That was something Ahmed Bukhamseen wanted to change, and his company Quant set out to transform the property investment market and open up pricing information to buyers, speculators, and investors.

As you might expect, at the core of Quant is data – advertised and realised prices, mapping, details of proposed and ongoing construction, geo-spacial data, imagery, and much more. We spoke to Ahmed at the recent MWC (Mobile World Congress) in Barcelona, to talk about the company’s vision and its journey to date. But first we asked what his chosen data platform of choice was – the basis on which the business runs.

“Our main challenge was to first comply with data protection. So, Saudi implemented new regulation laws about data protection, similar to GDPR, run by the Saudi Data and AI Authority. And for that we needed to have a local host for data protection. So that was our main driver for moving from Azure – Microsoft here in Europe – back to Saudi.”

Saudi Arabia, the Middle East, Africa and the APAC are target markets for Chinese tech giant Huawei, but it wasn’t an immediate given for Quant. “We discovered that Huawei offer cloud services late […] about this time last year. We started comparing Huawei with others.”

As Quant’s business model developed, so too did its requirements. The company started out just supplying information it drew from the Saudi municipal data registries, but has since enriched that core information with local data, and high-res satellite imagery.

“We were using a data centre [here] and the data had to go from Saudi to Europe and back. We were running AI on the edge, on images without storing them. But now we have different use cases, so we needed to store data before and after processing, after enrichment or maximisation. There was a huge cost, and we didn’t expect it at the beginning.”

Part of the issue was the imagery the company was pulling down from satellites to get up-to-date pictures of existing buildings on the ground, and those in the process of construction. It was grabbing detailed images once every two weeks, and is about to go live with a daily satellite photography cadence.

“We started testing Huawei in terms of latency, especially, because we streamed high volume data, and would like the individuals using our mobile application to get a seamless experience, and start navigating in-app.”

The app released by Quant opens the property market to anyone: individuals buying their first place, portfolio managers scoping new possibilities, investors of all sizes, landlords, and those – like Ahmed was once himself – hoping just to find a place to rent without the massive variance in prices that was the norm before Quant began its service.

Quant combines different data sources such a property prices, maps, satellite imagery, and government-approved planning documents among them to give its app users everything they need to find property or land to buy and sell.

The future will see Quant revising its data, further enriching it, and adding value for its clients. “We need to develop specific feature detection or object detection. For example, municipality data could state a company has zoning for a warehouse or extension, and we don’t know which one the owner decided on. But from space, we can build models and see the exact the building plan.”

Although Quant likely has the technical ‘chops’ to build its own models, it’s turning to Huawei:

“With our new development, computing the model [ourselves] would take us six months to deploy. Now when we run it, it takes four hours. And last time I checked with the team, we run it every week,” Ahmed said.

Quant’s plans include a service for retailers that will advise on the best areas to site their stores, given the known demographics of a neighbourhood – all data it collects, collates, enriches, and presents. Plus, there is a project on the table that extends the data scope to take in the rental market.

Given the speed at which Saudi Arabia develops, Quant and delivery partner Huawei need to move at least as fast. “We want to see you in Saudi, and see the return on real estate! We don’t talk about 2% or 5% growth: We’re talking about multiplying investments similar to the Bitcoin market. Foreign investors will be able to buy property or land with just the touch of a button, and they need data for that. They need to know the growth areas, how much it costs in perspective, before making decisions.”

You can download the Quant app, read more about the company on its website, and check out Huawei’s service offerings for Saudi and beyond.

(Image source: European Space Agency, licensed under CC BY-SA 3.0 IGO.)

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Unlike anything seen to date, the Manus AI agent doesn’t just chat with users – it is allegedly capable of independently tackling complex multi-step tasks with minimal human guidance.

Developed by Chinese startup Butterfly Effect with financial backing from tech giant Tencent Holdings, Manus AI agent has captured global attention for its ability to bridge the gap between theoretical AI capabilities and practical, real-world applications. It uses an innovative multi-model architecture that combines the strengths of multiple leading language models.

Breakthrough autonomous task execution

In a post on X, Peak Ji Yichao, co-founder and chief scientist at Butterfly Effect, said that the agentic AI was built using existing large language models, including Anthropic’s Claude and fine-tuned versions of Alibaba’s open-source Qwen.

Its multi-model nature allows Manus to use different AI strengths according to what’s demanded of it, resulting in more sophisticated reasoning and execution capabilities.

“The Manus AI agent represents a fundamentally different approach to artificial intelligence,” CNN Business stated. According to coverage, Manus “can carry out complex, multi-step tasks like screening resumés and creating a website,” and “doesn’t only generate ideas but delivers tangible results, like producing a report recommending properties to buy based on specific criteria.”

Real-world performance assessment

In an extensive hands-on evaluation, MIT Technology Review tested the Manus AI agent in three distinct task categories: compiling comprehensive journalist lists, conducting real estate searches with complex parameters, and identifying candidates for its prestigious Innovators Under 35 program.

“Using Manus feels like collaborating with a highly intelligent and efficient intern,” wrote Caiwei Chen in the assessment. “While it occasionally lacks understanding of what it’s being asked to do, makes incorrect assumptions, or cuts corners to expedite tasks, it explains its reasoning clearly, is remarkably adaptable, and can improve substantially when provided with detailed instructions or feedback.”

The evaluation revealed one of the Manus AI agent’s most distinctive features – its “Manus’s Computer” interface, which provides unprecedented transparency into the AI’s decision-making process.

The application window lets users observe the agent’s actions in real time and intervene when necessary, creating a collaborative human-AI workflow that maintains user control while automating complex processes.

Technical implementation challenges

Despite impressive capabilities, the Manus AI agent faces significant technical hurdles in its current implementation.MIT Technology Reviewdocumented frequent system crashes and timeout errors during extended use.

The platform displayed error messages, citing “high service load,” suggesting that computational infrastructure remains a limitation.

The technical constraints have contributed to highly restricted access, with less than 1% of wait-listed users receiving invite codes – the official Manus Discord channel has already accumulated over 186,000 members.

According to reporting from Chinese technology publication36Kr, the Manus AI agent’s operational costs remain relatively competitive at approximately $2 per task.

Strategic partnership with Alibaba Cloud

The creators of the Manus AI agent have announced a partnership with Alibaba’s cloud computing division. According to a South China Morning Post report dated March 11, “Manus will engage in strategic cooperation with Alibaba’s Qwen team to meet the needs of Chinese users.”

The partnership aims to make Manus available on “domestic models and computing platforms,” although implementation timelines remain unspecified.

Parallel advancements in foundation models

The Manus-Alibaba partnership coincides with Alibaba’s advances in AI foundation model technology. On March 6, the company published its QwQ-32B reasoning model, claiming performance characteristics that surpass OpenAI’s o1-mini and rivalling DeepSeek’s R1 model, despite a lower parameter count.

CNN Businessreported, “Alibaba touted its new model, QwQ-32B, in an online statement as delivering exceptional performance, almost entirely surpassing OpenAI-o1-mini and rivalling the strongest open-source reasoning model, DeepSeek-R1.”

The claimed efficiency gains are particularly noteworthy – Alibaba says QwQ-32B achieves competitive performance with just 32 billion parameters, compared to the 671 billion parameters in DeepSeek’s R1 model. The reduced model size suggests substantially lower computational requirements for training and inference with advanced reasoning capabilities.

China’s strategic AI investments

The Manus AI agent and Alibaba’s model advancements reflect China’s broader strategic emphasis on artificial intelligence development. The Chinese government has pledged explicit support for “emerging industries and industries of the future,” with artificial intelligence receiving particular focus alongside quantum computing and robotics.

Alibaba will invest 380 billion yuan (approximately $52.4 billion) in AI and cloud computing infrastructure in the next three years, a figure the company notes exceeds its total investments in these sectors during the previous decade.

As MIT Technology Review’s Caiwei Chen said, “Chinese AI companies are not just following in the footsteps of their Western counterparts. Rather than just innovating on base models, they are actively shaping the adoption of autonomous AI agents in their way.”

The Manus AI agent also exemplifies how China’s artificial intelligence ecosystem has evolved beyond merely replicating Western advances. Government policies promoting technological self-reliance, substantial funding initiatives, and a growing pipeline of specialised AI talent from Chinese universities have created conditions for original innovation.

Rather than a single approach to artificial intelligence, we are witnessing diverse implementation philosophies likely resulting in complementary systems optimised for different uses and cultural contexts.

The post Manus AI agent: breakthrough in China’s agentic AI appeared first on AI News.

Can you tell us a little bit about SS&C Blue Prism and what it does?

SS&C Blue Prism is a specialist in the field of Intelligent Automation, providing products and solutions that change the way in which our customers deliver the work they undertake.

We talk about automation augmenting the workforce, and we can do that by using a digital workforce that brings additional capacity to your human workforce. The rationale being we get a digital worker to do those repetitive, high volume, low value added tasks, and we then allow your employees to focus on the value add that they can bring. 

Intelligent Automation is really looking at the whole cycle of how to deliver the required work through the most efficient channel. That could include orchestration using business process management capabilities. It could also look at process identification through Blue Prism Process Intelligence technologies, where we’re trying to identify those tasks that lend themselves to be automated by technology. 

The dual effect of automation and orchestration of tasks that customers have to do day in, day out is where SS&C Blue Prism brings most value to its customers. A digital workforce could be aimed at improving an HR onboarding process, improving your finance period end close process or  transferring information from an outpatient system to an electronic patient record system and vice versa.The use cases are many and varied but the principle remains the same; use the right channel to deliver the work effort. The beauty of a digital workforce comes in the ability to flex work demands as and when necessary. 

What have been the latest developments at the company?

We’ve been putting a lot of our time, effort and resources into our Next Gen platform. That’s our cloud-native platform that provides access to intelligent automation capabilities, delivered in a way that suits our customers best. It helps customers enjoy the benefits of the cloud while keeping the work where it needs to be. With this hybrid deployment, Next Gen allows customers to take advantage of using the cloud, while having a self-hosted digital workforce that operates behind the customer’s firewall, on their own secure infrastructure – meaning no sensitive data leaves their network.

For many customers that operate in highly regulated industries, that really does drive the opportunity for us to enhance the way we can deliver that through the Next Gen platform. And Next Gen also brings together, in a single repository, all the capabilities that allow us to improve the business processes that we’re undertaking on behalf of our customers. 

Also, I think we’d have been living under a rock if we hadn’t appreciated the fact that Gen AI is really where the market is pivoting. We’re heavily looking into understanding how we can use that technology to really change the way that we work. We’ve introduced capabilities that allow us to integrate with a variety of large language models so our customers can adopt Gen AI. And the way in which we consider that is by using this concept that Gen AI is the capability, which is effectively the brain that allows you to have the emotional, considered response, and the digital workers are the arms and legs that deliver that work. 

So the brain, the Gen AI, does the thinking, and then the digital workforce does the actual doing. When Gen AI is wrapped into Intelligent Automation processes, it means it’s fully auditable and secure. With many customers hesitant to fully dive into using Gen AI due to security concerns, the combination is compelling. That’s something that our customers are really excited about in terms of driving use of Gen AI. And we’re seeing that in a number of places now where we’re looking at Gen AI to manage those customer facing interactions, manage those employee interactions, manage those supplier interactions. They have that ability to respond to any of those queries through a variety of channels, be that telephone,  email or chat capability, then Gen AI can pick up and author the response, executed by the automation platform. 

I speak to a lot of end users and the main thing they say about AI, because it’s so topical right now, is they think they should be utilising it. The problem for many though, is they don’t know how and why. They’re worried that they’re going to be left behind if they don’t get on board with it but maybe it’s not even suitable for them.

I couldn’t agree more. I think for a lot of our customers, and a lot of customer conversations you have, there is this view that we’ve got to do something. We’ve got to have a budget. And invariably there are budgets around for Gen AI. A lot of that is in pilot phase right now. And if you look at some of the evidence in support of it, they haven’t necessarily gone that well.

Part of the problem is that for many they are actually considering Gen AI without thinking of the business problem that they’re trying to solve. We know we’ve got this new shiny bit of kit and that we should be using it. How to use it and what to do with it is almost a secondary consideration. 

The conversation that we really try to move to with the customer is ‘what is the problem that you’re trying to solve? What is the customer issue that you’re trying to solve?’ And we’re certainly seeing that through three main lenses in terms of that use case for Gen AI.

The customer interaction, the employee interaction, or the citizen interaction, if it’s a member of the public. We’re seeing some really interesting things right now about how we are supporting our Gen AI partners, because most of what we are doing is facilitating the use of a third party large language model. We are effectively providing the framework by which our partners can interact with the customer and solve the customer problem.

What kind of trends have you seen developing in Intelligent Automation recently? 

There are a number of things that our customers talk to us about. One of the things we’ve already spoken about, and that is this notion of Gen AI. We’ve got to do it. What are we going to do? How are we going to do it? We need to use Gen AI, and we need to automate it. And there are a number of pilot initiatives that we see because of that. There’s been so much hype around the business value of Gen AI that I think it’s quite scary for some. 

There was a recent industry report by McKinsey that talked about a $4.4 trillion market opportunity with Gen AI. There are some absolutely unbelievable numbers that are thrown out about that. I think the reality of that is slightly more considered. And I think it’s not just about how we can change the way we work. It’s really about how can I get a better outcome for the stakeholder, whomever that may be, by deploying Gen AI with automation? So that’s one of the first trends. 

The second thing that’s really interesting is our customers that have adopted process automation. They’ve used digital workers to either reduce existing costs or improve productivity. So they’ve used it initially as an opportunity for maybe a bit of cost control around improving and automating some processes. But that now is taking them to the next level, which is looking at how to use process intelligence to really identify further process enhancements that they can make. We’re talking about targeting huge organisational objectives through the use of Intelligent Automation, such as growth, customer satisfaction, or employee satisfaction, to name just a few.

I think many companies have taken the low hanging fruit by automation, and now they are investing in those technologies around process identification so they can actually be sure that what they’re automating are the right things and delivering value. But are we? Are we leaving things uncovered by not using the process intelligence in support of the business operation? That is becoming more of a story that our customers are really getting into, and we’ve had a number of deployments where customers have done those initial automation activities, and are now looking to take it to the next level.

The third thing we see more of is this co-existence with Microsoft Power applications. We’re seeing customers adopting those capabilities alongside technologies such as ours, and actually coexisting together in support. We see that more and more, and I think that’s a trend that many customers recognise in terms of the way that they’re working. It’s not just a one size fits all approach. What is the most appropriate technology?

What are your customers biggest challenges? And how can Intelligent Automation help them deal with those? 

The number one challenge is cost control. How do we manage in a market of rising prices? How do we make sure that we’re getting value for money from the automation? We continue to advocate and demonstrate the value that automation is bringing. Be really structured in terms of how you are assessing the benefit that the automation is bringing, because you are accounting for that spend, you’ve got to prove that it’s worthwhile.

For example, what’s the impact on FTE savings? What’s the volume of automations that I’m delivering? What’s the average cost of an employee that’s doing that work? Multiply one by the other and that’s my FTE saving that goes into the business case. So actual cost control, but measured in the term of the business efficiency that I get as a consequence of it. But, where the magic happens is being able to demonstrate what those extra hours have enabled you to do. Have you been able to launch better, quicker products? Have you improved employee satisfaction? Cost factors are always important, but customers must look beyond this to make full use of automation. 

Many, if not most, of our customers have their own centres of excellence that need to be able to demonstrate a value to the business. So that’s the number one conversation we get with our customers. How do we continue to justify the investment in the technology? 

What advice would you give to any companies thinking about implementing Intelligent Automation?

For any customer considering introducing Intelligent Automation, what is the problem that you’re looking to solve? That’s the crux of the matter. And often you find that customers will look to technologies such as ours, where they know they have a challenge with existing technology estate. They’ve got a high degree of technology debt in their IT estate, and one of the ways that they can overcome some of those limitations is by adopting Intelligent Automation. 

So think about the problem that you’re trying to solve, and in order to do that, we need to get a really good understanding of what the actual business processes look like. Or, more importantly, what you think those business processes look like, because often what you think they look like and what they actually look like are very different. That’s where things like process intelligence come in to support that. So what is the problem that you’re looking to solve? 

The next thing that needs to be considered is how do you plan to support that moving forward? Because where our customers have continual investment in the technology and the development of the solution capability, they need to then start being advocates for automation technologies within the business. And once you are doing that, then you are the ones that are effectively going to other parts of the business and trying to identify those automation use cases. 

Our really successful customers are the ones that have got an internal champion who is going out to other parts of the business, because for many areas of the business, this is quite a well kept secret. So helping people understand what this technology can deliver by way of automation and streamlining process, and improvement of process because it’s not that widely understood. We often find that when employees realise what benefits it brings to their team, demand for those internal champions becomes huge. 

For some people, this notion of Intelligent Automation with digital workers has got this sort of Metal Mickey robot-type notion, and we’re not talking about that at all. You’re talking about using computers to emulate human interactions and, using Gen AI, they’re then emulating the human interaction that goes with it. 

So it becomes really quite powerful, but you’ve got to think about how you’re going to sustain that. What does a centre of excellence look like? What have I got by way of developers that can write the automations? What have I then got, by way of business analysts, that can then help us support and find the automations that we need? 

Think about what the initial use cases could look like. A business case on the whole is very easy to write. Where the challenge comes is how do I then sustain and grow the automation footprint? And the customers that are doing it really successfully are either partnering with someone who continues to deliver that function for them, or they’re bringing together their own centre of excellence in house, and they are then tasked with being the champions for further deployment. 

What plans does SS&C Blue Prism have for the year ahead?

It’s something we’ve already touched upon. We are absolutely focused on transitioning our customers to the Next Gen capability, and embracing the technology opportunity that comes with that is something that customers have really input into the the development roadmap for the technology, and how we are moving with that technology. 

Our customers are really looking at when is the optimum opportunity for them to deploy Next Gen. That’s going to be a focus in the short to medium term. And the benefit that offers to our customers is really exciting, particularly when you’re talking about a global customer, where they have operations in a variety of geographies. And actually by having that central automation capability you can deploy the actual workers within each of the regions. That gives you a real step change in terms of the efficiency of automation and the ease by which you monitor and manage those automations as well. 

And then, as others are also encountering, the whole value that Gen AI brings, again, we have got a lot of focus on that. We’ve got a number of customers that are doing some really interesting things. We’ve just been successful with a customer project – a public sector body that is looking at the way they transform the citizen experience – and Gen AI has a huge part to play in that. We see that as something that will continue to improve over time.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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At TickLab, we are committed to harnessing the power of our quant hedge fund resources with just a single click. This ease of access ensures that our clients can seamlessly integrate our advanced financial tools into their investment strategies.

A cornerstone of our innovation is E.D.I.T.H., an AI language model meticulously developed and trained by TickLab.IO. Unlike other AI models like ChatGPT, Bard, or Grok, E.D.I.T.H. is designed specifically for the finance and real estate industries. It provides comprehensive services including financial analysis, investment advice, portfolio management, market predictions, real estate insights, regulatory compliance, and risk management. Leveraging extensive financial and real estate data, E.D.I.T.H. delivers accurate and relevant information, making it an indispensable tool for professionals in these fields.

Harnessing the power of machine learning and deep learning

At TickLab, our innovative approach is deeply rooted in the advanced capabilities of machine learning (ML) and deep learning (DL). Our quant hedge fund leverages these technologies to analyse vast amounts of data, identifying patterns and trends that are invisible to traditional financial analysis methods. By utilising sophisticated ML algorithms, we can predict market movements with high precision, allowing us to execute trades at optimal times.

Deep learning, a subset of ML, plays a crucial role in our data analysis and decision-making processes. Our deep learning models are designed to process complex data sets, learning from historical data to make informed predictions about future market behaviour. This enables us to create robust trading strategies that adapt to ever-changing market conditions.

Artificial intelligence: The future of finance

Artificial Intelligence (AI) is the backbone of TickLab’s operations. Our AI systems are designed to perform tasks that traditionally require human intelligence, such as analysing market trends, managing portfolios, and providing investment advice. By automating these processes, we not only increase efficiency but also reduce the potential for human error.

Our AI-driven approach extends beyond simple automation. We develop intelligent systems that continuously learn and improve, ensuring that our hedge fund stays ahead of the curve. This dynamic learning capability allows us to refine our strategies and maintain a competitive edge in the fast-paced world of finance.

Connecting through advanced APIs

Our sophisticated API connects seamlessly to our quant auto-trading systems, ensuring that our clients can leverage the full potential of our AI-powered solutions.

By integrating with our API, clients gain access to real-time data and analytics, enabling them to make informed investment decisions quickly and efficiently. This integration ensures that our advanced trading algorithms are accessible and easy to use, empowering investors to maximise their returns.

At TickLab, we are not just keeping up with the future of finance; we are leading the way. Join us on this exciting journey and experience the future of investment and financial analysis today.

Follow TickLab.IO on Twitter and visit their website and Medium to find out more.

By Jason Derik PR Director at Ticklab

The post TickLab: Revolutionising finance with AI-powered quant hedge fund and E.D.I.T.H. appeared first on AI News.

Typically, a BI tool works by pulling data together to help end users draw their own conclusions. They aggregate data, slice and dice, figure it out, come to the insight and then, take action. 

Whereas, infusion speaks towards broadening perspective on what embedded analytics means to include more than just a chart to figure out. An example is the Apple Health Rings on Apple watches, Scott says.

“One of the cool things about this application is that when you’re walking, and it’s counting your steps; it won’t show you a chart. It bypasses that to provide an insight like, ‘Wow, you’re taking twice as many steps as usual; keep it up,’” he says.

That’s infusion. It allows a user to internalise that as opposed to a chart where the user would have to figure out what the information provided meant.

“Infusion is a bigger, broader, more inclusive term. And fundamentally, it’s vital to reaching the billions of underserved knowledge workers — all 90% of them for whom ‘drag and drop BI’ is not really self-service at all,” Scott says.

Three real-world examples of infusion:

1. Decision support: Applications like Outreach use embedded analytics to help users determine the best time to send an email for it to get read. It works by taking the insight, putting it in the UI, and guiding users to the best answer.

2. Connecting users to data: Building a plug that goes from your BI system into Excel, PowerPoint, Google Slides, etc allows users to access the data they need in a tool they already know how to use.

3. Data literacy: Teaching people to use data in their every day, minute-to-minute decision-making is data literacy. One of the things you can do with BI systems is to take a presence app like Slack or MS Teams and attach an NLQ on top of it. That allows you to write a query and the system comes back with a quick cut of data to see if it’s what you’re looking for. Basically, it allows people to start toying with data literacy.

Getting infusion right

Gerimedica, a multi-disciplinary Electronic Treatment Record SaaS platform company that serves the aged-care market, is getting it right. With around 60% of the market share as well as strong relationships with universities and the government, Gerimedica recently decided it was time to evolve and level up its offerings. Its customers were ready for something more. 

“They wanted to connect not only with our applications but their care, staffing, and financing applications as well as Salesforce and Microsoft Office too,” says Hamza Jap-Tjong, CEO and Co-Founder of Gerimedica Inzicht, a Gerimedica subsidiary.

However, as Gerimedica further discussed this with customers, it noticed that, to them, BI had become synonymous with dashboarding. “But BI isn’t synonymous with dashboarding. BI is about bringing intelligence back to your business,” he points out.

Bringing intelligence back to business

Gerimedica knew it had to rewrite the narrative. When it initiated the rollout with its customers, the first thing Gerimedica asked them was, “Who needs what, when, and how?” Hamza regards the “how” as the most important.

“We serve doctors, nurses, and other healthcare providers like psychologists and physical therapists. They cannot incorporate viewing dashboards into their daily practice while providing care for patients,” Hamza says.

“But they still need data to drive their decision-making. So, instead of making them adjust to BI; we make BI work for them by incorporating insights and business intelligence, into their natural workflow. We infuse data from the Sisense platform within our user base.”

For some, that looks like a notification where an alert about something that requires attention is sent. For others, it looks like an email report. However, for the majority of users, integrating data from the Sisense data platform within Gerimedica’s user base looks like collaboration via Microsoft Teams — the biggest in its user base.

Invisible insights infused

Gerimedica leverages BI on a daily basis to help customers, without them ever needing to log into Sisense. In fact, roughly 70% of its users have never even seen Sisense. They’ve never seen a chart. They just have the insights they need in a format that suits them best.

The company also creates ward overviews and sends roughly 350 of them every day to customers. This was initiated to save healthcare providers precious time. Prior to creating word overviews, nurses on the night shift had to copy and paste information from Gerimedica’s system or other systems into a Word or Excel sheet every day. It was taking them approximately 30 minutes for each ward.

By integrating that need into the Sisense platform, Gerimedica now provides nurses not just a dashboard, but an overview in the format they desire and that makes sense to them. Even more, it saves all organisations combined roughly 175 hours of administrative copy and pastes each day across wards. On a per organisation level, that equates to a time savings of 5-8 hours per day.

Serving the underserved means better outcomes

While some of the healthcare providers are what Hamza calls “power users” — those that are able to create data models and dashboards themselves or use dashboarding to create charts, drill-downs, and explanations, he says the majority (95%) aren’t.

With Sisense, more healthcare providers have access to insights, leading to better outcomes. Now they can see the total number of patients admitted and predictions, detailing the number of patients that will likely be discharged. They can also determine how much staff is needed in a particular ward. 

“Instead of just scheduling 30 hours for a psychologist and 20 hours for a dietitian and either overshooting or falling short, with the forecasting system in Sisense, we can predict a more accurate need for future scheduling,” Hamza says.

Even more, healthcare providers are able to see how their patients are doing. “When they see green, then everything is fine. When it’s orange or red, that indicates there might be an issue. They know to give those patients extra attention,” he points out. 

“When they click on the patient names, they get a pop-up with a detailed overview of the medical measurements that are important, helping them make better decisions. With the press of a button, healthcare providers can deep dive into the medical record system to find out more about what’s happening with the patient.”

At the same time, Hamza says that the customers who embed dashboards into their negotiation strategies increased tariffs by 2% on average. “That is a lot of money when you consider that an average organisation rakes in roughly €10 million in revenue for this kind of care,” he points out.

“Their expert negotiation skills based on data have resulted in roughly €200,000 of ‘free money’ — money that can be put towards real estate, education, workforce improvements, whatever they want.”

To access the on-demand version of this webinar please click here.

Editor’s note: This article is in association with Sisense.

The post The future of AI: Is ‘infusion’ the key to data democratisation? appeared first on AI News.

Web data – The AI goldmine 

First, we will look at where the data comes from, and it is more easily available than you might have assumed. That’s because it often comes from the largest source of information that has ever existed – publicly available web data. Public social media data, to give just one example, is being utilised by organisations as a source of information about consumer sentiment and behaviour. This data is being used to develop AI systems by businesses in industries as varied as insurance, market research, consumer finance, and real estate to gain an edge over their competition.

In these instances, information such as Twitter posts and online reviews data is leveraged to develop the AI insights needed to stay afloat in a volatile business environment. For example, hiring announcements on Twitter or other job websites for positions in the automotive industry could indicate an economic rebound in that sector, or that the industry itself anticipates an uptick in demand.

Overcoming data hurdles 

Although the data is widely available, accessing public web data at this mammoth scale is not without its challenges. Organisations are often blocked by competitors or for other reasons in the process of retrieving data, or they encounter difficulties accessing data in every region they are looking to target globally. Therefore it is important businesses adopt a web data platform that can consistently feed them the data they need. It will need to be a global network, with the capacity to handle gargantuan data volumes. 

Being able to access the correct data is essential as teaching AI systems properly is impossible without following the proper data retrieving protocols because only “clean” accurate data can create the right level of ROI for businesses. Often, requests seen as coming from data centres are blocked by websites, or fed incorrect information, as businesses want to prevent accessing data by their competition to gain a competitive advantage. Using a flexible web platform solves this problem as it provides you with a transparent view of the internet – just like it initially intended to do. 

The power of correct data 

Data is growing at an exponential rate and although businesses can benefit from this, they must take steps to ensure the right technology and processes are in place to generate real value. When looking at building an AI system, you could see it like building a house. You can have the best architect or the best team of builders on the planet, but if there are any flaws with the raw materials, they are the wrong type, or there are simply not enough of them, there are going to be serious issues with the final product. If you build on a foundation consisting of clean and accurate web data sources, you will have a robust base that you can build powerful AI systems on top of. These systems will be able to provide effective, dependable, and relevant business insights despite the unprecedented volatility in market trends.

Editor’s note: This article is in association with Bright Data

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