Category: Enterprise Tech

The explosion of interest in generative artificial intelligence (AI) applications has left many of us worried about the future of work. While it has exciting implications for transforming just about every industry, there is uncertainty about who might become redundant and what skills we will need to remain useful in the future.

The truth is just about every job that requires creativity or working with information is likely to be affected in some way. It’s essential to understand that generative AI is a tool, and those who learn to harness its potential are those who are likely to prosper rather than find themselves being replaced. So here are some of the jobs that are certain to change, along with the tasks that you should learn to delegate to AI.

Customer Services Agents

ChatGPT and large language models like GPT-4 can be used to build chatbots that answer customer inquiries, create transcripts of calls and summaries of interactions, giving an instant overview of issues that are important or are causing problems for customers. It can provide automated and personalized responses and offer support in many different languages. It can also be used as a training system to simulate customer inquiries.

Marketers

Marketing content, including blog posts, social media messaging, email campaigns, and product descriptions, can be automated. This content can be personalized to target different customer segments. It can also be used to automate the creation of marketing strategies and identify the most relevant targets and goals that marketers should aim for.

Doctors

Doctors and other health professionals can use generative AI to generate and summarize medical reports based on patient data. It can summarize patient histories and even suggest diagnoses or treatments based on symptoms and patient presentations. Image-based generative AI can create simulated medical imagery such as X-rays, and CT scans to assist with the training of medical image recognition systems.

Journalists

Journalists and writers can use generative AI to assist with writing reports by getting it to suggest outlines as well as determine the important facts that need to be covered in their reporting. They can also use it for research by having it create summaries of information or the latest developments in a field that they are covering.

Architects

Architects can feed in relevant information such as site dimensions, local building regulations, and availability of materials and get it to generate design ideas based on these criteria. Generative image-based AI can create initial design proposals or instructions for creating 3D models or 3D-printed prototypes to assist in visualizing and communicating their ideas to clients.

Computer Programmers

Generative language-based AI is proficient in creating computer code as well as human languages and can also suggest structures that should be used when creating programs, tools, and applications. It can debug code, quickly find errors and point out more efficient methods of achieving the desired results. It can also assist with creating technical documentation and explaining how code works, and as a tutor to help humans improve their own coding skills.

Web and Graphic Designers

Web designers can use language-based generative AI to automate the creation of code, allowing them to spend more time on creative tasks. It can be used to quickly generate prototypes of websites or individual graphic design elements such as logos, which the designer can then apply their human design skills to in order to create a finished product. It can be used to quickly gather together ideas that are similar to the project the designer is working on in order to provide inspiration or insights into trends.

Translators

Tools like ChatGPT can quickly translate almost any human language. They understand different alphabets and scripts and can create personalized translations that target specific information within a source text to different audiences, depending on the amount and depth of the information they need.

Teachers

Teachers can use generative AI to automate the creation of lesson plans, suggesting the best ways to teach subjects and highlighting the important information that needs to be communicated to students. It can automate the personalization of teaching materials for students of different levels of maturity or ability. It can create and grade tests, providing in-depth insights into the level of understanding of individual learners. It can also provide teachers with information and assistance with their own professional development, ensuring they are up-to-date with the latest teaching methodologies and resources.

Banking and Financial Services Professionals

Generative AI can be used to create financial reports, including assessing credit risk, fraud detection, and many of the routine reports that financial services professionals need to file regularly. It can be used to automatically process documents such as loan applications, KYC forms, or new account applications. It can also be used in training and professional development applications to ensure employees are aware of the latest regulations and compliance requirements affecting their roles.

Product Designers

Product designers can take advantage of generative design applications that automate the creation of design documents, blueprints, and prototypes by analyzing information such as material specifications and customer requirements. Then, it can automatically create instructions for 3D-printed prototypes or machining tools to go from natural-language input to prototype or even finished products.

Lawyers

Lawyers can use generative AI to research relevant case law and rulings and to automatically create summaries of information relevant to cases they are working on. By doing this, lawyers can vastly reduce the amount of time they spend going through documents and spend more face-to-face time with their clients, getting a more in-depth understanding of their individual requirements. It can also automatically generate contracts and other legal documents to personalized specifications.

Data Analysts

Large language models can automatically analyze, review and summarize large and complicated datasets, providing overviews and insights. It can also automate the generation of reports communicating these insights, personalizing them to the individuals who need the information in a way that’s specifically relevant to them, in a language they will understand.

Project Managers

Here, generative AI can be used to automate project planning, timelines, resource allocation, and risk management. It can assist with creating individual workflows for team members and with administrative tasks such as scheduling meetings and creating minutes, capturing key decisions and action points as they occur. It can be used to create reports that track performance during project delivery, automatically identifying obstacles and suggesting methods of optimizing processes and workflows.

Illustrators

Illustrators can use AI to generate inspiration in the form of sketches, as well as suggest elements such as color palettes and styles. It can help with keeping stylistic elements consistent across a set of images and provide image enhancements such as accurate shadowing and lighting. It can also help illustrators improve their skills by suggesting areas for improvement.

Interior Designers

Generative AI can help interior designers to visualize the way that they will transform spaces by analyzing inputs such as room dimensions, client preferences, and functional requirements. This can help designers to come up with a range of possibilities within a space. It can also provide information on new trends to allow them to provide up-to-date styles for their clients. It can help with material and product selection and provide 3D visualizations to help communicate their ideas to clients.

Technical Support

Generative AI can be used to analyze incoming support tickets and automatically assign them to categories or to agents best placed to handle them. In many instances, it may be able to provide automated responses. It can get to know the problems and pain points most frequently experienced and create automated FAQ documents with relevant solutions. It can also be used to train human agents by role-playing as customers or users with unique problems and analyze their performance, offering input on how they can improve their support skills.

HR Officers

HR can be used to automate the creation of documents, including policy documents, employee handbooks, and onboarding guides. It can be used to communicate policy in any number of different languages, using content personalized to specific audiences, such as different job roles or levels of seniority. It can create surveys and questionnaires to help monitor employee satisfaction and analyze the results to create automated reports and feedback summaries. It can also be used during recruitment to determine which applicants have relevant skills by analyzing their resumes, personal statement, and application letters.

Researchers

Generative AI can assist with gathering the latest information on whatever topics are being researched and creating summaries of the most important points or whatever information is relevant to the topic at hand. It can analyze datasets and assist with the design of scientific experiments. It can create research reports and ensure that research is being carried out in line with legal and ethical standards.

Video Game Designers

Generative AI can be used to automate the generation of procedural content, creating environments and challenges that will engage the player. It can enable designers to fill their worlds with dynamic narratives and storytelling that react on-the-fly to players’ choices and actions. It can also enable them to populate their worlds with more realistic characters that react in a natural, believable way. And it can analyze player feedback, such as social media discussions or online reviews, in order to provide feedback on how well a game is received and highlight areas for improvement or bug-fixing.

To stay on top of the latest on new and emerging business and tech trends, make sure to subscribe to my newsletter, follow me on Twitter, LinkedIn, and YouTube, and check out my books Future Skills: The 20 Skills and Competencies Everyone Needs to Succeed in a Digital World and The Future Internet: How the Metaverse, Web 3.0, and Blockchain Will Transform Business and Society.

A government report shows the notion that H-1B visa holders are “cheap labor” is a myth, with the average salary for H-1B professionals in computer-related occupations reaching nearly $130,000 a year. The information contained in a recent U.S. Citizenship and Immigration Services (USCIS) report is unlikely to silence critics, even though asserting H-1B visa holders earn low salaries has been the standard argument against the admission of foreign-born scientists and engineers.

H-1B Visa Holders Earn High Salaries

The average annual salary for an H-1B visa holder in computer-related occupations in 2022 was $129,000, according to the Characteristics of H-1B Specialty Occupation Workers, Fiscal Year 2022 Annual Report to Congress published by USCIS. The median salary for H-1B professionals in computer-related occupations was $123,000 in 2022.

“The USCIS data show H-1B visa holders are paid high salaries, and it contradicts the idea that these are low-skilled people since employers would not pay people with low skills such high salaries,” said Mark Regets, a labor economist and senior fellow at the National Foundation for American Policy (NFAP).

Regets notes these salaries do not include the substantial fees employers must pay when employing foreign-born scientists and engineers. NFAP estimates a company could spend up to $31,800 in government and legal costs to file an initial H-1B petition (for three years) and an extension for three additional years. Sponsoring an individual for permanent residence can add $10,000 to $15,000 or more.

The median salary for H-1B professionals in computer-related occupations increased by 26% between 2018 and 2022, and the average salary rose by 23%. At the current pace, the average salary for H-1B visa holders in computer-related occupations could reach about $200,000 a year within a decade.

Research: H-1B Visa Holders Paid Comparable Or Higher Salaries Than U.S. Counterparts

Under the law, to gain approval of an H-1B petition, an employer must pay “at least- (I) the actual wage level paid by the employer to all other individuals with similar experience and qualifications for the specific employment in question, or (II) the prevailing wage level for the occupational classification in the area of employment, whichever is greater.”

Several economists have examined the topic and have concluded that H-1B visa holders earn the same or more than comparable U.S. professionals:

– An analysis by Glassdoor found, “Across the 10 cities and roughly 100 jobs we examined, salaries for foreign H-1B workers are about 2.8% higher than comparable U.S. salaries on Glassdoor.”

– After examining the skills and compensation of over 50,000 IT professionals in the United States, University of Maryland researchers Sunil Mithas and Henry C. Lucas, Jr. wrote, “[C]ontrary to popular belief, non-U.S. citizen IT professionals are not paid less compared to American IT professionals.”

– A paper for IZA by economists Magnus Lofstrom and Joseph Hayes with the Public Policy Institute of California concluded, “The data indicate that H-1Bs are younger and more skilled, as measured by education, than U.S.-born workers in the same occupations. We fail to find support for the notion that H-1Bs are paid less that observationally similar U.S.-born workers; in fact, they appear to have higher earnings in some key STEM occupations, including information technology.”

– The Government Accountability Office (GAO) found in electrical/electronics engineering occupations (age group 20-39) the median salary for an engineer in H-1B status was $5,000 higher than for a U.S. engineer.

– In a May 2020 NFAP study, University of North Florida economics professor Madeline Zavodny found, “[T]he evidence points to the presence of H-1B visa holders being associated with lower unemployment rates and faster earnings growth among college graduates, including recent college graduates.”

Because H-1B visa holders are new to the labor market and often recent college graduates, analysts note a less than forthright argument some critics make is that H-1B professionals should be paid the median wage for all people in an area in that occupation, without regard to experience level. This argument appears to reflect a desire to price H-1B visa holders out of the labor market by discarding normal workplace hiring practices. Plaintiffs noted this when the Trump administration attempted to enact a similar policy in a Department of Labor regulation that courts struck down.

It is well known that employers consider experience level when setting salaries. According to USCIS, 43% of H-1B visa holders approved for initial employment were age 29 or under in 2022, and 72% were 34 or younger.

As noted, even though the data show H-1B professionals overall are paid well and not less than their comparable U.S. counterparts, the Department of Labor every year finds some H-1B visa holders have been underpaid, and some companies have violated the law. However, analysts point out, it appears those who assert companies employ H-1B visa holders because they are “cheap labor” hope to diminish the talent of H-1B professionals or, in some cases, even devalue them as people in the eyes of others.

Many H-1B Visa Holders Change Jobs

In FY 2022, 123,888 H-1B petitions were approved for H-1B visa holders to change to a new employer. Such a large number raises doubts about assertions that H-1B professionals are “indentured servants.” The challenge for H-1B professionals waiting years for their green cards is that changing employers could create the need to have a new employer file for a green card, thereby starting an individual’s wait again. H-1B portability rules passed by Congress were supposed to prevent those circumstances.

The problem would be solved by eliminating the per-country limit, which primarily affects immigrant applicants from India and China, and increasing the employment-based green card limit. Exemptions from the annual limit on green cards for foreign nationals with a master’s degree or Ph.D. in science and engineering fields would be law—and have ended the long waits for many green card applicants—if Sen. Charles Grassley (R-IA) had not blocked the measures from being included in the CHIPS and Science Act in 2022. The House of Representatives had passed those exemptions as part of the bill.

H-1B Visa Holders Have High Levels Of Education

In FY 2021, 66% of approved H-1B beneficiaries earned a master’s degree or higher, according to USCIS. (Education levels for FY 2022 contained incomplete data.) The high levels of education are another indication that H-1B visa holders represent highly skilled professionals whose skills are sought in the United States and other countries.

Many H-1B Professionals Were International Students

In 2022, 57,938 students in F-1 status (or F-2 if a spouse or child) were approved for an H-1B petition for new employment via a change of status, or approximately 44% of approved petitions for initial employment. That does not include international students who obtained H-1B status through consular processing. Over 5,000 spouses or children of H-1B professionals, approximately 4,300 exchange visitors and 3,038 individuals in L-1 status also were approved for an H-1B petition via change of status inside the United States in 2022.

Requests For Evidence Plummeted After USCIS Settled Lawsuit

In FY 2020, during the Trump administration, approximately 29% of H-1B petitions that USCIS adjudicated had a time-consuming Request for Evidence. After a successful lawsuit in 2020 against USCIS and a legal settlement, that number declined to about 10% by FY 2022.

Premium Processing

In FY 2022, employers requested premium processing for 75% of approved H-1B petitions for continuing employment and 53% for initial employment. Premium processing costs $2,500 and generally guarantees processing within 15 calendar days under current UCSIS guidelines. That so many employers pay the additional fee reflects the importance of the workers and the length of processing times.

An H-1B visa is typically the only way a high-skilled foreign national, including an international student, can work in the United States long term. A 2022 NFAP study found 55% of America’s startup companies valued at $1 billion or more had at least one immigrant founder, and almost 80% have an immigrant founder or an immigrant in a leadership role, such as CEO or vice president of engineering. The vast majority of those individuals, including international students, needed H-1B status and an employment-based green card to work in the United States.

More than ever, organizations are relying on digital initiatives as a key driver of growth. These include digital offerings that are used by customers as well as those leveraged by an organization’s employees. The results of Forrester’s Digital Business Strategy Survey, 2022, show that 68% of digital strategy professionals believe digital strategies have a major impact on or are a primary driver of growth.

It’s clear that digital offerings need digital product managers to ensure their success. But at many organizations, there’s a lack of clarity on what responsibilities these product managers should perform. The truth is, a digital product manager must practice the same activities as those of a traditional product manager — such as managing the vision, strategy, and roadmap of existing offerings and launching new offerings. However, digital products require the use of data for business growth, leverage emerging technologies, have new channels and touchpoints, and move through product journeys that evolve more rapidly than traditional products. This means that digital product managers must be super data-driven and continuously collaborate with and learn from customers.

Therefore, product leaders must reinforce a set of key attributes when nurturing a digital product management function. Innovation is one of these key attributes.

The Innovative Digital Product Manager

Digital product managers should be innovative. But what exactly does that mean? There are some tenets of an innovative quality and skill set:

• Ready for change. The innovative digital product manager expects and is ready for change. The role works with the broader product team to identify change long before it occurs and develop strategies for offering adoption and growth.
• Look beyond the ordinary. The innovative digital product manager inspires the team to look beyond the company, market, and industry they are focused on for new ideas and partnerships — and encourages an openness to different ways of thinking.
• Investigate ideas safely. The innovative digital product manager leads the product team to experiment with potential ideas, while responsibly managing risk and protecting the customer and the company. Failure is ok, so long as it is not realized in a commercial setting.

Digital and business leaders have cited important skill sets that drive the above tenets within a digital product management team. These include active learning, learning strategies, complex problem-solving, creativity, originality, reasoning, and ideation.

Foster Innovation Within Your Digital Product Management Team

What can product leaders do to help their digital product managers become more innovative? Consider the following across three core elements of successful digital offering strategies:

• Culture. Lead a culture of discovery, in which failure is not only accepted but expected. Encourage the discovery of new opportunities and solutions through customer engagement and experimentation.
• Practice. Clear and aligned practices ensure the product team can accomplish product lifecycle activities (e.g., continuous discovery) while safeguarding customer privacy and inclusive experiences.
• Resources. Tools and technology support the deep understanding of customer feedback in context and help digital product managers determine how offerings are being used.

Innovation is a key attribute of digital product managers, one that is necessary to ensure digital offerings remain unique in enabling customer outcomes and value. Product leaders should evaluate how well this attribute is harnessed within the digital product management team and define upskilling strategies that develop this quality.

To learn more, register to attend B2B Summit North America here.

This post was written by Principal Analyst Sam Somashekar and it originally appeared here.

As the world witnesses unprecedented growth in artificial intelligence (AI) technologies, it’s essential to consider the potential risks and challenges associated with their widespread adoption.

AI does present some significant dangers — from job displacement to security and privacy concerns — and encouraging awareness of issues helps us engage in conversations about AI’s legal, ethical, and societal implications.

Here are the biggest risks of artificial intelligence:

1. Lack of Transparency

Lack of transparency in AI systems, particularly in deep learning models that can be complex and difficult to interpret, is a pressing issue. This opaqueness obscures the decision-making processes and underlying logic of these technologies.

When people can’t comprehend how an AI system arrives at its conclusions, it can lead to distrust and resistance to adopting these technologies.

2. Bias and Discrimination

AI systems can inadvertently perpetuate or amplify societal biases due to biased training data or algorithmic design. To minimize discrimination and ensure fairness, it is crucial to invest in the development of unbiased algorithms and diverse training data sets.

3. Privacy Concerns

AI technologies often collect and analyze large amounts of personal data, raising issues related to data privacy and security. To mitigate privacy risks, we must advocate for strict data protection regulations and safe data handling practices.

4. Ethical Dilemmas

Instilling moral and ethical values in AI systems, especially in decision-making contexts with significant consequences, presents a considerable challenge. Researchers and developers must prioritize the ethical implications of AI technologies to avoid negative societal impacts.

5. Security Risks

As AI technologies become increasingly sophisticated, the security risks associated with their use and the potential for misuse also increase. Hackers and malicious actors can harness the power of AI to develop more advanced cyberattacks, bypass security measures, and exploit vulnerabilities in systems.

The rise of AI-driven autonomous weaponry also raises concerns about the dangers of rogue states or non-state actors using this technology — especially when we consider the potential loss of human control in critical decision-making processes. To mitigate these security risks, governments and organizations need to develop best practices for secure AI development and deployment and foster international cooperation to establish global norms and regulations that protect against AI security threats.

6. Concentration of Power

The risk of AI development being dominated by a small number of large corporations and governments could exacerbate inequality and limit diversity in AI applications. Encouraging decentralized and collaborative AI development is key to avoiding a concentration of power.

7. Dependence on AI

Overreliance on AI systems may lead to a loss of creativity, critical thinking skills, and human intuition. Striking a balance between AI-assisted decision-making and human input is vital to preserving our cognitive abilities.

8. Job Displacement

AI-driven automation has the potential to lead to job losses across various industries, particularly for low-skilled workers (although there is evidence that AI and other emerging technologies will create more jobs than it eliminates).

As AI technologies continue to develop and become more efficient, the workforce must adapt and acquire new skills to remain relevant in the changing landscape. This is especially true for lower-skilled workers in the current labor force.

9. Economic Inequality

AI has the potential to contribute to economic inequality by disproportionally benefiting wealthy individuals and corporations. As we talked about above, job losses due to AI-driven automation are more likely to affect low-skilled workers, leading to a growing income gap and reduced opportunities for social mobility.

The concentration of AI development and ownership within a small number of large corporations and governments can exacerbate this inequality as they accumulate wealth and power while smaller businesses struggle to compete. Policies and initiatives that promote economic equity—like reskilling programs, social safety nets, and inclusive AI development that ensures a more balanced distribution of opportunities — can help combat economic inequality.

10. Legal and Regulatory Challenges

It’s crucial to develop new legal frameworks and regulations to address the unique issues arising from AI technologies, including liability and intellectual property rights. Legal systems must evolve to keep pace with technological advancements and protect the rights of everyone.

11. AI Arms Race

The risk of countries engaging in an AI arms race could lead to the rapid development of AI technologies with potentially harmful consequences.

Recently, more than a thousand technology researchers and leaders, including Apple co-founder Steve Wozniak, have urged intelligence labs to pause the development of advanced AI systems. The letter states that AI tools present “profound risks to society and humanity.”

In the letter, the leaders said:

“Humanity can enjoy a flourishing future with AI. Having succeeded in creating powerful AI systems, we can now enjoy an ‘AI summer’ in which we reap the rewards, engineer these systems for the clear benefit of all, and give society a chance to adapt.”

12. Loss of Human Connection

Increasing reliance on AI-driven communication and interactions could lead to diminished empathy, social skills, and human connections. To preserve the essence of our social nature, we must strive to maintain a balance between technology and human interaction.

13. Misinformation and Manipulation

AI-generated content, such as deepfakes, contributes to the spread of false information and the manipulation of public opinion. Efforts to detect and combat AI-generated misinformation are critical in preserving the integrity of information in the digital age.

In a Stanford University study on the most pressing dangers of AI, researchers said:

“AI systems are being used in the service of disinformation on the internet, giving them the potential to become a threat to democracy and a tool for fascism. From deepfake videos to online bots manipulating public discourse by feigning consensus and spreading fake news, there is the danger of AI systems undermining social trust. The technology can be co-opted by criminals, rogue states, ideological extremists, or simply special interest groups, to manipulate people for economic gain or political advantage.”

14. Unintended Consequences

AI systems, due to their complexity and lack of human oversight, might exhibit unexpected behaviors or make decisions with unforeseen consequences. This unpredictability can result in outcomes that negatively impact individuals, businesses, or society as a whole.

Robust testing, validation, and monitoring processes can help developers and researchers identify and fix these types of issues before they escalate.

15. Existential Risks

The development of artificial general intelligence (AGI) that surpasses human intelligence raises long-term concerns for humanity. The prospect of AGI could lead to unintended and potentially catastrophic consequences, as these advanced AI systems may not be aligned with human values or priorities.

To mitigate these risks, the AI research community needs to actively engage in safety research, collaborate on ethical guidelines, and promote transparency in AGI development. Ensuring that AGI serves the best interests of humanity and does not pose a threat to our existence is paramount.

To stay on top of new and emerging business and tech trends, make sure to subscribe to my newsletter, follow me on Twitter, LinkedIn, and YouTube, and check out my books, Future Skills: The 20 Skills and Competencies Everyone Needs to Succeed in a Digital World and The Future Internet: How the Metaverse, Web 3.0, and Blockchain Will Transform Business and Society.

For months now, all the hype in technology circles has been around artificial intelligence (AI). But an upcoming announcement from Apple could reignite the excitement around virtual and augmented reality (VR/AR).

The closely-related technologies, often collectively known as extended or mixed reality (XR/MR), were the hot topic a couple of years ago. This was largely thanks to Meta’s Oculus Quest devices, which it successfully marketed as a package of affordable hardware and an accessible software ecosystem.

This played a big part in building up the hype and excitement around the metaverse concept as we looked towards a future where we would interact across online, digital spaces, and digitally-augmented real-world environments in more immersive and engaging ways.

Since then, AI has undoubtedly stolen the limelight thanks to the arrival of ChatGPT and the explosion of interest in generative technologies. But even back in the Quest days, Apple made no secret of the fact it was working on its own headset technology. Now, it seems those plans are set to come to fruition, with the first concrete announcements expected to arrive soon.

So, could this put VR and the Metaverse back on the agenda? And how will this generation of VR/AR be affected by the fact that AI now plays a much bigger part in our lives than it did, even just a couple of years back?

Apple’s History with AR/VR

Although it has not so far released any hardware, there are signs that Apple has been interested in the space for a long time. It developed QuickTime VR back in 1995, one of the first standards for using 360-degree images to create immersive experiences. More recently, it has made a number of acquisitions during the last decade that confirm its interest. These include AR startup MetaIO back in 2015 and NextVR in 2020.

In 2017, it released ARKit, a platform for creating AR applications that can run on its iOS devices, such as Macintosh computers, iPhones, and iPads. In particular, Apple co-founder Steve Wozniak has frequently expressed his interest as the technology has evolved over several generations, from the earliest headsets to those based around smartphones and later initiatives such as Google Glass.

From this history, we can see that although it might not have seen a need to rush a product to market, it has always considered that the field of mixed reality technology will play an important role in the future of technology.

The Apple Headset

Although we’ve known a headset is on the table for some time, the company itself has remained fairly tight-lipped. However, as this is Apple, there is a huge community of fans dedicated to delving into whatever the California-based tech giant is believed to be working on. This has led to a great deal of speculation and rumors about what we can expect.

Fans believe that it’s likely that the headset will be known as the Reality Pro. It’s also expected that it will have a hefty price tag of around $3,000. This would put it into a very different category than the firmly consumer and entertainment-oriented devices launched by Meta. Like the company’s first computers, as well as its more recent Mac Pro and Macbook Pro devices, it’s likely to be aimed at professionals and creatives.

It’s also believed that the launch will bring the debut of a new operating system called xrOS. This will be the software layer that the company will use to build its future VR/AR products around, just as its desktop computers and mobile devices each have their own operating system platforms, MacOS and iOS.

It’s expected that it will feature 8K OLED screens for super-sharp displays and, at the assumed price point, is also likely to include Apple’s powerful M2 processor, as used in its Macbook Pro laptops. The hardware is likely to track hand and eye movements, thanks to a large number of cameras and sensors, allowing users to explore virtual environments in a natural, fluid manner.

It is also important to note that Apple’s new headset and the accompanying xrOS operating system might pave the way for more consumer-focused AR glasses. It is rumored that Apple is working on lightweight glasses that could blur the line between AR and everyday life, making the technology more accessible and inviting to the everyday consumer. Whether it’s for navigation, shopping, or even social interactions, such consumer-focused devices could revolutionize our experience with AR technology, embodying Apple’s unique blend of function, design, and consumer appeal. Similar to how the iPhone transformed mobile technology, “Apple Glasses” could have the potential to redefine the AR industry, pushing it toward mainstream acceptance and usage.

Return to the Metaverse

It may seem that the topic of the metaverse has gone off the boil a little, with everyone paying far more attention to uses of generative AI rather than plans for building the “next level” of the internet.

This is just a natural evolution of the “hype cycle” that drives technology discussion; however – there’s always something new and shiny to capture our attention. It doesn’t mean that companies responsible for spending billions of dollars to bring these ideas into reality simply forget about what they were doing 18 months ago.

The previous wave of excitement around VR and the immersive internet was driven by Meta, as well as, to a lesser extent, the pioneers of the decentralized internet concept referred to as Web3.

It’s certainly possible that Apple’s upcoming announcement and launch could thrust it back into the limelight. A device of the power that’s being suggested would enable far more impressive visuals and a smoother experience. Bearing in mind earlier attempts (including Meta’s) were sometimes unkindly likened to the level of graphical fidelity offered by decades-old video games, this leap in performance could be all that’s needed to finally launch XR into the mainstream.

A key factor in this could be the rumor Apple will enable both VR and AR experiences using the same device. Meta’s Horizons has, so far, planted itself firmly as a VR experience, whereas it has often been suggested that AR will be just as important as VR to the “true” metaverse.

Given Apple’s strong user base and its history of catapulting specialist products into the mainstream by reinventing them as sleek lifestyle accessories, it’s possible that its arrival will generate a new wave of excitement around the metaverse concept – leading to AR and VR playing a bigger part in our lives.

MR and AI – A Perfect Match?

The last time we got excited about VR, no one outside of a group of researchers at OpenAI had heard of ChatGPT or the language models that power it. But thinking of them as distinct, unrelated technologies would be a mistake. It’s almost certain that Apple is factoring in the implications of the emergence of generative AI on its plans for a new generation of XR hardware and software.

For a start, generative AI will make it easier to design and build the 3D worlds we will explore with our headsets, as well as the immersive AR experiences that will mesh with the real world around us.

Being able to simply describe the experiences and applications we want to create in natural language will open the floodgates for businesses wanting to establish their presence and for designers hoping to create immersive environments for us to explore.

There’s also the potential for AR and VR worlds to be populated with AI-powered inhabitants. This could include life-like simulated humans (or any other creatures we can imagine) who we can interact and converse with as if they were real people standing in front of us.

It’s clear that a VR environment (or metaverse) is likely to be far more realistic and engaging than any other 3D environment we’ve experienced so far. AI can be used to generate realistic facial expressions and simulated voices and to allow 3D characters to behave and move in an increasingly life-like manner. These AI constructs will also be capable of speaking in as many languages as they need to, simultaneously – simply being translated in real-time according to the language preference of whoever is communicating with them. The implications for entertainment, education, business, and industry are literally unlimited.

How Will Apple Impact The Future of AR and VR?

Apple doesn’t tend to invent new categories of products – it brings them to the masses in slick, shiny packages with an appeal that goes beyond the early adopters who are the first to buy into new tech. We saw this with their early Macintosh computers, the later range of iMacs, and, more recently, with iPhones, iPads, and Apple Watches.

If the rumors are true, we can expect to learn the first concrete details about the Reality Pro at Apple’s World Wide Developer Conference (WWDC) 2023, scheduled to kick off in just a few days’ time on June 5.

Apple’s major new hardware launches rarely disappoint – unlike its biggest competitors, it doesn’t rush to join every trend as it’s happening but often bides its time until it’s sure it has the right project for the right market.

On the other hand, in today’s age, nothing can be taken for granted, and it’s fair to say that the emergence of AI has had enormous consequences that perhaps even Apple hasn’t fully comprehended just yet.

Time will tell whether this could finally bring an end to its run of ground-breaking product launches. But going by its track record, it would certainly be foolish to believe that anything is a foregone conclusion.

Immigration policy functions through arcane rules and publications. One of these publications is the State Department Visa Bulletin. For 23 years, until his retirement in December 2022, Charlie Oppenheim was the chief of immigrant visa control and reporting at the U.S. Department of State and oversaw the monthly publication of the Visa Bulletin. Oppenheim was one of a select few people working in the federal government who understood the interaction between the annual limits and per-country limits in the U.S. immigration system. He is currently the Director of Visa Consulting for WR Immigration. I interviewed Oppenheim, who replied in writing, and he explained how the Visa Bulletin works and affects immigration policy and family and employment-based immigration.

Stuart Anderson: What is the Visa Bulletin?

Charlie Oppenheim: The Visa Office is responsible for administering the various annual numerical limits on immigrants, subdivided by preference category and country, as established in various sections of the Immigration and Nationality Act (INA). It does so by subdividing those annual preference and foreign state chargeability limits into monthly allotments throughout the fiscal year. The Visa Office aims to utilize a specific total of visa numbers each month to maximize the number use under the applicable annual limit.

Stuart Anderson: What needs to be taken into account each month?

Charlie Oppenheim: During this process, several variables must be considered, such as how many numbers have already been used, future number use and how many additional numbers U.S. Citizenship and Immigration Services (USCIS) may use for adjustment of status cases based on Final Action Date movements. This process ultimately results in the establishment of Final Action Dates. Once this has been done, the Visa Office publishes the Visa Bulletin, which is a monthly periodical summarizing the availability of visas for the coming month. It also includes other visa-related information.

The Visa Bulletin may also provide other information, such as projections of visa availability in the coming months, issues which may impact visa processing, changes in policy, etc. This is done to keep those interested in the immigrant visa process informed and minimize the chance that people may be caught off guard regarding any potential changes in visa availability.

Anderson: Can you explain how the dates in the Visa Bulletin operate?

Oppenheim: The Visa Bulletin contains two different charts which represent visa availability for that specific month.

Chart A represents the Final Action Dates that will govern the potential scheduling of cases for that month. There are three options that may be displayed:

1) A Final Action Date is listed. This date is intended to represent the priority date (typically the date a petition for immigrant status was filed) of the first applicant for whom a visa number was not available during that month. Therefore, only persons with a priority date earlier than that date are eligible for potential final action on their case—resulting in visa issuance at an overseas post or adjustment of status if being processed in the United States.

2) The preference category is listed as “C” (i.e., Current). This designation indicates that the Visa Office has determined that applicant demand is less than the visa numbers available for use in that particular month. For example, there are 500 numbers available for use during June, but there are only 350 applicants who are eligible to be scheduled for potential final action on their case during that month. In such a case, the category would be current.

3) The preference category is listed as “U” (i.e., Unavailable). This would indicate that either the annual limit for that preference category has already been reached or is expected to have been reached by the beginning of the month.

Chart B represents the Application Filing Dates. The Visa Office estimates that the applicable Final Action Date is likely to be at a specific period in the future. These dates would typically be based on the known demand within the currently established Application Filing Date for that preference, plus an estimate of the future (new) applicant demand required to maximize the number use under the annual limit at that future time.

The Visa Bulletin also lists a chart with information on the numbers that can be made available for use under the annual Diversity Visa (DV) Program. These listings are like those in Chart A, with the main difference being that each DV case is assigned a Rank number during the selection process for that annual program, not a priority date. Therefore, listing a DV Rank number would indicate the first case which could not be scheduled for potential final action during that month.

Anderson: What is the biggest misconception people may have about the bulletin?

Oppenheim: The monthly dates announced in the Visa Bulletin are based on the best information available when those dates are determined and only intended to represent visa availability for that specific month. Many people assume that everyone with a priority date before the applicable Final Action Date listed in the Visa Bulletin has been processed to conclusion. Nothing could be further from the truth, and there are thousands of people who have not acted on their case in a timely manner and could potentially do so at any time in the future.

Therefore, subsequent changes in the demand pattern for numbers could affect the future availability of numbers, causing dates to slow, be held or be retrogressed.

Every individual in a case, whether principal, spouse, or child, counts against the applicable annual limit for that preference category. There also are sometimes extenuating circumstances, such as those experienced during the Covid-19 pandemic, which can impact visa processing. This may prevent the State Department or USCIS from acting on all the currently eligible cases because of limited processing capacity.

Anderson: What is “retrogression”?

Oppenheim: A retrogression is when the newly announced Final Action Date for the upcoming month (i.e., June) is earlier than that of the current (i.e., May) month listed in the Visa Bulletin. During the Visa Office’s determination of visa availability for the upcoming month, it may determine it is necessary to retrogress a final action date to limit the future use of numbers in a particular visa category. Such action is more likely to occur towards the end of the fiscal year to hold numbers used within a prescribed annual numerical limit. Often such action may only be temporary, and ideally there could be a full recovery to the previously established date for October, the first month of the new fiscal year. Unfortunately, this is not always the case when the demand for numbers remains much higher than originally expected.

Anderson: What employment-based immigration issues do you think people should be looking at in 2023?

Oppenheim: The employment-based annual limits for FY 2021 (262,288), FY 2022 (281,507), and FY 2023 (197,000) have been artificially high limits. This was due to the negative impact the Covid-19 pandemic had on family-sponsored processing at overseas posts during the preceding year. These high limits may have resulted in some people developing unreasonable expectations of the future movement of the employment-based Final Action Dates and expedited processing times.

By the end of FY 2023, overseas processing should return to relatively normal levels, and I would not expect there to be more than 10,000 unused family-sponsored numbers. Those numbers would be added to the FY 2024 employment-based (minimum 140,000) annual limit. That would result in far fewer numbers available for employment-based use next year [than in FY 2023], which is likely to mean slower forward movement of the final action dates, and longer wait times than those experienced in recent years.

Anderson: How does U.S. Citizenship and Immigration Services (USCIS) use the Visa Bulletin?

Oppenheim: USCIS uses the Final Action Dates listed in Chart A to determine which adjustment of status cases are eligible for potential final action each month. USCIS also uses the Application Filing Dates that are listed in Chart B to determine which applicants will be allowed to proceed with the early filing of their adjustment of status case. Such filings increase the likelihood that all required processing will have been completed by the time the applicant’s priority date actually becomes eligible for final action (based on the forward movement of the applicable Final Action Date).

This practice also provides much more visibility into potential future demand, which can help reduce volatility in the movement of future final action dates. USCIS makes its determination of whether Chart A or B will govern the filing of new adjustment of status on a monthly basis. Once USCIS has estimated that there will be sufficient demand to use all immediately available numbers, the agency will then only allow Chart A to be used for filing purposes through the end of that fiscal year.

Anderson: Do you see any issues with USCIS in the current fiscal year on employment-based green card processing?

Oppenheim: That is difficult to say without having access to all the required statistical data to make the most informed decision. That being said, I am a little confused as to why it has been necessary to retrogress so many of the employment-based preferences this year, in some cases several times, and for the sudden need to implement a Rest of World EB-2 final action date. Except for seven months, the Rest of World EB-2 preference category has continuously been “current” since November 2012.

Anderson: What advice do you have for people waiting for their green cards who read the Visa Bulletin?

Oppenheim: Patience is the key. Future forward movement of the final action dates is likely to be slowed in many employment-based preferences. However, I would expect continued forward movement of most of the family-sponsored Final Action Dates.

It will be increasingly important to closely monitor the monthly Visa Bulletin for any items that may be included on future visa availability issues. When I was responsible for the publication of the Visa Bulletin I always attempted to provide at least one or two months of advance notice whenever it became apparent that any type of “corrective” action of a final action date may be required. I knew this was essential information for applicants and visa practitioners for future planning. The State Department seems to be continuing with that practice and has done an excellent job in that respect.

Anderson: Why do you think so many people around the world want to immigrate to America?

Oppenheim: Being able to immigrate to America provides people around the world with unique opportunities. They can maximize their talents, gain an education, tap into America’s outstanding culture for starting businesses, escape ethnic, religious or political persecution and do everything within their power to ensure their children have a better life. That is why so many people want to immigrate to America.

As I’ve written about recently, for years Synopsys has been using AI to push the envelope on the electronic design automation (EDA) software that makes today’s semiconductors possible. Now its latest quarterly numbers are out, and if anything I’m even more impressed by what the company is doing and the impact it’s having in the industry.

At a moment when lots of companies are shaky because of broader economic conditions—and when the chip industry is facing lower demand—Synopsys’ numbers are moving up and to the right in every important area. Sure, some of this have to do with the offset between designing chips and building and inventorying them, but it’s still impressive.

The latest results blew past previous guidance, the company hit another record for quarterly revenue, operating margin is up, EPS is up and the company raised its guidance for annual revenue. It’s all a testament to strong execution of its “Smart Everything” strategy that Synopsys launched more than a decade ago.

After the earnings call, I had a chance to talk with CEO Aart de Geus and CFO Shelagh Glaser to get the Synopsys perspective on what’s happening at an interesting time for AI and the tech landscape as a whole. In this piece, I’ll share some of the highlights of that conversation and where they see things heading in the months and years to come. Let’s dig in.

Synopsys beats guidance for revenue, EPS and margins

First, the numbers. Quarterly revenue of $1.395 billion was above the high end of guidance, as was non–GAAP EPS of $2.54. The company generated $703 million in quarterly operating cash flow, and its non-cancelable backlog rose to $7.3 billion.

Given how it’s firing on all cylinders, the company now expects revenue for the full year to be about $5.8 billion, or 14 to 15% higher than its $5.08 billion of revenue in fiscal 2022. In line with that, it expects non-GAAP EPS to grow 21 or 22% year over year, and it projects an even bigger improvement in non-GAAP operating margin—150 basis points total—than its previous guidance.

That’s what I’d call a great quarter: top-line growth, bottom-line growth, higher guidance for the year. And from talking with Glaser, it’s very clear that Synopsys is serious about continuing to improve margins, both short- and long-term. By the way, the company also has plenty of cash and almost no debt.

The numbers are even more impressive when you consider the volatility of the market that Synopsys operates in. Demand for semiconductors has been down, especially for the chips that go into consumer products. But Synopsys says that its customers continue to invest in R&D for new chips so they can take advantage of opportunities when demand does pick up again. In de Geus’ words, for the chipmakers, “The worst is to miss an upturn, because that’s where most of the money is made.”

Leadership in AI helps Synopsys navigate a turbulent market

Synopsys is able to deliver such strong results because it’s in the right market space with the right technology—and AI is at the heart of that. “I think there has been great sensitization in the last few months around the whole notion of AI,” de Geus told me. “And you know, this is not new to us, because we had predicted the world was going to go to ‘smart everything’ about 12 years ago and built in that direction.”

Right now, generative AI like ChatGPT is getting most of the attention in the press, but Synopsys has already spent many years using machine learning (ML), big data and different types of AI to help develop and debug chips. As covered in my earlier post, its recent announcement of the Synopsys.ai platform means that the company is now applying AI to the entire EDA stack: design, verification, test and manufacturing—even for analog chips.

How’s that approach working? In a word, great. Nine of the top 10 semiconductor companies are already using AI-driven tools from Synopsys, and the uptake for these tools is only picking up pace. It started in mid-2021, when Samsung announced it had achieved the first AI-driven commercial tape-out in the world by using Synopsys’ DSO.ai. (DSO stands for “Design Space Optimization.”) By the end of 2022, Synopsys customers had already achieved 100 AI-driven tape-outs, and when I talked to de Geus in the past two weeks, he told me that it’s now “well over 200 tape-outs.”

To pick one example out of the many design wins Synopsys achieved in the past quarter, the company is now collaborating with TSMC to deliver EDA flows for the foundry giant’s most advanced 2nm process node. Renesas achieved up to 10X improvement in reducing functional coverage holes and up to a 30 percent increase in verification productivity.

Just think about the demands of that in terms of design complexity and zero tolerance for error.

The role of hyperscalers and generative AI

While he didn’t name specific companies, de Geus did verify that the list of his customers using AI-driven tools includes not only pure-play chipmakers, but also hyperscalers. This makes sense when you consider that about 45% of Synopsys’ business comes from what de Geus calls “systems companies,” meaning companies that intersect both hardware and software. This includes big cloud providers, car makers and others that are massive users of computing power and that have particular needs for operating at lower power consumption.

For example, think about how much onboard computing power will be needed by the end of this decade for fully autonomous cars—and yet all that computing power must still be delivered at the lowest possible level of energy consumption. As de Geus said, “If [chips] go into a car, they also better be as low-power as possible, because every 100 watts that you use in compute essentially takes away 10 to 15 miles of distance that you can drive on the battery.”

To address these needs, generative AI of the type used in ChatGPT isn’t yet ready to contribute to EDA processes. “Generative AI has fantastic results” in other contexts, de Geus told me, “but its results can have imperfections. In what we do, there’s zero room for that. No, we are a company that has to deliver 99.99999—you know the number of nines can never be enough.” This is so because even the slightest error in a chip design can have huge consequences in terms of efficiency, yield, time-to-market and so on. “So therein lies a very profound distinction between the level of precision that our AI has to have for these designs versus these more search-type AI capabilities.”

Mind you, de Geus’ enthusiasm for generative AI came through loud and clear. “I think it’s fantastic, what’s happening” with generative AI, he said, “and this will impact the world massively.” In the longer term, Synopsys president and COO Sassine Ghazi believes that although these are early days of research and development, we believe generative AI is at an inflection point and a game changing technology that will offer significant opportunities for EDA applications.

Meanwhile, Synopsys engineers are exploring how cutting-edge large language models (LLMs) of the type used by ChatGPT can help streamline internal processes and augment existing solutions. According to Ghazi, “Synopsys pioneered AI-Driven chip design and this is the only the beginning of our AI journey to deliver productivity breakthroughs for customers.”

If you want to know more about the AI journey Synopsys has been on for the past several years, I recommend a fascinating post the company published after its recent SNUG conference, where Synopsys.ai took center stage. At the conference, Ghazi celebrated the crucial work done by Synopsys engineers who were inspired by the 2016 and 2017 victories of Google’s AI-driven AlphaGo over masters of the ancient game of Go. Those engineers began to dig into the ways that AlphaGo’s reinforcement learning (RL) techniques might be applied to EDA. What started as merely an idea then quickly became an MVP, and is now being used to design some of the world’s most complex multi-die architectures.

While generative AI will definitely have a role in the future of EDA—and Synopsys is bullish about taking a leadership role there as well—the company’s existing AI tools are providing big improvements in productivity and speed for chipmakers in the here-and-now.

The future of EDA is “Smart, Secure and Safe”

For de Geus and his team, the mission is broader than just using more AI to speed up and smooth out EDA. In his earnings report and again in his call with me, the CEO reiterated the need for Synopsys to help deliver technology that is smart, secure and safe.

The “smart” part gets harder all the time as companies demand more from chips: greater speed, more sophistication, higher performance-per-watt and so on. Besides the automotive example mentioned above, this certainly applies to things like AI-driven search, which requires several times as much computing power as traditional text search. Other chips used for AI functions are similarly compute-hungry.

De Geus made the point that Synopsys itself is adding to this demand. “We are using AI, very advanced AI, now ourselves to help our customers design super advanced chips,” he said. He compared it to the famous M. C. Escher picture of two hands drawing each other, adding that “I’ve always loved that representation because we are in the midst of that.”

He also believes that safety and security are “increasingly, jointly important” alongside smarts. This is so, he told me, because “the very complex system, if it’s not secure—there are enormous dangers in that.” He added that “these dangers get aggravated if they touch human life in any form.” It’s not hard to come up with contexts—cars, medical devices, navigation systems and so on—where any of us would insist on super-high safety and security.

For customers looking to bolster security and safety, de Geus said that Synopsys helps “by virtue of putting mechanism in chips that help encrypt the data, provide root of trust, a unique identification, et cetera. And that’s built into the IP and the design flows.”

For de Geus, all these considerations are “interwoven and looped, and the really fantastic thing for us is we’re literally in the middle of many of these loops” of design, AI utilization and so on. In his view, that gives Synopsys lots of room to run. As he put it, “The roadmap of optimizing, automating and generative AI use cases is wide open to deliver productivity breakthroughs for years to come.”

The future of AI in EDA

As he answered a question on the earnings call, de Geus summed up his business philosophy when he said, “Whenever the world changes, there’s opportunity.” I found that telling and ironic, given that is where technology industry analysts grow, when there’s chance.

Synopsys has certainly been following that path since it launched its Smart Everything strategy a dozen years ago, and especially since it started going deep into RL and other AI techniques around 2017. What once sounded like science fiction is now penetrating deeper and deeper into every part of the creation process for semiconductors, from the early stages of design to the final steps of manufacturing.

I agree with de Geus that Synopsys’ opportunities in this space are diverse and open-ended. Better, the company has shown year after year that it can take good ideas and quickly turn them into real-world engineering that addresses customers’ challenges. The combination of strategy and execution has really paid off for them, and I expect that to continue.

Many B2B organizations excel at customer experience (CX), but most don’t apply the same discipline to the buyer experience – the pre-purchase part of the customer journey. It’s an often-overlooked lever for accelerating growth. With B2B buyers more savvy and demanding than ever, taking a CX approach to understanding and managing the buyer experience can be a competitive advantage.

But what exactly do we mean by “buyer experience”? Forrester defines buyer experience as a buyer’s perceptions of the full set of actions, interactions, and engagements that buyers work through, from defining a problem and exploring solutions through to the selection of a vendor and the finalization and initial deployment of a purchase. Basically, this entails thinking beyond the conception and execution of a buyer’s journey map to doing the work to understand how buyers themselves feel about the process of purchasing from you. Here are three things to consider when thinking through how to manage the buyer experience at your organization.

1) B2B Buyers Are Not Satisfied

Understanding the buyer experience begins with understanding the buyer. This includes grasping both what satisfies the buyer and what doesn’t. After all, according to Forrester’s Buyers’ Journey Survey, 2022, most global purchase influencers reported at least one area of dissatisfaction with the winning vendor at the conclusion of a purchase. Buyer dissatisfaction doesn’t just mean the factors that dissuade them from moving forward with the purchase; it extends to winning vendors that don’t consistently help buyers realize value (what they get exceeds what they give up in pursuit of their goal) during the purchasing process. And that’s where customer value comes in.

2) Customer Value Is The Key To Customer Obsession

One of the most important aspects of B2B buying behavior is how buyers perceive value from vendors. They are looking for relationships, connections, and benefits that go well beyond the product or service being offered. And that starts with the buying process. Understanding customer value is vital to managing a customer-obsessed buyer experience in which a buyer perceives value in their journey through the buying process itself. For example, how easily are they able to find the information they need? Was the process smooth and efficient, or did they have to make multiple request to move forward? Or how well do they feel understood and respected? B2B buyers that perceive value are more likely to be satisfied during each phase of the buyer’s journey and become better, more profitable long-term customers.

3) Portfolio Marketing Teams Should Take The Lead On Buyer Experience

Because of their focus on understanding buyer personas and buyer journey mapping, portfolio marketing teams are uniquely positioned and well equipped to orchestrate improvements to the buyer experience. With these skills already in their toolbox, portfolio marketers should expand persona profiles and journey maps to include how buyers perceive value during the buying process. Many functions across the organization contribute to the buyer experience – campaign, digital and web teams, demand generation, content strategy, sales, and more. Portfolio marketers need to coordinate an aligned effort to orchestrate improvements across the various teams that deliver the touchpoints that make up the buyer’s holistic experience.

To learn more, register to attend B2B Summit North America here.

This post was written by VP, Principal Analyst Barbara Winters and it originally appeared here.

Definitions are important, and from day one DevSecOps has never been strongly defined: there is no universal acceptance, and so no deep understanding, of what it actually means.

Is it terminology related to how an organisation approaches a product, or the organisational framework, or maybe a cultural and technical shift left within the integrated development environment? Or all of these things?

Is it time that we deconstructed DevSecOps and look for something better in 2023

Accelerating change to escape the silo

Let’s start at the beginning; that time before DevSecOps was a buzzword when developers didn’t look further than building the application. Security, be that in terms of vulnerabilities or misconfiguration, was not their concern, not their job. That would be down to the security people, assuming there were any. And, even if there were, we all know how well that went when security was considered an add-on rather than integral to the project.

Something had to change, and what changed was everything.

And nothing.

Agile management required quicker, continuous product development as organisations necessarily transformed into digital entities. Mistakes were made, vulnerabilities exploited, and now they impacted the entire business rather than being just another ‘software bug.’ Gradually, but inevitably, cybersecurity became a business issue. Security shifted left into the development process so as to be identified and redressed without causing later delays.

Is DevSecOps just an aspiration then?

That’s the ‘everything’ that changed. The nothing, for a great many organisations at least, is that DevSecOps remains nohing more than an aspiration, some may even say a fantasy. Those silos remain in practical terms, with DevOps teams and security teams both now trying to address and enforce security across all areas. In some ways, the shift left has happened: when a vulnerability is exploited in production, it’s the DevOps leaders who wield the power rather than the security team. Sure, security is involved, of course, but it remains the devs who are required to not only put things right but also find the vulnerability.

The DevOps-centric approach

DevSecOps, in reality, is actually more of a bridge building exercise: DevOps are asked to be that bridge to the security teams. Yet, simultaneously, DevOps are asked to enhance the technology used (for example, strong customer authentication, or SCA for short) often without the full input of security teams and so new potential for risk is introduced. These are DevOps security tasks, in effect, rather than DevSecOps. These need to be approached from the top down and bottom up: an organisational risk assessment to prioritise the software security tasks, and then a bottom-up modelling of how to incorporate something like SCA in our example. This is a DevOps-centric approach to security rather than the commonly accepted DevSecOps one.

The right tools for the job

Correct tooling is at the core of this DevOps-centric approach to security. DevOps itself is largely dependent on automation, and continuous, automated testing, in particular. Having the right tools and solutions to help discover and mitigate security issues should be a no-brainer.

Yet too many disparate tools, like cooks, will quickly enough spoil the seamless visibility broth. And here, we meet the key obstacles preventing organisations from realising a truly integrated, DevOps-centric approach to security.

These can best be summed up as being two extremes of the same problem: appropriate tooling. While DevOps is in the firing line when a security incident occurs, oftentimes, they won’t have the budget to purchase the tools required to best respond as those will be purchased by security teams.

Or, of course, they will have too many, resulting in an unruly tool landscape.

Joined-up thinking is not optional

The solution rests with joined-up thinking, where not only does DevOps have the right security tools but also a ‘single pane of glass’ solution to integrate the resulting findings with mapping of where in the organisation that software appears. And it’s not just DevOps that need this single pane viewpoint: security teams require such visibility of software vulnerabilities and remediation strategies as well. A DevOps-centric approach to security has to be all-embracing for it to be effective.

Maturity matters

Security risks cover the entire software lifecycle from the initial open source building blocks right through to deployed and in production. Understanding this level of maturity is essential to a DevOps-centric approach, with a shift right (to when code is operational) being equally important to the shift-left focus of old.

You can think of this as modernising DevSecOps, reducing alert ‘noise’ within developer range, and ensuring contextual threat levels are brought into focus. The Log4shell critical vulnerability in the Log4j logging tool that exploded into view just as 2021 was coming to an end is a great example to demonstrate the effectiveness of this. Organizations needed to know they could react not just quickly but immediately to such incidents. A map of all instances, so any that were at risk could be remediated, was essential. But understanding whether or not circumstances existed that could make it vulnerable, the contextual analysis, was equally as vital.

The painful truth about DevSecOps

It’s not so much that DevSecOps is dead, but maybe it never really existed at all. What most people mean when they talk of DevSecOps is a DevOps-centric approach to security.

Perhaps the time is right that we start talking about this broader, back-to-basics, integrated vision instead.

Your ability to get the most from technology and services partners is a core competency for success in today’s ecosystem-driven businesses. We’ve been highlighting the strategies and execution to achieve this over the years. We call it co-innovation — working with partners to co-create solutions that drive your business forward. To illustrate the benefits, we mined the submissions for the 2022 Technology Strategy Impact Awards and identified four best practices for getting the most value from partners:

1. Co-create solutions that transform your business. Co-innovation works when you stop asking “How much value can I extract from partners?” and start asking “How much value can we create together?” You unlock the power of co-innovation when both partners are motivated to achieve the same goal. The control dynamic changes, with innovation moving throughout the ecosystem of partners to create opportunities for all parties.
2. Example of co-creation to transform: A retailer partnered with a modern application development shop to reimagine commerce as a digital retail backbone. This business platform scales to support all the retail businesses and has now evolved to power the marketplace and modernize store checkout experiences. One of the initiatives done in the spirit of partnership was to build a store traffic management solution to adhere with local legal regulations and pilot it in in just 19 days.

1. Get help with developing and operating your core business capabilities. In a strategy we call future fit partner strategy, the best firms tap co-innovation partners’ end-to-end expertise assets, alliances, automation, and solutions to accelerate and scale the platforms that drive your business.
2. Example of operating core business capabilities: For example, during the pandemic, a liquidation specialist moved quickly to serve customers curbside with the help of a systems integrator and a cloud-based platform developed for restaurants to scale their buy-online, pick-up curbside. By adapting the platform capabilities developed for a different industry, the company dramatically accelerated their time to deployment without a lot of custom build.

1. Improve innovation through emerging technologies. Co-innovation partners don’t spend as much time learning and coding on your time. Instead, they invest in their own partnerships with software, cloud, data, and business providers to jump-start the work. You benefit from those alliances and their offerings because with partnership comes expertise and access to the engineering teams and roadmaps. This allows quicker innovation and, in time, faster and more business-ready adoption of emerging technology.
2. Example of innovation through emerging technologies: A network of health providers worked with an implementation partner and utilized the core AI capabilities of the Google Cloud Platform (GCP) to analyze millions of clinical notes and elevate its understanding, clinical practices, and billing outcomes. The combination of technology acumen and business relevance made the difference.

1. Increase the value and effectiveness of your ecosystems. A key part of your future fit tech strategy is building the capability to choose and manage an ecosystem of partners, including technology, business, and services partners. A co-innovation partner will help you integrate and coordinate their contributions to achieve your outcomes.
2. Example of ecosystems: A life science company’s partnership strategy focuses on engaging with four key types of partners: 1) strategic partners, including ISVs, cloud vendors, and innovation/change management partners; 2) systems integrators; 3) startups; 4) academia.

Companies in North America can visit here, companies in EMEA can visit here, and companies in APAC can visit here to review complete Forrester Technology Award nomination criteria and submit an entry. The deadline to submit a nomination is June 19, 2023 for all three regions.

This post was written by VP, Principal Analyst Ted Schadler and it originally appeared here.