Enhancing Patient Interaction

AI-driven chatbots and virtual assistants are revolutionizing patient interaction. These systems provide instant responses to queries, assist in scheduling appointments, and offer reminders for medication or follow-up visits. Patients can access healthcare information anytime, reducing wait times and improving overall satisfaction. For instance, a virtual assistant can guide a patient through pre-operative instructions, ensuring they are well-prepared and reducing pre-surgery anxiety.

Advancements in Diagnostics and Treatment

AI algorithms are increasingly being used to analyze complex medical data, enhancing diagnostic accuracy and treatment plans. Machine learning models can process vast amounts of data from medical records, imaging studies, and genetic profiles to identify patterns that may elude human clinicians. This results in earlier diagnosis of conditions such as cancer, leading to more effective and timely treatments. AI can also predict patient outcomes and suggest personalized treatment plans based on individual health data, improving the quality of care.

Personalized Patient Care

AI enables highly personalized patient care by leveraging data from various sources, including electronic health records (EHRs), wearable devices, and patient-reported outcomes. Personalized care plans can be developed to address the unique needs of each patient. For example, AI can analyze a patient’s lifestyle, genetic makeup, and environmental factors to recommend personalized wellness plans, dietary adjustments, and exercise regimens. This personalized approach not only enhances patient engagement but also improves health outcomes.

Streamlining Administrative Tasks

The administrative burden on healthcare providers is significantly reduced through AI-powered automation. Routine tasks such as billing, coding, and documentation can be automated, allowing healthcare professionals to focus more on patient care. AI-driven systems can manage patient flow, optimize scheduling, and ensure that resources are used efficiently. This reduces administrative errors, enhances operational efficiency, and improves the overall patient experience.

Continuous Monitoring and Support

Wearable devices equipped with AI technology offer continuous monitoring of patients’ vital signs and health metrics. These devices can detect anomalies in real time and alert healthcare providers or caregivers, enabling prompt intervention. For patients with chronic conditions, continuous monitoring can provide peace of mind and ensure timely management of their health. AI-driven systems can also offer support through virtual health coaches, providing personalized advice and encouragement to help patients manage their conditions effectively.

Future Prospects

The future of AI in healthcare looks promising, with ongoing advancements in natural language processing, predictive analytics, and machine learning. As these technologies evolve, AI is expected to become even more integrated into patient care, offering more proactive and preventive health solutions. AI will likely play a crucial role in population health management, helping to identify public health trends and develop strategies to address them.

Integration Capabilities with Platforms like Certainly

For healthcare providers looking to implement AI solutions, platforms like Certainly offer robust integration capabilities. Certainly provides tools that allow healthcare chatbots to be seamlessly integrated with existing systems, enhancing their functionality and ensuring they deliver personalized and contextually relevant experiences. For more details on integrating AI in healthcare using Certainly’s platform, visit Certainly’s integration and channels page.

Conclusion

The integration of AI in healthcare is not just an innovation but a necessity for improving patient experiences and outcomes. As AI technology continues to advance, it promises to revolutionize healthcare, making it more personalized, efficient, and responsive. This transformative potential underscores the importance of adopting AI-driven solutions to meet the evolving needs of patients and healthcare providers alike.

The post How AI Can Improve Patient Experiences in the Healthcare Sector appeared first on Certainly.

• Certainly: Renowned for its robust integration capabilities, Certainly stands out for creating tailored customer experiences through conversational AI. The platform excels in seamlessly connecting with various business systems, streamlining both customer support and internal workflows.
• Sprinklr: Sprinklr is praised for its sophisticated handling of out-of-context queries and robust in-platform testing, making it a top choice for enterprises seeking to refine customer engagement across multiple digital channels.
• SleekFlow: Known for its easy integration with numerous messaging platforms including WhatsApp and Facebook Messenger, SleekFlow offers a seamless user experience, blending automated responses with timely human interventions.
• IBM Watson Assistant: This platform is distinguished by its deep natural language understanding and security features, making it ideal for handling sensitive and complex interactions in sectors like finance and healthcare.
• Amazon Lex: Leveraging the technology behind Alexa, Amazon Lex provides powerful tools for developers to create advanced conversational interfaces, enhancing user interactions with sophisticated natural language understanding.
• Kore.ai: Kore.ai is noted for its extensive automation workflows and omnichannel deployment, enabling businesses to maintain consistent and efficient interactions across all customer touchpoints.
• Avaamo: Specializing in deep learning, Avaamo delivers personalized and intelligent conversational experiences, aimed at businesses focusing on detailed and empathetic customer interactions.
• Zendesk: Integrating AI into its customer service solutions, Zendesk uses conversational AI to efficiently manage and resolve customer inquiries, improving overall satisfaction and response times.
• Rezolve.ai: Excellent for internal corporate applications, particularly in IT and HR, Rezolve.ai enhances employee support and productivity with its rapid response capabilities and automation of routine inquiries.
• Boost.ai: Known for its scalability and robust auto-resolution capabilities, Boost.ai is effective in automating customer service and internal support queries, suitable for a wide range of industries.

These platforms illustrate the versatility and effectiveness of conversational AI across various applications, from improving customer service to enhancing internal communications. For businesses interested in leveraging sophisticated conversational AI capabilities, Certainly offers robust solutions that can be integrated into numerous operational facets to streamline interactions and processes. More about how Certainly achieves this can be explored on their customer stories page.

The post 10 Best Platforms to Win with Conversational AI in 2024 appeared first on Certainly.

Voice Technology Integration in Chatbots

One of the most notable trends is the integration of voice technology into chatbots. Advancements in voice recognition and natural language processing are making chatbots more adept at understanding and responding to spoken queries. This enhancement is not only making chatbots more accessible and human-like but also broadening their utility for various tasks, from setting reminders to controlling smart devices​​​​.

Advancements in Generative AI

Generative AI models are becoming increasingly sophisticated, enhancing chatbot interactions by making them more engaging and intelligent. These improvements are crucial for ensuring the accuracy and reliability of chatbot conversations​​.

Multimodality in Chatbots

Another trend is the evolution of chatbots beyond text-only interactions. Incorporating multimodality, chatbots will soon be able to process and respond to inputs from videos, sounds, images, and spoken words, significantly expanding their functionality​​.

Enhanced Integration Capabilities

Chatbots are set to become more adept at integrating with other systems, such as health apps and food delivery services, enabling them to offer more personalized and relevant suggestions and services​​.

Custom AI Models for Businesses

Businesses are increasingly adopting custom AI models tailored to their specific needs, improving the quality and relevance of chatbot interactions in various business contexts​​.

The Rise of AI Assistants

AI assistants are emerging as a key trend, streamlining tasks and enhancing performance for both individuals and businesses. These assistants will have applications ranging from cooking to fitness coaching​​.

Collecting Consumer Insights Through AI

AI assistants in 2024 will also play a crucial role in gathering consumer insights. By interacting with users, these systems can collect and analyze data on consumer preferences and behaviors in real-time, providing valuable insights for businesses​​.

Personalization and Human-Like Interactions

There’s an increased focus on making chatbots more personalized and human-like, improving user experience and making interactions more natural and engaging​​​​.

Social Media Bots

Chatbots on social media platforms will become more widespread, enabling businesses to engage in conversations and sales on platforms where their customers are most active​​.

Multilingual Capabilities

Multilingual chatbots will become more common, allowing businesses to reach a global audience and build trust among customers who speak different languages​​.

Certainly, with its advanced chatbot solutions, plays a crucial role in these trends, offering scalable and customizable solutions that enhance customer experience and streamline business processes. Explore Certainly’s platform here and check their pricing structure here.

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But it isn’t just the developers of these tools who must ensure user safety when interacting with Large Language Models (LLMs). Due to the occasionally unpredictable nature of LLMs, those of us integrating this cutting-edge technology into our business need to be aware of how to safeguard effectively while the rough edges are sanded off.

Safeguarding against AI hallucinations

One of the most spoken-about issues concerning generative AI and LLMs is the possibility of “hallucinations.” These are when the AI responds tangentially to the initial prompt—the request given to the model—providing non-sequitur or incorrect information. This lack of control, especially with such a new technology, is sure to concern anyone wishing to implement an LLM in their business. But rest assured; you can mitigate the risks in many ways.

Indeed the best way, in my experience, to train a new LLM for a customer-facing role is to think of it as if you are onboarding a new employee. When you take on an employee to represent your company, you’re giving up control. Yet, with the proper training and onboarding, you ensure the new team member is prepared to give your customers factual answers to their queries.

When a new salesperson or customer service agent joins a company, there’s a lot they have to learn. Among other things are the company’s values, which products or services they sell, and internal guidelines. The same goes for an LLM powering a chatbot on your store. Out of the box, the custom LLM instance will only have a generic understanding of ecommerce and no knowledge of your company. Thankfully, however, you can teach it.

Training your LLM

Training your LLM on this data is vital for AI safeguarding, as the more knowledge it has on such subjects, the less likely it is to stray from them. Once it knows your FAQs, branding guidelines, and store inventory, it has a stable “source of truth” for its answers. This is as opposed to the entirety of the internet, as the base LLM does. It is, therefore almost impossible for your custom instance to deviate from the source of truth you have provided.

You can do this by:

• Customizing the tone of voice to align with your brand’s
• Uploading your business policies, catalog, and FAQs into the LLM or connecting it to your help center platform
• Integrating it into your ecommerce platform so that it has live knowledge of information such as product availability; Shopify, for example, is an excellent source of structured store information for an LLM

Of course, in some instances, you don’t want the LLM to paraphrase specific copy, such as legal terms. In such cases, the bot sends it verbatim, like giving an agent a script to follow. But unique responses reacting to what the customer has written are usually preferential over canned answers. Customers prefer a more humanizing, personalized experience, after all.

These generated responses are only helpful if they align with your internal policies and brand identity. Once the LLM is trained, the next step is to test the model to ensure it consistently answers factually constantly. In the same way that you wouldn’t give a new team member a handful of training sessions and never check in with them again, you should have regular test scenarios to audit your LLM.

Ensuring GDPR compliance and data safeguarding with AI

Another significant AI safeguarding issue is with GDPR. The base technology of the LLM is a third-party service, after all, which you will constantly be sending data back and forth to. However, your customer is ultimately conversing with you rather than with OpenAI or whichever provider you choose.

That’s why, at Certainly, our LLM integration anonymizes all information that is not crucial to the smooth conversational flow. For instance, email addresses are identified and, instead of being sent to the LLM, are sanitized, and the LLM itself only receives “<EMAIL_ADDRESS>.” As such, no sensitive customer data is leaving your tech ecosystem. This is part of our wider commitment to keeping your data and your customers’ data, secure.

LLMs are the future. We are ready.

Large Language Models, whether GPT, LLaMA, Bard, or any other, will become a core technology for most industries in the near future. So, we need to ensure that we’re using them in ways that are safe for our businesses and customers.

This is something we’re deeply aware of at Certainly. We’re working hard to provide solutions for our customers to allow them to use this new technology safely and effectively. To learn more about what we’re doing with LLMs, look at our recent series on OpenAI.

Michael Larsen & Fergus Doyle wrote this article with visuals by Vital Sinkevich.

The post AI safeguarding tips for large language models appeared first on Certainly.

This post overviews the adoption and business applications of Generative AI (GAI) and Large Language Models (LLMs), such as ChatGPT, in the market as of February 2023. It will also cover how we are working with them at Certainly. Our ideas will evolve, and we will have new ones as we learn. Consider these a starting point.

You can find a primer on Generative AI, Large Language Models, and the differences between them and Certainly here.

Predictions on adoption

The natural language user experience of ChatGPT – that it simulates an instant messenger – was crucial to its viral success. Its predecessor, GPT playground, did not achieve the same fast adoption due to its API interface.

The natural language interface was key to its success as it allowed the masses to understand the power of language AI. This shows that, while the underlying technology is the same, the most successful AI products will be those obsessed with the user experience.

In the wake of ChatGPT’s success, we will see an explosion in new products using Generative AI and LLMs to solve problems for businesses and consumers. These LLMs will improve massively and become standard infrastructure in many B2C and B2B products.

The applications of these seem to follow this progression:

Wave one: immediate business applications for ChatGPT

The first wave has been focused on content generation. It is relatively easy to take language models and finetune them only to spit out content for specific use cases. Lensa AI, for example, is a consumer-focused app where you can upload headshots of yourself. Then, using GAI, the app will spit out new AI-generated profile pictures of you. Other examples are Jasper.ai, which helps marketers generate texts, and Copilot, which allows developers to generate code. This breed of products is essentially a finetuned LLM with a custom interface on top.

These tools serve a function for the user. However, they’re not commercially defendable as others can quickly build a similar product. That is what is happening; many competitors are launching, and when the products are identical, it becomes a race to capture the most customers with an easy-to-copy product. At the same time, you figure out how to monetize your customer base and build a better moat.

After last week’s announcement that Microsoft will invest as much as $10 billion in OpenAI over the coming years, adding ChatGPT-like functionality to their business solutions seems more of a certainty. Github’s Copilot has been helping coders since 2021, and just yesterday, the tech giant rolled out Teams Premium, which uses the technology to streamline meetings. The next logical conclusion is that they add Generative AI tools to the 365 Suite.

What is GREAT, though, is that these products help accelerate AI adoption and awareness in both businesses and the public.

Wave two: a replacement to search?

The next wave will be information retrieval. For example, custom search engines using a natural language interface that doesn’t just return a headline and a link to a webpage like Google does today. Rather, it’ll generate an actual answer to your query.

All indicators say that Bing will attempt a comeback in 2023. After Microsoft’s above-mentioned investment in OpenAI, a ChatGPT-integrated version of Bing (remember Bing?) is rumored to be on the horizon. This new breed of search engines could be a potential Google competitor.

Microsoft establishing Bing as a real competitor to Google will be the most significant disruption in the industry. Microsoft’s investment represents a multi-year partnership to not only potentially redefine search but also bring OpenAI’s capabilities to the enterprise and build countless applications on top. Interestingly enough, Microsoft does not have to earn revenue from Bing. It is enough for them to steal market share from Google, which will hurt Google a lot since the vast majority of revenue comes from their search and ads business. The King is dead. Long live the Bing?

Natural language search engines will also be used in narrower use cases. You can, for example, finetune an LLM on the transcript of your 1,000 podcast episodes and let users search for relevant information within that corpus of data.

These are great use cases but relatively straightforward if it is nothing more than an easier-to-use search engine. Search engines are essentially single-turn Q&A conversations that chatbots can also do, bringing me to the third wave, where I see Certainly fits in.

Wave three: Actionable AI

The third wave is what we do. In this context, I will call it “Actionable AI”; that is to say, an AI system that performs actions on your behalf based on what you tell it to do. The high-level tech stack needed is a natural language user interface, LLMs, and technology that can control other systems and send/receive data.

The Actionable AI tech stack has a lot of opportunities as it will allow non-coders like me to, for example, build software applications and connect third-party systems without knowing how to code.

Actionable AI is especially well-suited for digital commerce. It enables consumers to shop from brands using their natural language and for brands to provide a more human-like experience.

Now, the chatbot can do the shopping for you as a consumer. It will understand your purchase needs and, based on that, take actions such as navigating to the right product, taking you through check-out, and returning a previous purchase.

Actionable AI products are the hardest to build and monetize compared to the first waves. This means, though, that they are equally hard to copy. The main reasons are:

• Building a platform that enables all the different layers to work together – the natural language interface, the AI models, the data exchange layer, and the action layer – requires a massive investment.
• Their jobs are to handle conversations between businesses and their customers and resolve use cases where there is no gray area between success and failure: it either helps me with what I need or doesn’t.

At Certainly, we have been bullish on the opportunities in this space from day one. We’ve spent years building out Actionable AI tech specifically for ecommerce businesses. Today we have happy customers in more than 20 countries and have handled more than half a billion interactions between brands and consumers since inception.

ChatGPT’s business opportunities for the Conversational AI industry

The most significant improvement is that LLMs will help make the most annoying thing about chatbots disappear; their inability to understand what you want. No more “Sorry, I am a dumb chatbot. I didn’t understand you; please try again”.

They’ll help chatbots be much better at understanding the user’s intent and will make it much easier for businesses to build useful and loveable bots. Bots that excel, not only in single-step conversations like providing an answer to a question but also in taking part in a multi-step conversation, for example, to help find the right product.

The improvement in the utility and increase in general awareness will lead to a rapid rise in chatbot usage for businesses and consumers across many industries and use cases. It will increase consumers’ desire to interact with chatbots, increasing the ROI businesses get from using the technology. It is a positive cycle leading to faster adoption of chatbots. This is an ample opportunity for us and everyone thinking about using chatbots in their business.

Incorporating LLMs into our product will accelerate what we already focus on

The recent progress in language technology, including the release of ChatGPT, has brought us closer to our vision of enabling any merchant to use a human-like digital sales assistant to help their customers. In fact, Large Language Models have accelerated our efforts and focus.

We can benefit from using language models from companies like OpenAI as part of our infrastructure rather than building all the generic models in-house. This shift is similar to the move from on-premise to cloud computing, where companies no longer had to spend resources on managing their servers but could focus on building products that solved their customers’ problems. By outsourcing the generic language model infrastructure, we can focus on the application layer and create valuable products for our customer segments. This will ultimately benefit our customers and make good business sense for us.

At Certainly, we are experimenting with LLMs and adding them in ways that are useful in end-user-conversations, our customers’ workflow, and our internal work. We want them to do one of two things:

1. Improve on something we/our customers do today.

2. Enable us/customers to do something new.

In our product, this means we’re experimenting with:

• Enhancing the human-like conversations by enabling natural language generation, confined by and based on the individual brand’s content and ethos

• Improving bot-building workflows via generating content for answers, intents, and entities, even simulating entire conversational flows
• Simulating a user journey: Showing customers the quality of their flow in the bot builder and pointing out opportunities to improve

• Automating the testing of complex bots to avoid configuration mishaps
• Model finetuning on industry-specific data and customers’ products/faqs so bots can automatically answer relevant questions and drive conversations around purchases within minutes
• Prompting the Certainly Supportbot to update bots based on customers’ instructions
• Provide OpenAI webhook templates for customers to freely experiment with and incorporate into their bots

And in our day-to-day work:

• Assisting QA with test cases before releasing new features
• Automating the generation of standard code
• Creation and clean-up of datasets
• Assisting in content generation for helpcenter articles and documentation

ChatGPT business opportunities for Certainly

Incorporating LLMs as part of our infrastructure enables us to do several things to accelerate growth:

• Getting the time-to-live for customers from weeks down to minutes
• Lower the barrier of entry for merchants to adopt useful chatbot technology
• Reduce customer acquisition costs and cost to serve
• Expand our addressable market
• Solve the challenge for bots for retailers with many types of products
• Provide template bot solutions for partners
• Focus our Product team on building differentiation instead of infrastructure

Using LLMs to accelerate the naturalness of conversations, including understanding multi-intents and entities and remembering what was said previously in the conversation, helps improve the end-user experience and the ROI for merchants. This means merchants will scale their usage of Certainly bots faster. The improved UX means more end-users want to chat with bots. Better UX and business ROI means more adoption of bots by businesses and merchants, which expands the addressable market. Faster time to live through specialization means lower CAC and cost to serve, which means our addressable market increases significantly.

And that’s the dream. Ultimately, what we want to do is provide a service that will unlock more time for you to do the things that give you energy and that you love.

If you want to learn more about what we’re doing with ChatGPT here at Certainly, you can check out these use cases for product recommendation, support, and customer engagement.

This article was written by Henrik Fabrin. The visuals were by Vital Sinkevich, and it was edited by Fergus Doyle.

The post ChatGPT for business: general adoption of Generative AIs & Large Language Models and how we’re working with them at Certainly appeared first on Certainly.

We are certainly living in an exciting time. We are fortunate to be right at the center of what looks to be the most significant technological shift since the cloud or mobile. If you learn how to leverage this shift, it is a fantastic opportunity for you – as a business and as an individual.

You’ve probably seen many screenshots of your friends and colleagues talking to something called ChatGPT, a bot that responds to almost any prompt as if by magic. Here is a non-technical write-up of how Generative AI (GAI) and Large Language models (LLM) work, why they are important, and some initial thoughts on how we will use them at Certainly. For the sake of this post, I will use ChatGPT, Generative AI, and Large Language Models interchangeably.

What are they, and how do they work?  

Generative AI and Large Language Models are unsupervised and semi-supervised machine learning algorithms that use existing content like text, images, audio, video, and even code to create new content. The main purpose is to generate original content that seems real (that is, human-authored). Furthermore, there is no limit to how much new content they can create. You can get them to generate the new content via API or, as of late, via natural language interfaces such as the chat app ChatGPT by Open AI.

Why does it matter, and why now? 

Creating unlimited new content (text, image, video, code) using a natural language interface or API opens enormous opportunities. Suddenly,  

• you don’t have to be a coder to generate code 
• you don’t have to be a writer to generate texts 
• you don’t have to be a designer to generate visuals 

Tell it what you want, and it will generate the content for you. The content may not always be “production ready,” but it drastically reduces the time spent on creation. The immediate effect is that it lowers the bar to create, which means efficiency increases and more people can participate.  

The consumer benefits are fairly clear; you’ve surely seen it for yourself on social media. As you can generate new cool, fun, engaging content super easily, it has already spurred new consumer use cases. Lensa AI, for example, lets you create the superhero headshot you wish you had, and ChatGPT helps you write engaging content.  

As long as the content is engaging, it doesn’t always matter if it is correct.  

For businesses, it’s a tool for knowledge workers such as creatives and coders that can help them be even more productive. Many tools are focused on this, like Github’s Copilot for code generation, Jasper.ai for copywriting, and Midjourney for images. They allow domain experts and non-experts to get more done faster than ever before. Still, because it is in a business context, it requires a human in the loop to edit and approve the generated content before using it commercially.   

Differences between ChatGPT and Certainly  

There are specific differences between tools like ChatGPT and Certainly. We have our individual strengths, and understanding how to leverage both together creates opportunity. This comparison between Certainly and ChatGPT is a good place to start. 

Data: Timeboxed vs. Evolving, General vs. Brand/industry specific 

Large Language Models (LLMs) require enormous datasets to train. This is costly, so the data they use is a snapshot in time, which timeboxes their general knowledge. That means ChatGPT currently doesn’t know about any events in 2022.

Furthermore, the models underneath ChatGPT are not trained on data from a specific brand. So it cannot answer brand-specific questions such as policies and product info. 

LLMs have proven to be great for knowledge workers for ideation and productivity but not for using them out-of-the-box as a tool that engages directly with customers. For this, brands need tools that are based on content that is up-to-date and specific to their business. That continues to be our opportunity; to enable brands to build chatbots and use AI that is easy to use and understands their particular business as it evolves. 

Training cost: High vs. Low 

Training an LLM adequately on enough data to achieve a sufficient performance level costs millions of dollars in computing power and takes a very long time. This makes it economically out of reach for most businesses. A better option for most is to train smaller models or finetune a third-party general LLM to specific use cases. That is what we do at Certainly with our unique, domain-specific data. We offer such AI models as part of our platform that are finetuned to ecommerce and even tailored to each individual customer. 

Content: Probabilistic vs. Deterministic, On-the-fly vs. Database  

ChatGPT generates its content by trying to predict the right next word in a sentence. In basic terms, the model uses Natural Language Understanding to understand what you are saying. It then uses Natural Language Generation to assemble a likely answer in real-time. In its answer, it tries to construct words and new sentences resembling texts from the dataset on which it was trained. It does not know if what it has generated is actually true or false. Neither does it know if the dataset from where it generated the answer holds true or false information. That is why it is “probabilistic” and can be wrong. 

That means it is not a fact machine but a prediction machine. And as we know, predictions will sometimes be correct but can also be very wrong. (I predicted Denmark would reach the 2022 Fifa World Cup Finals… Oh my…). 

Probabilistic is probably ok when it generates text you can edit or use as inspiration, but it is certainly not enough for a brand to use to answer customers in real-time. Brands want to ensure the answers are correct every time, not provide answers that are probably right and may differ from time to time.  

Certainly is built for businesses to leverage AI in their customer interactions with high explainability and certainty. The AI matches content owned and controlled by the business in its answer. The answer is always what the business wants it to be (i.e., deterministic). It’s explainable as it is generated from its content and aligned with its policies. Any actions, such as recommending the right product or looking up an order, are based on live data pulled from the customer via API. 

All interactions are controllable and explainable, which makes it very useful in a business context.  

Actionable: ChatGPT vs. Certainly 

ChatGPT enables you to generate new content, but it cannot take actions on your behalf. For example, edit a database or change a website’s content. 

Our product does that, and that has been part of our vision from the start: To bring digital communication back to being on human terms.  

As an online shopper, you can use your natural language to say what you need, and the Certainly chatbot takes actions on your behalf. For instance, it can help navigate a website to find the right product, take you through checkout, cancel an order, or find answers to questions. 

Using a product that takes actions based on what the end-user wants vastly increases the potential use cases and value for our customers. It is not just about generating new content to be more productive; it is about getting things done as an end-user and brand.  

What does ChatGPT mean for us at Certainly? 

There are three important interlinked areas we consider when assessing the opportunities presented by LLMs. Not only for us at Certainly but for any business:  

• What problems can they help us solve for our customers? 
• How can they accelerate our product strategy and commercial strategy? 
• How can they help our day-to-day work? 

Our customers want to get more done faster when AI handles customer interactions without sacrificing control and explainability. That is at the core of what we offer online merchants, and adding third-party LLMs to our technology and workflows will only make us more accessible for more merchants and at a faster pace.   

In the next post, I will give an overview of the general adoption and business applications of Generative AI and Large Language Models and how we are working with them at Certainly.  

If you want to find out more about what we’re doing with ChatGPT here at Certainly, you can check out these use cases:

https://certainly.io/chatgpt-customer-engagement/

https://certainly.io/chatgpt-product-recommendation/

https://certainly.io/chatgpt-support/

This article was written by Henrik Fabrin. The visuals were by Vital Sinkevich, and it was edited by Fergus Doyle.

The post ChatGPT, Generative AI, & Large Language Models: a primer appeared first on Certainly.

The Impact of Artificial Intelligence on Ecommerce 

But first, a quick primer. Artificial intelligence (AI) and machine learning (ML) are transforming ecommerce, while data processing has reached an entirely new level, leading to more significant business and market insights. McKinsey’s latest Global Survey presents best practices across the broad spectrum of AI implementations. This includes MLOps (machine-learning operations) that drive success and more efficient spending on AI. 

The major benefit of using AI and ML technologies is that, though they require some maintenance, they can act semi-autonomously once they’re up and running. This allows for significant scaling without a huge team working round the clock. They’re also constantly collecting customer data, as we’ll see later. This data from the retail industry can be used to inform content and strategy better. This, in turn, will help create better experiences for your retail customers. Here is our selection of AI-powered ecommerce trends and innovations that every business should know to leverage AI properly. 

Top 3 AI-driven Ecommerce Trends 

1. Conversational AI-powered Chatbots: The Abiding Ecommerce Trend 

Thanks to constantly improving technology, chatbots are one of the perennial ecommerce trends. Finances Online’s annual Chatbot Statistics report found that 80% of retail and ecommerce businesses use AI chatbots or plan to use them in the near future.   

Using chatbots, customers can talk to brands 24 hours a day, 7 days a week. As a result, chatbots are one of the most valuable AI technologies. No matter the inquiry, AI-powered virtual assistants can solve or streamline the process for the customer support agents in the case of more complicated tickets. This is especially important when 24/7 support is a core driver of customer satisfaction. A Zendesk study on “Multi-channel Customer Care” found that nearly half of customers expected instant responses when engaging with brands online. The need becomes even more acute if you’re responding to customers in multiple time zones. 

Artificial intelligence chatbots use ML and Natural-Language Understanding (NLU). For example, chatbots are being developed which can learn and adapt from customer conversations, understand consumer intent, and predict future actions based on observed existing behavior patterns. Furthermore, these chatbots can use sentiment analysis and gather and analyze conversation information to understand consumer needs. 

In addition to providing first-line support and generally improving CSAT, chatbots are vital for collecting first- and zero-party data to understand your customers better. You can build a better image of your customers by asking and answering questions about sizing and style preferences. This kind of data is vital for customer retention and reacquisition, providing a deeply personalized experience, something Salesforce’s 4th State of the Connected Customer report found over half of the customers expected. 

2. AI-powered Visual Search for Product Recommendations 

At its most basic, a reverse image or visual search does what it says on the tin; it searches for an image or similar images to the one provided by the user, using visuals instead of keywords. The ecommerce application of this trending technology not only links the image up to similar images. It also identifies things such as items of clothing there and finds related products in your online shop. 

Visual search is a tried and proven AI application in the world of fashion ecommerce. It is used by business administrators and Heads of Ecommerce to improve their online shop performance and create a more intuitive customer experience.  

According to Research and Markets, the computer vision market will be worth $18.9 billion by 2027. Ecommerce today is being transformed by AI-enabled visual search engines and applications, such as Google linking their image search function to the shopping tab or browser plugins provided by Amazon.  Leading companies use it to optimize their ecommerce websites and offer a smoother and more spontaneous customer experience.  As AI technology for ecommerce and particularly visual search tech advances, it’s smart to start future-proofing. You can start by optimizing the search engine of your ecommerce website for image searches right now. 

3. Augmented Reality (AR) and Virtual Reality (VR) technologies: A Magical New Trend 

One of the most powerful and fast-growing ecommerce trends is Augmented Reality (AR). You’re probably familiar with its use in video games. In 2016, it felt like everyone (or everyone I knew, anyway) was playing Pokémon Go. Now the technology has spread into online shopping, with the industry expected to grow to almost $300 billion by 2024. The Covid-19 pandemic has exacerbated a pre-existing problem in ecommerce: customers don’t have the opportunity to see the product in context. As Nadin Kempel Sigh of Tiger of Sweden said in a past webinar with Certainly: “The main factor in the fashion industry is that people shopping online can’t go into fitting rooms and try items on. So, people might buy 10 items and return 9, just like they might do if they were trying on those 10 items in a fitting room with a mirror.” 

To get around this problem, new “virtual mirror” technology is on the rise. This allows potential customers to try on clothes from homes without expending the time, money, and environmental impact of returns. Companies like Memomi use AI to tailor the experience, making it more than a paper doll overlaying an image. Instead, they adapt to the user’s body shape and movements. 

An extensive study conducted in the US, U.K., France, and Saudi Arabia indicated that branded AR experiences increase consumers’ willingness to purchase products, particularly when used to support product customization (73%), virtual try-on (72%), and product demonstration (70%). In addition, the findings suggest that shoppers see AR as a desired ecommerce tool. Furthermore, over 75% of consumers think augmented reality will play a role in retail’s next five years. 

The Future of AI in Ecommerce is Bright 

Want to learn more about how AI-powered chatbots can cut support costs, increase profits, and teach you more about your customers? Then you should read about MrBeast’s Feastables, who achieved all that and much more. 

This article was written by Fergus Doyle. The visuals were by Vital Sinkevich & George Radu, and it was edited by Beatrice Carraro.

The post AI-powered Ecommerce Trends to Scale Your Business in 2022 appeared first on Certainly.

In this post, we take a look at how Denmark is serving as one of the centers of the AI revolution.

Denmark recently ranked fourth in the Digital Economy and Society Index, enjoying a position at the top of the leader board for digital performance in the EU.

Globally, the country is among the elite in digitalisation.

Denmark puts research high on the agenda and this is where Danish AI comes in.

In the Organisation for Economic Co-operation and Development, Denmark sits amidst other countries with the highest public investment in research and development, this being measured in regard to GDP.  

In AI territory, Denmark has stellar research space.

The country’s public sector is one of the most digitised on the planet, earning it international recognition.

The public research budget in 2019 was 23 billion DKK.

This figure indicates the level of attention the Danish government is placing on becoming a digital front-runner.

It also illustrates the scale of financial backing that will power this ambition. 

Denmark has built a solid platform for this, and seems ready to integrate new technologies into society.

Digital Hub Denmark brought some of Denmark’s strongest tech companies & start ups to Web Summit in Lisbon 2019.

Certainly were selected to join the delegation to represent Denmark.

The Promise of Danish AI

A report published in 2019 by a collaboration of authors from The Innovation Fund Denmark, McKinsey & Company and the University of Copenhagen, explores the current situation concerning AI in Denmark and forecasts the future of the technology. 

Key figures estimated in the report give insight into the effects it will have on Danish society in the future.

By 2030, AI may boost the economy by 35 billion DKK annually in additional GDP.

Furthermore, it shows promise in improving the well-being of Danish citizens with an increase of 0.4 % annually by 2030.  

The report covers Denmark’s present standing regarding research within AI.

Three Danish universities rank within the top 15 in Europe in AI sub-disciplines.

The subcategories are natural language processing, algorithms, and complexity.

The University of Copenhagen, IT University of Copenhagen and Aarhus University boast third, seventh and fifteenth place.

Additionally, from 2018 to 2020, Danish universities endeavour to scale-up on the number of courses in AI-related subjects.

Denmark is taking long strides in its pursuit of AI superiority and fashioning a digital society.

However, the report is not shy in addressing the hurdles that must be overcome to achieve success. 

Overcoming Obstacles

There is a pressing need for talent in this branch of computer science.

Denmark lacks enough individuals equipped with the skills and competencies for working in artificial intelligence.

The past decade has seen a 20% annual increase in the need for AI-specific expertise and skillsets.

The talent deficit is impeding the implementation of AI, say 51% of Danish enterprises.

Meanwhile, 35% of Danish AI start ups concur that this is restricting movement in the field.  

Anders Søgaard, a professor in natural language processing and machine learning at the University of Copenhagen, states that one of the contributing factors of this is that graduates and academics relocate for work outside of Denmark.

None of the big players in the industry have headquarters or substantial research labs in the country, much to the detriment of Denmark’s AI scene.

Tech giants in the US attract hordes of individuals in the field, who leave their home nations for Silicon Valley.

Jobs with the big players in the industry are gold dust for aspiring tech wizards and are highly sought after. 

Zendesk, who are one of Certainly’s partners, are a great example of a healthy Danish tech company that made it to Silicon Valley and now has 160 customer countries and territories.

The monopolisation of AI

Camilla Rygaard-Hjalsted, CEO of Digital Hub Denmark addresses the problem head-on:  

“One of the crises Europe currently faces is that the large tech corporations are monopolising AI technology.

“A way in which Europe can back its corner is by channelling efforts into the green agenda and to also prioritise ethical principles that coincide with the development and application of AI, with a specific focus on algorithms and decision-making,” she said.

“Denmark is doing digital differently.

“We are ambitious and optimistic for what AI has in store for the future of our nation, and we extend a special invitation to women to come and join us in the tech industry in Denmark,” Rygaard-Hjalsted finished.

Canada is facing the same problem.

In April 2019, Montreal-based software company Element AI published their second annual report on the global talent pool within AI.

The report found that despite being one of the highest-ranking countries for employment and training within AI, the number of researchers leaving the country for work elsewhere beats that of those taking up roles in Canada. 

It’s also important to note that computer science is a male-dominated area. Tackling the gender gap in the industry is imperative, as this will help in solving the talent problem. 

Denmark’s National Strategy for Artificial Intelligence

In March 2019, the Danish government launched its new National Strategy for Artificial Intelligence.

The strategy details how the government will invest in the research and development of AI.

This will aid the progression of the technology in the public and private sectors, business and industry.

It stresses that the bedrock for growth of AI within these areas must be a human-centric, ethical approach that upholds the core values of Danish society: equality, security, liberty and freedom.

The government’s vision is that “Denmark is to be a front-runner in the responsible development and use of artificial intelligence.”

The strategy sets forth four objectives for how to fulfil this vision:

• Denmark should have a common ethical and human–centred basis for artificial intelligence
• Danish researchers should research and develop artificial intelligence
• Danish businesses should achieve growth through developing and using artificial intelligence
• The public sector should use artificial intelligence to offer world-class services 

Danish AI offering revolutionary solutions

Projects that are contributing significantly to the country’s infrastructure are already underway across Denmark.

For example, scientists and researchers at Odense University Hospital in Southern Denmark are channelling their efforts into using AI to detect cancer in its early stages.

Through studying images of cancer cells, AI can judge whether a cell is cancerous. The technology has a high success rate within this field.

This will allow for early diagnosis and treatment, increasing a patient’s chances of survival. Ultimately, AI will be saving lives.  

Researchers at the Carlsberg laboratory in Valby, Copenhagen, are also experimenting with AI.

The goal is to abolish the gruelling task of screening thousands of types of beer yeasts manually.

The lab will test thousands of yeast types using chemical sensors. AI will then examine the data from the yeast types.

Researchers are then able to conclude whether the yeast is of the right quality.

There is a high likelihood of the technology being used in the future to measure other things, such as air pollution.

Both projects are examples of how AI is pioneering revolutionary approaches to science and technology. 

AI is conducive to saving time and energy.

It improves efficiency of processes and offers solutions to problems that hinder advancement in these areas.

Funding for Danish AI

Little expense is being spared on Denmark’s quest to achieve technological excellence.

The Danish government has designated 60 million DKK to fund the strategy from 2019 to 2027. 

This is on top of a hefty sum of 295 million DKK granted from the research reserve in the Finance Act 2019.

This will fund research into the latest digital technologies and evolution in the area, and the establishment of a national centre for research into digital technologies.

The strategy emphasises four priority areas.

These are energy and utilities, healthcare, transport and agriculture.

These areas have taken precedence as authorities and businesses have access to quality data, a prerequisite for moving forward with AI.

How technology will strengthen 4 priority areas

The EU has also identified these areas as having notable potential for partnering with AI, noting that it could prove lucrative within these industries.

Europe should exploit the expanding capabilities of AI and welcome the changes it will bring to socioeconomic landscapes across the continent. 

Energy and Utilities

Within the energy and utility sector, use of AI will enable businesses to create new services, products and business models.

This is particularly crucial for businesses in environmental technology and green energy.

This, in turn, will help other businesses and consumers lower their carbon footprint and expenditure through energy conservation.

AI could be essential at helping to reducing waste and injecting hope for a greener, more sustainable future.

Healthcare

The use of AI within the Danish healthcare system is a breakthrough for medical science, as it plays a role in preserving life.

Equally, AI can help medical professionals prioritise patients with severe conditions, who require attention sooner rather than later.

Transport

In transport, AI is useful for devising solutions when dealing with the logistics of traffic management.

This will create smooth traffic flows for road users.

It will also improve road safety and play a part in reducing vehicle emissions.

Finally, there are applications for self-driving cars and enhanced public transport services.

Agriculture

In agriculture, AI will be instrumental in the continuance of sustainable farming in Denmark, through precision agriculture.

Compiling and analysing real-time environmental data such as weather patterns, soil conditions, crop maturity, allows farmers to make decisions based on the information collected and act accordingly.

This type of agriculture can save time, increase yields and cut costs. It allows farmers to keep records and lessens the negative impact on the environment.  

Framework for the development of AI

Alongside the four priority areas, the government have presented four focus areas in the strategy: 

• A responsible foundation for artificial intelligence
• More and better data
• Strong competencies and new knowledge
• Increased investment

These areas form the practical framework for the development of AI within the priority areas.

The nucleus of the strategy in its entirety is the six ethical principles laid out by the government: self-determination, equality and justice, responsibility, explainability, development and dignity.

These will inform the practices of researchers, developers and organisations who are setting the strategy in motion.

The principles highlight the importance that the Danish government places on nurturing the trust and confidence of its citizens, especially when navigating the waters of AI and its associated risks. 

The strategy stipulates that artificial intelligence must be neutral and detached from personal circumstances.

It must avoid algorithmic bias and produce fair and non-discriminatory outcomes.

The data inputted into a computer system to train an algorithm must therefore be correct, impartial and free from prejudice. 

This will serve and protect the four core values that characterise Danish.

24 initiatives for Danish AI

The strategy consists of 24 initiatives that will inevitably undergo changes and alterations as progress is made and obstacles crop up. 

The demand for new initiatives will arise as developers make headway with new technology.

Energy levels must be maintained in all areas of research and implementation if the strategy’s vision is to be accomplished.

The government will keep track of the progress by regularly touching base with researchers, decision–makers, professional networks, political stakeholders and the like.

In short, the necessary points of contact who are instrumental in the process.

The strategy will be assessed annually, and any revisions made accordingly.

Denmark is gearing up to be a major driving force in the development of AI, in the long run, having devised a comprehensive strategy adaptable to the sands of time. 

There are five central initiatives, spanning across the private and public sector, that are the key ingredients for the strategy’s success:

• Stronger investment in Danish businesses
• More open public-sector data for artificial intelligence
• Common Danish language resource
• Signature projects in the public sector
• Principles for responsible development and use of artificial intelligence

Solid Foundations

Denmark is primed and ready to embrace a digital future. The field of AI and machine learning is experiencing exponential growth and reshaping modern society.

The country has seized the opportunity to build upon this growth, with the view of transforming lives for the better.

The prevalence of AI is to increase rapidly in the years to come, therefore it is of the utmost importance that countries welcome a future shared with machines.

More and more widespread adoption of AI and machine learning will also imply that job roles will need to adapt.

No, robots will not take over our jobs.

The Danish approach to artificial intelligence aims to be a smart and an ethical one at the same time: robots will help automate repetitive tasks, optimise processes, and ultimately redefine what it means to work in customer service.

Ultimately, this will redefine the level and quality of service companies can provide to their customers.

As a society that has always welcomed innovation and is now embracing digital growth, Denmark is likely to be at the forefront of the drive to redefine the future of work.

Find out more about the latest Conversational AI and Chatbot news in the Certainly blog

Article written by Elizabeth Garnett

The post Danish AI: How Denmark is Contributing to The World’s Digital Dream appeared first on Certainly.

Certainly asked three experts to share their insights into digital HR, and how the industry is adapting to fast paced change.

What is the digitalization of HR?

Digital HR, or the digitalization of HR, involves using data to automate processes and reduce time spent on repetitive tasks.

The desired result of digital transformation is the shift from manual to digital processes and introducing HR technology trends to the core of HR departments and Recruitment agencies.

The importance of digitalization lies in creating an automated and data-driven department that will contribute to business agility.

This, in turn, leads to a reduction in costs for the industry and the maximization of profit for businesses.

Digital HR can lead to improvements to internal and recruitment processes, resulting in increased staff performance and improved customer experience.

Certainly spoke to the following experts to get their insights.

Christian Payne

Christian Payne is the Founder of Payne Search Recruitment agency, which specializes in recruiting IT professionals.

Payne has over twenty years of experience within the recruitment industry, and previously handled recruitment for Microsoft.

Randi Mørk Lildballe

Randi Mørk Lildballe is Global Talent Onboarding Manager at Odense Robotics and a specialist in HR analytics and digitalization.

Lildballe has 20+ years of experience in HR, and previously served as the HR Manager for international construction giant YIT.

Ahmed Salama

Ahmed Salama is the Regional Human Resources Director at Otis Middle East.

He is an expert in organizational design and cultural transformation, and has over 14 years experience within the industry. 

Digitalization of HR and Recruitment

Digital HR can enable a more sustainable, fast, and objective way of operating. 

The early benefits that can result from HR transformation and digitalization can include:

• Decreasing/eliminating manual HR work.
• A more systematic and objective approach when it comes to talent assessment and decisions.
• Reduction of time spent on tasks that are automatable.
• Enhancing customer experience.

“The most straightforward processes to automate are employee and manager self-service, payroll and time management,” said Ahmed Salama.

“These are extremely easy and can be automated with minimum effort, investment and time.

“However, some areas are currently not yet ready to be automated; those related to ethics & compliance, grievances, disciplinary actions and employee improvements,” he finished.

Randi Mørk Lildballe says the following areas are ripe for HR innovation:

• Recruitment
• Pre- & onboarding
• Employee data
• Salary, benefits and holiday requests

One AI tool that can transform HR and Recruitment is a chatbot, or conversational AI. 

Christian Payne, the founder of Payne Search Recruitment agency specializing in recruiting IT professionals, explained that HR technology trends can benefit the sector in many different ways.

“It can speed up the recruitment process, making the process easier for candidates, and it also adds more efficiency for the recruiter,” he said.

Furthermore, a bot that provides answers to common questions can facilitate hiring and onboarding processes.

Factors of Digital HR: Where to Start?

According to Randi Mørk Lildballe, there are many opportunities for improving HR and Recruitment operations.

Many tasks and procedures are automatable and could change HR as we know it today. However, many companies are still not joining the digital transformation.

The main factor for the transformation is about making a strategic decision for the whole company as HR is not a lonely island.

It has to be prioritized and financed. Digitalization projects require a specific skillset. If employees lack a desired skill, training should be provided.

Randi Mørk Lildballe explains that to get started those responsible for the digitalization process should ask the following questions:

• WHY do you need digital HR solutions?
• What is the purpose of digitalization?
• What processes should be supported?

“Jumping into digitalization for the sake of it is not a good idea,” Lildballe said.

“A better approach is to find out where you can get the biggest output. Moreover, finding the right talents might be the biggest challenge.

“Transformation takes time; it does not happen overnight. Those responsible should take one step at a time,” Lildballe finished.

The Challenges of Digital HR

However, digital HR solutions, as with any other transformation or change, are not without obstacles.

Ahmed Salama highlighted several key challenges.

“Firstly, the mindset and resistance of some managers who rely on traditional school of thoughts,” he said.

“Secondly, the legislation in some countries can place limitations on technologies and data privacy.

“Last but not least, the initial investment, which is required for the HR transformation.

“This type of change has to come from the top as part of the company’s strategic plan.” Salama finished.

The leadership team need to believe in the importance of the HR transformation, as part of the complete business transformation.

The human factor is equally important; people need guidance to build strategies to sustain the change. HR tech resources should plan and lead the change.

Finally, it comes down to allocating the initial investment.

Within two to three years, businesses generally break even. From the third year, they generate savings.

“HR digital transformation is essential to position HR in the center of the organization and operate as a business accelerator,” Salama said.

Randi Mørk Lildballe emphasizes that HR innovation and transformation always comes down to people.

“We should keep in mind how it can affect people and their emotions.

“Moreover, wrong timing, lack of prioritization of the digitalization project, unsuitable tools, lack of engagement of employees, to name a few, can harm the process,” Lildballe added.

AI Technology as a Competitive Advantage

Do the technological advancements that enable a digital HR system give businesses a competitive advantage?

Christian Payne explains that automation is key. 

“It allows businesses to scale faster. A chatbot using NLU and NLP is without a doubt a competitive advantage.

“It can communicate like a human and be faster and more efficient in conducting different tasks.

“Besides, using a chatbot can make the user experience more pleasant,” Payne said.

“The big advantage of using the technology is that it can free up some of the recruiters time,” he continued.

“This allows them to spend more time on other ‘human tasks’ such as finding the best candidates for their clients, and detailed interaction with shortlisted candidates.”

Will Digital HR Change the Role of HR?

Some people worry that HR technology trends could replace workers.

Randi Mørk Lildballe explains that automation of some of the functions in the HR department can increase efficiency.

“Some skillsets will become out of date and should be replaced with more relevant skillsets,” she said.

“The Digitalization of HR is no different from transformation of other parts of the business.

“People’s roles can shift and different opportunities can be created. It is up to people how they adjust.”

Instead, different skills will be required as a consequence of this change. 

More technical, as well as digital communication skills, will be in demand.

HR will need more data analysis. And let’s not forget social skills.

Human Resources and Recruitment are all about people, and social skills are essential. Equally important are leadership, coaching, engagement… the list is very long.

Whilst the digitalization of HR can improve efficiency, it seems highly unlikely that technological solutions will make human personnel obsolete.

“The HR digital journey can transform nearly all HR processes that impact the employee life cycle,” said Salama.

“The role of the HR team will be essentially in planning, implementing, communicating and sustaining the change.”

Likewise, Payne agreed, stating that “AI tools, such as chatbots, can take care of repetitive tasks in recruitment, which saves a lot of time.

“However, decision making still belongs to humans.”

How Can a Chatbot Help HR and Recruitment?

An AI chatbot is one of the digital tools that’s great for HR innovation.

It can reduce manual work by conducting activities such as candidate screening, answering common questions, scheduling interviews and meetings, and taking care of employee onboarding.

These are just a few of countless possibilities of chatbot applications.

“The investment in chatbots is very reasonable now and can be beneficial in the areas such as employee self-services, or manager self-service.

“It’s even useful during recruitment when candidates want more details about the company, and the status of the application,”​ Salama said.

AI Chatbot – Your Virtual Assistant

A chatbot can be a virtual assistant responsible for handling routine processes and repetitive administrative tasks.

This allows HR professionals to be more productive.

Payne, who recently introduced an AI chatbot on his website to innovate recruitment services, gives it access to the calendar.

In this way, the bot has all the information needed to book a meeting. In other words, it became his personal assistant, a virtual twin of the recruitment assistant.

Candidates and clients are now able to book a meeting without the need to speak to a human, and the bot can also ask a number of questions to ensure both parties have all the information they need for the meeting.

The bot can also ensure meetings are only scheduled with candidates that have successfully passed the screening process, therefore meetings are validated.

A Chatbot as a Recruitment Assistant

Recruiters spend a lot of time on screening candidates, and this can take a lot of time when done manually.

With a chatbot as a first contact for candidates, businesses can save lot of time.

This can then be devoted instead to the final selection of the best candidates.

Payne explained that a bot can easily handle some parts of the recruitment process.

For example, his (affectionately named)  ‘Payne Bot’ already helps candidates to:

• Search for jobs on his website
• Search for blog posts
• Upload CVs
• Assist with signing up for a newsletter
• Schedule meetings with a recruiter, via either video or phone.

The best part of it is that a chatbot can do all of this 24/7.

This is extremely convenient for recruitment agencies that are receiving applications from different parts of the world.

In other words, communicating with candidates in different time zones requires a lot of time planning. Meanwhile, a chatbot for recruitment is always available for users.

Besides, the candidates might have a lot of questions about the company.

Instead of contacting an HR employee, a candidate can ask questions to a chatbot.

The bot can provide answers regarding interview questions, company culture and structure, and many others.

“An AI chatbot can actually make the recruitment process more engaging and increase retention,” Payne said.

“A conversational application process can add value to the candidate experience.”

Payne also emphasized the fact that the chatbot improves user experience, as it also makes a good first impression of the hiring company.

Lack of Bias in Conducting Tests

Many employers want candidates to take competency or personality tests.

Payne said that using a chatbot for conducting competency tests is an interesting option, and he is currently trialling this.

The test can be conducted online at any time, at the candidate’s convenience.

Rather than set questions, the bot can actually make the test more conversational, it actually brings more of a ‘human element’, the feeling of conversing.

Moreover, the bot can of course run multiple interviews at the same time. 

The data the chatbot collects can be very useful, where you can observe how the conversation went.

The answers can be converted to pdf and sent as a report to the clients, who might wish to further analyze the candidate.

“The full transcript of the conversation and answers can help the clients to make a more-informed choice. This has a very positive impact on decision making,”​ Payne said.

A cheaper option

This solution saves a lot of time and resources and is much more efficient than tests done by humans.

“A chatbot can be a cheaper option than hiring an agency that uses human ‘man-hours’ to conduct these kind of tests,” added Payne.

“A chatbot conversation is fully transparent. Candidates can freely type their answers, which cannot be plagiarized.

“That’s an unbiased element of technology, so the conversation script is pure and not edited,” Payne finished.

AI chatbots can therefore carry out the recruitment and hiring process in an unbiased way. As a result, this leads to more equal and fairer candidate evaluation and selection.

AI Chatbot for Onboarding Processes

A chatbot can also come in handy when onboarding new employees to the team.

Onboarding looks more or less the same for the majority of employees.

New employees might have a lot of questions regarding the organization of the company, and many of them are repetitive: what is the company dress code? How can I get my ID card? Where can I pick up my laptop and phone?

There is no need to visit or call the HR department. This process can be easily automated.

A chatbot can communicate with employees through text messages or on the website.

“When clear processes are put in place, one can extract solid FAQ’s that cover all scenarios and possible situations that need answers,”​Ahmed Salama said.

Choose the Right Technology for HR and Recruitment

There are many benefits of implementing a chatbot in digital HR and Recruitment.

This AI tool can handle administrative tasks such as scheduling meetings, conducting interviews, reporting and information extraction. It is also a flexible and efficient solution available 24/7.

Randi Mørk Lildballe summarized her approach to implementation of AI chatbot in HR and Recruitment.

“It depends on the company or organization. I can see great opportunities with recruitment, pre-, on- & offboarding, company policies, training, benefits, reviews etc.

“The list is long – but again we need to keep the WHY in focus.

What is the purpose of implementing an AI chatbot in this specific company/organization, what is the benefit and how does it support the strategy?

When we have answered these questions, I would be more than happy to get started,”​ she said.

This article is based on:

1. The interview between Beatrice Carraro, Head of Marketing at Certainly, and Christian Payne, the Founder of Payne Search.

2. The interview between Patrycja Hala Saçan, former Marketing Specialist at Certainly and Randi Mørk Lildballe, Global Talent Onboarding Manager at Odense Robotics.

3. The interview between Patrycja Hala Saçan, former Marketing Specialist at Certainly and Ahmed Salama, Regional Human Resources Director at Otis Middle East.

Written by Patrycja Hala Saçan.

The post Digital HR: How Conversational AI Is Providing Profitable Solutions appeared first on Certainly.

We have gathered top tech publications about latest big data trends including Artificial Intelligence, Big Data and Machine Learning, Deep Learning and trends in the IT industry. 

1. Analytics Insight

Analytics Insight is one of the top tech publications reporting on big data projects, tech trend developments, and achievements made by AI, Big Data and Analytics companies across the globe.

The domain provides daily big data news as well as analysis, opinions, and research reports about robotics, AI, big data and machine learning problems and solutions, Cyber Security, Digital Transformation and much more.

2.KDnuggets

Another of the top tech publications, focusing on AI and big data news is KDnuggets. An authoritative source on AI, ML, data science, and big data trends with over 500,000 unique visitors per month.

The platform was created in 1997 and since then has been edited by Gregory Piatetsky-Shapiro, who was the first recipient of ACM SIGKDD Service Award in 2000 and was ranked no. 1 in LinkedIn Top Voices 2018: Data Science & Analytics.

Excellent tutorial materials, courses, webinars and online events make this platform a comprehensive source of knowledge.

3.MIT Technology Review

MIT Technology Review, with 120 years of experience in reporting on technology and innovation. This publication is one of the most authoritative resources providing reliable and up-to-date information.

The site focuses on creating approachable for everyone big data news analysis, business reports, reviews, and essays. This top tech publications 3.5 million followers demonstrate the high level of expertise and authority of the editors.

4. Inside Bigdata

Inside Big Data provides big data news, reports, white papers, podcasts, and job postings within the fields of Big Data, AI, ML, and Deep Learning.

It is for data scientists as well as IT and business professionals. Readers also have access to interviews with researchers and business executives who provide insight into tech research and their daily work.

5.Dataversity

Dataversity creates educational resources on implementation and the management of data for businesses and IT professionals.

The site keeps users up-to-date about big data news, data conferences, and live webinars. If you are interested in big data trends, you can apply for online courses related to big data projects and data governance.

6.Datafloq

Datafloq is one of the top tech publications that’s a great source for big data news, blockchain and AI.

Dr Mark van Rijmenam, the founder of the website, is a globally recognized speaker, adviser and influencer. He is also the author of the bestselling book “Think Bigger – Developing a Successful Big Data Strategy for Your Business”.  

You can join the Datafloq forum to exchange knowledge on big data projects. Users have a chance to publish their articles too, providing an excellent opportunity for exposure within the field.   

7.Dataconomy

Founded in Berlin, Dataconomy focuses on big data news, expert opinions, and events within data-driven technology.

The “conversation” section hosts interviews with founders and executives of top tech companies, providing insight big data and machine learning application in different sectors.  

The platform also connects professionals to relevant companies by posting employment opportunities on the job board.  

8.Datanami

Datanami details the latest big data news and big data trends for its readers, focusing on sectors such as academia, biosciences, financial services, government, healthcare, manufacturing, retail, and science.

The site is available in English, Chinese, French, Japanese, German, Korean and Spanish, providing a hub for data scientists to connect on big data projects around the world.

9.Data Science Central

Data Science Central is an educational resource for Big Data practitioners providing educational materials and IT, AI, deep learning, and big data trends.

For those who prefer audio learning, the site offers podcast series on problems related to big data projects, AI, ML and others. You can also join Data Science Central forum-based support to facilitate your ideas and work.  

10.Database Trends And Applications

This magazine make the top tech publications list for covering data management, big data news, and data science.

It includes downloadable white papers on companies’ big data and machine learning, tech solutions, business guides, and data application and management.  

11.Informs

The Institute for Operations Research and the Management Sciences is a large international community of 12,500 professionals and students from 90 countries.

The organization connects its users to big data news, research, conferences, and educational sources in operations research, management sciences, analytics, engineering, IT, and others. 

12.Analytics Vidhya

“Vidhya” meansknowledge or clarity and Analytics Vidhya focuses on providing knowledge and career guidance to Data Science professionals.

As the creators believe in self-practice, you can access tips and tricks related to big data and machine learning, business analytics, and case studies of big data projects and their analytical solutions.  

13.Distill

Distill is an academic journal for big data and machine learning research. This top tech publications USP is acting as a platform to educate yourself and publish your work.

Contributors are encouraged to go beyond traditional academic forms and find a unique way to communicate about science. Moreover, Distill offers a prize for outstanding ideas in ML and related fields.

If you think that your big data project provides a new perspective, clarifies things, or you created a useful tutorial you can submit your candidacy for the prize.

14.IBM Developer

Developer IBM is an excellent resource for anyone interested in coding, packed with open source code, instructional tutorials, and tools videos.

In one place, you have access to security and compliance information, speed and reliability and support, big data news, and advice from a community of expert developers. 

15.AI Trends

AI Trends helps you to be up–to–date with the latest big data trends. Big data news in efficiently collected into one easy-to-use platform.

With more than 2000 studies, this archive includes interviews with tech professionals working for companies such as Uber, Thomson Reuters, or SAP. 

16.Machine Intelligence Research Institute

The Machine Intelligence Research Institute (MIRI) is a non-profit research organization studying the mathematical foundation for safe and reliable application of Artificial Intelligence.

The blog includes analysis of problems and solutions to show the reader obstacles related to big data projects and building responsible AI.  

17.AI Playbook

Andreessen Horowitz, also known as 16z, was founded in 2009 by Marc Andreessen and Ben Horowitz. As the founders understand the hardship of building a successful tech company, they aim to make the process easier for others.

The platform offers an approachable information for those who are involved in creating and managing software, as well as big data news and trends.  

18.MIT News

MIT News, straight from the Massachusetts Institute of Technology aims to promote education, research and innovation.

The platform mostly communicates the institutes announcements, community events, and students and staff achievements.

The big data news section is a great source of information about recent big data projects from MIT alum.

19.Science Daily

ScienceDaily is on the list of top tech publications to follow for the latest news research. The research is divided into 12 sections and more than 500 different topics.

They cover physical sciences and technology, business, education and big data news. They feature big data trends from around the world, and since the news are divided into specific topics, you will find news relevant to you in one place. 

20.Emerj

Emerj Artificial Intelligence Research is working with businesses to develop cutting-edge AI solutions. Emerj’s rich content is a useful source of knowledge for everyone just starting their journey with AI.

This publication offers in-depth case studies on big data and machine learning applications in marketing, finance, security, and other sectors. Moreover, podcast interviews highlight the iconic figures in business and big dat news.

21.R-bloggers

R-bloggers is a blog aggregate of content shared by a community working with the R language and open-source software for statistical software and Data Analysis.

You can also add content and connect with other data professionals. Content is not limited to the R language exclusively, more general topics related to big data news and big data projects are covered too.

22.Journal Of Artificial Intelligence Research

Another of the top tech publications worth your attention is Journal of Artificial Intelligence Research, established in 1993 as one of the first scientific research journals on the internet.

This open-access journal has an editorial focus on all aspects of AI, including automated reasoning, natural language, big data and machine learning, robotics and vision.

This publication is an outstanding source of big data news for its high standard and original articles.

23.Jaxenter

Jaxenter is all about big data news, views and tutorials related to Java, Machine Learning, Development and Operations. This top tech publications tutorials were created by the experts, with detailed descriptions and visual representations.  

The platform accepts tutorials for publications accompanied by a $50 charity donation! You can share your skills in big data projects and at the same time support a good cause.

24.Hunch

Hunch is led by John Langford, who holds a PhD in Computer Science and works on big data and machine learning for Microsoft.

He calls Hunch an “experiment in the application of a blog to academic research in Machine Learning and learning theory”.

The founder welcomes everyone from the field to contribute to the blog with original articles, studies, and big data news.  

25.Analytics

Analytics Magazine is published by INFORMS. This top tech publications distributor is the leading international association for professionals working on Operations Research and Analytics.

With an editorial focus on data analytics, big data and machine learning. Written by academics as well as professionals working, the platforms resources are a good source for the latest big data news.

Interested in More Big Data News?

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Article written by: Arta Vitola and Patrycja Hala Saçan

The post TOP 25 Artificial Intelligence and Big Data News Publications appeared first on Certainly.