Evolving Landscape: AI Startups Take Centre Stage in 2024

5/5 (1)

5/5 (1)

The tech industry tends to move in waves, driven by the significant, disruptive changes in technology, such as cloud and smartphones. Sometimes, it is driven by external events that bring tech buyers into sync – such as Y2K and the more recent pandemic. Some tech providers, such as SAP and Microsoft, are big enough to create their own industry waves. The two primary factors shaping the current tech landscape are AI and the consequential layoffs triggered by AI advancements. 

While many of the AI startups have been around for over five years, this will be the year they emerge as legitimate solutions providers to organisations. Amidst the acceleration of AI-driven layoffs, individuals from these startups will go on to start new companies, creating the next round of startups that will add value to businesses in the future. 

Tech Sourcing Strategies Need to Change 

The increase in startups implies a change in the way businesses manage and source their tech solutions. Many organisations are trying to reduce tech debt, by typically consolidating the number of providers and tech platforms. However, leveraging the numerous AI capabilities may mean looking beyond current providers towards some of the many AI startups that are emerging in the region and globally. 

The ripple effect of these decisions is significant. If organisations opt to enhance the complexity of their technology architecture and increase the number of vendors under management, the business case must be watertight. There will be less of the trial-and-error approach towards AI from 2023, with a heightened emphasis on clear and measurable value. 

AI Startups Worth Monitoring 

Here is a selection of AI startups that are already starting to make waves across Asia Pacific and the globe. 

  • ADVANCE.AI provides digital transformation, fraud prevention, and process automation solutions for enterprise clients. The company offers services in security and compliance, digital identity verification, and biometric solutions. They partner with over 1,000 enterprise clients across Southeast Asia and India across sectors, such as Banking, Fintech, Retail, and eCommerce. 
  • Megvii is a technology company based in China that specialises in AI, particularly deep learning. The company offers full-stack solutions integrating algorithms, software, hardware, and AI-empowered IoT devices. Products include facial recognition software, image recognition, and deep learning technology for applications such as consumer IoT, city IoT, and supply chain IoT. 
  • I’mCloud is based in South Korea and specialises in AI, big data, and cloud storage solutions. The company has become a significant player in the AI and big data industry in South Korea. They offer high-quality AI-powered chatbots, including for call centres and interactive educational services. 
  • H2O.ai provides an AI platform, the H2O AI Cloud, to help businesses, government entities, non-profits, and academic institutions create, deploy, monitor, and share data models or AI applications for various use cases. The platform offers automated machine learning capabilities powered by H2O-3, H2O Hydrogen Torch, and Driverless AI, and is designed to help organisations work more efficiently on their AI projects. 
  • Frame AI provides an AI-powered customer intelligence platform. The software analyses human interactions and uses AI to understand the driving factors of business outcomes within customer service. It aims to assist executives in making real-time decisions about the customer experience by combining data about customer interactions across various platforms, such as helpdesks, contact centres, and CRM transcripts. 
  • Uizard offers a rapid, AI-powered UI design tool for designing wireframes, mockups, and prototypes in minutes. The company’s mission is to democratise design and empower non-designers to build digital, interactive products. Uizard’s AI features allow users to generate UI designs from text prompts, convert hand-drawn sketches into wireframes, and transform screenshots into editable designs. 
  • Moveworks provides an AI platform that is designed to automate employee support. The platform helps employees to automate tasks, find information, query data, receive notifications, and create content across multiple business applications. 
  • Tome develops a storytelling tool designed to reduce the time required for creating slides. The company’s online platform creates or emphasises points with narration or adds interactive embeds with live data or content from anywhere on the web, 3D renderings, and prototypes. 
  • Jasper is an AI writing tool designed to assist in generating marketing copy, such as blog posts, product descriptions, company bios, ad copy, and social media captions. It offers features such as text and image AI generation, integration with Grammarly and other Chrome extensions, revision history, auto-save, document sharing, multi-user login, and a plagiarism checker. 
  • Eightfold AI provides an AI-powered Talent Intelligence Platform to help organisations recruit, retain, and grow a diverse global workforce. The platform uses AI to match the right people to the right projects, based on their skills, potential, and learning ability, enabling organisations to make informed talent decisions. They also offer solutions for diversity, equity, and inclusion (DEI), skills intelligence, and governance, among others. 
  • Arthur provides a centralised platform for model monitoring. The company’s platform is model and platform agnostic, and monitors machine learning models to ensure they deliver accurate, transparent, and fair results. They also offer services for explainability and bias mitigation. 
  • DNSFilter is a cloud-based, AI-driven content filtering and threat protection service, that can be deployed and configured within minutes, requiring no software installation. 
  • Spot AI specialises in building a modern AI Camera System to create safer workplaces and smarter operations for every organisation. The company’s AI Camera System combines cloud and edge computing to make video footage actionable, allowing customers to instantly surface and resolve problems. They offer intelligent video recorders, IP cameras, cloud dashboards, and advanced AI alerts to proactively deliver insights without the need to manually review video footage. 
  • People.ai is an AI-powered revenue intelligence platform that helps customers win more revenue by providing sales, RevOps, marketing, enablement, and customer success teams with valuable insights. The company’s platform is designed to speed up complex enterprise sales cycles by engaging the right people in the right accounts, ultimately helping teams to sell more and faster with the same headcount.  

These examples highlight a few startups worth considering, but the landscape is rich with innovative options for organisations to explore. Similar to other emerging tech sectors, the AI startup market will undergo consolidation over time, and incumbent providers will continue to improve and innovate their own AI capabilities. Till then, these startups will continue to influence enterprise technology adoption and challenge established providers in the market.

AI Research and Reports
0
Shift Your Focus from Omnichannel to Opti-Channel

5/5 (2)

5/5 (2)

Customer Experience teams are focused on creating a great omnichannel experience for their customers – allowing customers to choose their preferred channel or touchpoint. And many of these teams are aware of the challenges of omnichannel – often trying to prise the experience from one channel into another. Too often we create sub-optimal experiences, forcing customers to work harder for the outcome than if they were using other channels.

I know there have been times when I have found it easier to jump in the car and drive to a store or service centre, rather than filling in a convoluted online form or navigating a complex online buying process. I constantly crave larger screens as full web experiences are often better than mobile web experiences (although perhaps that is my ageing eyes!).

One of the factors that came out in a study conducted by Ecosystm and Sitecore is that customers don’t just want personalised experiences – they want optimised experiences. They want to have the right experience on the right device or touchpoint. It is not about the same experience everywhere – the focus should be on optimising experiences for each channel.

We call this “opti-channel”.

Use an Opti-Channel Strategy to Guide Investment and Effort

This is what you are probably doing already – but by accident. I suggest you formalise that strategy. Design customer experiences that are optimised for the right channel or touchpoint – and personalised for each customer. Stop forcing customers into sub-optimal experiences because you were told to make every customer experience an omnichannel one.

The move towards opti-channel accelerates your ability to provide the best experience for each customer, as you ask the important question “Does this channel suit this experience for this customer?” before the fact – not after the experience has been designed. It also eliminates the rework of existing experiences for new channels and provides clear guidance on the next-best action for each employee.

Customer Experience Insights

There Will be Conflict Between Opti-Channel and Personalisation

The challenge for opti-channel strategies will be to align them to your personalisation strategy. How will it work when you have analytics driving your personalisation strategy that say customer X wants a fully digital experience but your opti-channel strategy says part of the digital experience is sub-standard? And the answer to this lies in understanding the scope of your experience creation – are you trying to improve the existing experience or are you looking to create a new improved experience?

  • If you are improving the existing experience, then you have less license to shift transactions and customer between channels – even if it is a better experience.
  • If you are creating a new experience, you have the opportunity to start again with the overall experience and prove to customers that the new experience is actually a better one.

For example, when airlines moved away from in-person check-in to self-check-in kiosks, there was an initial uproar from customers who had not yet experienced it – claiming that it was less personal and less human. But the reality is that the airlines took the check-in screen that the agents were using and made it customer-facing. Travellers can now see the seats and configuration and select what is best for them.

This experience was reinvented again when the check-in moved to web and mobile. By turning the screen around to the customer, the experience actually felt more human and personal – not less. And by scattering agents around the screens and including a human check-in desk for the “exceptions”, the airlines could continue to optimise AND personalise the experience as required.

Opti-Channel Opens Many New Business Opportunities

Your end-state experience should consider what is the best channel or touchpoint for each step in a journey – then determine the logic or ability to shift channels. Pushing customers from a chatbot to web chat is easy. Moving from in-store to online might be harder, but there are currently some retailers looking to merge the in-store and digital experience – from endless aisle solutions to nearly 100% digital in-store. Some shoe and clothing stores offer digital foot and body scans in-store that help customers choose the right size when they shop online. And we are beginning to see the rollout of “magic mirrors” – such as one retailer who has installed them in fitting rooms and you can virtually try different colours of the same item without actually getting them off the shelf.

Businesses are trying to change customer behaviour – whether it is getting them into stores or mainly shopping online or encouraging them to call the contact centre or to even visit a service centre. Creating reasons for why that might be a better option, while also providing scaled-back omnichannel options is a great way to meet the needs of existing customers, create brand loyalty and attract new customers to your company or brand.

More Insights to tech Buyer Guidance
0
Intelligent ‘postcards’ from the Edge: Machine learning model usage

5/5 (2)

5/5 (2)

Organisations have found that it is not always desirable to send data to the cloud due to concerns about latency, connectivity, energy, privacy and security. So why not create learning processes at the Edge? 

What challenges does IoT bring?

Sensors are now generating such an increasing volume of data that it is not practical that all of it be sent to the cloud for processing. From a data privacy perspective, some sensor data is sensitive and sending data and images to the cloud will be subject to privacy and security constraints.

Regardless of the speed of communications, there will always be a demand for more data from more sensors – along with more security checks and higher levels of encryption – causing the potential for communication bottlenecks.

As the network hardware itself consumes power, sending a constant stream of data to the cloud can be taxing for sensor devices. The lag caused by the roundtrip to the cloud can be prohibitive in applications that require real-time response inputs.

Machine learning (ML) at the Edge should be prioritised to leverage that constant flow of data and address the requirement for real-time responses based on that data. This should be aided by both new types of ML algorithms and by visual processing units (VPUs) being added to the network.

By leveraging ML on Edge networks in production facilities, for example, companies can look out for potential warning signs and do scheduled maintenance to avoid any nasty surprises. Remember many sensors are linked intrinsically to public safety concerns such as water processing, supply of gas or oil, and public transportation such as metros or trains.

Ecosystm research shows that deploying IoT has its set of challenges (Figure 1) – many of these challenges can be mitigated by processing data at the Edge.

Challenges of IoT Deployment

Predictive analytics is a fundamental value proposition for IoT, where responding faster to issues or taking action before issues occur, is key to a high return on investment. So, using edge computing for machine learning located within or close to the point of data gathering can in some cases be a more practical or socially beneficial approach. 

In IoT the role of an edge computer is to pre-process data and act before the data is passed on to the main server. This allows a faster, low latency response and minimal traffic between the cloud server processing and the Edge. However, a better understanding of the benefits of edge computing is required if it has to be beneficial for a number of outcomes.

Perception on Edge Analytics in IoT Users
AI Research and Reports

If we can get machine learning happening in the field, at the Edge, then we reduce the time lag and also create an extra trusted layer in unmanned production or automated utilities situations. This can create more trusted environments in terms of possible threats to public services.

What kind of examples of machine learning in the field can we see?

Healthcare

Health systems can improve hospital patient flow through machine learning (ML) at the Edge. ML offers predictive models to assist decision-makers with complex hospital patient flow information based on near real-time data.

For example, an academic medical centre created an ML pipeline that leveraged all its data – patient administration, EHR and clinical and claims data – to create learnings that could predict length of stay, emergency department (ED) arrival models, ED admissions, aggregate discharges, and total bed census. These predictive models proved effective as the medical centre reduced patient wait times and staff overtime and was able to demonstrate improved patient outcomes.  And for a medical centre that use sensors to monitor patients and gather requests for medicine or assistance, Edge processing means keeping private healthcare data in-house rather than sending it off to cloud servers.

Retail

A retail store could use numerous cameras for self-checkout and inventory management and to monitor foot traffic. Such specific interaction details could slow down a network and can be replaced by an on-site Edge server with lower latency and a lower total cost. This is useful for standalone grocery pop-up sites such as in Sweden and Germany.

In Retail, k-nearest neighbours is often used in ML for abnormal activity analysis – this learning algorithm can also be used for visual pattern recognition used as part of retailers’ loss prevention tactics.

Summary

Working with the data locally on the Edge, creates reduced latency, reduced cloud usage and costs, independence from a network connection, more secure data, and increased data privacy.

Cloud and Edge computing that uses machine learning can together provide the best of both worlds: decentralised local storage, processing and reaction, and then uploading to the cloud, enabling additional insights, data backups (redundancy), and remote access.

More Insights to tech Buyer Guidance
1
Industries of the Future – Ecosystm Bytes

5/5 (2)

5/5 (2)

Industries continue to innovate and disrupt to create and maintain a competitive edge – and their technology partners evolve their solution offerings to empower them.

We bring to you latest industry news from the Healthcare, Financial Services, Retail, Travel & Hospitality and Entertainment & Media industries to show you how organisations are leveraging technology. Find out more about organisations such as Services Australia, Paypal, Walmart, Zara and Amex – and how tech providers such as IBM, Oracle, Google and Uplift are supporting organisations across industries.

View the latest Ecosystm Bytes on Industries of the Future below, and reach out to our experts if you have questions.


Industries-of-the-Future-EcosystmBytes-1
Industries-of-the-Future-EcosystmBytes-2
Industries-of-the-Future-EcosystmBytes-3
Industries-of-the-Future-EcosystmBytes-4
Industries-of-the-Future-EcosystmBytes-5
Industries-of-the-Future-EcosystmBytes-6
Industries-of-the-Future-EcosystmBytes-7
Industries-of-the-Future-EcosystmBytes-8
previous arrowprevious arrow
next arrownext arrow
Industries-of-the-Future-EcosystmBytes-1
Industries-of-the-Future-EcosystmBytes-2
Industries-of-the-Future-EcosystmBytes-3
Industries-of-the-Future-EcosystmBytes-4
Industries-of-the-Future-EcosystmBytes-5
Industries-of-the-Future-EcosystmBytes-6
Industries-of-the-Future-EcosystmBytes-7
Industries-of-the-Future-EcosystmBytes-8
previous arrow
next arrow
Shadow

For more ‘byte sized’ insights click on the link below

Ecosystm Bytes
0
Nuance Acquisition Strengthens Microsoft’s Industry & AI Capabilities

5/5 (2)

5/5 (2)

Last week Microsoft announced the acquisition of Nuance for an estimated USD 19.7 billion. This is Microsoft’s second largest acquisition ever, after they acquired LinkedIn in 2016. Nuance is an established name in the Healthcare industry and is said to have a presence in 10,000 healthcare organisations globally. Apart from Healthcare, Nuance has strong capabilities in Conversational AI and speech solutions to support other industries. This acquisition is in line with Microsoft’s go-to-market roadmap and strategies.

Microsoft’s Healthcare Focus

Microsoft announced their Healthcare Cloud last year and this acquisition will bolster their Healthcare offerings and market presence. Nuance’s product portfolio includes clinical speech recognition SaaS offerings – Dragon Ambient eXperience, Dragon Medical One and PowerScribe One for radiology reporting – on Microsoft Azure. The acquisition builds on already existing integrations and partnerships that were in place over the years.

Microsoft Cloud for Healthcare offers its solution capabilities to healthcare providers using a ‘modular’ approach. Given how diverse healthcare providers are in their technology maturity and appetite for change, the more diverse the  ‘modules’, the greater the opportunities for Microsoft. This partnership with Nuance also brings to the table established relationships with EHR vendors, which will be useful for Microsoft globally.  

The Healthcare industry continues to struggle as the world negotiates the challenges of mass vaccination. But on the upside, the ongoing Healthcare crisis has given remote care a much-needed shot in the arm. Clinicians today will be more open to documentation and transcription services for process automation and compliance. The acquisition of Nuance’s Healthcare capabilities will definitely boost Microsoft’s market presence in provider organisations.  

However, Healthcare is not the only industry that Microsoft and Nuance are focused on. The Microsoft Cloud for Retail that was launched earlier this year aims to offer integrated and intelligent capabilities to retailers and brands to improve their end-to-end customer journey. Nuance has omnichannel customer engagement solutions that can be leveraged in Retail and other industries. As Microsoft continues to verticalise their offerings, they will consider more acquisitions that will complement their value proposition.

Microsoft’s Focus on Conversational AI

Microsoft already has several speech recognition offerings, speech to text services, and chatbots; and they continue to invest in the Conversational AI space. They have created an open-source template for creating virtual assistants to help Bot Framework developers. In February, Microsoft announced their industry specific cloud offerings for Financial services, Manufacturing, and Non-Profit, and also introduced a series of AI and natural language features in Microsoft Outlook, Microsoft Teams, Microsoft Office Lens and Microsoft Office mobile to deliver interactive, voice forward assistive experiences.

“There is no slowing down in this space and the acquisition clearly demonstrates the vision that Microsoft is building with Nuance – a vendor that has made speech recognition, text to speech, conversational AI the foundation of the company. This is a brilliant move by Microsoft in the Conversational AI space and a win-win for both companies.

This move could also mark further inroads for Microsoft into the contact centre space. With Teams now being integrated into contact centre technologies, working with large customers using speech and conversational AI, Dynamics 365 could herald the start of more acquisitions for Microsoft to bolster a wider customer engagement vision.

The Conversational AI war is heating up and various other cloud vendors such as Google and AWS are starting to get aggressive and have made investments in recent years to enhance their Conversational AI capabilities. Google Dialogflow has been seeing rapid uptake and they now have deep partnerships with Genesys, Avaya, Cisco and other contact centre players. Microsoft coming into the game and acquiring a company with years of history and IP in the speech space, demonstrates how the cloud battle and the war between Google, Microsoft and AWS is heating up in the Conversational AI. All of a sudden you have Microsoft as a powerhouse in this game.”


New call-to-action
1