master data management (MDM) toolsets for managing data on their cloud or on-premises solutions. Increasingly enterprises using these systems are adopting and deploying these MDM toolsets as part of their overall data governance strategies.

MDM tools offer user-friendly ways to manage system data and to import data from outside sources. MDM software provides a single view of the data, no matter where it resides, and IT sets the MDM business rules for data consistency, quality, security, and governance.

Data Management Using AI/ML

While the trend of using artificial intelligence and machine learning (AI/ML) for data management is not new, it continues to grow in popularity driven by big data concerns as the unprecedented volume of data enterprises are faced with managing collides with an ongoing staffing shortage across the tech industry as a whole—especially in data-focused roles.

AI and ML introduce highly valuable automation to manual processes that have been prone to human error. Foundational data management tasks like data identification and classification can be handled more efficiently and accurately by advanced technologies in the AI/ML space, and enterprises are using it to support more advanced data management tasks such as:

• Data cataloging
• Metadata management
• Data mapping
• Anomaly detection
• Metadata auto-discovery
• Data governance control monitoring

As AI/ML continues to evolve, we can expect to see software solutions that offer intelligent, learning-based approaches including search, discovery, and capacity planning.

Prioritizing Data Security

In the first quarter of 2023, over six million data records were breached worldwide. A data breach can destroy a company’s reputation, impact revenue, endanger customer loyalty, and get people fired.This is why security of all IT—especially as more IT moves to the edge and the IoT—is an important priority for CIOs and a major IT investment area.

To meet data security challenges, security solution providers are moving toward more end-to-end security fabric solutions. They are offering training for employees and IT, since increases in user citizen development and poor user security habits can be major causes of breaches.

Although many of these security functions will be performed by the IT and network groups, clean, secure, and reliable data is foremost a database administrator, data analyst, and data storage concern as well.

Automating Data Preparation

The exponential growth of big data volumes and a shrinking pool of data science talent is stressing organizations. In some cases, more than 60 percent of expensive data science time is spent cleaning and preparing data.

Software vendors want to change this corporate pain point with an increase in data preparation and cleaning automation software that can perform these tedious, manual operations. Automated data preparation solutions ingest, store, organize, and maintain data, often using AI and ML, and can handle such manually intensive tasks as data preparation and data cleansing.

Using Blockchain and Distributed Ledger Technology

Distributed ledger systems enable enterprises to maintain more secure transaction records, track assets, and keep audit trails. This technology, along with blockchain technology, stores data in a decentralized form that cannot be altered, improving the authenticity and accuracy of records related to data handling. This includes financial transaction data, sensitive data retrieval activity, and more.

Blockchain technology can be used in data management to improve the security, shareability, and consistency of data. It can also be used to provide automatic verification, offering avenues to improve data governance and security.

Bottom Line: The Future of Data Management

As businesses confront the need to collect and analyze massive volumes of data from a variety of sources, they seek new means of data management that can keep pace with the expanding need. Cutting edge technologies like AI/ML and blockchain can be used to automate and enhance some aspects of data management, and software vendors are incorporating them into their platforms to make them an integral part of the work. As new technologies continue to evolve, data management methods will evolve with them, integrating them into processes driven by increasing demand.

Read next: Structured Data: Examples, Sources, and How it Works

SaaS provider GoTo conducted a report on the impact of AI in the workplace and society that included a survey sent to U.S. industry professionals to shed light on how businesses and employees are being impacted by the introduction of AI.

Results showed that 56 percent of respondents expressed trust in AI technology but stated they would manually fact-check the information provided for safety. Only 10 percent indicated distrust in the information generated by AI systems.

While AI is currently not being regulated in the U.S., 70 percent of workers believe the software and technology should be regulated. The survey also found that 57 percent believe they have a basic knowledge of privacy and copyright laws around AI.

Other Key Findings

Here are some of the report’s key findings:

• 42 percent of U.S. workers believe AI will be a good time management tool. A significant majority of respondents are enthusiastic about the positive changes AI will bring to their daily work tasks.
• 47 percent expressed excitement that AI can effectively solve smaller tasks, freeing up time to concentrate on more complex tasks. Additionally, 45 percent believe AI can enhance their job efficiency.
• Among industry professionals, 71 percent believe AI can improve productivity. The same percentage are confident that AI could be incorporated into their jobs.

The full findings can be found below:

Find the full report here.

While postings change frequently, this article highlights 10 companies currently recruiting for IT automation roles to provide some insight into the types of jobs that are available and the benefits they offer. Note that not all companies share salary information on job postings, either on their websites or on job boards—for those without specific salary information, we’ve instead listed the national average for similar roles according to data collected from Glassdoor, LinkedIn, Ladders, and Indeed.

Some of these companies have global presences, but all jobs listed here are U.S.-based.

Table of Contents

• ABB Ltd
• Accenture
• Amazon Web Services (AWS)
• Apple
• Cognizant
• IBM
• Intel
• Rivian
• Schneider Electric
• Siemens

ABB Ltd

ABB Ltd is an automation company that optimizes how technology is manufactured, moved, powered, and operated. It supplies electrical equipment and automation products in many different industries. ABB Ltd supplies products for robotics and other industries for a company that needs both discrete and process automation.

Employees on Glassdoor rated ABB Ltd four out of five stars, and 82 percent said they would recommend working at the company.

Best Perks

ABB Ltd offers such employee benefits as adoption assistance and family medical leave; flexible work schedules and remote work; dental, disability, health, life, mental health, and vision insurance; flexible spending account (FSA), commuter benefits, company outings, and tuition reimbursement; 401k, company equity, stock purchase plan, charity contribution matching, and performance bonus; and paid time off (PTO) and sabbatical. ABB Ltd allows work from home (WFH) depending on the position.

Headquarters

ABB Ltd Global Headquarters is in Zurich, Switzerland; North American Headquarters is in North Carolina.

Salary and Role Examples

ABB Ltd does not consistently post salary information for job listings on its website or job hiring boards. The salaries below are U.S. national averages.

• Automation and Controls Engineer, National Electrical Manufacturers Association (NEMA) Motors: $91,404
• Senior Project Engineer, Protection, Controls, and Automation Engineer: $73,731 to $90,992
• Test Automation Engineer, Automation Machinery Manufacturing: $102,918
• Technical Support Specialist, Process Automation: $96,207
• Quality Specialist, Process Automation Measurement & Analytics: $43,000 to $98,000

Accenture

Accenture is a professional services company offering digital, cloud, and security technology capabilities. It provides strategy and consulting, technology, and operations services for enterprises looking to improve their infrastructures.

Employees on Glassdoor rated Accenture four out of five stars, and 78 percent said they would recommend working there.

Best Perks

Accenture provides benefits for health, family, time away, and financial security. This includes medical, dental, and vision coverage; parental leave; care for dependents; flexible work arrangements, PTO, and holidays; 401k match, life insurance, and spending accounts. Accenture offers remote and WFH options for some of its open roles.

Headquarters

Accenture is headquartered in Dublin, Ireland; Accenture North America is headquartered in Chicago, Illinois.

Salary and Role Examples

• Federal – Automation Tester, Software Engineering: $73,900 to $115,100
• Test Automation Engineering Associate Manager, Software Engineering: $90,699 to $113,002
• Federal – Automation Test Engineering Sr. Analyst, Software Engineering: $77,000 to $116,000
• Federal – Automation Test Engineering Specialist, Software Engineering: $93,000 to $150,000
• Federal IT Operations Analyst, Software Engineering: $47,500 to $92,400

Amazon Web Services (AWS)

AWS, under parent company Amazon, focuses on building technologies, inventing products, and providing services that help its customers. AWS is a top cloud computing company that is accessible for small businesses to large enterprises using multiple software and services including database services, applications, data centers, and data storage.

Employees on Glassdoor rated Amazon almost four out of five stars, and 71 percent said they would recommend working there.

Best Perks

Amazon provides many benefits for its employees, including medical, dental, and vision plans; company-paid basic life and accidental death insurance; company-paid short-term and long-term disability; PTO, The Amazon Career Choice Program, and adoption assistance; flexible spending accounts, 401k plan, and restricted stock units (RSU). Amazon has remote and WFH options as well.

Headquarters

Amazon is headquartered in Seattle, Washington.

Salary and Role Examples

• Marketing Automation Manager, Amazon Flex: $87,300 to $179,600
• Manager, Tooling and Automation, Kuiper Production: $120,800 to $234,900
• Manager, Security Automation, Security Operational Research: $78,000 to $122,000 (U.S. national average)
• Senior Security Engineer, Research and Automation: $77,413 to $101,280 (U.S. national average)
• Senior Software Dev Engineer, Continuous Infrastructure Automation: $70,000 to $121,000 (U.S. national average)

Apple

Apple Inc is one of the most widely known brands globally, creating a line of popular products and software applications as well as third-party digital content. In addition to computer hardware and related products, it provides different services such as AppleTV, Apple Music, Apple Arcade, and fitness and health applications.

Employees on Glassdoor rated Apple four out of five stars, and 82 percent said they would recommend working there.

Best Perks

Apple offers a variety of benefits and perks for its employees including medical, life, disability, accident, and retirement insurance, PTO and parental leave; commuting help; gym credit; stock purchase program; tuition and self-improvement benefits; and product discounts, subsidized meals, and a beer bash once a year. Apple has remote and WFH options.

Headquarters

Apple is headquartered in Cupertino, California.

Salary and Role Examples

• Automation Engineer, Hardware: $136,000 to $207,000
• CAD Flow Automation Engineer, Hardware: $161,000 to $278,000
• Embedded QA Automation Engineer, Software and Services: $105,500 to $196,500
• RF PHY Test Automation Engineer, Hardware: $50.72 to $76.44 per hour
• Health Software Automation Engineer, Software and Services: $104,000 to $190,000

Cognizant

Cognizant is a professional technology services company whose employees operate and build technology models for the digital era. Cognizant helps enterprises implement IT business transformation practices and assists in positioning enterprises with new innovative solutions to improve customers’ competitive edge.

Employees on Glassdoor rated Cognizant nearly four out of five stars, and 75 percent said they would recommend working there.

Best Perks

Cognizant has many benefits, including health, dental, and vision insurance; PTO, maternity, and disability leave; flexible WFH options; bonus pay, stock purchase plans, flexible spending accounts, and more.

Headquarters

Cognizant is headquartered in Teaneck, New Jersey.

Salary and Role Examples

Cognizant does not consistently post salary information for job listings on its website or job hiring boards. The salaries below are U.S. national averages.

• Mobile Automation Lead, Technology & Engineering: $131,995
• Automation Tester, Technology & Engineering: $63,000 to $87,000
• Junior Automation Engineer, Technology & Engineering: $87,000 to $126,000
• Automation Test Manager, Technology & Engineering: $112,700
• Senior Automation Tester, Technology & Engineering: $100,173 to $122,943

IBM

IBM is widely known for its cloud computing and data analytics solutions, but it also provides technology research, consulting, and hosting services in areas from mainframe computers to nanotechnology. It is the largest industrial research organization in the world.

Employees on Glassdoor rated IBM four out of five stars, and 75 percent said they would recommend working there.

Best Perks

IBM offers many perks, including health and well-being services, family support, tax-advantaged spending accounts, financial benefits, and PTO. Some unique benefits include IBM MoneySmart which offers financial education and planning at no cost; Wellbeing@IBM which provides resources to improve your health and well-being; and IBM offers three weeks off a year for employees who work for under ten years and four weeks for employees who work over ten years. IBM also offers remote and hybrid options for employees.

Headquarters

IBM is headquartered in Armonk, New York.

Salary and Role Examples

• Automation Developer, Watsonx: $66,565 to $79,833
• RPA/Automation Developer, Data & Analytics: $98,000 to $183,000
• Network Automation Engineer, Tool Development: $141,000 to $263,000
• SAP Quality Assurance/Test Consultant, Automation/Consulting: $78,000 to $146,000
• Senior Automation Architect, IT Management Consultant: $150,000 to $250,000

Intel

Intel is the largest manufacturer by revenue of semiconductor chips, and is a large technology company that conducts research in artificial intelligence (AI), analytics, and cloud-to-edge technology. Intel is a developer for the microprocessors found in most personal computers.

Employees on Glassdoor rated Intel over four out of five stars, and 84 percent said they would recommend working there.

Best Perks

Intel offers many benefits including health, vision, and dental insurance; behavioral health, fertility, and retiree health insurance; quarterly, annual, and stock bonuses; PTO, travel discounts, and maternity leave; family programs and more. Intel also offers three working models including fully remote, fully on-site, and hybrid remote arrangements.

Headquarters

Intel Corporation is headquartered in Santa Clara, California.

Salary and Role Examples

• DevOps Software Development Engineer, HPC Storage Architecture and Development: $94,000 to $135,000
• Software Development Engineer, Generative AI, GPT, and Automation: $129,846 (U.S. national average)
• PDK Design Automation Engineer, Design Enablement Group: $119,130 to $178,690
• Software Tools & Automation Engineer, High-Performance Data Division: $102,918 (U.S. national average)
• Engineering Intern, Automation: $54,210 or $26.06 per hour

Rivian

Rivian is an electronic vehicle (EV) manufacturer that produced the first electric pickup truck and sport utility vehicle (SUV). The company is committed to building products that use renewable energy sources. Rivian’s vehicles are designed for off-road capabilities, which is not typically a focus for electric transportation, making its technology unique within its industry. Rivian has around 7,500 employees.

Employees on Glassdoor rated Rivian four out of five stars, and 55 percent of employees said they would recommend working there.

Best Perks

Rivian provides many perks and benefits for employees, including health, dental, and vision insurance; health savings accounts, PTO, and flexible spending accounts; mental, financial, and physical wellness programs; and unique perks include tuition assistance, LGBTQIA2+ benefits, and pet insurance.

Headquarters

Rivian is headquartered in Irvine, California.

Salary and Role Examples

Rivian does not consistently post salary information for job listings on its website or job hiring boards. The salaries below are U.S. national averages.

• Senior Automation Engineer, Equipment Engineering: $111,703
• Senior Vehicle Test Automation Engineer, Manufacturing: $137,207
• Staff Automation Controls Engineer, Manufacturing: $65,216 to $124,567
• BMS Software Engineer, Test System Automation and Simulation: $101,000 to $113,000
• Staff Automation Controls Engineer, Integration: $60,663 to $115,877

Schneider Electric

Schneider Electric is a digital and environmental company. It works with energy and automation digital solutions to combine energy technologies, automation, software, and services for solutions for homes, buildings, data centers, infrastructure, and other industries.

Employees on Glassdoor rated Schneider Electric over four out of five stars, and 87 percent said they would recommend working there.

Best Perks

Schneider Electric offers unique benefits and perks for employees, including BenefitBuck$, which helps with benefits costs including vision, supplemental AD&D insurance, short and long-term disability, spouse life, child life insurance, and more. For employees who have to pay for transportation and parking, Schneider Electric offers commuter benefits and if an employee does not wish to commute, the company also offers home office enhancements.

Schneider Electric offers a program called Care@Work to provide emergency backup child care, adult/elder care, or pet care. The company also offers PTO, health programs, legal services plans, and more.

Headquarters

Schneider Electric is headquartered in Rueil-Malmaison, France; Schneider Electric North America is headquartered in Andover, Massachusetts.

Salary and Role Examples

• End User Automation Sales Executive, Sales: $150,000
• Building Automation System – Technician I, Customer Projects & Services: $74,101 (U.S. national average)
• Senior Application Design Engineer, Industrial Automation: $96,000 to $144,000
• Automation Engineer for Microgrid: $82,009

Siemens

Siemens is a digital engineering, manufacturing, and electronics company. Siemens designs and develops electrification, automation, and digitalization solutions and has the capability to install complex systems and projects into enterprise infrastructure.

Employees on Glassdoor rated Siemens over four out of five stars, and 83 percent said they would recommend working there.

Best Perks

Siemens provides many benefits to employees, including medical, dental, and vision insurance; 20 days of PTO, life insurance, and long-term and short-term disability plans; 401k plan with up to six percent company match, tuition reimbursement, career development plans; commuter benefits, and financial planning programs. Siemens does allow WFH, but it is limited.

Headquarters

Siemens is headquartered in Munich, Germany; Siemens USA is headquartered in Washington D.C.

Salary and Role Examples

• Building Automation Operations Manager I, Smart Buildings/Automation: $131,995 (U.S. national average)
• Senior Deep Learning Scientist, AI Automation: $48,000 to $91,000 (U.S. national average)
• Atellica Product Portfolio Manager Automation, Product Management, Portfolio & Innovation: $134,000 to $184,000
• Sr Power Automation, Protection and Controls Engineer: $74,226 (U.S. national average)
• SaaS Site Reliability Engineer and Automation Developer, Research & Development: $116,900 to $210,400

Bottom Line: IT Automation Jobs

Automation is becoming increasingly a part of day-to-day work across many industries, and as more enterprises incorporate it into their workflows, the need for a skilled automation workforce grows. Jobs in automation combine technical expertise, problem-solving skills, and the opportunity to help shape a company’s future with technology-driven operations, and offer a wide range of benefits and perks.

Read next: What Is Automation?

AI is a versatile technology, so its potential applications span a huge range of sectors and purposes. Here’s a look at how five leading companies in different industries use it.

1. Facebook

Facebook is one of the most recognizable names in social media. Even though the sector is more crowded now than when it first started, Facebook remains the second most popular social platform in the U.S., according to Pew. With so many users, ensuring everyone sees what’s most relevant to them can be challenging.

In 2016, Facebook introduced DeepText, a proprietary deep-learning text analysis model, to gain a better understanding of user intent and interactions. DeepText can analyze thousands of posts per second in more than 20 languages to enable more personalized suggestions, identify spam, and highlight relevant content.

Industry: Social Media
AI Product: DeepText
Outcomes:

• More accurate user intent analytics
• Better spam and cyberbullying responses
• Transparency into user sentiment across locations and languages

2. Daimler Trucks Asia

Daimler Trucks Asia (DTA) is a branch of the world’s largest truck manufacturer, so its internal processes have ripple effects throughout global supply chains. In light of this massive industry presence, catching potential safety or quality issues early is crucial.

DTA’s old, manual quality control system could take up to two years to identify and address safety issues. As those losses mounted, the company turned to Deloitte to build a custom AI analytics platform to streamline the process. The resulting solution, dubbed proactive sensing, analyzes a wide range of data, from vehicle metrics to social media engagement, to predict and quantify safety issues earlier and more accurately.

Because AI is better than humans at spotting trends in data, the solution can find indicators of potential problems human analysts may overlook. It can also predict issues before they happen, enabling faster, more cost-effective responses.

Industry: Vehicle manufacturing
AI Product: Deloitte
Outcomes:

• 50% reduction in issue detection time
• $8 million reduction in warranty costs in first two years
• Preserved client reputation

Learn more about artificial intelligence in supply chains.

3. Humana

Humana is one of the largest insurers in the U.S., serving over 13 million customers across the country. Managing queries from all those customers can be a challenge, especially when you have to determine which cases are the most pressing. Humana had a voice chatbot to field these calls, but it transferred too many to human agents or expensive outsourced call centers when most calls were about routine questions.

Using an AI solution from IBM, Humana built a new chatbot that could understand conversational language better, leading to more helpful responses. The bot can interpret a wider range of customer needs more accurately, enabling it to provide customized responses and help instead of sending lengthy FAQ pages or directing calls to human agents.

Industry: Insurance
AI Product: IBM Watson
Outcomes:

• Handled inquiries at a third of the cost
• Doubled response rate
• Faster customer care responses

Learn more about how to use chatbots to improve customer service.

4. Panasonic

Natural language processing (NLP) AI services have uses outside of customer-facing chatbots, too. Panasonic, a global leader in electronic manufacturing, uses AI translation services to enable easier communication between its more than 240,000 employees and 500 affiliate companies.

Using global teams gives companies a wider talent pool and promotes flexibility, but language barriers can hamper productivity. To get around that issue, Panasonic implemented an AI system to translate documents between English and Japanese with remarkable speed and accuracy.

Another crucial advantage of AI over manual translation is that it provides more security over trade secrets. Panasonic found that other methods and services may expose sensitive documents to data leakage. AI, by contrast, removes intermediaries and can work securely, ensuring classified company documents stay secure through the translation process.

Industry: Electronics Manufacturing
AI Product: MiraiTranslate
Outcomes:

• Faster, more accurate translations
• Protected trade secrets
• Enabled more productive global collaboration

5. Česká Spořitelna

Banking is another industry with high security and productivity needs, making it an ideal use case for AI. Česká Spořitelna, the largest retail bank in the Czech Republic, uses the technology extensively.

As a customer-facing bank, Česká Spořitelna uses AI to analyze its ad performance and user trends to inform more effective marketing strategies and boost lead generation. It also applies AI to its data processing practices to automate regulatory compliance. Similarly, AI helps the bank automate credit risk scoring, leading to faster, more reliable loan approvals.

With so many use cases, it’s unsurprising that the AI-in-banking market could be worth more than $64 billion by 2030, according to ReHack. AI is adept at understanding the real-world implications of numbers, making it the ideal tool for many banking processes.

Industry: Banking
AI Product: Keboola
Outcomes:

• Streamlined credit risk scoring
• Improved regulatory compliance
• More effective marketing campaigns

Bottom Line: AI Business Use Cases

As these five use cases highlight, AI has uses across virtually every industry. This technology serves many purposes, from language comprehension to data analysis to trend prediction. Such a wide range of applications makes it a promising technology for any modern business.

Read next: AI in Education–The Future of Teaching.

Here are our picks for the top five AI image generators based on features and price.

Table Of Contents

• Top AI Image Generators
• AI Image Generators Comparison Table
  • DALL-E 2: Best Overall Image Generator
  • DeepAI: Best For Ease Of Use
  • Deep Dream Generator: Best For Style Prompts
  • Artbreeder: Best For Customization
  • Midjourney: Best For High-Quality Images
• Key Features Of AI Image Generators
• How To Choose What AI Image Generators Best For You
• Bottom Line: AI Image Generators

Top AI Image Generators

The top five providers all offer feature-rich AI image generators at varying prices. Your company’s specific needs will determine which is right for you. When selecting a provider, consider use cases and costs for each provider as outlined below.

AI Image Generators Comparison Table

DALL-E 2: Best Overall AI Image Generator

DALL-E 2 is OpenAI’s AI image generator, released in 2022—a year after the initial version of DALL-E. Users can prompt DALL-E 2, and it will create an original and realistic image based on that prompt. DALL-E 2 will not create “violent, hate, or adult images,” and OpenAI removes explicit data from its system.

Features

• Strong Composition: DALL-E 2 generates images with strong composition due to the vast amount of text and image training, making the images more accurate and natural to the prompt.
• Deciphers Images: DALL-E 2 can decipher generated images and compare them to the prompt, providing feedback to refine output.
• Image Normalization: Using image normalization, DALLE-2 standardizes and transforms prompts to ensure they have consistent characteristics and contain the properties for a better generated image.

Pros

• Free and paid options
• Simple image creation
• Users own image copyright

Cons

• Possible copyright and trademark issues
• English language only
• Unable to do hyperrealistic images at this time

Pricing

Pricing for DALL-E 2 works on a credits-purchased model, based on image resolution. For 1024×1024 pixel images, the cost is $0.020 per image; 512×512 pixels is $0.018; and 256×256 pixels is $0.016. Credits are a single charge of $15 per 115 credits, with 50 free credits at signup and 15 additional free credits per month.

DeepAI: Best For Ease Of Use

DeepAI is a well-known image generator that is mostly free for public use, and lets users create an unlimited number of unique images. The model is based on stable diffusion, which creates images from scratch from a text description, and DeepAI includes a free text-to-image API that can connect to another software project.

Features

• Art Style Options: DeepAI’s tool lets users pick an image style based on the options they create.
• Text-to-image API: DeepAI’s text-to-image API allows users to give creative inputs through text prompts, which the tool uses to produce the image.
• Saves Image Drafts: Unlike many image generators, DeepAI allows users to save a draft of an image to finish later.

Pros

• Easy to use
• Access to the full database
• No account required

Cons

• Free edition contains ads
• Only one image generated at a time
• Slow

Pricing

DeepAI has limited free accounts, but if you plan to use the image generator often, DeepAI has two pricing options. The DeepAI Pro plan starts at $4.99 per month, while its pay-as-you-go plan starts at $5 per 100 API calls.

Deep Dream Generator: Best For Style Prompts

Deep Dream Generator creates unique images with a “dream-like” appearance. Users have three image-processing options: Text 2 Dream, Deep Style, and Deep Dream. Text 2 Dream uses text prompts; Deep Style modifies users’ photo prompts based on their choice of style prompts (Starry Night, colorful abstract, and Pieter Bruegel); Deep Dream modifies prompt images by turning them into dream-like photos with varied color schemes and overlaid images of animals.

Features

• Community Platform: Deep Dream Generator acts as a community, letting users share and comment on generated images.
• Three Modes: Unlike many other generators, Deep Dream Generator offers three generator modes: Text 2 Dream, Deep Style, and Deep Dream.
• Strong Neural Networks: The strong algorithms and neural networks generate realistic, dream-like art.

Pros

• Diverse options
• Change aspect ratio
• User-friendly interface

Cons

• Limited credits
• Expensive high-resolution downloads
• Slow processes

Pricing

Deep Dream Generator offers a free version that functions more like a free trial. The three payment plans are based on credits. The advanced plan is $19 per month, the professional plan is $39 per month, and the ultra plan is $99 per month.

Artbreeder: Best For Customization

Artbreeder is an image generator that runs on the BigGAN and StyleGAN style-based generator architectures to let users create and modify images, including portraits, landscapes, and paintings. Users move sliders to change clothing, facial features, and more; another slider lets them compare the original to the modified image. Artbreeder will also build images based on text input. The terms of use explain that any images created and publicly shared are released to the public domain.

Features

• Image Modification: Users have four function options when generating portraits: random, remixed, uploaded, and animated. Original images can be modified widely using the various controls.
• Specific Criteria: By specifying criteria for the generator and discriminator models, users can prompt the system to produce wanted outcomes.
• Trained With Many Images: Artbreeder has been trained with thousands of images to create multi-scale transformations.

Pros

• Extensive image library
• Downloads as both JPG and PNG
• Customizable

Cons

• Difficult interface
• Limited free features
• Lower-quality images

Pricing

Artbreeder has four pricing plans. The free plan allows 10 credits per month. The starter plan allows 100 credits for $8.99 a month, while the advanced plan allows 275 credits for $18.99. The champion plan allows 700 credits and costs $38.99.

Midjourney: Best For High-Quality Images

Midjourney is an image generator known for rapidly generated, high-quality images, but it is exclusively a text-to-image platform. Midjourney is hosted on Discord. Users must join a Discord server and use its bot commands to work with the tool.

Features

• High-Quality Images: Midjourney is considered the highest-quality image generator, and can create unique oil-paint style images, which many generators cannot do.
• Shared Images: Users can share images and get other ideas from the community.
• Beta Tool: Midjourney uses ATM, a beta tool used by Discord bots, to assist first-time users.

Pros

• High-quality images
• Four images created with one input
• Community built on Discord

Cons

• Limited realistic images
• Complicated setup
• Pay for unlimited inputs

Pricing

Midjourney has a free plan and three paid plans. The basic plan allows 200 images per month and costs $8; the standard plan allows unlimited image generations, but only has 15 hours a month of fast GPU time and costs $24 per month; the pro plan has unlimited generations and allows 30 hours a month of fast GPU time, and costs $48 per month. All plans are billed yearly rather than monthly.

Bottom Line: AI Image Generators

The technology is still new and changing rapidly, and is becoming more widely adopted in enterprise uses after its initial period of consumer use. Not all AI image generators are capable of creating the same type or quality of imagery, so your use case should be a determining factor when considering which one to use. You’ll also want to consider copyright, production time, and cost—while none of the tools are expensive, if your company plans to generate images in higher volumes, you should take that into account.

Read next: 100 Top Artificial Intelligence Companies 

Table Of Contents

• Top Robotics Companies
  • UiPath
  • ABB Ltd
  • Universal Robots
  • KUKA
  • FANUC
• Benefits of Robotics
• How To Choose a Robotics Company
• Bottom Line: Top Robotics Companies

Top Robotics Companies

All the robotics companies covered here offer feature-rich services in differing areas of focus, from mobile robots that integrate with your infrastructure to sensors that can monitor for and respond to different conditions. While your business’s specific needs and budget will be a key factor in finding the right company, pricing is another factor—and comparing pricing can be a challenge.

Because robotics is not an off-the-shelf solution, customization means pricing can vary widely. We’ve gathered publicly available pricing information where possible, but for accurate quotes you’ll need to contact the vendors.

UiPath

UiPath is a leading provider of robotic process automation (RPA) technology, sometimes referred to as “software robotics,” and provides both automation and operations to customers. UiPath’s RPA platform uses automation and artificial intelligence (AI) to streamline business practices. It offers visual design tools and integration capabilities, and allows bots to interact with different systems, applications, and databases. UiPath provides solutions for many industries, giving businesses direct control with a drag-and-drop user interface.

Features

• AI and Machine Learning (ML): UiPath integrates AI and ML capabilities with software bots to handle unstructured data, make better decisions, and adapt to unexpected changes, improving automation potential for users.
• Robust Integration: UiPath bots can interact with a broad range of systems, applications, and databases, including web and desktop applications, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and more.
• Screen Scraping: UiPath’s screen-scraping enables bots to extract data from business applications including legacy systems and virtual environments to fuel the RPA technology.

Pricing

UiPath provides three pricing options: There is a free option for personal use. The pro plan starts at $420 a month; for enterprise plan pricing, contact sales.

ABB Ltd

As one of the leading robotics and machine automation suppliers, ABB Robotics offers a large range of robots, software, and services. ABB Robotics and Discrete Automation has a wide portfolio of tools in a wide range of sizes, payloads, and reach capabilities, and provides robotics, machine automation, digital services, and other innovative solutions for many industries.

Features

• Vision And Sensor Integration: ABB Robotics emphasizes the seamless integration of vision systems and sensors into robotics tools to facilitate data exchange and coordinated workflow within the automation setup.
• Flexibility: ABB’s robots are designed to be flexible and capable of performing various tasks including assembly, welding, material handling, painting, inspection, and more.
• Affordability: While prices can range based on solutions, ABB Ltd offers robotics options for businesses with a lower budget.

Pricing

ABB Ltd has a pricing page; price ranges from $33,000 to $90,500 depending upon maximum reach, maximum payload, arms, and types.

Universal Robots

Universal Robots, part of Teradyne Inc., aims to “create a world where people work with robots, not like robots.” To meet this goal, it uses collaborative robots, or cobots, that can automate repetitive tasks and shift them away from human workers. The cobots are meant to share work with human employees, making its automation possible to be used for a variety of applications.

Features

• Build-Your Own-Application: Users can choose from 400 components, kits and solutions, software tools, and safety accessories for robot integration, giving a wide range of options.
• Robot Collaboration: The company can connect operators to robots for successful automation by building a program, selecting functions, and executing them line by line, or via physical interaction by manually moving the robots’ arms.
• No Coding, Easy Setup: Universal Robots are designed to be used without robotics or coding experience. The company offers classes and has experts on call.

Pricing

Universal Robots does not have a pricing page. Based on user reviews, customers should budget between $50,000 and $100,000 depending on cobots and accessories.

KUKA

KUKA provides industrial robots for factory automation, with customers ranging from BMW and Ford to Siemens, Airbus, and Volkwsagen. The company also offers cobots with various payload capacities and reaches, and builds them to size and specifications for a wide range of deployments in automotive, electronics, food and beverage, foundry and foreign, medical, plastics, and other industrial applications. KUKA also sells used robots to help make entry into robotics more affordable for smaller companies.

Features

• Motion Control: KUKA robots have advanced mobile control systems that enable precise movements, optimizing performance, speed, and productivity.
• Programming Interface: KUKA robots provide an user-friendly interface during programming and operations, facilitating a wider range of tasks.
• Robot Controllers: The company sells both manual and autonomous robots that self-navigate, minimizing intervention.

Pricing

Pricing varies based on a wide range of factors. Used robots are available on the KUKA Marketplace.

FANUC

FANUC offers a range of industrial robotics products and services including computer numerical control (CNC) machining systems and factory automation solutions. It has more than 100 models of robots and cobots for manufacturing in a wide range of industries designed to be easy-to-operate and flexible, with payloads up to 5,000 pounds.

Features

• Wide Range: Existing models make it easier for customers to find the right fit for their specific industrial application based on payload capacities, capabilities, and configurations.
• Customer Support: The company provides a wide range of support services for customers, including system installation, training, maintenance, and technical support.
• Robust And Durable: Known for robust construction and durability, FANUC robots are designed for demanding industrial environments.

Pricing

FANUC does not provide pricing details. Contact sales for quotes. Customer reviews list prices in a range from $6,000 to $200,000.

Benefits Of Robotics

Robotic tools provide comprehensive support in a wide range of industrial applications.

• Enhanced Productivity: By automating routine tasks, robotic tools enable your company to devote more time to critical tasks.
• Consistent Performance: Once an automation framework is established, robotic tools operate consistently, minimizing human intervention and distraction.
• Improved Safety: Incorporating robotics provides safety measures, especially when dealing with heavy machinery. Human employees are vulnerable to potential harm from objects in the workplace, but robots can be repaired.
• Accelerated Efficiency: Robots achieve constant productivity, minimizing downtime.

How To Choose A Robotics Company

There are several criteria to weigh when looking to choose the right robotics provider.

Type of Robotics

Depending on the industry and the specific tasks the robot will undertake, it is important to explore various types of robots. Popular types include autonomous mobile robots (AMRs), automated guided vehicles (AGVs), articulated robots, humanoids, cobots, and hybrids. AGVs are beneficial for delivery and transportation services, while humanoids are well-suited for research.

Maintainability and Availability

Consider the reliability, repairability, and availability of robotics tools—factors like battery duration and part complexity can have an impact on efficiency. Providers should offer support for maintenance and repairs to help avoid downtime.

Cost

While it can be difficult to compare prices with robotics tools, establish a budget that incorporates the initial investment as well as upkeep, repair, and downtime costs. Narrow down providers by their ability to meet your robotic needs, and then work with their sales teams to receive an exact cost.

Bottom Line: Top Robotics Companies

The top robotic companies offer a wide range of features and advantages for businesses looking to automate processes. While these robots offer numerous benefits, there are also potential disadvantages to consider, such as initial investment costs, integration complications, and the need for coding professionals. However, with proper training, planning, and maintenance, these robots can revolutionize industry automation and drive your business toward increased productivity and competitiveness.

In this article, we’ll explore 11 practical ways organizations can use chatbots to improve customer service, client intake, and overall user experience.

Jump to:

• A Brief History of Customer Service Chatbots
• 11 Ways to Use Chatbots to Improve Customer Service
• The Future of Customer Service Chatbots
• Bottom Line: How to Use Chat Bots to Improve Customer Service

A Brief History of Customer Service Chatbots

The origins of customer service chatbots can be traced back to U.S. contact centers in the 1960s, where voice synthesizing technologies were first being applied at scale in early automated response systems. During this period, companies began implementing interactive voice response (IVR) systems to handle customer inquiries via phone calls. IVR systems used pre-recorded voice prompts and menu options to guide customers through simple transactions or provide basic information. While not true chatbots, these early systems laid the foundation for automated customer service interactions.

Rule-Based Chatbots

In the 1990s and early 2000s, rule-based chatbots emerged as a significant advancement. These automated assistants operated on predefined sets of rules and responses, enabling them to automatically handle specific customer queries and frequently asked questions (FAQs).

During this period, firms began integrating chatbots into websites and messaging platforms, enabling customers to access self-service support. Although limited in their capabilities, rule-based chatbots provided quick and consistent responses, reducing the need for human intervention in routine inquiries.

Natural Language Processing (NLP) and Machine Learning

Advances in natural language processing (NLP) and machine learning (ML) in the late 2000s marked a turning point in chatbot development for customer service. NLP enabled chatbots to understand and interpret human language, allowing for more complex interactions. ML algorithms empowered chatbots to learn from user interactions, improving their responses over time.

Omni-Channel AI-Powered Chatbots

The rise of intelligent virtual assistants (e.g., Apple Siri, Amazon Alexa) brought chatbots to the mainstream in customer service. These AI-powered chatbots used advanced NLP and ML to engage in more natural, human-like conversations. And as customer service expanded beyond traditional phone calls and websites, chatbots evolved to support omni-channel experiences.

Businesses began integrating chatbots seamlessly with messaging apps, social media platforms, and voice assistants, providing customers with multiple avenues for support. These integrations enabled enterprises to meet customers’ expectations of consistent and personalized experiences across channels.

Today’s Customer Service Chatbots

With recent advancements in AI and ML, chatbots have become even more sophisticated in their ability to provide a full range of customer service functions. Conversational AI allows chatbots to understand context, maintain context throughout a conversation, and provide intelligent responses. On the customer service operations and logistics side, AI-powered chatbots can handle complex queries, perform tasks like order tracking, and even initiate proactive conversations based on customer behavior.

Modern chatbot implementations also facilitate human-agent collaboration; in these scenarios, complex issues are escalated to human agents, while routine and repetitive tasks are relegated to chatbots. These types of hybrid, human-AI customer support models combine the strengths of chatbots and human support, ensuring a seamless customer experience that optimizes efficiency, reduces response times, and provides personalized support when needed.

11 Ways Chatbots Can Improve Customer Service

From providing on-demand support around the cloud to automatically setting appointments, the following are 11 ways that organizations can use chatbots to improve customer service.

1. Instant Customer Support

One of the primary benefits of using chatbots is their ability to provide instant customer support. Chatbots can handle routine queries and FAQs, allowing businesses to provide round-the-clock assistance without requiring human intervention. This ensures that customers receive timely responses and reduces the need for customers to wait in queues. Additionally, chatbots can analyze customer messages to identify the sentiment and urgency of their inquiries, enabling them to prioritize and escalate issues accordingly.

2. Personalized Recommendations

Chatbots can analyze customer preferences and behavior to deliver personalized recommendations. Chatbots can use ML algorithms to understand individual customer preferences and provide tailored product or service suggestions. This not only enhances the user experience but also increases the likelihood of conversions. For example, leading e-commerce websites are using chatbots to analyze a customer’s browsing history and purchase patterns for offering relevant product recommendations, leading to higher customer satisfaction and improved sales.

3. Efficient Order Processing

Integrating chatbots into order processing systems can streamline the entire buyer’s journey. Customers can place orders, make payments, and track their deliveries directly via chatbot interactions. This eliminates the need for customers to navigate complex websites or interact with multiple systems, resulting in a faster and more efficient ordering process. Moreover, chatbots can provide order status updates in real-time, keeping customers informed and reducing the need (and the overhead) of reaching out to the vendor for order status updates.

4. Automated Appointment Scheduling

Chatbots can automate the appointment scheduling process, allowing businesses to save time and resources. Customers can book appointments, check availability, and receive confirmation without the need of human intervention. By integrating with an organization’s scheduling processes, chatbots can provide real-time availability and even send reminders to customers before their scheduled appointments. Unsurprisingly, service-based organizations like healthcare providers and utility companies were the first to integrate chatbot-powered automated appointment scheduling into their operations.

5. Lead Generation and Qualification

Chatbots can assist in lead generation and qualification by engaging potential customers in meaningful conversations. By asking targeted questions and capturing essential information, chatbots can identify qualified leads and assign them to the appropriate sales staff or queue. This helps businesses prioritize their efforts and improve the efficiency of their sales processes. Chatbots can also qualify leads based on predefined criteria, ensuring that sales teams focus on leads with a higher likelihood of conversion.

6. Proactive Customer Engagement

Chatbots can initiate proactive conversations with customers based on predefined triggers. For example, if a customer abandons a shopping cart, a chatbot can send a personalized message offering assistance or a special discount. Proactive engagement helps businesses increase customer satisfaction, recover lost sales, and foster stronger customer relationships. By using chatbots to proactively address customer concerns or offer assistance, businesses can demonstrate their commitment to providing exceptional service as well as meet/exceed predefined metrics for customer success.

7. Interactive Tutorials and Onboarding

Unless programmed otherwise, computers are ever-faithful and eternally patient tutors, which means organizations can use chatbots to deliver interactive tutorials and onboarding experiences to users. Chatbots can guide new customers through the initial setup or educate existing customers about advanced features. By providing real-time assistance and interactive guidance, chatbots enhance the user experience and reduce the learning curve. Additionally, chatbots can provide step-by-step instructions, answer questions, and offer relevant resources, ensuring that users get the most out of the products or services they have purchased.

8. AI-Powered Messaging Apps

Chatbots can be seamlessly integrated with popular messaging apps to engage with customers on the platforms they frequently use. For example, Microsoft recently incorporated the Bing AI Co-Pilot into Skype, effectively extending ChatGPT capabilities to its chat messaging user base. By providing a familiar and convenient communications channel, businesses can improve customer satisfaction and increase engagement. Integrating chatbots with messaging apps also enables businesses to reach a wider audience and expand their customer base.

9. Language Support and Translation

Of all the AI subdisciplines, NLP has arguably been the most well-researched and developed. It’s therefore not surprising that chatbots are especially adept with language processing, supporting multiple languages, and even providing real-time translation services. These capabilities enable organizations to address a broader, more diverse customer base with multilingual support, resulting in an expanded reach and more inclusive customer service apparatus.

10. Feedback Collection and Surveys

Continuous improvement requires a continuous influx of data to inform course-corrective efforts. To this end, chatbots can be employed to collect feedback and conduct surveys in a conversational manner. By integrating survey questions into chatbot interactions, businesses can gather valuable insights, measure customer satisfaction, and identify areas for improvement. This enables businesses to make data-driven decisions, refine products or services, and enhance the overall customer experience. Chatbots can also prompt customers for feedback after specific interactions or transactions, ensuring that businesses receive timely and relevant feedback.

11. Data Analysis and Customer Insights

Chatbots automatically capture valuable customer data during interactions, which can be used for performing data analysis and generating customer insights. By analyzing chat logs and user behavior patterns, businesses can identify customer trends, preferences, and pain points. This information can inform strategic decision-making, drive product/service improvements, and help firms stay ahead of their competition. Moreover, chatbot analytics can provide businesses with actionable metrics, such as response times, customer satisfaction ratings, and conversation flow analysis, enabling them to continuously optimize their chatbot performance and customer engagement strategies.

The Future of Customer Service Chatbots

Tomorrow’s chatbots will inevitably reach new levels of sophistication, with a deeper ability to understand customer intent, emotions, and preferences. However, organizations must continue to strike a balance between leveraging automation and providing customers with their desired levels of personalization and human interaction.

Collaboration with Human Agents

While chatbots continue to evolve and develop, human agents will remain integral to the customer service process. The future will see increased collaboration between chatbots and human agents, leveraging each other’s strengths—for example, chatbots may handle routine inquiries and transactions, freeing up human agents to focus on complex or emotionally sensitive issues that require a human touch. Seamless handoffs between chatbots and human agents will ensure a smooth transition and provide customers with both efficient automation and personalized human assistance.

Delivering Hyper-Personalization

Customer service chatbots will deliver increasingly hyper-personalized experiences. Leveraging AI algorithms and vast customer data, chatbots will have the capacity to understand customer preferences, behaviors, and historical interactions. By analyzing this data, chatbots can offer tailored recommendations, anticipate customer needs, and provide highly targeted assistance. From personalized product suggestions to customized support, hyper-personalization will enable chatbots to create individualized experiences that deepen customer engagement and loyalty.

Seamless Integration Across Channels

As customer service channels continue to diversify, future chatbots will need to integrate seamlessly across various touchpoints. Chatbots will transcend individual platforms and be able to provide consistent experiences across websites, messaging apps, social media platforms, voice assistants, and more. This integration will allow customers to switch between channels effortlessly, while chatbots maintain the context of conversations. The ability to seamlessly transition between touchpoints ensures a cohesive and frictionless customer journey, resulting in enhanced satisfaction and a positive brand perception.

Intelligent Automation and Predictive Support

Continuing AI/ML developments will bring about ever more powerful intelligent automation capabilities—for example, customer service chatbots will become more adept at handling complex queries, understanding natural language, and executing tasks autonomously. They will also use predictive analytics to anticipate customer needs and offer proactive support. By analyzing customer data and behavior patterns, future chatbots will be highly skilled in identifying potential issues before they arise and provide relevant assistance or information, saving time and effort for both customers and businesses.

Emotional Intelligence and Empathetic Interactions

Humans are emotional creatures, and customer chatbots must evolve to meet their emotional requirements. Specifically, this means developing emotional intelligence and the ability to engage in empathetic interactions. Current advancements in natural language processing are already giving way to chatbots that understand and respond to customer emotions effectively. Future customer service chatbots will be equipped with sentiment analysis capabilities, allowing them to adapt their tone and responses accordingly. Empathetic interactions will help create a more human-like experience, fostering stronger customer relationships and enhancing overall satisfaction.

Bottom Line: How to Use Chatbots to Improve Customer Service

From providing instant customer support to automatically creating personalized recommendations and proactive engagement, customer service chatbots have revolutionized the way enterprises engage with customers, capture client information, and deliver exceptional user experiences. Enterprises looking to the future of customer service chatbots can anticipate more hyper-personalization, seamless integrations, intelligent automation, emotional intelligence, and collaboration capabilities with human agents. By embracing these technologies now, businesses can gain a competitive advantage through delivering and maintaining optimal customer support levels and meaningful connections while continuously scaling these efforts and service levels as the organization and customer base expands.

One area in which they are especially useful is customer service, where they can improve the accuracy and efficiency of call center and help desk personnel, facilitate customer self-service, and speed up everything from employee training to problem-solving and information recovery.

Organizations looking to implement knowledge management for customer service or other uses have a number of options from which to choose. While budget will play a part in an software selection decision, it’s just one of many factors to consider, and this guide ranks the best knowledge management systems by use case to help you see how they compare to your own particular needs.

• Best for Collaboration: Confluence
• Best for Multi-Channel: ZenDesk for Service
• Best for SMBs: Zoho
• Best for Self Service: Jira
• Best for Sales and CRM Integration: Salesforce
• Best for Agent Assistance: KMS Lighthouse
• Best for Customer Engagement: Verint

Top Knowledge Management Software at a Glance

Knowledge management software is very much in demand, with Gartner reporting that 74 percent of customer service and support leaders have set a priority of improving knowledge and content delivery to customers and employees. The recent boom in artificial intelligence (AI) is affecting this market, like so many others, with systems that incorporate AI features and chatbots becoming increasingly popular.

Each of the top systems takes a slightly different approach to knowledge management, offering a mix of features and benefits. Here’s a quick look at how they compare.

Jump to:

• Key features of knowledge management software
• Benefits of working with knowledge management software
• Bottom Line

Confluence

Best for Collaboration

Atlassian’s Confluence is all about content collaboration across Android, iOS, Linux, and Windows devices. This cloud-based system enables companies to publish, organize, and access knowledge from a single place, and is especially well-suited to helping organizations collaborate on knowledgebase data across multiple channels.

Features

• Works across multiple channels on Android, iOS, Linux, and Windows devices
• Cloud-based
• Lets users create documents, publish, organize, and access knowledge from a single place
• Collaboration features include feedback on new documents, keeping track of versions, sharing documents, exporting PDFs, and copy/pasting images
• Includes project management and Jira integration

Pros

• Can collaborate with Asana, Slack, Miro Board, Google Sheets, and other tools
• Good ease of use
• Enterprise-grade permission handling

Cons

• Lack of a flowchart builder
• Dated user interface
• Lack of Microsoft Teams integration

Pricing

Confluence costs $5.75 per user, per month for the standard version and $11 for premium. The price goes down by almost half after 1,000 licenses. A free “lite” version for up to 10 users lacks enterprise features and includes just 2 GB of storage.

ZenDesk for Service

Best for Multi-Channel

Zendesk for Service provides an open, flexible platform designed to enable customer self-service. It helps organizations provide personalized documentation across any channel, can scale to the large-enterprise size, and has an integrated Help Desk ticketing system.

Features

• Users can interact via phone, email, chat, and social media
• Easy to implement, use, and scale
• Integrated ticketing system
• Includes AI and automation for faster issue-resolution
• Facilitates customer self-service

 Pros

• Offers a unified workspace with a contextual interface
• Omnichannel support

 Cons

• Can be too complex to use for SMBs
• Expensive
• Can be difficult to integrate, especially for small businesses

Pricing

ZenDesk starts at $49 per user, per month. For the self-service customer portal, AI, customizable tickets, and multilingual support, the price rises to $79. The professional version at $99 also includes a live agent activity dashboard, integrated community forums, private conversation threads, and more.

Zoho

Best for SMBs

Zoho Desk can manage all customer support activities and is context aware. It has integrated Voice over IP (VoIP) features and comes with analytics and AI tools as well as a ticketing system, making it a good choice for SMBs and mid-sized enterprises.

Features

• iOS and Android compatible
• Provides features for interacting with agents through VoIP and social media
• Agent, manager, and customer-specific features
• Includes a ticketing system
• Strong reporting capabilities
• Tracks customer requests across channels

Pros

• Cloud-based system is easy to use and makes ticket-tracking easy
• Users can manage tickets and everything else in one place
• Includes AI-based chat and analytics

Cons

• Not designed for large enterprises
• Some customization and integration limitations

Pricing

Zoho is free for up to three users. The Professional plan costs $12 per user, per month, and the Enterprise plan costs $25 per user, per month.

Jira

Best for Self Service

Jira Service Management is a tool for self-service knowledge management for employees and customers. It helps trace knowledge usage frequency and can identify content gaps and flawed articles. AI-powered search is available as well as good editing and formatting capabilities.

Features

• Tracks document changes, incident runbooks, and playbooks so teams can continuously learn and improve
• Helps monitor knowledge usage to identify content gaps, optimize articles, and see which articles deflect the most requests
• Provides a federated knowledge base

 Pros

• Self-service management of knowledge articles
• Provide companies and employees with relevant articles quickly
• AI-powered search that surfaces relevant knowledge articles

Cons

• Knowledge management is one facet of a much larger suite; may not be suitable for people who only need knowledge management.

Pricing

Jira Service Management is free for up to three users. Its premium plan starts at $47 per user, per month. A custom enterprise plan is also available.

Salesforce Service Cloud

Best for Sales and CRM integration

Salesforce Service Cloud is part of the vast Salesforce universe. Its aim is to help customers find answers quickly across any channel, which it accomplishes by empowering agents with the best answers to questions. This multichannel solution also incorporates AI.

Features

• Centralized knowledgebase for all agent and customer information
• Uses analytics to identify which knowledge articles are working and to identify new articles that need to be created
• Automatically suggests articles for conversations
• Can share across multiple channels
• Can embed knowledge articles into a website, portal, community, and mobile app

Pros

• Can quickly deliver answers to customers by adding the knowledgebase to the Salesforce agent workspace
• Integrates fully with Salesforce customer relationship management (CRM)
• Uses AI chat bots to recommend articles
• Integrated computer telephony capabilities

Cons

• May be too much for companies that just want knowledge management, as it contains case management, service console, service contracts, computer telephony integration, web services, and more.

Pricing

Salesforce Service Cloud only provides knowledge management in the starter ($25 per user, per month) and unlimited ($300 per user per month) versions.

KMS Lighthouse

Best for Agent Assistance

KMS Lighthouse is all about knowledge management, and seeks to improve first-interaction resolution by intelligently directing agents to the right answer and reduce call center operational costs.

Features

• Built-in intelligence can cut agent training time in half to onboard agents and employees
• Lighthouse call center knowledgebase serves as a “single point of truth” to help call center agents speed up calls and avoid inaccuracies
• Lighthouse Chat enables agents to communicate and collaborate with knowledge-sharing via instant messaging and links to articles and relevant content

Pros

• AI provides instant responses to agents and customers during search
• Can function like a personal assistant to answer on-the-job questions
• Makes all product/service knowledge easy to tap into and compare to help with upselling and cross-selling

Cons

• Integration can be a challenge
• Needs better reporting

Pricing

KMS Lighthouse starts at $25 per user, per month.

Verint

Best for Customer Engagement

Verint Knowledge Management integrates across business operations with self-service contact center capabilities designed to help staff engage better with customers. Automated knowledge is embedded directly in tools and workflows.

Features

• Uses context from customer history to personalize results, resulting in the right knowledge appearing with little to no searching.
• Helps agents find answers via search using everyday language

Pros

• Guides decision trees help to resolve complex issues
• Helps agents understand what customers are looking for
• New content is automatically analyzed and optimized for search, removing the burden of manual tagging and linking

Cons

• Vendor is not transparent about pricing models
• Customer reviews say it is expensive

Pricing

Verint does not publicize its pricing models.

Key Features of Knowledge Management Software

While each platform takes a slightly different approach to knowledge management, all of the systems in this article share some common features.

Cloud-based

Knowledge management repositories should include all of the business’s articles and sources of knowledge, but locking it all on-premises can be limiting. Cloud-based systems integrate with other systems more easily and can better facilitate search and sharing among users and customers.

Multichannel

Knowledge management software should make it easy to collaborate across multiple channels, such as phone, email, chat, social media or other channels. Information should be always accessible, anywhere, on any channel, on tablets and mobile devices, and on PCs and laptops.

AI Chat

AI is being incorporated into a great many tools and IT systems, and knowledge management is no exception. Its best use case is in chatbots that provide users and agents with answers to questions, summarize information, and provide sales data.

Help Desk

Knowledge management systems can be tightly integrated with a help desk as well as with customer contact center systems, though not all users need this functionality, making it a selection point to narrow down choices when considering systems.

Price

Generally speaking, the more features and capabilities a knowledge management package includes, the higher the cost. Lower costs systems may suffice for organizations that need limited features. Those that need enterprise capabilities, help desk integration, and advanced AI and should expect to pay more.

Knowledge Management System Benefits

A knowledge management system can benefit a business in a number of ways. Here are a few of the most common:

• Provides all enterprise knowledge in one place
• Offers powerful search capabilities to find information quickly
• Helps customer service agents answer customer questions
• Lets customers access knowledgebase for self-service
• Makes it easy and fast to update information
• Improves both accuracy and consistency
• Helps with training new employees

Methodology

The items on this were chosen based on analyst evaluations, user reviews, and assessment of a wide range of lists suggested by knowledge management experts.

Bottom Line: Top Knowledge Management Systems

While knowledge management systems have broad utility for many aspects of an organization’s work, they can be especially useful to help reduce costs of customer service, facilitate self-service, and speed up everything from employee training to problem-solving and information recovery. Organizations should select knowledge management software based rigidly on their specific business needs. Some need all the bells and whistles that come with enterprise-class systems, such as scalability, help desk integrations, and more, while others will only need specific knowledge management functionality. Choose the system that best meets your specific needs without charging for unnecessary features.