As more large learning models (LLMs) are coming to market, you might find that your enterprise customers are asking how they can automate processes and drive efficiency with AI. While eager to inquire, enterprise decision-makers often hesitate to invest in generative AI for their business upon discovering the significant resources, security considerations, and operational changes required to deploy an effective generative AI tool. While trendy, AI adoption still carries a lot of uncertainty and risk.

AI and the AV Industry

The good news is that our industry is uniquely positioned to help in this new scenario. As a trusted resource and advisor for technology decisions, clients trust AV integrators to offer AI-powered solutions that are ready for prime time and have a meaningful impact. These four critical questions can help integrators and technology managers adapt their discovery phase to incorporate AI into their AV technology roadmap.

Q1. What are the strengths and limitations of AI?

In response to the increased demand, technology vendors have rushed to develop AI integrations for everything — from truncating transcripts into meeting minutes to brainstorming ideas beyond reality. However, integrators should recognize both the efficacy and limitations of AI in truly enhancing the end-user experience. While generative AI can do a lot in its current state, it’s still best suited as a support tool for enhancing and accelerating processes rather than completely overtaking operations end to end.

The key is to identify discrete tasks that AI can successfully handle. If a human must significantly revise an AI’s output to make it usable, AI will waste more time than it saves. Work with clients to establish clear operational guidelines and oversight for AI tools to avoid these limitation roadblocks. This includes defining responsibilities; establishing handoffs between AI and human operators; allocating maintenance resources and having reasonable expectations. Once these boundaries are established, integrators can introduce effective opportunities to accelerate tasks with AI.

Q2. How do the strengths of AI support the client’s goals?

Properly trained AI can extend enterprise bandwidth by accelerating mundane-yet-time-consuming tasks, like retrieving data to answer questions or inform project timelines; providing employee and customer support; and digesting information to guide brainstorming and decision-making. This kind of implementation improves existing information retrieval processes. AI can also automatically compile information from across sources or customize responses to align with user data permissions.

Once you understand the capabilities of AI, it’s time to bring them to your client. To understand how AI can best support their organization, work with them to understand their unique pain points. Remember, the goal is to improve efficiency in existing systems, not replace processes entirely with AI.

AI functions must align with the interests of the enterprise and the evolving processes, demands and expectations of the business. Deployments should complement and improve the existing employee experience. AI should work across devices and processes, offering a reliable and seamless user experience. If an AI application does not make the job easier for enterprise customers, it’s probably not ready for deployment.

Q3. What training resources are available?

When trained with the right data aggregations, AI assistants can produce qualitative and quantitative-driven outputs that help streamline employee experiences, audience engagement, and objective results that benefit an organization’s defined visual goals and output. That being said, an AI’s responses are only ever as accurate as its dataset. Before launching an AI implementation for an application like sales, customer service or troubleshooting support, a business should have solid and up-to-date documentation of its processes and internal knowledge.

While AI implementation can make operations easier, it’s not an effortless addition to a business model. Deloitte cites managing internal data and processes as one of the most reported obstacles in scaling AI. It is important to inform the client about the continuous investment required for AI and discuss how they plan to maintain it. Before deployment, the business should evaluate whether the AI solutions that client plan to adopt are maintainable. They should then develop a long-term support plan and appoint someone to oversee the ethics and accuracy of the AI system.

Finally, ensure your clients can deliver on processes continuously after deployment, even if their AI tool is down. Integrators can help customers identify reliable, intuitive foundational AI tools that will support their business best, but setting realistic expectations that no AI tool will be free of error or downtime is the key to effective process management and planning.

Q4. What data is acceptable to share?

Data security is top of mind for enterprises, and while the benefits of AI are worth discussing, it’s crucial to educate clients about the potential risks linked with AI.

One risk is the “black box” paradox: the inner workings of AI systems are obscure to humans — this lack of transparency may pose challenges and lead to unforeseen consequences as AI evolves. That being said, sharing data and training is essential to successful AI implementation. Integrators must educate their clients to ensure they understand the risk. This will help clients make more informed decisions regarding adopting and implementing AI technologies.

Security is also a discussion point when evaluating whether an AI tool will operate on premises or in the cloud. On-premises solutions offer several benefits, including that data never leaves the local area network. This is especially important for businesses that deal with highly sensitive data.

Additionally, on-premises solutions offer firewall protection and integration with internal contact/user data, which enables AI to learn identity and permissions. Finally, on-premises solutions provide access to highly secure employee-facing AI chat with responses derived from proprietary company data.

On the other hand, cloud-based solutions offer flexibility, high scalability and accessibility for distributed workforces. However, clients have less control over data distribution than an on-prem solution. Data and encryption keys are stored with third-party providers. This means that if there is downtime, clients may be unable to access their data.

While each option has its benefits and drawbacks, integrators should advocate for establishing a solid security protocol and best practices before sharing data with an AI assistant.

Concluding Thoughts

While the AI landscape fluctuates, these four questions can guide integrators in evaluating and recommending the right AI investments to meet enterprise customer demands. While some clients might be ready for AI, other organizations might need more preparation before taking the leap. Emphasizing genuine operational enhancements over fleeting trends is paramount, guiding clients toward integrations that yield enduring advantages.

Another version of this article originally appeared on our sister-site Commercial Integrator on March 19, 2024. It has since been updated for My TechDecisions’ audience.

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The White House Center for Science and Technology Policy defined the five core principles that underpin the Blueprint for an AI Bill of Rights. In July 2023, several large technology companies including Google and Meta convened at the White House to announce voluntary commitments advancing the safe, secure, and transparent development of AI. The latest order sets out to define standards for safety and security, advance equity and civil rights, promote innovation and competition, and ensure transparency to protect consumers. This sweeping order also promises to foster strong international collaboration to ensure the same principles guide global AI development. Let’s delve into the key areas and explore five main takeaways.

1) Defines New Standards for AI Safety & Security

The executive order requires that companies developing the most powerful AI algorithms notify the government when they are training these models and share critical safety test data with the U.S. government. This will apply to models that may pose national security, economic, public health, and safety risks. The National Institute of Standards and Technology (NIST) will develop rigorous standards to ensure that AI systems are secure and trustworthy prior to their public release. The order also builds on the Biden-Harris Administration Artificial Intelligence Cyber Challenge (AIxCC) with a cybersecurity program that encourages the adoption of AI tools to flag and mitigate vulnerabilities in critical software.

2) Emphasizes Privacy & Data Protection

The Biden Administration’s order is developing programs that will evaluate and develop best practices for federal agencies to protect data privacy for Americans. This program will fund the creation of a Research Coordination Network which will collaborate closely with the National Science Foundation to encourage widespread adoption of cutting-edge privacy technologies by federal agencies. This order also requires that federal agencies be provided with stronger guidance on how they collect and use commercially available information in order to mitigate the risks posed by AI.

3) Advances Equity & Civil Rights

The Blueprint for an AI Bill of Rights identifies algorithmic discrimination as an emerging area that undermines equality and civil rights. Guidelines and best practices will be developed to help ensure that AI is used responsibly in the criminal justice system, benefits programs, federal contractors, landlords, and in workplaces. Federal law enforcement agencies will be equipped with the knowledge, skills, and tools to properly investigate and prosecute civil rights violations that involve the use of AI.

4) Advocates for Patients, Workers & Consumers

Society enjoys real benefits from AI applications — including improved healthcare research and delivery, greater productivity, and more personalized experiences in several settings. However, AI brings with it increased workplace surveillance, bias, and potential for discrimination in medical, employment, consumer, and educational settings. The White House Executive Order on Artificial Intelligence benefits workers by defining guiding principles and best practices to ensure employers do not misuse AI to exploit or discriminate against workers. It also establishes a budget to ensure that AI is used effectively and ethically for the development of lifesaving healthcare treatments and to correct healthcare practices.

5) Promotes American Leadership in AI Innovation & Fosters Competition

This order, along with others issued in recent years, fosters breakthrough innovation by authorizing pilot programs, grants and education opportunities. It also emphasizes the need for international, multi-disciplinary collaboration to ensure that the future of AI is safe and trustworthy everywhere. To this end, the State and Commerce Departments will lead international efforts to institute effective frameworks and accelerate the creation of crucial international AI standards that will mitigate risks while still allowing people to take advantage of AI’s many benefits.

The executive order’s standards, best practices, and principles lay a strong foundation for developing responsible, equitable AI systems. The U.S., in collaboration with international partners, aims to ensure that AI is trustworthy, upholds individual rights, and its fullest potential is maximized to support critical global initiatives. AI is still in its early stages, but this Bill marks an important milestone.

Jennifer Mullen, Emerging Technology Solutions at Keysight Technologies (KEYS)

The post 5 Things You Need to Know About the White House Executive Order on Artificial Intelligence appeared first on My TechDecisions.

For decades, writers and filmmakers have imagined dystopian futures where AI displaces and tries to destroy humanity. So, it’s understandable that people are apprehensive about generative AI’s foray into the workplace. It’s important to acknowledge AI’s transformative effect on workplaces, industries, economies and everyday life in ways we’re just starting to envision.

Even before ChatGPT came on the scene, researchers projected AI would displace 85 million jobs by 2025, but that same study also predicted 97 million new jobs will be created. Organizations that embrace and develop AI, provide incentives for employees to use it and create a human-centric culture where AI helps employees succeed at work will win the future.

As CTO of a team that recently launched a successful AI product for the global employer of record market, I’m optimistic — not only about the value of the technology but also for the employees whose knowledge and expertise AI will augment. Here’s a closer look at how employees and AI complement each other’s strengths and can work together to create business value within a human-centric company culture.

AI Should Augment Human Capabilities Rather Than Replace People

To address the elephant in the room, AI won’t replace human knowledge workers. It requires our insight into what’s happening in the real world to grow and learn to accomplish new tasks. According to some studies, approximately 90% of content online is predicted to be AI generated by 2026, i.e., synthetic data. In some scenarios, synthetic data is useful for training specific models. In fact, a 2021 leveraging synthetic data to refine AI-powered fraud detection tools.

But past a certain point, synthetic data can corrupt large language models due to LLMs depending on unique, creative insight related to real-life events, systems and trends to produce reliable answers. Models like ChatGPT may curate and repurpose existing content, but they also rely on human knowledge and creative thinking that transcends linear reasoning. For example, if companies are trying to hire, recruit and pay international talent, turn to AI for the latest HR best practices, which often change in response to new legislation and emerging trends, they’d need to use an AI model that incorporates human expertise to ensure accurate answers.

Some of the most exciting applications of generative AI are solutions that combine proprietary business expertise with LLM platforms to create a generative AI chat interface. This type of knowledge base can expedite delivery of information to internal and external customers and become a more valuable business asset over time as it learns and improves. But that’s only the case if company leaders think of employees not just as people who help administer the technology but as partners in a sociotechnical system, where people and AI work together, each making contributions to generate value.

Redefining Employee Roles in AI-Powered Industries

The best way to create a system where interactions between humans and technology create value is to build AI solutions in collaboration with employees, not as a separate project to replace them. This is especially important in industries that offer expertise and knowledge as their primary product.

A productive AI-human partnership will require new ways of thinking about how to deliver knowledge to customers. For example, in some jobs, employees apply expertise by responding to emails, submitting helpdesk tickets or collaborating with customers on phone calls. A new role for those employees might be analyzing emerging trends, writing content and training models, bringing the same expertise to solve customer problems in a novel way.

To develop AI solutions that augment employee expertise and knowledge, technology leaders will first need unfettered access to company data — including information that might be difficult to reach due to the use of legacy systems. They’ll also require AI-specific talent to help build out the solution, a blend of expertise from the software engineering and data science disciplines. That’s a commodity already in short supply, so finding people with the right skills should be a priority for an AI project.

Equally as important, building AI solutions will require incentives across the workforce to fine-tune the generative AI solution after launch. As the model ingests more and more information, it learns and becomes more adept at solving problems. Employers who bring more people into the conversation with AI will create additional value by providing multiple perspectives. So, if a customer has a question, the AI chat interface can provide an initial answer that can later be refined by human partners to create a solution that includes well thought-out strategies.

Building a Human-Centric Culture

So how can companies create a generative loop where humans and AI work together to constantly improve output? My AI project team created a chat interface that automates some of the mundane and time-consuming aspects of our knowledge workers’ jobs, and this has proved to be a powerful motivator for employees to keep engaging with the system. It frees staff from tedious tasks and gives them more time to focus on higher level work. That benefit answers the “what’s in it for me?” question that employees naturally ask themselves.

Other real-world examples of ROI from our AI project include employees getting instant answers to questions, when before they used to have to wait hours or days for a subject matter expert to weigh in so they could resolve an issue for a client. Another internal user called the chat interface a gamechanger because it can instantly access data across multiple systems that a person would have to laboriously assemble by consulting several different applications and datasets.

If you can create an architecture that seamlessly accesses data companywide and put together an innovation team that’s encouraged to experiment and explore new possibilities with emerging AI technology, you can build a solution that adds value immediately. And if you operate within an environment where people are adaptable and motivated to access AI to fulfill your company’s mission, you can leverage that human-centric culture to transform the business.

Every business will adapt in its own unique way, but being transparent about how AI will affect roles, encouraging employees to embrace change and instituting a more collaborative approach are essential across the board. Ready or not, generative AI will continue to transform the way companies operate, and an AI-driven business transformation, that leverages human capital, can offer exciting opportunities for businesses to serve customers better and gain a competitive edge.

Duri Chitayat is CTO of Safeguard Global

The post Leading the Evolution Towards Human-Centric AI Work Cultures appeared first on My TechDecisions.

Productivity plays a huge role in a company’s financial health. Recent data from McKinsey & Company shows that productivity has barely grown in the U.S. (1.4%) since 2005. They also estimate that boosting U.S. productivity represents a $10 trillion opportunity.

Few teams feel the effects of unproductive work quite like IT. Their teams are typically small, and yet their work is overwhelming and mission-critical to keeping businesses moving forward. For instance, countless SaaS applications are now integral to the operations of every company, and yet one, often lean team is responsible for their upkeep and management.

That makes IT operations a potential bottleneck. Unsurprisingly, according to Evanta (a Gartner company), CIOs say increasing efficiency and productivity are top priorities for 2023.

The Manual Trap

SaaS applications, from project management, CRM and collaboration to MarTech and expense tools, are inseparable from our daily work. And our IT stacks are continuously increasing, with companies worldwide using an average of 130 apps in 2022.

My experience in the IT space has taught me that most IT teams still manage SaaS applications manually, attempting to track their licenses, users, and renewals via spreadsheets and provisioning/deprovisioning access to applications one by one. These bandwidth-crunched teams are wasting valuable (and expensive) time completing important yet mundane and repetitive tasks that are humanly impossible to keep up with – and, therefore, prime candidates for an automation-driven productivity boost.

The Onboarding/Offboarding Bottleneck

Two of the most time-intensive IT operations are onboarding and offboarding employees from SaaS applications.

Consider this scenario: A company is growing rapidly, hiring to fill the gaps they have across their organization to keep up with the high volume of work required to hit their increasing targets. Maybe they bulk-start new employees on one specific day, every two weeks.

If they have five employees starting on that day, IT first needs to receive the information that those employees are starting. They need to learn what departments those employees will work within and what tools are required for their roles. From there, they must provision access to those applications per employee and application. Endless Slack messages, clunky application backends and likely incomplete information eat up their day.

By the time they finish provisioning that group of new hires, the next group is likely just about to start. Any failures along that scattered onboarding process create a less-than-ideal onboarding experience and derail a new employee’s initial productivity.

On the flip side of this process is offboarding employees.

Manual offboarding requires IT to work with a departing employee’s manager to learn what apps they used (a less than scientific endeavor, particularly when you consider that many employees subscribe to apps on their own without others’ knowledge), and then manually deprovision them from every application. Beyond the time sink for IT, there is also a risk factor — failing to deprovision an employee from even one could leave sensitive company information accessible.

While handling these critical tasks in this manner is inefficient, it’s also indicative of the wasteful nature of manual IT operations. IT pros are highly technical and talented. Having them spend their time on tedious tasks distracts them from working on higher-value projects.

Related: ChatGPT-Like Microsoft 365 Copilot Will Be Coming to Microsoft Productivity Apps

Increase IT Productivity Through Automation

Gartner predicts that by 2025, 70% of organizations will implement structured automation to deliver efficiency, an increase from just 20% of organizations in 2021.

Despite that, Torii’s  survey of over 200 IT professionals found that less than half of respondents have fully automated most tasks, and only 13% reported great success with automation.

So what’s stopping IT from automating? Time: 58% cite insufficient staffing or time as significant challenges in automation implementation. It’s a chicken and egg scenario.

The only way forward is through investing in tools that help manage their SaaS stack, that also include intuitive automation capabilities.

Investing in any old automation, won’t cut it either. In the context of IT, how that automation integrates and interacts with your SaaS stack is key.

Solutions such as SaaS Management Platforms, with automation built-in, provide the visibility IT needs to understand their SaaS stack and associated users and costs, as well as with the actionability required to manage it.

For the onboarding/offboarding bottleneck, this means automatically discovering and surfacing every application in use at your company, first and foremost. From that point of centralized visibility, you can create workflows that automate previously time-intensive tasks (and eliminate the chance of things falling through the cracks) to free up your IT staff’s time (and improve results).

For example, the system can detect which department new hires work in and automatically provision access to the applications they’ll require. Equally for offboarding, when an employee leaves — since you’re aware of every application they’re utilizing, the same platform can automatically deprovision them once triggered by your IDP or HRIS.

These are just small examples of the automation opportunities for IT, but when you compare them to the manual alternatives, it’s easy to imagine the productivity gains you can garner.

As companies continue looking to save costs, greater productivity could fit the bill — as it improves the value of every dollar spent.

By better using time, you’ll enable your organization to optimize resource use and focus on the objectives that drive revenue. Those that invest in productivity now stand to earn more from the efficiency gains of the future.

The post Now’s The Time to Focus on IT Productivity appeared first on My TechDecisions.

Artificial Intelligence (AI) is quickly becoming a mainstay in our everyday lives. Whether it be text-to-image generators like DALL-E or language processing tools like ChatGPT, sophisticated AI tools are revolutionizing the way we work.

That’s especially true in the hybrid workplace, where organizations are searching for new ways to bridge the gap between distributed employees. Given this challenge, it’s no wonder that videoconferencing has emerged as AI’s next step in revolutionizing how we live and work.

Let’s explore AI’s role in videoconferencing and how its latest innovations are improving the meeting experience.

The Rise of AI-Powered Videoconferencing

It should come as no surprise that demand for videoconferencing apps has grown exponentially since the start of the pandemic in spring 2020. Without question, virtual meetings are the bedrock of hybrid work, enabling organizations to seamlessly connect and collaborate across a distributed workforce.

This uptick in demand runs parallel with the emergence of several new competitors in the videoconferencing market. In turn, vendors have been compelled to improve their services by releasing feature-rich additions to their existing applications.

AI technologies have also advanced in leaps and bounds over the same period. In fact, reports show that AI adoption is 2.5 times greater now than it was in 2017. The market is projected to skyrocket twentyfold by 2030 to nearly $2 trillion.

Now, the two worlds are converging. Leading platforms are weaving AI capabilities into their videoconferencing tools to enhance the meeting experience for maximum productivity and performance.

Four Ways AI Can Transform the Videoconferencing Experience

Gone are the days when organizing, running and managing your virtual meetings was a clunky, cumbersome endeavor. Thanks to AI’s advantages, videoconferencing platforms are empowering organizations to kick up their internal and external meetings several notches.

Here are four of the most impactful ways AI-driven solutions can improve the meeting experience.

#1 Natural Language Processing (NLP)

Natural language processing is a branch of AI that enables computers to understand text and spoken words in the same way a real person can. In other words, it’s about giving AI programs the power of conversational intelligence.

Modern videoconferencing tools are leveraging NLP across a host of innovative capabilities, including:

• Voice-to-text transcription: Using NLP, an AI-enabled platform can recognize and process complete sentences as they’re spoken during the meeting. This allows the system to automatically capture meeting notes, summarize conversations and generate searchable transcripts. When machine learning tools are incorporated, videoconferencing platforms can even identify who is speaking during the meeting.
• Real-time translation: Global enterprises may be holding internal or external meetings that stretch beyond borders and include attendees who speak different languages. With AI, the meeting software can give everyone an equitable experience by automatically translating live audio into the language of any user’s choice.
• Closed captions: Likewise, NLP capabilities can analyze audio and generate closed captions to make the meeting experience more inclusive for all participants.
• Noise removal: From cars and pets to housemates and more, there’s no telling what sounds might suddenly disrupt and derail a meeting. Machine learning can be used to train AI to identify and remove those noises before they’re picked up. For hybrid teams, this is especially useful, as automatic background noise removal and acoustic fencing filters out distractions in different locations to keep meetings running smoothly.
• Sentiment analysis: Consider the customer experience use case for videoconferencing: contact center agents working on resolutions in real-time. One major benefit for agents is AI’s ability to understand sentiment and tone during a conversation. Programs can analyze a speaker’s voice to uncover pain points and frustrations during the interaction. This allows managers to evaluate both customer experience and agent performance for future improvements.

#2 Conversational AI

Although they’re also built on NLP, conversational AI programs stand on their own. In simple terms, conversational AI refers to platforms that can chat and interact with users. That said, their capabilities stretch far beyond simple back-and-forth communication.

For example, voice-based virtual assistants can be used to streamline meeting management. Users can simply speak to their endpoints and apps to kickstart a meeting, turn on their camera, capture important items and control the experience from start to finish.

Chatbots can also be asked to schedule follow-up meetings with attendees, assign action items, send automated reminders and more.

#3 Computer Vision (CV)

According to Aragon Research, computer vision will have the biggest impact on intelligent videoconferencing in the future. This type of AI uses algorithms to understand, analyze and reconstruct visual inputs from image or video data.

This has several innovative use cases for meetings:

• Optimized framing: CV can help participants stay in their cameras’ frame when presenting to colleagues and customers. By automatically tracking the speaker’s movements, the system keeps you centered in your video to eliminate unwanted distractions. In addition, multiple cameras in a room can now work together by switching cameras views to focus on the active speaker. The result? A cinematic video experience that follows the conversation from one speaker to the next.
• Adaptive tracking: For meetings with multiple speakers, CV programs can ensure everyone is equally represented on screen. During webinars, cameras can adaptively track speakers as they move across the room or auditorium stage, ensuring that they’re always in everyone’s view.
• Immersive presentations: Virtual presentations are challenging in hybrid settings. CV allows presenters to deliver more immersive content by automatically screen sharing in front of presentation materials.
• Meeting zones: AI will soon use machine learning to automatically learn and frame your space based on predefined boundaries. This will ensure that distraction from passers-by are removed from the meeting in glass-walled rooms or open space.

#4 Productivity Analytics

People spend a lot of time in meetings these days. So, it’s important for organizations to ensure that time is well spent on value-added activities and core business processes. Fortunately, AI-powered solutions are helping businesses capture meeting data and leverage it to their advantage.

AI enhances data collection and offers users the power to evaluate key metrics. For example, platforms can record time spent on certain agenda items and activities, back-to-back meetings and other real-time insights that can be delivered directly to employees to help improve their productivity.

The Future of AI-Driven Videoconferencing

In truth, we are only scratching the surface when it comes to what AI can bring to the table. But, with the videoconferencing market expected to double by 2030, it’s safe to say that AI-powered meetings will be a major asset for years to come.

Robyn Rawlings is director of campaigns and content marketing at Webex by Cisco.

The post AI’s Revolutionary Impact on the Videoconferencing Experience appeared first on My TechDecisions.

Technology has been a passion of mine for a long time, so I have personally found it fascinating to watch the variety of opinions and perspectives unfold as technologies mature. It has challenged my thinking and preconceptions, and I recognize that as a leader and a human I need to address them.

I’ve covered everything from fundamental ethics and whether it is good or bad, through to more specific considerations like ‘what do I want from AI?’, and therefore ‘what might others want from it?’.

Naturally, it’s been hotly debated by my colleagues. The leadership team is considering how today’s and tomorrow’s versions of AI should shape our own role in AI creation and adoption.

We already know that artificial intelligence for IT operations (AIOps) is poised to take advantage of the benefits. At Avantra, we’ve long evangelized the value of automation to offset risk, better utilize skill and boost productivity and innovation. But the debate that surrounds AI has helped us understand that our next phase of technical development must be underpinned with even greater pragmatism and responsibility.

The latest numbers I saw suggest that every day, 100 million people are experimenting with the likes of ChatGPT and other Large Language Models (LLM), such as Bard. Numbers like this highlight the popularity and allure that machines still have. Just like the washing machine, if it makes life easier, why would you not use it?

Download: ChatGPT and Generative AI in the Workplace

Proceeding with Caution

However, even though world renowned university colleges are condoning the use of conversational AI tools, leading experts are urging caution — legalities, politics, economics and ethics are top of the list of concerns.

It was eloquently summed up by The Future Life Institute, which is made up of over 1,000 experts, in an open letter asking the industry to pause AI development, or risk humanity and society. LLMs are learning so much so fast, that we, as a species, haven’t had time to truly process the long term impact. Ethics are at stake.

Taking Responsibility is Urgent

I think it’s a responsible challenge. As I said before the headlines have provoked my own thinking to evolve and prompted me to consider whether such a warning could, should or even will stop our own industry from forging on.

This is where I think the application of AI must be balanced against the dilemma. Take the example of producing project documentation or new product technical summaries. Is using ChatGPT to create the first draft irresponsible or a boon for productivity, freeing up time for innovation in other areas? Similarly, with the introduction of ‘copilot’ tools, like Microsoft assistant, people can increase their productivity and have more time for other things, even just going to the gym. I can see how it could make a sustainable argument for a four day week and happier colleagues.

Can Conversational AI Help Our Industry?

Of course, in my world, the real advantage of introducing ML and AI is the ability to help customers find answers to the problems they face. Using conversational AI to mine a database of known and defined errors other businesses have encountered — be that on SAP or Google — would help practitioners arrive at answers far sooner and avoid a degradation in productivity.

The process would augment the value of the intelligence we aggregate and own and, as it’s a trusted source, accelerate decision making and the time to resolution (TTR). No human can realistically (nor would they want to) hold in their brain all the common problem scenarios and fixes.

I’ve tried to do this in my professional career, and though possible, it is exhausting. That’s why I believe, applying conversational AI to the common challenges our customers face would help highly qualified and skilled humans validate and implement the decisions they take.

I’d advocate that automating the interrogation of vast knowledge banks makes complete sense, especially when it helps skilled people get on with doing what they do best — running, managing, and developing world class ERP.

I should be clear that I am wedded to the notion that it’s important the wider industry runs the AI race in tandem with the ethics that protect humanity. We must thoroughly understand the implications at every point in development and put in place the checks, balances and regulation to ensure the values we hold dear are protected and enhanced, not obliterated.

In the world of AIOps there is real value to its adoption not least to ensure mission critical systems related to food supply or energy stay online. We must therefore consider the broad view of AI technology as well as our narrower domain. Only with a balanced view and appreciation of the accountability we assume as leaders, can we make the right choices.

John Appleby leads Avantra as the Chief Executive Officer. Before Avantra John served as the Global Head of DDM/HANA Center of Excellence at SAP and as the Global Head of SAP HANA solutions at Bluefin Solutions, subsequently acquired by Mindtree. John is a recognized thought leader in the SAP market and was part of SAP’s Mentors Group. John holds an MA in computer science from the University of Cambridge.

The post Does Conversational AI Have A Role to Play in AIOps? appeared first on My TechDecisions.

Specifically, the Zoom IQ tools launched today are Zoom Meeting summary and Zoom Team Chat compose, which leverage both large language models from ChatGPT creators OpenAI as well as Zoom’s own large language model.

Meeting summary allows Zoom Meeting hosts to create a summary powered by the company’s own AI models, and the hosts can then share it via Zoom Team Chat and email without having to record the conversation, Zoom says.

Hosts will receive automated summaries, which can be shared with both attendees and those who didn’t attend so they can catch up on what they missed.

Team Chat compose leverages OpenAI’s technology to enable Zoom Team Chat users to draft messages based on the context of a Team Chat thread in addition to changing message tone and length as well as rephrasing responses to customize text recommendations, the company says.

To use these Zoom IQ features, customers will need to go to the Zoom admin console and opt into the free trials for each feature. As part of the opt-in, customers will also select data-sharing options with Zoom. Account admins may change this data-sharing selection at any time. Customer data will not be used to train third-party models, the company says.

Zoom is working on several other AI-powered Zoom IQ features, including email compose, Zoom Team Chat thread summaries, meeting queries, whiteboard draft and whiteboard synthesize.

According to Zoom, its federated approach to AI leverages its own proprietary large language AI models, those from leading AI companies like OpenAI and Anthropic, and select customers’ own models.

Zoom wants that flexibility to incorporate multiple types of models to provide the most value for its customers’ diverse needs.

“With the introduction of these new capabilities in Zoom IQ, an incredible generative AI assistant, teams can further enhance their productivity for everyday tasks, freeing up more time for creative work and expanding collaboration,” said Smita Hashim, chief product officer at Zoom. “There is no one-size-fits-all approach to large language models, and with Zoom’s federated approach to AI, we are able to bring powerful capabilities to our customers and users through Zoom’s own models as well as our partners’ models.”

The post Zoom Launches Trials for Zoom IQ Meeting Summary, Team Chat Compose appeared first on My TechDecisions.

The Build conference comes as Microsoft becomes fully invested in Copilot, AI and Windows 11, with much of the announcements spanning across those product categories.

Windows Copilot for Windows 11

Microsoft has already unveiled Microsoft 365 Copilot to help workers be more productive while using Microsoft’s productivity tools such as Word, PowerPoint, Outlook and more. Now, the company is launching Windows Copilot, available in preview next month, which Microsoft calls the first PC platform to provide centralized AI assistance for users.

This comes along with Bing Chat and first- and third-party plugins to help users create complex projects and collaborate more efficiently across multiple applications. Windows Copilot, essentially a virtual assistant, can be invoked from the taskbar and will stay consistent across apps, programs and windows, Microsoft says.

In a blog, Panos Panay, Microsoft’s chief product officer of Windows and devices, says Windows Copilot makes every user a power user.

“The things you love about Windows – copy/paste, Snap Assist, Snipping Tool, personalization – they are all right there for you, along with every other feature on the platform, and they only get better with Windows Copilot,” Panay writes. “For example, you can not only copy and paste, but also ask Windows Copilot to rewrite, summarize or explain your content.”

Similar to ChatGPT, Bing Chat and other chatbots driven by large language models (LLMs), Copilot can be asked a range of questions.

Since the tool was announced during the Build developer conference, Microsoft says Windows Copilot gives developers new ways to reach and innovate for shared customers.

“We welcome you to be part of the Windows Copilot journey by continuing to invest in Bing and ChatGPT plugins so your investments will carry forward to Windows Copilot,” Panay writes.

Bringing the new Bing to ChatGPT, plugins

Microsoft is also bringing its new Bing to ChatGPT to act as the default search experience, giving ChatGPT users access to Bing’s search engine which will be built-in to provide additional information from the web.

This makes ChatGPT answers grounded by search and web data, with citations. ChatGPT Plus subscribers will first get access, and it will be rolling out to free users “soon” by enabling a plugin with brings Bing to ChatGPT, Microsoft says.

Additionally, Microsoft and ChatGPT creators OpenAI are making it possible for developers to use one platform to build and submit plugins that work across both consumer and business surfaces, including ChatGPT, Bing, Dynamics 365 Copilot, Microsoft 365 Copilot, and Windows Copilot.

As part of the shared platform, Bing is adding to its support for plugins by adding several others to the Bing ecosystem.

With Microsoft launching Windows Copilot and essentially bringing Bing Chat to Windows 11 in a “more robust way,” Microsoft says Windows Copilot and Bing Chat enable those plugins to be enhanced through applications on Windows.

Microsoft says it is also natively integrating the common plugin platform into Microsoft Edge.

Microsoft Fabric

Also as part of Microsoft’s announcements is Microsoft Fabric, a new unified platform for analytics that includes data engineering, data integration, data warehousing, data science, real-time analytics, applied observability and business intelligence connected to a single data repository called OneLake, the company says.

According to Microsoft, Fabric enables customers of all technical levels to experience capabilities in a single, unified experience. It is infused with Azure OpenAI Service at every layer to help customers unlock the full potential of their data, enabling developers to leverage the power of generative AI to find insights in their data.

Fabric also incldues Copilot, allowing customers to use conversational language to create dataflows and pipelines, generate code and entire functions, build machine learning models or visualize results, Microsoft says.

Other developer tools

Microsoft also announced Hybrid AI loop to support AI development across platform, and across Azure to client with new silicon support from AMD, Intel, Nvidia and Qualcomm. This builds on Hybrid Loop, which Microsoft launched at last year’s Build conference to enable hybrid AI scenarios across Azure and client devices.

Microsoft also announced Dev Home, which it calls a new Windows 11 experience designed to help developers be more productive and streamline workflows. The preview is available in the Microsoft Store now.

Read Microsoft’s blog for the full list of new developer tools.

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According to a study from enterprise automation software firm UiPath, employees largely view AI as a tool to help them do their jobs and relieve burnout as they are asking to do more with less.

Specifically, 60% of respondents to a UiPath survey say AI and automation can address burnout and job fulfillment, and 57% of employees view employers that use automation to help support employees and modernize operations more favorably that those that don’t leverage AI.

The New York City-based firm says 28% of employees report being asked to more work with less support as organizations reduce headcount in response to a looming economic recession. That is leading to 29% of workers globally reporting feelings of burnout.

Those feelings of burnout are felt more in younger generations, as 41% of Gen Z respondents and 34% of Millennial respondents reported burnout, compared to just 25% of Gen Xers and 16% of Baby Boomers. With those younger generations set to replace older generations and become business leaders, burnout levels could rise.

While companies like Microsoft, Google, UiPath and others have been presenting AI tools as a solution to burnout and helping workers be more efficient, some organizations have blatantly said that they plan to replace some jobs with AI, including BT and IBM.

Nevertheless, 60% of respondents say AI will help reduce burnout and help them do their jobs better, with younger generations reporting higher levels (69% of Gen Zers and 63% of Millennials, 51% of Gen Xers compared to 44% of Baby Boomers) of reception to AI-powered automation.

When asked what they want to change with the help of automation, 34% of respondents said they wanted more flexibility in their work environment, 32% said they want more time to learn new skills, and 27% said they want more time during the day to focus on critical tasks.

When it comes down to specifics, employees want automation tools to help with largely technical tasks, including analyzing data (52%); inputting data/creating datasets (50%); resolving IT/technical issues (49%); and running reports (48%), UiPath’s report finds.

In a statement, UiPath’s chief people officer Brigette McInnis-Day said workplace disruption and economic factors don’t have to result in employee burnout, and using AI and automation for some of that work can help.

“Businesses that deploy AI in an open, flexible, and enterprise-ready way are best positioned to attract and retain the types of employees that will help them thrive in an automation-first world,” McInnis-Day says. “Automation is a key differentiator for companies to attract and retain by empowering employees and driving engagement.”

The post Report: Employees Want AI, Automation to Help Alleviate Burnout appeared first on My TechDecisions.

According to OpenAI, the ChatGPT app syncs a user’s chat history across devices and integrates Whisper, the company’s open-source speech-recognition system, allowing users to prompt ChatGPT with their voices.

The mobile app also gives subscribers to ChatGPT Plus–OpenAI’s $20 subscription plan–exclusive access to GPT-4’s capabilities, early access to features and faster response times on iOS devices, the company says.

While the launch of the ChatGPT mobile app is beginning with iOS users, the company says an app for Android devices will be coming soon.

The iOS app offers essentially the same functionality as using ChatGPT from the OpenAI website, including instant answers, tailored advice, text generation, professional assistance and learning opportunities, the company says.

OpenAI will begin the rollout of the generative chatbot iOS app in the U.S., with expansion to additional countries to come in the following weeks.

“With the ChatGPT app for iOS, we’re taking another step towards our mission by transforming state-of-the-art research into useful tools that empower people, while continuously making them more accessible,” the company says in a blog post.

OpenAI’s release of the ChatGPT app for iOS comes amid a wave of new features and updates from OpenAI designed to make ChatGPT more secure and safe, including new data control tools and a teased business subscription package designed to give organizations more control over their data.

With this new way of accessing ChatGPT, organizations may need to further educate and train employees on the use cases and capabilities of ChatGPT and other generative AI, as well as enact other policies around its usage for business purposes as data security has become an issue.

Read our guide, “ChatGPT and Generative AI in the Workplace,” for more information on how to manage the use of ChatGPT and generative AI.

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