For example, modern artificial intelligence is capable of understanding and creating art, carrying on intelligent conversations, identifying objects by sight, learning from past experience, and making autonomous decisions.

Organizations have deployed AI to accomplish a wide range of tasks. AI creates personalized recommendations for online shoppers, determines the content social media users see, makes health care decisions, determines which applicants to hire, drives vehicles, recognizes faces, and much more.

Given the countless business opportunities that this new technology brings, the global market for AI technologies has exploded over the past decade and is continuing to grow. Gartner estimates that customers worldwide will spend $65.2 billion on AI software in 2022, an increase of 21.3 percent from the previous year.

While AI technology is new and exciting and has the potential to benefit both businesses and humanity as a whole, it also gives rise to many unique ethical challenges.

Also see: Top AI Software 

Examples of Unethical AI

News stories have no shortage of examples of unethical AI.

In one of the more well-known of these cases, Amazon used an AI hiring tool that discriminated against women. The AI software was designed to look through resumes of potential candidates and choose those that were most qualified for the position. However, since the AI had learned from a biased data set that included primarily male resumes, it was much less likely to select female candidates. Eventually, Amazon stopped using the program.

In another example, a widely used algorithm for determining need in healthcare was systematically assessing Black patients’ need for care as lower than white patients’ needs. That was problematic because hospitals and insurance companies were using this risk assessment to determine which patients would get access to a special high-risk care management program. In this case, the problem occurred because the AI model used health care costs as a proxy for health care need, without accounting for disparities in how white and Black populations access health care.

But discrimination isn’t the only potential problem with AI systems. In one of the earliest examples of problematic AI, Microsoft released a Twitter chatbot called Tay that began sending racist tweets in less than 24 hours.

And a host of other less widely published stories have raised concerns about AI projects that seemed transphobic, that violated individuals’ privacy, or in the case of autonomous vehicles and weapons research, put human lives at risk.

Challenges of AI Ethics

Despite the many news stories highlighting concerns related to AI ethics, most organizations haven’t yet gotten the message that they need to be considering these issues. The NewVantage Partners 2022 Data and AI Leadership Executive Survey found that while 91 percent of organizations are investing in AI initiatives, less than half (44 percent) said they had well-established ethics policies and practices in place. In addition, only 22 percent said that industry has done enough to address data and AI ethics.

So what are the key challenges that organizations should be addressing?

Bias

As we have already seen, perhaps the biggest challenges to building ethical AI is AI bias. In addition to the cases already mentioned, the AI criminal justice tool known as COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) is one egregious example. The tool was designed to predict a defendant’s risk of committing another crime in the future. Courts, probation, and parole officials then used that information to determine appropriate criminal sentences or who gets probation or parole.

However, COMPAS tended to discriminate against Black people. According to ProPublica, “Even when controlling for prior crimes, future recidivism, age, and gender, Black defendants were 45% more likely to be assigned higher risk scores than white defendants.” In actuality, Black and white defendants reoffend at about the same rate — 59 percent. But Black defendants were receiving much longer sentences and were less likely to receive probation or parole because of AI bias.

Because humans created AI and AI relies on data provided by humans, it may be inevitable that some human bias will make its way into AI systems. However, there are some obvious steps that should be taken to mitigate AI bias.

And while situations like the COMPAS discrimination are horrifying, some argue that on the whole, AI is less prone to bias than humans. Difficult questions remain concerning to what degree bias must be eliminated before an AI can be used to make decisions. Is it sufficient to create an AI system that is less biased than humans, or should we require that the system is closer to having no biases?

Data Privacy

Another huge issue in AI ethics is data privacy and surveillance. With the rise of the internet and digital technologies, people now leave behind a trail of data that corporations and governments can access.

In many cases, advertising and social media companies have collected and sold data without consumers’ consent. Even when it is done legally, this collection and use of personal data is ethically dubious. Often, people are unaware of the extent to which this is going on and would likely be troubled by it if they were better informed.

AI exacerbates all these issues because it makes it easier to collect personal data and use it to manipulate people. In some instances, that manipulation is fairly benign, such as steering viewers to movies and TV programs that they might like because they have watched something similar. But the lines get blurrier when the AI is using personal data to manipulate customers into buying products. And in other cases, algorithms might be using personal data to sway people’s political beliefs or even convince them to believe things that aren’t true.

Additionally, facial recognition AI software make it possible to gather extensive information about people by looking at photos of them. Governments are wrestling with the question of when people have the right to expect privacy when they are out in public. A few countries have decided that it is acceptable to perform widespread facial recognition, while some others outlaw it in all cases. Most draw the lines somewhere in the middle.

Privacy and surveillance concerns presents obvious ethical challenges for which there is no easy solution. At a minimum, organizations need to make sure that they are complying with all relevant legislation and upholding industry standards. But leaders also need to make sure that they are doing some introspection and consideration of whether they might be violating people’s privacy with their AI tools.

Transparency

As already mentioned, AI systems often help make important choices that greatly affect people’s lives, including hiring, medical, and criminal justice decisions. Because the stakes are so high, people should be able to understand why a particular AI system came to the conclusion that it did. However, the rationale for determinations made by AI is often hidden from the people who are affected.

There are several reasons for this. First, the algorithms that AI systems use to make decisions are often protected company secrets that organizations don’t want rival companies to discover.

Second, the AI algorithms are sometimes too complicated for non-experts to easily understand.

Finally, perhaps the most challenging problem is that an AI system’s decision is often not transparent to anyone, not even to people who designed it. Deep learning, in particular, can result in models that only machines can understand.

Organizational leaders need to ask themselves whether they are comfortable with “black box” systems having such a large role in important decisions. Increasingly, the public is growing uncomfortable with opaque AI systems and demanding more transparency. And as a result, many organizations are looking for ways to bring more traceability and governance to their artificial intelligence tools.

Liability and Accountability

Organizations also need to worry about liability and accountability.

The fact that AI systems are capable of acting autonomously raises important issues about who should be held responsible when something goes wrong. For example, this issue arises when autonomous vehicles causing accidents or even deaths.

In most cases, when a defect causes an accident, the manufacturer is held responsible for the accident and required to pay the appropriate legal penalty. However, in the case of autonomous systems like self-driving cars that make their own decisions, legal systems have significant gaps. It is unclear when the manufacturer is to be held responsible in such cases.

Similar difficulties arise when AI is used to make health care recommendations. If the AI makes the wrong recommendation, should its manufacturer be held responsible? Or does the practitioner bear some responsibility for double-checking that the AI is correct?

Legislatures and courts are still working out the answers to many questions like these.

Self-Awareness

Finally, some experts say that AI could someday achieve self-awareness. This could potentially imply that an AI system would have rights and moral standing similar to humans.

This may seem like a farfetched scenario that is only possible in science fiction, but at the pace that AI technology is progressing, it is a real possibility. AI has already become able to do things that were once thought impossible.

If this were to happen, humans could have significant ethical obligations regarding the way they treat AI. Would it be wrong to force an AI to accomplish the tasks that it was designed to do? Would we be obligated to give an AI a choice about whether or how it was going to execute a command? And could we ever potentially be in danger from an AI?

Also see: How AI is Altering Software Development with AI-Augmentation 

Key Steps for Improving your Organization’s AI ethics

The ethical challenges surrounding AI are tremendously difficult and complex and will not be solved overnight. However, organizations can take several practical steps that toward improving their organization’s AI ethics:

• • Build awareness of AI ethics within your organization. Most people have either no familiarity or only a passing familiarity with these issues. A good first step is to start talking about ethical challenges and sharing articles that bring up important considerations.
  • Set specific goals and standards for improving AI ethics. Many of these problems will never completely go away, but it is useful to have a standard that AI systems must meet. For example, organizations must decide to what degree AI systems must eliminate bias compared to humans before they are used to make important decisions. And they need to have clear policies and procedures in place for ensuring that AI tools meet those standards before entering production.
  • Create incentives for implementing ethical AI. Employees need to be commended for bringing up ethical considerations rather than rushing AI into production without checking for bias, privacy, or transparency concerns. Similarly, they need to know that they will be held accountable for any unethical use of AI.

• • Create an AI ethics task force. The field of AI is progressing at a rapid pace. Your organization needs to have a dedicated team that is keeping up with the changing landscape. And this team needs to be cross-functional with representatives from data science, legal, management, and the functional areas where AI is in use. The team can help evaluate the use of AI and make recommendations on policies and procedures.

AI offers tremendous potential benefits for both organizations and their customers. But implementing AI technology also carries the responsibility to make sure that the AI in use meets ethical standards.

Also see: Best Machine Learning Platforms 

The post AI Ethics: An Overview appeared first on eWEEK.

Industry reports reflect this skyrocketing interest in deep learning. To look a few years back, a 2018 report titled How Companies Are Putting AI to Work through Deep Learning found that only 28 percent of enterprises surveyed were using deep learning. But the more recent AI Adoption in the Enterprise 2021 survey found that the percentage of respondents using deep learning had more than doubled to 67 percent.

You can also see this increasing use of deep learning reflected in spending. Gartner doesn’t break out spending on deep learning specifically, but it forecasts that the total AI market will reach $62.5 billion in 2022. That’s a 21.3% increase from the $51.5 billion spent in 20221.

Grand View Research says that just the deep learning portion of the AI market was worth $34.8 billion in 2021. And it estimates that spending will grow by more than 34.3 percent per year between 2022 and 2030.

Clearly, deep learning is becoming big business. But what exactly is deep learning?

Also see: Top AI Software 

Deep Learning, Machine Learning and Artificial Intelligence

Before you can understand deep learning, you need to understand two foundational concepts: artificial intelligence and machine learning.

Artificial intelligence encompasses all the technologies that allows machines to think like humans. It includes capabilities like understanding and speaking human languages, describing the contents of an image, ascertaining a speaker’s emotional state, and learning new concepts.

Machine learning is a subset of artificial intelligence. It refers to all the technologies that allow computers to learn something new without being explicitly programmed.

Deep learning is a subset of machine learning. It refers to ML that takes place on artificial neural networks.

Also see: The Future of Artificial Intelligence

What Is Deep Learning?

Several different organizations have offered definitions of deep learning. But many of these definitions are difficult to understand unless you have a background in data science.

For example, Gartner says, “Deep learning, a variant of machine learning algorithms, uses multiple layers of algorithms to solve problems by extracting knowledge from raw data and transforming it at every level.”

IBM offers a slightly more comprehensible definition:

Deep learning attempts to mimic the human brain—albeit far from matching its ability—enabling systems to cluster data and make predictions with incredible accuracy. Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data.

In essence, deep learning represents an attempt to allow computers to learn the same way that human babies do. When a baby is born, it knows nothing. Babies use the networks of neurons in their brains to take in information about the world around them and make sense of it, slowly coming to conclusions about the world.

Deep learning systems rely on artificial neural networks designed to be very similar to the neurons in an infant’s brains. These networks include multiple layers that allow the system to process and re-process data until it learns the important characteristics of the data it is analyzing.

Also see: How AI is Altering Software Development with AI-Augmentation 

Types of Deep Learning

You can organize the different kinds of deep learning into three different categories depending on the type of data they use:

1. Supervised deep learning relies on tagged data. In this kind of deep learning, you feed the data model a lot of different data and tell the model what it is. The computer then learns on its own which characteristics cause data to fall into one category or another so that it can extrapolate on its own. The human equivalent is adults pointing at different objects and telling a baby the name of those objects. Eventually, the baby learns on its own what makes a “dog” different from a “bottle” or a “book.”
2. Unsupervised deep learning relies on untagged data. Essentially, the system learns by mimicry. One example that may be familiar are the deep learning systems that take in examples of human art and then generate their own artwork. The human equivalent is a baby learning to say sounds like “mamamama” or “dadadada” by mimicking the adults it hears. Over time and with reinforcement the sounds turn into recognizable words that have meaning.
3. Semi-supervised deep learning involves a combination of tagged and untagged data. It’s probably the closest to how actual babies learn, with some explicit training combined with a lot of observation and mimicry.

Each of the kinds of deep learning has its own pros and cons. Supervised learning generates the fastest and most accurate results, but it requires a lot of work up front on the part of the people operating the system. Unsupervised learning doesn’t require the same level of setup, but it’s less reliable and takes a long time. Semi-supervised is in between the two—requiring much less setup than fully supervised learning, while generating significantly more reliable results than unsupervised learning.

Also see: Top Digital Transformation Companies

Deep Learning Architectures

The artificial neural networks that deep learning relies on can take many different forms. Fully explaining these would require an entire book, but here are some short descriptions of some of the most common deep learning architectures:

• Convolutional neural networks are probably the most widely used deep learning architecture, particularly for image processing, but they require graphics processing units (GPUs) and advanced processing capabilities to perform the complicated calculations required.
• Feedforward neural networks were the first type of artificial neural network; they feed data in one direction without any loops or cycles.
• Radial basis function neural networks are a type of feedforward neural network that include an input layer, a radial basis function activation layer, and an output layer.
• Recurrent neural networks follow a temporal sequence and are particularly useful for speech and handwriting analysis.
• Kohonen self-organizing neural networks are useful for creating feature maps from unsupervised data.
• Modular neural networks include a series of independent neural networks that can be joined together.

Data scientists are developing new types of architecture, as well as variants of the existing kinds, all the time, so the list is constantly growing and changing.

Also see: Best Machine Learning Platforms 

Deep Learning Use Cases

Deep learning is most useful for very complex problems with a lot of different variables. Some of the most common use cases include the following:

• Natural language processing allows computer systems to understand and generate human speech. It can include voice-to-text, text-to-voice, machine translation, tagging, named entity recognition, and sentiment analysis. It enables applications like digital assistants (like Siri and Alexa), chatbots, spam detection, social media analytics, and many more. Deep learning helps natural language processing engines improve over time by identifying and mimicking patterns in human speech.
• Image processing is one of the most common uses for deep learning. It enables a wide array of different applications, including biometrics and facial recognition, analyzing medical scans, autonomous vehicles, identifying faulty parts on an assembly line, image sharpening, and even the filters popular in social media and video call software.
• Fraud prevention becomes much more accurate when organizations use deep learning techniques to identify anomalous patterns in sales and financial data. Financial institutions, retailers, law enforcement, transportation and other organizations use deep learning to quickly identify and halt fraudulent activity.
• Cybersecurity tools have traditionally lagged behind cybercriminals, who are always developing new techniques to circumvent existing prevention measures. Deep learning techniques allow cybersecurity software to identify expected and unexpected patterns in network traffic, making it possible to detect and prevent brand new attacks that no one has ever seen before.
• Drug development becomes much faster when researchers employ deep learning techniques. During the coronavirus pandemic, scientists trained models on biochemical datasets and then used the resulting algorithms to identify drugs that could potentially treat the illness. These same techniques could speed development of pharmaceuticals for a variety of different diseases.
• Climate science models involve a huge number of variables—making it an ideal candidate for deep learning approaches. These techniques are helping scientists refine their models and make more accurate forecasts.
• Video games are making extensive use of deep learning techniques. Deep learning allows the developers to create more lifelike characters and animations, to enable better audio interactions, and to develop bots whose abilities are on par with the best human players.
• Predictive analytics has become a critical tool to enable businesses to create strategies and optimize their operations. While not all predictive analytics requires the use of deep learning techniques, applying deep learning in situations with high numbers of variables and large sets of historical data can yield excellent results.

Deep Learning Tools and Services

Because deep learning requires extensive compute, graphics processing, memory, and storage capabilities, many deep learning projects run either on high-performance computing systems or in the public cloud. Most of the major public cloud vendors have platform as a service (PaaS) offerings that support deep learning. Options include:

• AWS SageMaker
• Google Cloud AI and Machine Learning Services
• Microsoft Azure Machine Learning
• IBM Watson Studio
• Oracle Machine Learning

Other popular deep learning tools include the following:

• Caffe
• Caffe2
• Deeplearning4J
• Gluon
• H20.ai
• Keras
• MATLAB
• PyTorch
• TensorFlow
• /Theano

The Future of Deep Learning

Looking ahead, the future of deep learning is tied closely to the outlook for AI as a whole. Many analysts say that artificial intelligence is at something of a crossroads currently. Enterprises have invested heavily in these technologies, but AI initiatives aren’t always achieving the desired results.

“The AI software market is picking up speed, but its long-term trajectory will depend on enterprises advancing their AI maturity,” explains Alys Woodward, senior research director at Gartner.

“Successful AI business outcomes will depend on the careful selection of use cases,” adds Woodward. “Use cases that deliver significant business value, yet can be scaled to reduce risk, are critical to demonstrate the impact of AI investment to business stakeholders.”

The advancement of deep learning also depends heavily on the development of faster hardware that can quickly process ever-larger datasets. Some researchers believe that the development of quantum computing systems will eventually enable deep learning systems with capabilities that we can’t even imagine today.

Until those systems arrive, look for researchers to continue refining their deep learning techniques and scaling deep learning systems to handle more data and find the answers to more questions.

The post What Is Deep Learning? appeared first on eWEEK.

If you dig deeper into edge, you’ll see that edge computing deployments – often supported by cloud computing providers – are part of a distributed infrastructure, which enables the compute power to be closer to the people who produce or consume that data.

Key to the idea of edge, whether your edge deployment supports machine learning, artificial intelligence or data analytics, is that it extends resources far outside the once-dominant datacenter. Edge is forward-looking today in the same way that the datacenter was a leader some dozen years ago.

The most important part of edge technology is that it’s a form of distributed computing. If you look back at computer history, you can see a cycle between more centralized computing (like the early mainframes) to more distributed models (like networked PCs). In recent years, the trend toward cloud computing has been a move to a more diffuse, multicloud computing model. The newer trend toward edge computing is a further extension of that distributed model.

Edge computing enables computing beyond the data center and cloud perimeter, which allows it to support mobile and IoT devices, including cell phones. 

Edge Computing Examples

You might not realize it, but you probably interact with devices leveraging edge computing every day. For example, if you work in a remote office or back office (ROBO) environment with your own computing infrastructure, that’s an example of edge computing.

The smartphone you have in your pocket does edge computing. So does your car. Your printer. Probably your TV.

Here’s a non-exhaustive list of edge computing devices:

• Smartphones
• Smartwatches
• Wearables
• Laptop and desktop PCs
• Edge servers
• Gaming systems
• Printers
• Smart routers
• Smart appliances
• Smart home speakers
• Computerized medical devices
• Connected cars
• Autonomous vehicles
• Smart traffic lights
• Smart agriculture
• Smart grid
• Cell phone towers
• Kiosks
• Point-of-sale devices
• Internet of Things (IoT) gateways
• Industrial Internet of Things (IIoT) gateways
• Military and defense vehicles and weapons
• Robots

Edge Computing Architecture

Because there are so many different kinds of edge devices, there is no single edge architecture that covers all use cases. However, in general, most edge computing deployments do have some typical characteristics in common.

First, edge devices usually collect data from sensors. Those sensors might be part of the device itself (as in the case of smartphones and autonomous vehicles) or they might be separate (as in the case of gaming systems and many IIoT deployments).

Then the edge device does some processing and storage locally. In theory, a device could store the data at the edge indefinitely, but in most deployments, the device then sends a portion of the data up to the cloud for additional processing and analytics. Other devices and users can then access the processed data via the cloud.

It might be easier to understand this architecture by considering a particular use case. Think about the tablet-style kiosks you might see at each table in a chain restaurant. These edge devices collect data input by users, such as order information, payment details, and/or survey responses.

Those tablets then transmit all that data via Wi-Fi to a centralized server in the restaurant. That server processes and stores data, as well as forwarding it to various Internet-connected servers that process payments, monitor company financials, and analyze customer orders and survey responses. Administrators and business managers can then access that cloud-based data through various applications.

This combination of edge computing and cloud computing is becoming increasingly common in a variety of different use cases and industries.

What Are the Benefits of Edge Computing?

Edge computing offers a number of benefits over centralized computing models, such as faster processing speed, reduced network loads, reduced costs, and more. See the other the benefits below:

• Speed. If you process data near where it is generated, you don’t have to wait for it to go up to the cloud and back again. This reduction in latency results in faster performance.
• Reduced network loads. Today’s devices are generating so much data that it can be difficult for networks to keep up. Doing more processing at the edge reduces network bandwidth loads, freeing up capacity for the most important workloads.
• Reduced costs. Transmitting less data can also result in lower data transmission costs. This can be significant, particularly in parts of the world where mobile data fees are high.
• Improved security. If you store and process all your data in one location, that gives attackers a big, attractive target, but edge computing makes it less likely that attackers will gain a huge trove of data. In addition, edge computing makes distributed denial of service (DDOS) attacks more difficult.
• Compliance. Some regions of the world have data protection laws that require data to be stored and processed in the area where it was created. Edge computing can make it easier for organizations to comply with these regulations.
• Better reliability. Spreading your data across multiple physical locations is a fundamental tenet of disaster recovery/business continuity (DR/BC) best practices. If many smaller devices are processing your workloads, you are less likely to experience a catastrophic failure if a single device goes offline.
• Unique products and services. Edge computing makes possible a number of mobile devices that wouldn’t otherwise be available to end users. Consumer demand for “smart” products continues to rise, and these products rely on edge computing.
• Improved monitoring. Edge computing also allows businesses to keep track of a lot of things they wouldn’t otherwise be able to track. This is particularly important for smart factories, smart agriculture, smart grids, and IIoT use cases.

What Are the Challenges of Edge Computing?

As you might expect, edge computing also has some downsides. Here are some of the most significant:

• • Increased maintenance burdens. If your enterprise has dozens or hundreds or even thousands of edge computing devices, your staff then needs to maintain all those devices. That can add more burden to IT departments and require staff to travel to a lot of different locations, all of which can increase costs.
  • Security risks. As mentioned above, edge computing does decrease some security risks, but it also creates some others. In some cases, attackers might be able to gain access to networks by compromising an edge device. And instead of having a few centralized systems to protect, teams must now secure many smaller devices. Ensuring that each device has an adequate level of protection can be time-consuming and costly.
  • Missing data. If you transmit only a subset of your data to the cloud after it has been processed at the edge, it’s possible that you might be missing a critical piece of information. Organizations need to design their edge computing environments with care to ensure that they have access to all relevant information when they need it.
  • Scalability. Scaling out your edge computing network requires you to deploy new hardware. That is generally more difficult than scaling in a cloud computing environment.
  • Environmental challenges. Cloud servers live in highly controlled data centers, but edge devices are often outdoors where they can be affected by weather, dust, pollution, human beings, or even animals. Depending on the use case, you might need to design your edge devices so they can withstand being hit by lightning, run over by a truck, or nibbled by wildlife.
  • Lack of standards. In many edge computing use cases, industries have not yet settled on one standard for key pieces of the technology stack. This makes it difficult to achieve interoperability, and it exposes organizations to the risk that they might bet on a technology that soon becomes obsolete.
  • Logistical hurdles. Getting an edge computing environment up and running can be difficult from both a technological and human resources point of view. For example, organizations may face challenges in powering devices, ensuring that devices turn on automatically when necessary, or even finding room for devices in use cases where physical space is limited. While these hurdles are not insurmountable, organizations should consider them before embarking on an edge computing initiative.

Why Is Edge Computing Important?

Despite these challenges, enterprises should be paying attention to the edge computing trend and considering how their company might participate. Here’s why:

• • • The market for edge computing devices is huge—and growing. According to IDC, the edge computing market will be worth about $250.6 billion in 2024. And the market is growing every year. Your competitors are certainly participating in this trend, and if you want to keep up, you need to be examining how your company could profit.
    • Edge computing complements cloud computing. For more than a decade, cloud computing has been on the rise. But it doesn’t make financial sense to do all your data processing and storage in the cloud—and it might not be feasible for security or compliance reasons as well. A solid edge computing strategy is often a necessary balance for a good cloud computing strategy.
  • Edge computing can make enterprises more efficient. As previously mentioned, edge computing can save both time and money. Organizations should carefully examine ROI to determine where edge computing might make sense for their operations.
  • Edge computing enables new products and services. Consumers and businesses alike are looking to purchase new products and services that integrate computing capabilities into daily life. Edge computing can open up new business models and new ways of serving customers.
  • Edge computing can make life better. Edge computing drives a whole lot of innovation that makes the world safer and more enjoyable. It helps make cars safer, shopping and dining more convenient, farms and factories more productive, supply chains more efficient, and living rooms more fun.
  • Edge computing is here to stay. Given the tremendous benefits offered by edge computing and how deeply it is integrated into daily life, it’s highly unlikely that edge devices will be going away anytime soon. Instead, as with cloud computing, it’s likely that we’ll just begin to think of edge computing as “computing.”

The post What Is Edge Computing? Your Complete Guide appeared first on eWEEK.

However, digital transformation projects can take a lot of different forms, and many different organizations have different definitions of what counts as digital transformation. In eWeek’s view, digital transformation is “the adoption of digital technology that has the capability to transform the business.”

In order to help them complete a digital transformation project, some organizations choose to hire a digital transformation company, or consultancy. These firms have experts with broad-ranging technology and business experience that can assist in creating and implementing digital strategy.

Often DTCs work very closely with the highest-ranking executives in an enterprise. In Gartner’s words, digital transformation companies and service providers are “strategy and transformation consulting services supporting senior business stakeholders, such as CEOs, COOs, chief marketing officers (CMOs) and other business leaders. DTC particularly helps these leaders in efforts to leverage digital technologies that enable the innovation of their entire business or elements of their business and operating models.”

What are the benefits of hiring a Digital Transformation Company?

The benefits of hiring a DTC may include the following:

• • Faster transformation
  • An outside perspective on your organization’s strengths and weaknesses
  • Experience with unfamiliar technology
  • Industry and regulatory knowledge
  • Innovative new ideas
  • Relieving employees of some of the burden of strategic work

In short, a DTC augments your team with capabilities you may not possess in-house. And it usually speeds up the process of digital transformation so that you can start experiencing the results of your transformation more quickly.

How do you select a Digital Transformation Company?

If you are in the market for a digital transformation consultant, keep these tips in mind:

• • Expect to make a partially subjective choice. You can make grids and spreadsheets to compare the relative strengths and weaknesses of different DTCs, but in the end, a lot of the decision will come down to whether or not a consultancy is a good fit for your culture and goals. That also implies that you’ll be better prepared to assess that fit if you have a really good understanding of your organization’s culture and goals.
  • Read the white papers. All DTCs publish white papers. A lot of white papers. During the initial search, the best way to get an idea of a particular firm’s philosophy is to read as many of these papers and thought leadership articles as you can. When a particular approach resonates with your team, ask to talk to a consultant.
  • Get to know the team. Many consulting firms are huge operations with tens of thousands or even hundreds of thousands of employees. How well your project goes will depend in large part on the capabilities of the individuals you work with. Don’t be shy about interviewing these consultants directly and asking about their experience just as you would if you were adding a new person to your executive team.
  • Consider the funding model. Consultants get paid in a lot of different ways. Some get paid by the hour. Some want to be paid if they get deliverables completed within certain deadlines. Some only get paid when your organization meets certain key performance indicators (KPIs), such as revenue targets. You’ll need to think carefully about which kind of funding model is the best fit for your project and your organization’s needs.
  • Brace for the sticker shock. Hiring a digital transformation consultant is not cheap. Expect to pay a lot for your DTC. But in return, you should also get very high-quality services.

With those tips in mind, here are ten digital transformation consulting firms you might want to consider:

Jump to:

• Accenture
• Boston Consulting Group
• Capgemini
• Cognizant
• Deloitte
• EY
• HCL
• KPMG
• McKinsey & Company
• PwC

Best Digital Transformation Companies

Accenture

Founded in 1989, Accenture is a global IT services and consultancy headquartered in Dublin, Ireland. Ranked 258th on the Fortune Global 500 list of the world’s largest companies, it reported $50.53 billion in revenue in fiscal 2021. In addition to digital transformation consulting, it offers a wide range of other services including data and analytics, security, automation, cloud, zero-based budgeting, mergers and acquisitions, and sustainability, among others. Its clients include the USDA, Lenovo, NASA, Prisma, Nippon Express and many more.

DTC services fall under Accenture’s Technology Strategy and Advisory Practice. Its capabilities include assisting with cloud acceleration, data-driven enterprise, intelligent operating model and innovation, network connected services, resilient modern architecture, tech ROI and transformation office. It also offers services related to technology innovation, cloud, digital commerce, security, and other related areas.

Pros

• As one of the world’s largest companies, Accenture has more than 624,000 employees all over the world.
• The firm has extensive experience related to the cloud, automation, and DevOps, which are often critical pieces of a digital transformation initiative.
• Accenture is willing to base pricing on whether or not clients meet agreed-upon KPIs.

Cons

• Because it is itself a large company, Accenture is best suited to meeting the needs of other very large enterprises.
• Its technology-related offerings are confusing, with a lot of overlap in areas related to digital transformation.
• Clients say that Accenture’s fees are high, although the quality of work provided is also very high.

Boston Consulting Group

Founded by Bruce Henderson in 1963, BCG has annual revenues of $8.6 billion and more than 21,000 employees and 90 offices worldwide. Its capabilities include corporate finance and strategy, international business, marketing, pricing and revenue management, people strategy and more. Clients include H&M, Starbucks, GSK, Shell and others.

BCG describes its digital transformation capabilities as “bionic,” because they blend a focus on people and technology. It says that only 30% of digital transformation initiatives succeed and six factors are necessary for success:

1. 1. Craft a clear integrated strategy.
   2. Commit to leadership from the top through the middle.
   3. Put the best people in the right places.
   4. Adopt an agile governance mindset.
   5. Monitor and measure your transformation progress.
   6. Create a business-led tech and business data platform.

Pros

1. 1. • BCG is known for its stringent talent interview process that helps it build a stable of consultants that are among the best in the world.
      • The firm has a wide range of capabilities and can handle almost anything that a digital transformation project might require.
      • Fees are based on meeting agreed-upon KPIs.

Cons

1. 1. • Like many DTCs, BCG charges fees that can be quite high.
      • It might not be a good choice for smaller companies and small projects.
      • Its fast-paced approach might be challenging for some organizations.

Capgemini

Headquartered in Paris, Capgemini has more than 290,000 employees worldwide, including 125,000 staff members in India. In its most recent quarter, it reported revenue of €4.552 billion, and its 2020 revenues topped €16 billion. In addition to consulting, it offers technology, outsourcing and other managed services. Its digital transformation clients include LEONI, Eramet, and the Rugby World Cup. And the company is an extremely passionate supporter of international rugby.

Capgemini’s digital transformation services are provided by its Capgemini Invent division. It aims to combine “strategy, technology, data science, and creative design expertise with an inventive mindset” to help transform businesses. It takes a customer-first approach provided by enterprise transformation experts with an eye toward intelligent industry and inventing for society.

Pros

1. 1. • Capgemini is known for its deep technical expertise that helps its customers use the latest technologies in intelligent ways.
      • Its approach to DTC is highly flexible and customized for each client.
      • It helps clients design self-funded projects that rapidly achieve ROI.

Cons

1. 1. • The firm’s business strategy capabilities are not as highly developed as some other DTC firms.
      • The company does not have much expertise serving clients in the Asia Pacific, Africa, and South America.
      • Like many DTC firms, Capgemini’s prices are high.

Cognizant

With more than 318,000 employees, Cognizant reported $16.65 billion in revenue for 2020, making it number 185 on the Fortune 500 list and number 533 on the Forbes Global 2000. It is headquartered in Teaneck, New Jersey, and began as a division of Dun & Bradstreet. Highly technology focused, its services include application modernization, cloud enablement, artificial intelligence, digital experience, business process services, and others.

Cognizant’s offers digital transformation services under its Digital Strategy umbrella. Its specific product offerings include Insight to Transformation (strategy), Managed Innovation (agile processes), Workforce Transformation (a digital approach to HR and culture), Change Adoption and Transformation Enablement.

Pros

1. 1. • Managed cloud and cloud migration are particular strengths for Cognizant, making it a good option for organizations looking to do more cloud computing as part of their digital transformations.
      • It offers a wide array of technical services, which will benefit organizations with diverse technical needs.
      • It customizes its payment model to client needs with a high degree of flexibility.

Cons

1. 1. • Cognizant’s business strategy capabilities are not as strong as its technical abilities.
      • The company primarily does business in North America and may not be a good choice for companies in other parts of the world.
      • Some clients give Cognizant less favorable reviews for its responsiveness.

Deloitte

Although probably best known as an accounting and financial firm, Deloitte also offers an array of consulting services, as well as technology services related to cloud computing and analytics. Founded in 1845, it is one of the oldest consulting firms on this list. Its headquarters is in London, and it reported $50.2 billion in revenue for fiscal 2021.

Deloitte incorporates digital transformation capabilities in its Strategy and Analytics business and its Monitor Deloitte arm. It focuses on creating “exponential enterprises,” which it defines as companies with “the ability to win and the capacity for change.” It aims to help its clients fuel growth, catalyze tech, continuously transform and harness insights.

Pros

1. 1. • Deloitte excels at integrating technology, and it is particularly good at helping organizations set up multi-cloud environments.
      • The firm also has extensive expertise related to DevOps and automation.
      • The company offers flexible payment options, including some based on outcomes and some based on deliverables and deadlines. It can also tap its financial management resources to help make consulting services more affordable.

Cons

1. 1. • Deloitte’s technology capabilities are not as broad as some of the other DTCs.
      • Some clients complain that Deloitte’s prices are high.
      • Some analysts suggest that potential clients should pay close attention to service level agreements (SLA) to make sure that they are best-in-class.

EY

Also known as Ernst & Young, EY is a global accounting, consulting, and professional services firm headquartered in London. Founded in 1989, it posted revenues of $40 billion for fiscal 2021. Its services span tax, assurance, consulting and strategy. Well-known clients include Microsoft, Carrier Corporation, Discovery, Oklahoma City and others.

EY’s Digital Transformation framework has six different function areas: the Bridge (planning); the Engine Room (orchestration and acceleration); Innovation, Design & Iteration; Deployment Hub; and Digital Factory. Its website highlights its capabilities in robotic process automation (RPA), commercial transformation, digitally integrated customer experience, blockchain, smart factory, and energy systems.

Pros

1. 1. • The firm tries to help clients envision the future and figure out a plan for how to get there.
      • Customers say it has good project management capabilities.
      • EY has a large number of employees (more than 18,000) focused on digital transformation, and it is investing significant resources in improving its capabilities in this area.

Cons

1. 1. • EY’s digital transformation capabilities are not as advanced as some of the others on this list.
      • Like all DTCs, its fees can be high.
      • It might not be a good fit for very large organizations with wide-ranging needs.

HCL

Formerly known as Hindustan Computers Limited, HCL Technologies is based in Noida, India. With fiscal 2021 revenues of approximately $10 billion, it is number 695 on the Forbes Global 2000 list. It offers a wide range of technology and services, including hybrid cloud, product engineering, cybersecurity, software, analytics, IoT and more.

HCL’s Digital Consulting group, which provides digital transformation services, describes itself as “visionary, empathic, pragmatic, and enabling.” Key capabilities include agile delivery transformation, business process optimization, CX strategy & experience design, digital strategy & planning, industry capability definition, organizational agility & change management, and program & product management.

Pros

1. 1. • HCL touts its deep technology expertise, which helps set it apart from DTCs that are more business-focused.
      • It has extensive experience in financial services, manufacturing and life sciences, making it a good option for organizations in these industries.
      • It offers a lot of different kinds of products and services and can handle large projects easily.

Cons

1. 1. • HCL focuses more on very large enterprises and may not be as good a fit for smaller organizations.
      • Its broad array of products can make it difficult for clients to find exactly what they need.
      • HCL tries to help organizations learn to solve their own problems, which may not be a good fit for all cultures.

KPMG

Like several others on this list, KPMG offers accounting and professional services in addition to digital transformation consulting. Headquartered in Amstelveen, Netherlands, the company resulted from a 1987 merger, but the companies that were part of the merger trace their history back as far as 1818. It categorizes its services into four buckets: audit & assurance, tax & legal, advisory, and private enterprise.

KPMG’s Digital Adoption & Transformation business aims to help clients “rebuild your business around the customer to create a truly connected and highly profitable enterprise.” Its past digital transformation clients include AB Inbev, the city of Amsterdam, Hong Kong Broadband Network, SickKids, Spectris and Team DSM.

Pros

1. 1. • KPMG has a lot of expertise with emerging technologies, making it a good choice for organizations that want to be part of the cutting edge.
      • It has a broad range of capabilities that span technology, business strategy, and cultural transformation.
      • Customers say that its consultants are very hands-on and work hard for their clients.

Cons

1. 1. • It can be hard to schedule meetings with its consultants, as KPMG keeps them very busy.
      • Like most of the DTCs, KPMG charges high fees.
      • Its fee arrangements may be less flexible than some of the other DTCs on this list.

McKinsey & Company

Founded in 1926, McKinsey & Company is a management consulting firm with more than 30,000 employees and offices in more than 65 countries. It is privately held and does not report its revenue.

McKinsey offers three different approaches to Digital Transformation: enterprise-wide performance lift, tech-enabled performance transformation and strategic transformation. It says that five truths characterize successful transformations:

1. 1. 1. Execution is critical.
      2. The same routines won’t get you different results.
      3. Sustainable change starts by shifting mindsets.
      4. Address all levers and multiple time horizons.
      5. Inspired leadership is a must.

Pros

1. 1. • McKinsey’s digital unit has successfully completed digital transformation projects for many different companies.
      • Its approach involves pilot testing proposed changes before rolling them out company-wide.
      • Its consultants are among the most highly regarded in the world.

Cons

1. 1. • McKinsey has a reputation for being one of the most expensive consulting firms.
      • The firm does not have the same technological capabilities as some of the other companies on the list and focuses more on business strategy.
      • McKinsey’s intense, hard-driving attitude may not be a good fit for every company’s culture.

PwC

Also called PricewaterhouseCoopers, PwC is probably best known for its accounting and tax services, but it also offers a wide range of other professional services, including consulting. The firm was formed in 1998 from a merger of two companies that had been in business since 1849 and 1854. Headquartered in London, it has more than 295,000 employees and reported $45.1 billion in revenue for fiscal 2021.

Rather than a single digital transformation offering, PwC offers cloud & digital services that are focused primarily on technology and separate transformation services that are focused on business processes and strategy. Its cloud & digital capabilities encompass technology strategy, cloud computing, cybersecurity, data and analytics, emerging technologies and business applications. Its transformation capabilities, branded as “Fit for Growth,” include cost optimization, organizational strategy, operations and supply chain management, digital operations, office of the future and real estate strategy, finance transformation, IT strategy, global business services and return to growth.

Pros

1. 1. • PwC has a broad range of capabilities that it can bring to bear on very diverse projects.
      • It focuses on increasing its clients’ speed and innovation.
      • It takes a detailed, analytic approach to problem-solving.

Cons

1. 1. • Like most DTCs, PcW’s fees can be high.
      • Its patient, thorough approach to collecting information may not be a good fit for all company cultures.
      • Some customers complain about slow response times.

Digital Transformation Provider Comparison Table

The post Top 10 Digital Transformation Companies 2022 appeared first on eWEEK.

Indeed, for nearly a decade, industry pundits have been telling enterprises to embrace digital transformation, and many of them have been heeding that call. Enterprise technologies like cloud computing, data analytics, and artificial intelligence all drive the growth of digital transformation.

Today, the term “digital transformation” has become nearly ubiquitous. It’s hard to find a company that hasn’t embarked on at least one digital transformation project. In a 2021 Gartner survey, only 3 percent of the data and analytics leaders surveyed said they were not involved in a digital transformation project.

Firms have good reason for tackling these projects. Several studies have found that digital transformation correlates with better business outcomes. For example, a Deloitte Insights study found, “Greater digital maturity is associated with better financial performance. The higher-maturity companies in this year’s sample were about three times more likely than lower-maturity companies to report annual net revenue growth and net profit margins significantly above their industry average—a pattern that held true across industries.”

And while digital transformation was already well underway at many companies by 2019, the coronavirus seems to have accelerated the process. A 2021 BDO Digital study found that 43 percent of organizations surveyed where accelerating their existing plans in response to the pandemic, and 51 percent were adding new digital projects. Looking ahead, 90% of middle market organizations planned to maintain or increase their digital spending in 2021.

In the same vein, when Boston Consulting Group surveyed 5,000 managers and employees about how the pandemic was affecting their business, more than 80 percent said that their digital transformation efforts were helping them deal with the economic slowdown.

But what exactly is digital transformation? And what should organizations be doing if they want to experience the benefits associated with this trend?

Also see: Top Digital Transformation Trends Shaping 2022

What Is Digital Transformation?

You will get widely different definitions of digital transformation, depending on who you ask. And almost everyone shades the definition slightly so that it benefits them. For example, Salesforce, which sells customer relationship management software, will tell you, “Digital transformation begins and ends with the customer.”

Red Hat, which sells software and services that touch all areas of business, says:

Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers. It’s also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.

Analyst firm Gartner, which makes its money by selling information about broad, complex topics, makes digital transformation look broad and complex:

Digital transformation can refer to anything from IT modernization (for example, cloud computing), to digital optimization, to the invention of new digital business models. The term is widely used in public-sector organizations to refer to modest initiatives such as putting services online or legacy modernization.

The eWeek definition of digital transformation is a bit more general: “Digital transformation is the adoption of digital technology that has the capability to transform the business.”

Critically, digital transformation involves more than just deploying some new technology. And it is about much more than becoming more efficient or increasing productivity.

Digital transformation is about finding new markets and new ways of doing business, about interacting with customers in new and different ways. It involves harnessing the power of emerging technology to do things that have never been done before. It often leads to new revenue streams, increased margins, greater competitiveness and meeting other business goals.

It carries a high degree of risk. But it also brings significant opportunities.

Digital transformation represents the intersection of numerous constantly evolving sectors, from cloud computing to data analytics to social media. 

Also see: Top 10 Digital Transformation Companies

Types of Digital Transformation

Well-known companies that have publicly discussed their digital transformation efforts include GE, Nike, Home Depot, Walmart, Target, John Deere and many others. But because every organization is unique, digital transformation looks different at every single company that attempts it. And often organizations are deploying more than one kind of transformational technology at once.

Having said that, many organizations are drawn to similar types of technology. Here are some of the more popular types of digital transformation projects:

• Converting from primarily brick-and-mortar sales to ecommerce.
• Migrating from on-premise data centers to cloud computing.
• Using big data analytics to become more efficient and optimize business processes.
• Deploying artificial intelligence and machine learning into business analytics to uncover new insights.
• Creating mobile apps that allow customers to interact with the company from their phones.
• Integrating new technologies like Internet of Things sensors, digital twins, blockchain and others directly into products.
• Abandoning traditional advertising methods like direct mail, television and newspapers in favor of digital marketing and social media.
• Utilizing virtual reality and augmented reality to improve product design and service processes.
• Enabling employees to work remotely, thus improving their employee experience.

Today, most digital transformation efforts incorporate several of these types of technologies. And they also usually require a change in business strategy as well.

Challenges in Digital Transformation

As digital transformation continues to accelerate, businesses must consider the challenges that arise during implementation. Here are some of the most common challenges businesses face when implementing a digital transformation:

• Culture and Onboarding. Digital transformations are not as simple as adopting technologies for each aspect of your business. Businesses must provide the right tools, onboarding assistance, and general company culture for employees to encourage the transformation. A major factor in this challenge is having effective senior leaders in an organization, particularly a Chief Data Officer (CDO).
• Budgeting. Digital transformations are an investment. If businesses do not have a clear transformation strategy in place, budgeting becomes more challenging. Having foundational knowledge of what types of technologies you should adopt and why is a critical first step before budgeting comes into play.
• Lack of Strategy. As we previously mentioned, not having the right experts assisting with your digital transformation could ultimately prove detrimental. In the same vein, going into a digital transformation with a lack of strategy could lead to wasted time and money. If you’re looking to get a rundown of what a traditional strategy looks like before making any decisions, digital transformation frameworks can provide clarity and insight into proven strategies.
• Not Prioritizing Cybersecurity. Although this is becoming less and less of an issue as businesses take cybersecurity more seriously, businesses must put security at the forefront of their digital transformation. Prioritizing cloud migration is a practical start to ensuring your newly adopted technologies are secure.

How to Create a Digital Transformation Strategy

The most successful digital transformations are those with well-planned and well-implemented strategies. How can you create a successful strategy? Here are some tips:

• Identify your business goals. Deciding to start a digital transformation project because everyone else is doing a digital transformation project is a surefire recipe for disaster. Before you begin, make sure everyone on your team understands why you are undertaking the effort. You need to have agreement on your current situation and a clear vision for where you want to go. Often, the most effective digital transformation goals come with key performance indicators (KPIs), metrics that allow you to track your progress toward your goal.
• Hire an expert. Chances are good that no one on your current team has ever shepherded a successful digital transformation before. If that’s your situation, you might want to think about bringing in some help. This could take the form of hiring a full-time employee with digital transformation experience, or it could entail hiring an outside consulting firm. Some companies also hire other key personnel in IT, marketing, or other areas who have experience or expertise in the kind of transformation you want to make.
• Build on your strengths. It’s tempting to throw out everything you are currently doing and start from scratch, but that is usually a mistake. Think about what you already do well. Do you have loyal customers? What does your brand stand for? As you go through your digital transformation you need to make sure you aren’t doing anything to undermine the success you already have. The digital transformation projects that meet their goals often seem like a natural extension of a company’s previous efforts into the digital arena.
• Put yourself in your customers’ shoes. A common theme among digital transformation frameworks (more on that below) is an emphasis on the customer experience. Digital transformation will change the way your customers interact with your organization. You need to make sure that this new experience is positive and reinforces the brand image you want to have.
• Establish new procedures and policies. New technology is completely unhelpful if no one uses it. Before you roll out a new tool or application, think carefully about how you want your employees to use it. If you don’t clearly define procedures and institute some enforcement mechanisms to make sure that people follow them, your staff will likely revert back to the old way of doing things. In some cases, digital transformation will require a complete cultural transformation, which is a lengthy, difficult process.
• Iterate. You can’t transform your organization in an instant. Instead, make a small change, evaluate and repeat. Digital transformation projects that evolve through a series of steps are more likely to succeed than a wholesale change that you attempt to implement all at once. If you keep your strategy somewhat agile, you can make slight modifications along the way and react to changing conditions in a timely manner.
• Consider following a digital transformation framework. Several analyst and consulting firms have laid out digital transformation frameworks that can help give form to your strategy. Remember that your actual strategy should be unique to your organization, but following a proven strategy can help you avoid missing key steps or repeating mistakes that other organizations have made before. A framework isn’t necessary, but it might be helpful, particularly if your management team doesn’t have prior experience with digital transformation.

What is a Digital Transformation Framework?

According to Boston Consulting Group, less than a third of companies (30%) navigate their digital transformation successfully. In order to help businesses improve their odds, a number of consultants, vendors, and other experts have published digital transformation frameworks.

A digital transformation framework is a step-by-step plan for successfully implementing a digital transformation strategy. Usually, it comes along with a graphic representation that shows how the consultants perceive digital transformation.

Some of the most well-known digital transformation frameworks include the following:

• MIT Sloan’s Path to Digital Transformation The MIT Sloan Digital Management Review offers a host of resources related to digital transformation, including several different step-by-step guides that could be classified as digital transformation frameworks.
• Boston Consulting Group’s Six Factors of a Successful Digital Transformation include integrated strategy, leadership commitment, high-caliber talent, agile governance, effective monitoring and a modular platform. It says that 80 percent of organizations with these factors in place success in their digital transformation efforts.
• PwC’s Digital Transformation Framework has five steps: evolve your business, create new value, protect for the future, accelerate through technology and know your customers.
• Cognizant’s How to Win with Digital Playbook claims that only 30 percent of digital transformation projects succeed. It says that in order to be one of the winners you need to take a human-centered design approach, cope with emerging technologies and digitize your business strategy.
• Gartner’s IT Roadmap for Digital Business Transformation describes the key steps of digital transformation as ambition, design, scale, deliver, refine. And it notes that digital transformation continues to be a top priority for business leaders.No matter which digital transformation strategy and/or framework your organization chooses to pursue, it’s worth noting that planning and strategizing is only part of the effort. In order to succeed, you will not only need a good plan, you will need to execute it well. As more and more companies embrace digital transformation, those that rise to the top of their industries will be those that execute digital transformation well.

The post Digital Transformation Guide: Definition, Types & Strategy appeared first on eWEEK.