Abstract

Reviews Cisco System's approach to implementing Oracle's Enterprise Resource Planning (ERP) software product. This case chronologically reviews the diverse, critical success factors and obstacles facing Cisco during its implementation. Cisco faced the need for information systems replacement based on its significant growth potential and its reliance on failing legacy systems. The discussion focuses on where management was particularly savvy in contrast to where it was the beneficiary of good fortune.

Keywords

Citation

Electronic health record (EHR) systems are essential for maximizing profits and delivering a modern standard of care to patients. However, the implementation phase is among the most arduous stages of the electronic medical record (EMR) adoption journey. This phase is where the most problems occur and where shortcuts could lead to catastrophic issues later on. Here’s how to make sure your medical practice avoids a crisis scenario when transitioning to a new EHR system.

Editor’s note: Looking for the right EMR system for your business? Fill out the below questionnaire to have our vendor partners contact you about your needs.

The practical application of generative artificial intelligence has gone from an abstract, future concept to a concrete reality in a matter of mere months. Businesses and organizations large and small are scrambling to figure out if and how AI can help their people be more productive and efficient. For organizations using Microsoft software, the application of AI in a business environment is being led by the Microsoft Copilot platform.

Businesses at the enterprise level are also looking for ways AI can leverage the massive amounts of data generated daily by their organizations in increasingly productive ways. Many believe that such a massive undertaking can only be accomplished by a competent AI platform. Microsoft Copilot, with its integration into Microsoft 365, Azure, Windows and enterprise-wide data streams, is purported to be the AI that unlocks the creative and productive potential of an organization’s people and data.

Jump to:

What is Microsoft Copilot?

Microsoft Copilot is a new AI product that combines the power of large language models with in-house enterprise data generated by the Microsoft Graph and Microsoft 365 applications. Using the power of AI and natural language conversations, users can find better answers to their questions and potentially create content from those answers. Copilot was developed on the ChatGPT platform and announced as an in-development platform at the July 2023 Microsoft Inspire conference.

There are two versions of Copilot: Microsoft 365 Copilot and a more general Microsoft Copilot. It is important to note that Microsoft 365 Copilot is different from the consumer and small business-oriented Copilot platform found in Bing Chat or non-enterprise versions of Microsoft 365. These Copilot LLMs are trained on more generalized aggregate data gathered across the internet and therefore tend to have more generalized results. Microsoft 365 Copilot will be dependent on the data generated by a specific, and only a specific, enterprise.

Microsoft 365 Copilot will be dependent on in-house, enterprise-generated data, while the more general Microsoft Copilot will use aggregate data pulled from the internet.

Both Copilot versions will be embedded in the Microsoft 365 apps including Word, Excel, PowerPoint, Outlook and Teams. This complete Microsoft 365 integration will allow an organization’s workers to be more creative and unlock productivity gains and potentially Improve their skills. In addition, Microsoft 365 Copilot will add AI-enabled Business Chat to the productivity suite, which will work across enterprise-specific data like calendars, emails, chats, documents, meetings and contacts to help employees communicate easier and better.

What are business benefits to using Microsoft Copilot?

Assuming Microsoft Copilot works as advertised, an employee could jump-start a project (e.g., email, presentation, report, data visualization) with an AI-generated first draft. With that foundation, the employee can quickly move on to refining and iterating a second draft and then a final draft, cutting at least one step from the process. This should make the employee more efficient and productive, and it could allow for the development of new skills.

For Microsoft 365 Copilot, the key to these productivity gains will be the application of enterprise-specific data to the new project from the start. Using data internally generated by the enterprise from emails, documents, calendars, contacts and so on, presumably the project will be jump-started with limited, and more importantly, pertinent assets.

SEE: Hiring kit: Prompt engineer (TechRepublic Premium)

For example, if an enterprise employee at Ford is creating a presentation that needs to show movement, the AI – if properly trained – should use a Ford-related product rather than a similar product from one of its competitors. An AI trained by data culled from the internet may come to a different conclusion and defeat the purpose of deploying enterprise-specific generative AI.

How does Microsoft plan to integrate Copilot into its applications?

Copilot will be integrated into the fabric of all Microsoft 365 applications. When an employee starts a Word document, reads an email in Outlook, opens an Excel report or updates a PowerPoint presentation, Copilot and its generative AI abilities will be there to assist when called upon.

The specifics of how each Microsoft 365 application will use Copilot are still in development, and many use cases will likely only be discovered when users can actually use the platform. However, Microsoft has outlined some of its basic ideas for Copilot in a business environment.

Below are examples of commands a user might provide Microsoft Copilot.

Copilot in Word

• Draft a two-page project proposal based on data gleaned from a Word document (i.e., either the one you are currently working on or one that you specify by name) and an Excel worksheet.
• Make the third paragraph in the current document more concise and change the tone of the document to be more casual.
• Create a one-page draft based on this rough outline.

Copilot in Excel

• Break down this sales data by type and channel and then insert a table.
• Project the impact of a variable change in this data and then generate a chart to help visualize it.
• Model how a change to the growth rate for a variable would impact my gross margin.

Copilot in PowerPoint

• Create a five-slide presentation based on this Word document and include relevant stock photos.
• Consolidate this presentation into a three-slide summary.
• Reformat these three bullets (in a specific PowerPoint presentation) into three columns, each with a picture.

Copilot in Outlook

• Summarize the emails missed while out of the office last week and flag any important items.
• Draft a response thanking the senders of an email and asking for more details about their second and third points.
• Shorten a draft email and make the tone more professional.
• Invite everyone to a lunch-and-learn about new product launches next Thursday at noon. Mention that lunch is provided.

Copilot in Business Chat

• Summarize the chats, emails and documents about the syllabu being discussed.
• What is the next milestone for a project? What risks were identified? Brainstorm a list of potential mitigations.
• Write a new planning overview in the style of this (specified existing) document that contains the planning timeline from a different document and incorporate the project list in the email from this person (specifying a user’s name).

What are potential problems and caveats with Microsoft Copilot?

All AI platforms are only as good as their training; if Microsoft Copilot is modeled after data that is incomplete, biased, wrong or otherwise corrupt, the suggestions it generates, regardless of who is asking, will be incomplete, biased, wrong or otherwise corrupt. The old adage of garbage in equals garbage out still applies.

For Microsoft 365 Copilot, training will be particularly important because all the data used for that Copilot AI platform will be internally generated and gathered. Enterprises looking to properly apply AI will have to carefully monitor the data Copilot can access. For example, a business may not want brainstorming documents and meeting notes to be part of the data stream; rejected ideas may taint the data stream and propagate throughout the organization.

For general versions of Microsoft Copilot, organizations and users will have to be aware that inherent biases, fads, misguided trends and other transitory events will likely color some of the AI-generated output. Not every passing fancy on the internet should make its way into official organizational documents.

In addition to this potential problem, especially for Microsoft 365 Copilot implementations, is the tendency of some departments to silo their data behind firewalls. New products and services developing under non-disclosure agreements, for example, will often be cut off from the normal organizational data stream. Businesses will have to decide whether hiding data from the AI in such cases is more beneficial than allowing AI access.

The most important caveat for Microsoft Copilot is the platform is still in development. The use cases outlined by Microsoft at the 2023 Inspire conference are the company’s visions of how the platform will work. Once Copilot is released to the public, what the AI platform is capable of, good or bad, will be revealed. Until then, we will be dealing with potential and perhaps some wishful marketing.

What are the alternatives to Microsoft Copilot?

Generative AI is arguably the hottest trend in technology innovation for 2023, so it stands to reason there are many new and in-development AI platforms ready to compete with Microsoft Copilot.

Even though Copilot is based on ChatGPT, the AI chatbot is available as a standalone platform and therefore should be considered a competitor. AI is already incorporated into Microsoft Edge in the form of Bing Chat, and an AI has recently been released for the Google search engine and Chrome. Technology experts have reported that Apple is developing its own AI platform. It seems that AI will be integrated into just about every digital application we use on a computing device.

Major tech companies including Salesforce, Oracle and Adobe are all working on AI platforms. There are also dozens of smaller independent developers working on their own versions of an AI platform. Plus, there are a multitude of AI competitors working on specialized platforms that will bind LLMs and generative AI principles to specific applications. Businesses will likely spend a lot of time wading through AI platform possibilities.

How much does Microsoft Copilot cost?

As revealed during the 2023 Inspire conference, Microsoft 365 Copilot will cost $30/user/month. At first glance, this price point seems expensive, but it is vital to remember this version of the platform is designed for large business enterprises. For a large enterprise with thousands of employees, and assuming the platform delivers what Microsoft promises, that $30 could end up being a bargain. That is a big assumption to make at this early point in development.

The consumer and SMB versions of Microsoft Copilot will likely be priced lower than the enterprise counterpart. Bing Chat, which is also based on ChatGPT, is available now and for free as an integral part of Microsoft Edge. It is also likely that some features restricted or otherwise modified version of Copilot will be available for SMBs too small to have meaningful in-house generated data available to train the AI platform.

The pricing for all the versions of Microsoft Copilot is likely to change as development of the platform continues. With the large number of competitors in the AI platform space, it seems almost certain the cost of these services will change significantly.

Which businesses should consider Microsoft Copilot, and which should not?

At $30/user/month, only certain large enterprises will be able to afford large numbers of employees subscribing to the Microsoft 365 Copilot platform. In addition to the subscription fee, such employers will also have to account for the extra expenses associated with generating, collecting and collating accurate and useful data for the AI and LLMs to train on. This is a major undertaking, and the decision to implement the Copilot platform will take a significant commitment.

Large enterprises will have to decide whether the productivity benefits of using the Microsoft 365 Copilot platform outweigh the initial costs of developing and maintaining the platform – and then paying for it indefinitely.

For individuals and small businesses with little to no LLM-ready data, the consumer level versions of Microsoft Copilot is available for free. Bing Chat is already available in Microsoft Edge for everyone. Some form of low-cost or no-cost version of Copilot will likely be available for certain versions of Microsoft 365.

Only time will tell if the generative AI capabilities of Copilot are worth the time necessary to use them.

When will Microsoft Copilot be available?

As of August 2023, Microsoft Copilot is in the testing phase of development. A limited number of organizations and Microsoft Insiders are testing the AI and providing feedback on what works and what does not work.

There is currently no officially announced release date for any version of Microsoft Copilot.

Because of climate change, we are experiencing far more natural disasters than ever before in my lifetime. Yet we still seem to be acting as if each disaster is a unique and surprising event rather than recognizing the trend and creating adequate ways to mitigate or prevent disasters like we just saw in Hawaii.

From how we approach a disaster to the tools we could use but are not using to prevent or reduce the impact, we could better assure ourselves that the massive damage incurred won’t happen again. Still, we continually fail to apply what we know to the problem.

How can we Improve our approach to dealing with disasters like the accurate Maui fire? Let’s explore some potential solutions this week. Then we’ll close with my Product of the Week, a new all-in-one desktop PC from HP that could be perfect for anyone who wants an easy-to-set-up-and-use desktop computing solution.

Blame vs. Analysis

The response to a disaster recovery should follow a process where you first rescue and save the living and then analyze what happened. From that, you develop and implement a plan to make sure it never happens again. As a result of that last phase, you remove people from jobs they have proven unable to do, but not necessarily those that were in key positions when the disaster happened.

Instead, we tend to jump to blame almost immediately, which makes the analysis of the cause of a disaster very difficult because people don’t like to be blamed for things, especially when they couldn’t have done anything differently.

Generative AI could help a great deal by driving a process that focuses on the aspects of mitigating the problem that would have the most significant impact on saving lives both initially and long-term rather than focusing on holding people accountable.

Other than restrictions this puts on analyzing the problem, focusing on blame often stops the process once people are indicted or fired as if the job is done. But we still must address the endemic causes of the issue. Someone who has been through this before is probably better able to prioritize action should the problem arise again. So, firing the person in charge with this experience could be counterproductive.

Generative AI, acting as a dynamic policy — one that could morph to address a wide range of disaster variants best — could provide directions as to where to focus first, help analyze the findings, and, if properly trained, recommend both an unbiased path of action and a process to assure the same thing didn’t happen again.

Metaverse Simulation

One of the problems with disasters is that those working to mitigate them tend to be under-resourced. When disaster mitigation teams devise a plan, they often face rejection due to the government’s unwillingness to pay for the implementation costs.

Had the power company in Hawaii been told that if they didn’t bury the power lines or at least power them down, they’d go out of business, one of those two things would have happened. But they didn’t because they didn’t do risk/reward analysis well.

All of this is easy for me to say in hindsight. Still, with tools like Nvidia’s Omniverse, you can create highly accurate and predictive simulations which can visibly show, as if you were in the event, what would happen in a disaster if something was or were not done.

ADVERTISEMENT

Is Hawaii likely to have a high-wind event? Yes, because it’s in a hurricane path and has a history of high wind events. So, it would make sense to run simulations on wind, water, and tsunami events to determine likely ways to prevent extreme damage.

The answer could be something as simple as powering down the grid during a wind event or moving the electrical wiring underground if powering down the grid was too disruptive.

In addition, you can model evacuation routes. We know that if too many people are on the road at once, you get gridlock, making it difficult for anyone to escape. You must phase the evacuation to get the most people out of an area and prioritize getting out those closest to the event’s epicenter first.

But as is often the case, those farthest from the event have the least traffic, and those closest are likely unable to escape, which is clearly a broken process.

Through simulation and AI-driven communications, you should be able to phase an evacuation more effectively and ensure the maximum number of people are made safe.

Communications

Another significant issue when managing disasters is communications.

While Cisco typically rolls trucks into disaster areas to restore communications as part of the company’s sustainability efforts, it can take days to weeks to get the trucks to a disaster, making it critical that the government has an emergency communication platform that will operate if cell towers are down or have hardened the cell towers, so they don’t go down.

Interestingly, during 9/11, all communication was disrupted in New York City because there was a massive communications hub under the towers that failed when they collapsed. What saved the day was BlackBerry’s two-way pager network that remained up and working. In our collective brilliance, instead of institutionalizing the network that stayed up, we discontinued it and now don’t have a network that will survive the disasters we see worldwide.

It’s worth noting that BlackBerry’s AtHoc solution for critical event management would have been a huge help in the response to this latest disaster on Maui.

Again, simulation can showcase the benefits of such a network and re-establishing a more robust communications network that will survive an emergency since most people no longer have AM radios, which used to be a reliable way to get information in a disaster.

Finally, autonomous cars will eventually form a mesh network that could potentially survive a disaster. Using centralized control, they could be automatically routed out of danger areas using the fastest and safest routes determined by an AI.

Rebuilding

We usually rebuild after a disaster, but we tend to build the same types of structures that failed us before, which makes no sense. The exception was after the great San Francisco earthquake in 1906, which was the impetus for regulations to Improve structures to withstand strong quakes.

In a fire area, we should rebuild houses with materials that could survive a firestorm. You can build fire-resistant homes using metal, insulation, water sprinklers, and a water source like a pool or large water tank. It would also be wise to use something like European Rolling Shutters to protect windows so that you could better shelter in place rather than having to evacuate and maybe getting caught on the road by the fire.

With insurance companies now abandoning areas that are likely to be at high risk, this building method will do a better job of assuring people don’t lose most or all of their belongings, family, or pets.

Again, simulation can showcase how well a particular house design could survive a disaster. In terms of rebuilding on Maui, 3D-printed houses go up in a fraction of the time and are, depending on the material used, more resistant to fire and other natural disasters.

Heavy Lift

One of the issues with floods and fires is the need to move large volumes of water quickly. While the scale of the vehicle needed to deal with floods may be unachievable near-term, carrying enough water to douse a fire quickly that was still relatively small is not.

We’ve been talking about bringing back blimps and dirigibles to move large objects for some time. Why not use them to carry water to fires rapidly? We could use AI technology to automate them so that if the aircraft has an accident, it doesn’t kill the crew. AI can, with the proper sensor suite, see through smoke and navigate more safely in tight areas, and it can act more rapidly than a human crew.

Much like we went to extreme measures to develop the atomic bomb to end a war, we are at war with our environment yet haven’t been able to work up the same level of effort to create weapons to fight the growing number of natural disasters.

We could, for instance, create unique bombers to drop self-deploying lightning rods in areas that are hard to reach to reduce the number of fires started by lightning strikes. The estimate I’ve seen suggests you’d need 400 lightning rods per square mile to do this, but you could initially just focus on areas that are difficult to reach.

You could use robotic equipment and drones to place the lightning rods on trees or drop them from bombers to reduce the roughly $100-per-rod purchase and installation cost at volume.

Wrapping Up: The Real Problem

The real problem is that we aren’t taking these disasters seriously enough to prevent them. We seem to treat each disaster as a unique and non-recurring event even though in areas like where I live, they are almost monthly now.

Once a disaster occurs, we have the option of either moving to a safer location or rebuilding using technology that will prevent our home from being destroyed. Currently, most of us do neither and then complain about how unfair it is that we’ve had to experience that disaster again.

Given how iffy insurance companies are becoming about these disasters, I’m also beginning to think that spending more money on hardening and less on insurance might result in a better outcome.

While AI could contribute here, developers haven’t yet trained it on questions like this. Maybe it should be. That way, we could ask our AI what the best path forward would be, and its answer wouldn’t rely on the vendors to which it’s tied, political talking points, or other biased sources. Instead, it would base its response on what would protect us, our loved ones, and our assets. Wouldn’t that be nice?

HP EliteOne 870 G9 27-inch All-in-One PC

My two favorite all-in-one computers were the second-generation iMac, which looked like the old Pixar lamp, and the second-generation IBM NetVista.

I liked the Apple because it was incredibly flexible in terms of where you could move the screen, and the IBM because, unlike most all-in-ones, you could upgrade it. Sadly, both were effectively out of the market by the early 2000s.

Since then, the market has gravitated mainly toward the current generation iMac, where you have the intelligence behind the screen, creating a high center of gravity and a lower build cost. In my opinion, this design creates a significant tip-over risk if the base is too light — as it is in the current iMac.

The HP EliteOne 870 G9 has a wide, heavy base which should prevent it from toppling if bumped, Bang and Olufsen sound (which filled up my test room nicely), a 12th Gen Intel processor, 256GB SSD, 8GB of memory, and an awesome 27-inch panel.

Unlike earlier designs, it has a decent built-in camera that doesn’t hide behind the monitor. In practice, I think this is a better solution because it’s less likely to break.

The HP EliteOne 870 G9 27-inch All-in-One PC is a versatile desktop solution. (Image Credit: HP)

As with most all-in-ones, the 870 G9 uses integrated Intel graphics, so it isn’t a gaming machine. Still, it’s suitable for those who might do light gaming and mostly productivity work, web browsing, and videos. The game I play most often ran fine on it, but it is an older title.

The screen is a very nice 250 nit (good for indoors only), FHD, and IPS display. Also, as with most desktop PCs, the mouse and keyboard are cheap, but most of us use aftermarket mice and keyboards anyway, so that shouldn’t be a problem. The base configuration costs around $1,140, which is reasonable for a 27-inch all-in-one.

A fingerprint reader is optional, but I found Microsoft Hello worked just fine with the camera, and I like it better. The installation consists of two screws to secure the monitor arm to the base, and then the monitor/PC just snaps onto the arm. This all-in-one is a vPro machine which means it will comply with most corporate policies. At 24 pounds, it is easy to move from room to room, but no one will mistake this for a truly mobile computer.

The PC has a decent port-out with 2 USB type Cs, 5 USB type As, and a unique HDMI-in port in case you want to connect a set-top box, game system, or other video source and use it as a TV, so it is a decent option for a small apartment, dorm, or kitchen where a TV/PC might be useful.

Clean design, adequate performance, and truly awesome sound make the HP EliteOne 870 G9 a terrific all-in-one PC — and my Product of the Week.

The opinions expressed in this article are those of the author and do not necessarily reflect the views of ECT News Network.

Every day, people watch one billion hours of video on YouTube. And that’s just one of many video platforms on the web. According to Cisco, 82% of global traffic came from video streaming or downloads in 2022. 

The eLearning market is projected to reach $848bn by 2030. Video-based learning and video training solutions are a significant part of this trend.

After all, the way we learn has changed. We no longer reach for manuals or weighty instructional tomes. Instead, we visit Youtube, TikTok or video course libraries like Coursera and Udemy. 

Videos help to expand our mind, skill-set and potential. If you’ve got five minutes spare, you can visit Youtube now to learn how to make a chocolate raspberry martini, how to juggle or how to cut an onion without crying. Good luck with that last one. 

But training videos also have a role to play in structured workplace learning. You’re probably well aware of this fact. After all, 95% of organisations are already using video as part of their employee training toolkit. 

With this in mind, we’ve created the ultimate guide to video-based learning. This article includes a clear definition, a breakdown of the benefits, tips and tricks, case studies and a look to the future. Ready for reel results? Then let’s get started.

What Is Video-Based Learning?

Video-based learning refers to any learning experience that is facilitated through video. Video, as you probably know, is a recording or reproduction of moving images produced digitally or on videotape. 

Whenever video is used to share information or teach new skills, that qualifies as video-based learning. Simple, right? So let’s complicate things a little. 

There are numerous types of video you can incorporate as part of a video-based learning approach. Below, you’ll discover some of the video types that are most commonly deployed as part of a structured workplace learning initiative.

Common Types of Video:

• Instructional: This video type (also known as ‘explainer videos’) is used to deliver information in an easy to understand format. For instance, ‘What is a Light Bulb?’.
• Demonstrative: This video type is used to illustrate how to perform a specific task or skill. For instance, ‘How to Change a Light Bulb’.
• Scenario / Case Study: This video type presents real-life examples and scenarios and encourages the viewer to think critically. For instance, ‘Eco-Friendly Light Bulb Trial: The Results’.
• Personality-led: This video type uses a charismatic presenter to engage and inform the viewer. For instance, ‘Lumina Wattsworth’s Guide to The Best Light Bulbs’.

Common Video Formats:

• Talking head: This format utilises an interview setup that features subject matter experts talking directly to camera. This form of direct address can be quite powerful. 
• Performance-driven: You can also deploy actors or performers to create a narrative that conveys your message. This is great for adding detail to your scenario-based learning experiences.
• Animation: This video format uses motion graphics to communicate your learning objectives. This is useful for explaining abstract or complex concepts.
• Pre-recorded: This covers video that is recorded in advance so that it can be broadcast or shared later.
• Livestreaming: This covers synchronous video content that is streamed over the internet in real time. In other words, you’re viewing the footage as it happens.
• Interactive: This video format utilises interactive elements such as quizzes or decision pathways to create a more immersive experience. 

As you can see, there are a variety of video types and formats for you to consider. To pick the right approach, you’ll need to consider the personas within your audience. 

Take some time to understand what makes your learners tick. Then you can start building your training video plan with this in mind. 

Your Learners’ Viewing Habits

Video has never been more in vogue. Content has always been king. And in the digital age, video is the king of kings. 

• The number of digital video viewers across the globe is expected to reach 3.5 billion in 2023.
• We watch an average of 17 hours of online video per week. That’s 2.4 hours per day.
• We’re 52% more likely to share a video than other types of content. 
• And 75% of viewers watch short-form video content on their mobile devices.

It’s safe to say that we’re video obsessed. But is this time well spent?

What Are The Benefits of Video-Based Learning?

Structured workplace learning is one big battle against The Forgetting Curve. To overcome this almighty foe, we must strive to forge genuine connections with our learners. 

Without an engaged audience, you’ll never change your learners’ behaviour. And if you can’t achieve that, you won’t drive better outcomes or business impact. 

Thankfully, video-based learning can help on multiple fronts.

1. Video Is More Effective

In fact, According to a accurate study, viewers retain 95% of a message when it’s delivered via video. This compares to just 10% when it’s delivered via text.

What’s more, because videos provide a multi-sensory experience, they’re also good at conveying or showcasing complex concepts. It’s often easier to show than it is to tell. This leads to deeper and more meaningful learning experiences.

2. Video Is Easier to Process

Unfortunately, according to Email Audience, this claim has no actual basis in science. But don’t toss your video cameras in the bin just yet. 

They suggest that the actual figure is closer to 600x, which is still a significant boost. After all, this means your learners are capable of processing your videos hundreds of times faster than your other materials.

3. Video Is More Engaging

According to Wyzowl, 36% of us have a preference for video-based learning. This makes it the most appealing training option, with one-on-one training in second place (25%) and work-based learning in third (17%). 

Videos have a wide preferential margin. So, why not provide your learners what they want?

4. Video is More Memorable

After all, you’ll be in a better position to trigger an emotional response, which will help your content to live longer in your learners’ memories. Research shows that heightened states of emotion are more memorable than less dramatic experiences. It’s time to bring some flair!

There you have it. Video is more memorable, effective and engaging than alternative training methods. What’s not to love? 

Of course, you’ll want to make sure that you get your approach to creating training videos just right. That’s what we’ll tackle next.

What Are The Principles of Effective Video-Based Learning?

Creating high-quality training videos requires combining sturdy educational principles with technical best practice. Take note of the tips below. Following them will ensure that your training videos pack a real punch.

1. Set Clear Learning Objectives

You should be able to clearly state the learning outcomes you want to achieve. This will help both your video producers and your learners to stay focused.

2. Put Good In, Get Good Out

Don’t forget to follow best practice. Include an introduction, explain key concepts step-by-step, highlight benefits and conclude with a handy summary and a call to action.

3. Don’t Skimp on Production Value

As HubSpot notes, 28% of consumers feel that video production value is ‘very important’. That’s almost a third of your audience. It’s little wonder too, given how much video content we all interact with on a regular basis.

As we’ll see later in this article, that doesn’t mean you need to hire a high-end studio or see if Kevin Bacon’s free for a day of filming. Proper planning and preparation can go a long way.

4. Capture Attention With Strong Visuals

Ensure all your assets serve a clear purpose and fall in line with your brand guidelines. Any resources you create for your video should also match the tone and style of your other training materials.

5. Engage Your Learners’ Emotions

As such, attempt to generate curiosity, surprise and empathy amongst your learners. To do this, you can’t rely on facts alone. Use your video to tell a compelling story and lead your learners on a profound emotional voyage.

6. Create Mobile-Friendly Videos

In order to change behaviour, you will need to meet your learners where they are. According to Zippia, we check our phones 96 times a day. In fact, most of us can’t bear to be apart from our devices. 

As such, many of your learners will access your content on their smartphone. You should provide them with an optimised and rewarding experience.

7. Keep it Short And Sweet

The bad news doesn’t stop there. Evidence shows that our attention spans are decreasing. This creates a challenging environment for learning professionals. 

To circumvent this issue, you should follow microlearning best practice. Ensure your video content focuses on a single learning objective and is as short as possible. Don’t be afraid to make cuts and to break up larger syllabus into bite-sized chunks.

8. Improve Accessibility

This will help you to cater to learners with different abilities and preferences.

9. Seek Out Feedback

As such, gather feedback from your learners and make refinements based on their suggestions. Regularly update your video content where possible and carry over lessons learnt to future projects. 

How Can I Make Video-Based Learning More Engaging?

If you follow our previous suggestions, you’ll create video-based learning experiences capable of generating real impact. But if you’re seeking even higher levels of engagement, then there are still further steps for you to take.

1. Get Interactive

With this in mind, consider adding interactive elements to your training videos. This could include quizzes, polls or reflection questions. 

Interactive videos are also great for storytelling purposes. For instance, you could challenge your learners to make decisions that guide the outcome of your narrative.

2. Gamify the Experience

Adding experience points, badges and leaderboards to your video content (or supporting learning environment) can help to incentivise learner activity.

After all, you’re helping to create a more dynamic, interactive and feedback-driven environment for your learners.

3. Embrace User-generated Video Content

With this in mind, you should establish clear guidelines and provide all the tools necessary to help facilitate user generated video content. 

Active learner participation can help you to successfully scale your training programme, embrace diverse perspectives and drive higher levels of relevancy. What’s more, you’re also encouraging learner ownership and empowering your audience.

What’s not to love?

Is Video-Based Learning A Cost-Effective Approach?

According to F.Learning Studio, training video production costs around $300-$2,000 per minute. Yes, that’s a wide range. Ultimately, what you pay will depend on the type of video you’re making, production choices and the video effects you deploy.

As such, if you’re creating a library of video resources, it’s easy to see how you could quickly eat away at your budget. 

That said, thanks to advancements in technology, video production is considerably cheaper than it used to be. It’s also worth noting that it’s a one-time cost. Your video is highly scalable and can be used again and again — as long as it remains relevant.

The same cannot be said for instructor-led training. Your training videos will generate no travel costs, instructor fees or general overhead. In fact, it’s estimated that you can save as much as 50-70% with video-based learning.

Cost-Saving Tips for Your Training Videos

There are also a number of ways for you to keep your costs down. We would recommend the following approach:

• Plan thoroughly: Stick to your script and storyboard to minimise shooting and editing time. 
• Embrace minimalism: Don’t get too fancy with your set design, props and other equipment. Focus on the essentials. Talking head setups are relatively inexpensive.
• Bulk film: Shoot multiple videos at once to maximise your studio time and reduce your setup requirements. 
• Use templates: Where possible, use presets and stock imagery to keep costs down. Always keep your audience in mind when making trade-offs in quality.
• Use and reuse: Remember, once you’ve created your training video there are no further costs. Deploy it through multiple channels and reuse it wherever possible.

If video-based learning is still proving too expensive, don’t be afraid to embrace more of a DIY-style. After all, most of us have high-powered cameras on our smartphones. Purchase a tripod, check the lighting, find a quiet space and hit record!

And if that fails, don’t forget that you can always rely on user-generated video content.

Video-Based Learning Success Stories:

Video-based learning has proven to be a valuable tool for employee training and professional development. But don’t take our word for it. Check out the case studies below.

1. Bayt.com:

Bayt.com is a leading job-search platform in the Middle East. Since implementing video as part of their structured workplace learning approach, they’ve seen some impressive results. According to their co-founder and CTO, Akkram Asaaf: 

“Our training process is a lot cheaper than before. Knowledge retention is up by 40% and our employees are more receptive to training in their own time.”

2. EWR Digital

EWR is a digital marketing agency who focus on SEO and corporate video. They’ve found similar levels of success through video-based learning. Here’s what their CEO, Matt Bertram, has to say:

“We have found that video training boosts information retention. According to our internal data, the retention rate for video training is 60% higher than when using traditional methods.”

What Does The Future Hold for Video-Based Learning?

Emerging technologies and innovative approaches are set to reshape the way we learn. And video is right at the heart of this revolution.

1. New Levels of Immersion

Virtual reality (VR) and augmented reality (AR) have opened the door to new levels of immersion. Deploying video in these environments will enable learners to interact with content as if they were in the same physical space. 

Furthermore, 360-degree video enables learners to explore their learning environment thoroughly. This makes it easier to adopt different perspectives and viewpoints. As a result, your learners will be better placed to visualise and understand more complex concepts.

2. Real-time Collaboration

Social learning is a powerful training approach. Imagine if your video platform or learning management system enabled your learners to collaborate synchronously while watching videos. 

Your learners would be able to share their thoughts and feedback via live chat, like and dislike elements and answer poll questions. They could even work together as a group to decide the outcome of any interactive elements within your video content.

3. Better Data

As training videos become more robust and interactive, you can expect your data to follow suit. Learning platforms will begin to offer more robust analytics and insights. This means that you’ll be able to go beyond simple completion rate reporting. 

For instance, you’ll be able to analyse viewing behaviour. This includes when they start, stop and pause your content. You’ll also be able to access in-depth interaction reports, heatmaps, attention tracking and more. 

There we have it. An exciting future awaits. And that’s before we’ve considered the role that artificial intelligence (AI) will come to play in video production. For instance, it’s not inconceivable that AI algorithms could be used to generate video content from text. 

This will make video production faster, more cost-effective and more accessible. Now that’s a future that we could all get behind. 

Video-Based Learning: Final Words

Learner engagement happens when you blur the line between education and entertainment. Video-based learning can help you to remove this line altogether. 

We’ve seen that training videos are effective and engaging. We also know that they have a sticky quality that helps them to live rent-free in our minds. What’s more, technological advancements mean that they no longer need to cost an arm and a leg. 

We hope that our video production tips and engagement suggestions help you to upgrade your training strategy. The future awaits. You just need to press play. 

Ready to take your instructional strategy to the next level? Fuel your growth by downloading ‘The Ultimate eLearning Instructional Design Guidebook’.

Participants

Fay Tian; VP of IR; Remark Holdings, Inc.

Kai-Shing Tao; Chairman & CEO; Remark Holdings, Inc.

Todd Brown; Senior Manager of Financial Reporting; Remark Holdings, Inc.

Brendan O'Neill

Stephen Hans Wagner; President, Investment Advisor, CIO & CEO; Integrity Wealth Advisors, LLC

Unidentified Analyst

Presentation

Operator

Good day, and welcome to the Remark Holdings Fiscal Second Quarter 2023 Financial Results Earnings Call. (Operator Instructions) Please note that this event is being recorded. Now I'd like to turn the call over to Ms. Fay Tian, Investor Relations. Please go ahead.

Fay Tian

Thank you, Nick. Good afternoon, everyone, and welcome to Remark Holdings' Second Quarter Fiscal 2023 Financial Results Conference Call. I am Fay Tian, Vice President of Investor Relations for Remark. On the call with me this afternoon is Kai-Shing Tao, Remark's Chairman and Chief Executive Officer; and Mr. Todd Brown, Vice President of Finance. In just a moment, Mr. Tao will provide an update on our businesses and Mr. Brown will recap our second quarter 2023 fiscal results.
Following these remarks, we will open the call to questions. But before I turn the call over to Mr. Tao, I would like to take this opportunity to remind you that some of the statements made today may be forward-looking statements. These statements involve risks, uncertainties and other factors that could cause actual results to differ materially from those expressed or implied by these forward-looking statements.
Any forward-looking statements reflect Remark Holdings' current views, and Remark Holdings expressly disclaims any obligation to update or revise any forward-looking statements after this date hereof. This disclaimer is only a summary of Remark Holdings' statutory forward-looking statements disclaimer, which is included in full in its filings with the SEC.
I will now turn the call over to Remark's Chairman and Chief Executive Officer, Mr. Tao, so he can provide additional information on Remark's businesses and accurate developments.
Please go ahead, Shing.

Story continues

Kai-Shing Tao

Thank you for taking the time to listen to our Q2 update, and we're excited to share what lies ahead of us. But first, a recap of Q2, which was revenue that was strongly driven by our business in China by the current economic and political headwinds that China is facing.
First, the construction sector. In Q2 2023, we've completed 25 Remark AI construction product deployments, which contributed over $2 million. The deployments are for smart construction platform, which includes worker check-in and checkout, worker PPE monitoring, worker safety enforcement, hazard zone intrusion prevention, fire and smoke alerts, environmental protection monitoring. We continue to develop construction SOP and autonomous operation-related AI algorithms and applications, which will include autonomous crane operation system and autonomous cement mixing system.
Two, school sector. In Q2 '23, we continue our Remark AI school product development and deployment. We've added 43 schools in our customer base in Q2, which makes our AI school products to serve over 700 schools and 1.5 million students. Our school safety, food safety and AI energy controlling system, along with new products released in Q2, which include eye protection and restroom environment monitoring system are making us a unique AI product provider in the school education market.
We are now expanding our footprint into 2 new provinces in China and into the city of (inaudible), which is 1 of the 4 principal municipalities of China. To date, our systems cover less than 1% of the total of 400,000 campuses in China. Three, our community. We completed the entire deployment of our smart community projects, over 1,800 communities deployed in which we do that in conjunction with China Mobile for Phase I. We are in the process of negotiating additional Smart Community project Phase 2, which will cover an additional 3,000 residential communities and over 5 million residents. Phase 2 is expected to start in Q3, Q4 of this year.
Four, Airline sector. As we mentioned before in our calls, Remark AI has introduced the first AI aviation platform in the industry. We are currently in the final bidding process for 3 of the airline AI projects after delay due to COVID from 2022. The projects include airport safety system, engine inspection system and airplane collision prevention system. To remind you of the use cases here, typically, it takes 1 hour plus for 10 workers to inspect the engine to make sure there are no burns or cracks in the blade. With our computer vision AI, we are now able to do this inspection in under 10 minutes and with only 1 inspector.
And lastly, in the retail sector, we are currently working with 1 of the largest ice cream and pizza chains in China and have deployed our AI smart retail products to over 600 stores. The products include staff, check-in and checkout and performance analysis, food processing, safety, theft prevention, store food material waste management, product presentation and placement, and loss of sales prevention, replenishment management and et cetera.
We are expecting an additional 600 more chain stores to be online by the end of Q1 '24. Going beyond China. During the last 2 conference calls, we have shared with you the large total addressable markets, otherwise known as TAM, Remark Holdings is seeking to capture. To recap, there are currently approximately 100 million cameras in the United States and another 200 million globally, excluding Greater China.
Our mission is to utilize our award-winning AI power technology to bring intelligence to the 300 million sum cameras that are in the marketplace outside China, providing detailed analytics such as identity recognition, large density people counting, loitering object detection, weapons detection through our SSP, otherwise known as our Smart Safety platform that is camera and platform-agnostic. Modeling $100 per month analytic fee per video stream or $1,200 annually. That reflects an annual total addressable market of $3.6 billion in recurring revenue globally, excluding Greater China.
With such a large TAM, how are we going to capture this market opportunity. We have been in discussions actively with sales and marketing partnerships with technology market share leaders that have wide distribution and a vested interest in participating in the rapid growth in the implementation of AI technology. It's not a secret that the competition is very intense for market share out there, and these large companies want to fund the best partners to help them rapidly advance their AI business.
While Remark AI's platform is broadening its capabilities, what we have uniquely distinct ourselves is in the specific AI area of computer vision. Our focus is on making industrial applications run more efficiently as that is a large-scale business that is moving rapidly too and is what customers are paying for now. As we deepen our relationships and partnerships, it is very clear that we are 1 of the leaders in this area, and we are well positioned to further our lead in this race.
We also recently announced our partnership in working with global leaders with both NVIDIA and Intel. We have optimized our AI computer technology with each respective company's latest chip, which now allows us to jointly market our computer vision technology to their customer bases, utilizing their 5,000-plus sales force who are trained to help sell our Smart Safety Platform. The collaboration with Intel does not stop within the U.S. as we are actively partnering to deliver the SSP to their Latin American customers that complement our existing system integrator relationships.
With Intel, we will be initially working with them in retail and quick service restaurant space with both understanding how their customers shop and eat on the front end and then working with their food and customer safety and security on the back end. This is exciting for us as we first worked with Intel a couple of years ago in launching a kitchen safety platform in China and have now jointly upgraded offering globally.
Each group will be initially focused on different areas. Some may overlap, but in general, it's a divide and conquer strategy. Our partnership with NVIDIA through its metropolis ecosystem was recently acknowledged by NVIDIA program manager, David Gregory, who recognize our team's successful working relationship in establishing a strong foothold in the smart city space. This includes traffic control and providing meaningful analytics and data insights to cities, stadiums, and major events.
We are now also working with the largest global software company and the largest EV manufacturer in the world to train their product and salespeople on Remark's Smart Safety Platform and how they can help their end customers enjoy an immediate return on investment by providing real-time analytical data that helps to understand their customer preferences as well as augmenting their security teams monitoring capabilities with AI tools to prevent disruptive incidents.
This operating leverage gained from our partner's existing distribution channels changes the playing field and allows us to scale quickly. During the quarter, we announced a partnership with 1 of the best companies in the people counting space called WaitTime. We expect to share sales resources with them as they have very established sales channels with Cisco and Intel has made a strong entry here in the U.S., selling their people counting products.
Our strategy is to piggyback our sales team and relationships to help expand their offerings to their customers. As you know, once we win this, many similar opportunities will present themselves after we win. As a point of reference, these deals are all 7-figure opportunities with a combination of both hardware and software, which ultimately means high-margin business for Remark AI. To provide another example, previously, we've disclosed our relationship with the Barclays arena, where we are integrating our AI analytics with their existing Genentech video management system to help provide area, arena security, monitor crowd density as well as extend coverage for the exterior square attached to the public transit station.
Our software partner has direct relationships with 150 sports teams in the NBA, NFL, NHL, MLB, MLS as well as over 200 stadiums and arena, speeding up the sales conversations with decision-makers as we leverage our future placement in their platform marketplace as a preferred provider we sell directly by their sales force. More importantly, the credibility from this partnership provides an indirect technology stamp of approval for most corporate IT departments, which are already running on their operating systems.
Recently, we are in discussions with the world's largest EV manufacturer for them to bring EV transport buses and school buses to Nevada. Given Remark's extensive expertise in AI-powered video analytics and our proven track record of developing fast and effective innovative solutions, we are well positioned to have our suite of SSP products to outfit the EV bus to provide functions such as people counting, violence detection and driver monitoring. The federal government through the Federal Transit Administration, FTA will be funding Axis 2050, which is focused on improving the transportation system through the U.S., the #1 of the top 5 priorities of this plan is safety.
Safety is what Remark's products are built to provide as we have now shown through our deployments around the world. This includes here in Fremont Street Las Vegas to Brazil with the police departments and 2, the U.K. with border and traffic control. Our capabilities drive meaningful change, foster efficiency and contribute to the realization of Axis 2050's overarching mission solidifying our role as a front runner in the pursuit of the $3.74 billion that is allocated for improving safety from the Axis 2050 overall spend.
As you may recall in our last earnings, I noted about the opportunity for us to work with 1 of the largest providers of school buses in the U.S. using our AI safety security technology. We're able to make progress in those discussions and with this opportunity in Nevada will only significantly enhance the opportunity. In addition, we are partnering with large global systems integrators with deep industry relationships who already sell and service the end market that we are seeking to capture. One example of this partnership is with [Intellicity], working with them to outfit the Rio de Janeiro police car fleet with mobile facial and license plate recognition capabilities as police officers patrol the city looking for wanted persons or vehicles.
In a short period of time, we were able to customize our technology offerings for the Brazil market and subsequently win. While this is just the initial phase, it showcased our ability to move fast and make the requirements of our customers, and we're confident you will see many opportunities like this in Latin America in the second half of the year. One of the key advantages to Remark AI is the speed we are able to move in the versatility of our Remark AI platform. Recently, we are presented with a very small window of opportunity to present our platform to address the 911, 311 call center problems that cities around the U.S. are facing.
Our competitors thought we would be unable to customize our AI, however, in the short time frame, we were able to do so. We built a platform that was unique and embrace the latest AI technology to help address the issues that city was facing. Many of you know the wait times for 311 or 911 are very long. A key reason for that is the lack of dispatches. Remark AI's 311 platform can simultaneously service 20 sessions in 40 different languages. With the average 311 session costing $3.50 per session and as high as $7 in some major cities, Remark AI's platform immediately delivers cost savings and a return on investments for its urban customers, while increasing the speed and breadth of services through a wider audience.
Our platform combines the best in generative AI and computer vision AI. When a caller dials 311, our AI agent will speak to them to help understand this situation. Simultaneously as they are speaking, our AI agent fills out the necessary paperwork to document this call. Lastly, if they say a picture is worth a thousand words, the caller would take a picture or video to showcase the situation and our computer vision AI would better understand and independently verify the nature of the call.
This solution was unique and very well received by our listeners. We are confident that after we win this opportunity, similar opportunities will present itself not only in other cities, but any other -- in any business that deals with forms and paperwork, and obviously as well as anything to do with customer service, like airports and restaurants.
On a final note, we would like to begin letting you know about the new market, which will be a Q3, Q4 story is Remark AI entering the Middle East, in particular with Saudi Arabia. As it appears this is a special opportunity to take part in the rapid transformation of the Saudi's economy and national infrastructure. Unless you've been hiding under a rock, Saudi is spending hundreds of billions of dollars to transform the economy into 1 that is not reliant solely on oil.
We have been approached by several groups that are involved in this transformation, who want our expertise in handling large-scale construction projects like in China and bring it back to the Kingdom of Saudi Arabia. We are very excited about the opportunity as it is unprecedented. And also, we are confident we are 1 of the very few that can provide such solutions.
These include managing a workforce that will grow from a current number of 50,000 workers to 400,000 workers by the end of the year, using AI and LiDAR jointly for construction planning or having construction run 24/7 autonomously and in the dark to save power. This is a once-in-a-lifetime opportunity for Remark, and we intend to open an office sometime in 2023 in Riyadh to handle these opportunities.
In conclusion, I'd like to emphasize that our business model is built upon annual multiyear recurring subscription revenue that provides a total turnkey AI-powered platform for our customers and partners. Our technology has been proven by our partners and clients to be robust and flexible at the same time. As we set a solid footing into the various markets we have mentioned, our continuous focus is on the mutual goals of positive cash flow generation and growth and believe that this quarter has set us on the trajectory to reach our goals by the end of the fiscal year.

Todd Brown

All right. Thank you, Shing, and thank you to everybody for joining us on today's call. As we noted when we last spoke to you during the first quarter earnings call, we expected that our project completions in China would begin to accelerate in the second quarter and they have. Though China's former zero-COVID policy had been adversely affecting our business in China and such policies lingering effects as well as COVID infections continued during the first quarter of 2023, we were able to ramp up our work with vendors and customers during the 2023 second quarter to complete many more projects than we did in the first quarter of '23 and even more projects than we did in the 2022 second quarter.
The increase in project completions led to revenue for the 2023 second quarter, totaling $3.2 million, reflecting a 24% increase from the $2.6 million earned during the same quarter in 2022. The revenue in Q2 is almost entirely related to our smart construction project, under which we were able to complete installations of 25 more sites and our smart campus solutions, which we were able to deploy yet an additional 43 educational campuses in China, bringing total installations to more than 700 campuses and helping to protect more than 1.5 million students.
With revenue and cost of revenue increasing by about the same amount during the 2023 second quarter compared to the same period of 2022, gross profit remained essentially unchanged at approximately $0.7 million.
Our operating loss also remained essentially unchanged at $4 million. Though operating loss did not change, we did experience a $0.7 million decrease in general and administrative expense that resulted from a $0.5 million reduction in stock-based compensation and a $0.4 million decrease in business development expense. Those decreases in G&A expense were partially offset because we recorded a total of $0.4 million of asset impairments in the 2023 second quarter compared to having no impairments in the same quarter of 2022.
Increases in sales and marketing expense and technology and development expense also partially offset the decrease in G&A. Net loss totaled $5.9 million or $0.42 per basic and diluted share in the 2023 second quarter compared to a net loss of $12.5 million or $1.19 per basic and diluted share in the same quarter of 2022.
Since operating loss was unchanged, the decrease in net loss was primarily the result of the 2022 second quarter including $6.9 million of loss on an investment that did not recur in the 2023 second quarter. Also, a decrease in interest expense because significantly less debt principal was outstanding during the 2023 second quarter and during the same period of the prior year was offset by an increase in finance costs related to the obligations to issue our common stock that resulted from our transactions with Ionic Ventures LLC.
On April 12, 2023, we issued the second of 2 debentures to Ionic Ventures pursuant to the debenture purchase agreement that we and Ionic signed on March 14, 2023, and we received $1 million pursuant to such debenture. We also received an advance of $2 million during the 2023 second quarter pursuant to the equity line of credit with Ionic Ventures that was established in October of 2022. The convertible debentures we issued in March and April of 2023 plus the advance pursuant to the equity line of credit during the 2023 second quarter provide rise to obligations to issue our common stock and, in turn, caused the increase in finance costs.
As of June 30, 2023, we were obligated to issue an estimated 5,711,148 shares of our common stock to Ionic, representing an obligation with an estimated aggregate fair value of [5.6 million]. As of June 30, 2023, we still owed an aggregate total of $16.3 million to our lenders, an amount which currently must be repaid by October 31, 2023.
We continue to have ongoing conversations with our lenders regarding that outstanding debt and are confident that we will reach a positive solution that is beneficial to all stakeholders. Also as of June 30, 2023, our cash balance totaled $0.2 million compared to a cash balance of $0.1 million at December 31, 2022. Net cash used in operating activities was $5.2 million for the 6 months ended June 30, 2023, representing a 53% decrease compared to the $11.1 million we used in operating activities during the same period of 2022.
And with that, I will turn the call over to the operator to begin the question-and-answer portion of the call.

Question and Answer Session

Operator

(Operator Instructions) The first question will be from Steve Wagner of Integrity Wealth Advisors.

Stephen Hans Wagner

I just -- we are hearing so much about AI -- and I've had some -- I read -- actually, I read a really good book on it called The Spatial Web. And I'm learning that there -- everyone's doing generative AI, but -- it seems like that's a commodity. Is that what you're doing? Is there any difference between what you're doing than what everybody else is doing in this regard with the computer vision and (inaudible)?

Kai-Shing Tao

I would -- thanks, Steve. I think within AI and certainly generative AI with what we're seeing with ChatGPT has definitely garnered the attraction. Where we focus on is computer vision. We feel that's a much bigger market, that's where customers are paying. And I really believe the value that AI brings us in the industrial applications. So we've been -- we have our own large language model.
We -- actually, that was what we -- one of our first ventures in AI, and I think those back in 2017, 2018. So we've actually been able to update our model and been able to use that with some of our customers. But as a stand-alone business, it's very difficult. It's very difficult to get customers to pay for it. And so that's why we had pivoted almost 6 or 7 years ago. And we're glad that we do that. You can see once ChatGPT had released their product, very quickly, there were a lot of other companies that introduced their language model. So the barriers to entry is not very high.
For what we do, we think the barriers to entry are very high because it's not just the -- it's not just coming up with the technology, but it's the ability to deploy it as well. The implementation is not so simple.

Stephen Hans Wagner

Sure. As you look across the spectrum of AI outside of the magnificent 7, it seems like there's been just a gigantic fair market sell-off in market valuations. I mean what are you seeing in private wise -- private valuations on AI? And what -- I mean, how would you measure where Remark should be given what you guys are doing and seem to be really enthusiastic over?

Kai-Shing Tao

Yes. I mean it's obviously frustrating where our share price is and where our valuation is. But I think this is a -- this is not a sprint. It's certainly a long-distance race. And so yes, it's -- I think you see a lot of private companies right now that are raising monies with -- in the several hundred million dollar valuations with less than $2 million of revenue. I just don't think that's sustainable.
I feel that where there are companies that are incredibly overvalued in terms of their metrics where we obviously feel we're very undervalued. We have a technology that we built from the ground up. We've got a blue chip customers around the world, and we're generating real numbers without taking huge losses to get there. And we feel we're on track to breakeven this year, which I think will be the first for any AI company doing these kinds of numbers.
So it is -- inevitably, it is a distraction when you read it in the press but when it comes down to actual execution, I like where we are -- what path we're going down.

Stephen Hans Wagner

Okay. In terms of headcount, I mean where are you guys now that way? I mean -- and do you see -- obviously, as you get these contracts, there may be more of a need for capital? Or do you think that will be offset by upfront deposits or revenue. And I would like you to expand on that because I think 1 of the things we shareholders are kind of looking at is we understand that you've been hearing about the revenue. And obviously, it's great to see some money coming in from the China side of things. But like bringing on a domestic customer, is there any upfront revenue? How is that going to work?

Kai-Shing Tao

Yes. I mean we don't do unpaid POCs, right? So that's -- it's not -- when we go through a proof-of-concept, they're paying customers. So I think we're kind of -- we certainly feel we're past that stage where we're doing things for free, right? So -- but as we expand, I think certainly with our partnerships that we mentioned with Intel and with NVIDIA and the software partners that will -- there won't be that unformed cost for, just say, additional salespeople because we'll be able to leverage upon their existing sales team.
As far as deployment, we will be covering as these opportunities are being presented, of course, we're working with local systems integrators, but a lot of times, we'll need our guys on the ground as well. So I could see certainly an expansion there. Certainly, the ability to handle so many different projects. I mean, I think we're uniquely positioned to do that. So we didn't -- we've been doing this for almost 10 years now.
So we didn't do this spend all this time to just be a one-trick pony, right? So we had a vision 10 years ago to really build a comprehensive AI platform, and now we're seeing that. And these opportunities are presented to us that no one else is able to execute. They may claim they do it, but when it comes to execution, it's different. So yes, so obviously, we would have to staff appropriately for that. But obviously, with these partnerships, that upfront cost will come at a lot lower number than what it would have been if we're going to do everything ourselves.

Stephen Hans Wagner

Okay. So -- and again, I apologize. I was -- I came into the call literally 20 minutes late. So I missed a lot of it. So it sounds like these are partnerships, as you mentioned, I think, with Intel and NVIDIA that they will provide funding for. Is that -- am I hearing that correctly?

Kai-Shing Tao

No, you didn't hear that correctly. What they're providing is their sales team, right? So I think each company has their own marketing program where say, for example, if there is a potential customer that's a little bit hesitant on incurring the cost of a paid POC, that company will come in and provide $300,000, $400,000 that would pay us to execute that, right? So obviously, that's very -- that's -- that's exciting.
Even more exciting is the fact that now we can leverage they have over 5,000 kind of a sales force and being trained on how to sell the Remark's AI solutions obviously speeds everything up for us. We cover a lot of areas faster. I can get to the -- frankly, get to the key decision-makers great. So I think in any type of enterprise account, you have to hit the company from a bunch of different angles. And I think this just makes our pitch more compelling.

Operator

Our next question will come from Ross (inaudible), [Silvan International].

Unidentified Analyst

Yes. So a question I have for you is -- you look at a lot of companies in the AI space, you don't see a ton of execution. I look at Remark, and I see you've already won an initial $6 million agreement for -- with the Rio de Janeiro police cars. And then you've got a $1 trillion company in NVIDIA, who you've joined this metropolis ecosystem. I mean what's -- how are you so differentiated from these other, like what's differentiating you or what's your secret sauce as it relates to these opportunities? And what's making you stand out, I guess, is what my question is?

Kai-Shing Tao

Okay. So I think I'd answer that in 3 -- kind of the 3-part answer. The number 1 is we've been doing this for a long time, and we're not -- we didn't just start this AI company in the last 2 years and certainly over the last 6 months to capture the wave. So it took us 6 years to build the technology in just in the last 3 or 4 years to gain the experience on how to deploy it.
Each customer obviously has different requests and the actual expertise in putting up these cameras and the angles and the placements and all that takes time. And obviously, every type of vertical has their own intricacies. But I would say, taking a step back, the 2 most important part, which answers a bigger question, which is why are we able to go into these different industries, is really our core algorithm, right?
So the way that our algorithm is trained allows us to is number one, train any type of solution that we don't have under kind of our stable to do it in a very fast manner. So typically, it takes 9 to 10 months to train something new, we can do it under 1 month. And then the second part is you probably have heard about with these AI algorithms you need, it's all about the data sets and how much data you need.
A key point to ours is that we don't need a lot of data to train our algorithms. We actually call that our few-shot training algorithm. And we'll post a white paper on it just to provide further detail on it. But that allows us to train the algorithm much faster than anybody else and with a lot less data. And because many times, the solution pain point that you're looking to solve doesn't have the data sets to train it under.
So I think those are the things that really differentiate ourselves from a preparation standpoint and then number two, from an execution standpoint. I hope that answers it.

Operator

(Operator Instructions) Next question will be from Brendan O'Neill, Ionic Ventures.

Brendan O'Neill

I have a couple of questions for you around kind of your interest in developing the non-China-based revenue streams. And kind of going back to kind of the earlier calls, April and May calls, you touched on a couple of things. I was wondering if you could provide a further update on the yellow school bus program in partnership with Netwatch. I think at the time in April on the call, you had launched a pilot program and school starting up here, so I just wonder if you could update us on how that program is going.

Kai-Shing Tao

Okay. Sure. That was what I kind of alluded to in our discussions with the trends in school buses. I would say the discussions are moving along. As you can imagine, this is covering a large school bus space. But the opportunity is right there in front of us. So we continue to customize our algorithms for their needs, and we hope to have something for you over the next couple of months as this is a big deal for everyone, right? Because we would be outfitting over 50,000 buses using our algorithms. But we think there will be something where you'll see in the future where, with Taiwan, what we were doing with them. Also with the large EV company we mentioned on our call and what they're doing in Nevada.

Brendan O'Neill

Okay. And similar to other opportunities, I know on August 1, you announced your 311 product that you kind of alluded to on the May call of a various opportunity. Is that a product launch? Do you have a current customer? Or what would be your strategy to now getting municipalities and people to use this product?

Kai-Shing Tao

Yes. Right now, as you can imagine, when you do with municipalities is that you have to go through their technology governance board, which is always -- always a hurdle. We just passed one. And right now, we're waiting for the formal approval from the other one, from the other city (inaudible) right now, I'd say, close to the finish line for really on 2 major cities in the U.S. And it's a major problem. It's a very -- of very high urgency.
We were doing 1 presentation just 3 weeks ago. And tragically, a mother was shot while the daughter was waiting on hold for a 911 call. So this is something and this is what we were presenting to the technology board. So this is obviously something of very high importance. But obviously, we have to jump through the hoops. But I say we're -- because of the urgency, we're moving faster than any other group is on this area. But it would be 1 major city on the West Coast and 1 major city on the East Coast that we're doing this.

Operator

Our next question will be from Andrew (inaudible), Breton Capital.

Unidentified Analyst

I have a quick question. I know you mentioned the Neom, the -- the Saudi Arabian city of the future and all the investments they're making in AI. And so I'm wondering how you think you're able to compete there and what your competitive advantage is participating (inaudible) in the business? It seems like all the really big players are trying to get a stead in there.

Kai-Shing Tao

Yes. I mean it's not easy. I mean, a lot of these things were, for sure, the smallest company, that's probably participating there. But what we do have and what we keep on saying is our AI is unique. And very few companies have the experience in working on large-scale projects like we do. We've had over 5-plus years of working with the largest construction company in China working on their largest projects. So we can take that expertise and bring it to Neom, which is where similar-sized projects are now being designed and built.
So I think we're uniquely qualified for that. And it's not just a qualification, it's that we -- there aren't many, if you were to look around, there -- there are AI companies in the construction industry that provide 1 or 2 features rather than a real full complete platform.
So we're able to go in, provide 20 different solutions immediately. We don't have to wait 6 to 9 months to customize, but we can be up and running within 4 to 6 weeks. And everything you realize as you spend more time in Saudi is that they want to move fast. They don't want to wait. The capital is not an issue. The issues are you able to deliver and execute. And we think we are. And construction is just 1 industry of where we potentially may work with them.
They are building 1 of the largest sea ports in the world. They're building 1 of the largest airports in the world. So -- and these are all areas that we have uniquely developed a platform to handle. And we just don't see other competitors doing the same thing. I mean they tell us the same thing. So I think many of the counterparts, when we talk to them are surprised that there is even something out there like this.
So we are just going through the process. I think when we first started having conversations with them was back in late spring. And so things are moving along pretty fast where we can -- right now, when we talk about construction, but there are 3 to 4 other verticals within that we think we can bring to a project like Neom over the course by the end of this year.

Operator

That concludes our question-and-answer session. I'd like to turn the conference back over to Mr. Fay Tian for closing remarks.

Fay Tian

Thank you, Nick. Thank you, everyone, for participating in Remark Holdings Second Quarter Fiscal 2023 Financial Results Conference Call. A replay will be available in approximately 4 hours through the same link issued in our August 2 press release. Have a good afternoon.

Operator

Conference has now concluded. Thank you for attending today's presentation. You may now disconnect.

Abstract

Teaching Note for (9-699-022).

Citation

Nolan, Richard L., Robert D. Austin, and Mark J. Cotteleer. "Cisco Systems, Inc.: Implementing ERP TN." Harvard Business School Teaching Note 699-031, October 1998. (Revised June 2001.)