1. Certainly

Certainly excels in creating highly customized conversational experiences that cater to various industries. Its platform stands out for its robust integration capabilities with multiple systems and its ability to seamlessly handle complex queries. Certainly is praised for its intuitive design that allows for easy creation and management of chatbots, making it ideal for businesses looking to enhance their customer service without extensive technical expertise. For more about their features, visit Certainly’s official website.

2. IBM Watson Assistant

IBM Watson Assistant is renowned for its deep learning capabilities and natural language understanding, which allow it to deliver more meaningful and contextually relevant interactions. It is particularly favored by organizations that require a high degree of customization and secure data handling. Watson Assistant integrates well with existing business systems and offers extensive tools for detailed analytics.

3. Google Dialogflow

Dialogflow by Google is a popular choice for developers due to its powerful natural language processing (NLP) capabilities that support dynamic and rich conversational experiences. It’s compatible across multiple devices and platforms, making it versatile for creating chatbots that can operate on websites, mobile apps, and popular messaging platforms like WhatsApp and Messenger.

4. Microsoft Azure Bot Service

Microsoft’s Azure Bot Service leverages the company’s cutting-edge AI services to offer robust conversational interfaces that can be integrated with various Microsoft products and services. It supports a multilingual and multimodal experience, allowing businesses to reach a global audience effectively. Azure Bot Service is known for its enterprise-grade security, making it a trusted choice for large corporations.

5. Kore.ai

Kore.ai provides a powerful AI-powered platform that excels in automating complex business processes. It offers features like voice and text-based conversational capabilities, pre-built templates, and extensive analytics tools. Kore.ai is designed to deliver a seamless omnichannel experience, ensuring consistent interactions across all customer touchpoints.

These platforms are at the forefront of the conversational AI field, each offering tools and features that help businesses automate communications, enhance customer engagement, and streamline service operations. As AI technology continues to evolve, these platforms are expected to introduce even more advanced capabilities, further transforming the landscape of digital interaction.

Pricing Considerations

When selecting an AI chat platform, pricing is a critical factor to consider. Certainly offers a transparent pricing structure tailored to various business needs, making it accessible for startups and scalable enough for large enterprises. The platform provides different pricing tiers to accommodate the specific requirements and budgets of different organizations, ensuring that companies can find a plan that suits their specific use case and expected volume of interactions. For detailed information on the different pricing options available, you can visit Certainly’s pricing page. This will help potential users make an informed decision based on their budget and the features they require.

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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.

2021 in review 

2021: Kudos to us, but it’s all thanks to you. 

2021 was certainly quite a year. We doubled the team, did a complete rebrand, and reached or exceeded our goals. Kudos to the entire Certainly team for this accomplishment and here’s to a brilliant 2022! We would however be nothing without the value we provide to our customers, so I want to lead with that: It’s 110% about the value to our customers. 

Most companies today use us to drive more revenue or scale support. We are fortunate to see first-hand how our customers’ conversation-first user experience unlocks outsized value, and our mission is to accelerate their growth. Here are some examples from across our customer community: 

Higher CR: The average is 3x the usual conversion rate.

More conversations with more visitors help our customers better understand each visitor and what’s important for the visitor in each unique session. As their bot responds to questions, personalizes the webshop content accordingly, and drives the conversation forward, their visitors buy at a much higher rate. Here’s an example from a fashion brand: 

Sizing is amongst the top 3 reasons for concern when buying clothes and apparel and the #1 reason for returns. In the example above, the visitor is advised by the bot to purchase another size than the one they expected, based on their input. Only through conversation can you pick up on this and advise accordingly. 

Depending on how merchants use their Certainly chatbots (e.g., reactive vs. proactive), the conversion rate ranges between 3-18%. This number matches a multitude of third-party studies reporting that 70-85% of shoppers say personally relevant content from brands increases their purchase intent (M. Kaptein, R. McFarland, P. Parvinen, How personalized sales tactics can be automated online, 2019). 

Data from ecommerce businesses using Certainly chatbots shows The Average Order Value is 20% higher. 

Enabling personalized help at scale also means customers end up purchasing more per order. Merchants that are using Certainly to drive sales see on average 20% higher order values and often this is driven by cross-selling relevant products. Here’s an example; the bot has added a product to the cart and continues the conversation with a potential upsell: 

Online buying experiences that adapt to the individual shopping session – not just the individual user – generate outsized results.  
 
And again there is a lot of research, including from MIT, that suggests the same: “If you know the shopping type of the individual shopper and personalize the webshop accordingly, you will increase sales by +20%.” (J. R. Hauser, G.L. Urban, G. Liberali, M. Braun, Website Morphing, 2009. & more).

Customer insights: Forget big data – go for the right data. 

By the end of 2021, our customers were collecting an average of 100,000 zero-party data points each month using Certainly chatbots. This is data that their visitors have voluntarily provided via conversations, usually by requesting help in finding the right product for them. 

When you pair zero-party data with Certainly that enables unlimited sale conversations, you can tailor the experience to each purchase moment. When measuring the impact on a company’s business, it is proving itself.

Gross Margins – your margin is your opportunity. 

With the larger number of sales due to higher conversion rates and when each transaction is 20% higher, your gross margin per order improves significantly. 

Using benchmark ecommerce P&L data, the GM improves in the ranges of 200-550%. That is new revenue our customers are using to grow their customer base and revenues faster. As we see customers adopt our technology and see the results, they are rapidly increasing their usage of Certainly to help even more visitors and drive more value.  
 
In 2021 the revenue impacted by Certainly varied depending on the number of visitors and average order sizes of the individual merchant. Small to midsized merchants as an example, saw extra revenues of ∼ €400.000 via sales impacted by their bots, which added ∼ 10% on top of their 2021 overall online revenues. 

In 2022 we aim to help grow the revenue even more for merchants across our customer community. If you want to see how we can help drive more revenue in 2022, please reach out to your direct contact or via support@certainly.io. We’d love to hear from you! 

Support scales in quantity and quality.

As our customer’s bot handles more and more requests during 2021, their customer support teams experienced several things: 

1. The total number of requests from visitors increases as more people can get answers to questions whenever they want. This is good for gathering customer insights, solving their questions, and driving sales. 

2. As customers’ chatbots handle between 20-80% of end-user requests, the agent’s average response-times drop significantly, often by 50% or more. 

3. The combination of the bot’s scalability and the agent’s effectiveness means the overall customer satisfaction increases as faster resolutions are offered to a breath of requests and more quality time is available for agents.

Customers & partners growth.

We saw great growth in 2021 and we accelerated even more during the second half of the year. We hit the important end-of-year targets for new business and NRR which was 18% above the target. The new business indicates our ability to attract new customers and NRR indicates how good we are making current customers successful, so these are very important metrics for us.  

As of the end of 2021, we are helping customers from 17 markets across EMEA and the Americas with the US, UKI, and Benelux being the fastest-growing markets throughout the year. 

Our tech and agency partners played a big part in the 2021 growth, and we’ll continue to invest a ton in partnerships during 2022. Thank you to all our partners for a great year! Now, let’s make 2021 jealous of 2022. 

Team growth – investing in technology and support.

In 2021 we doubled our team in Copenhagen, Madrid, and remotely. The team now consists of 26 different nationalities and is a diverse group across all departments and functions from individual contributors to leadership. Many colleagues took on new responsibilities within the company and that we create personal growth opportunities for individuals, is something I am particularly proud of.   

Almost 70% of our team works in Product and Customer Success – this is a deliberate focus on investing heavily in providing value to you, dear customer, via our technology and support. In 2022 we’ll grow our team even more, with talented new colleagues ready to add to – and accelerate what we are already doing. 

New Board members.

In 2021 we announced Jesper Lindhardt, a SaaS veteran and the former Chief Commercial Officer of Trustpilot as Chairperson. Jesper joined the board along with the CEO of Templafy and the Strategy Director of Valtech. To me, this is like having Pep Guardiola, Zinedine Zidane, Diego Simeone, and Michael Laudrup as coaches (yes, I am a football fan). In 2021 they have played an important role and will continue to help us play our best games in 2022. 

From Botox..sorry BotOX…argh…BotXO, to Certainly. 

Even though I thought “BotXO” was a brilliant name when I came up with it, I slowly got the message as customers couldn’t pronounce it and colleagues didn’t dare to say our company’s name during job interviews.  

I am happy we decided to do a total rebrand towards an identity that is less tech and more human. We launched the Certainly brand in May and thank you for all the positive feedback we have gotten from you since. That meant a lot. 

For those of you who came after – or have forgotten how we looked like previously – here’s a before and after comparison.

From BotXO…

…to Certainly.

Product usage & features.

The volumes of conversations and zero-party data through our platform have grown 10x during 2020 and 2021. In the second half of 2021 alone, the volumes doubled. That is significant growth and we’re excited that our customers’ usage grows rapidly as they see the results and want to utilize our platform more.

In 2021 we launched a ton of new features, including a cutting-edge new Conversational AI engine that many customers are already using. In our efforts to continuously shorten the time-to-value for our product, we launched many new default domains, intents, and entities based on the use cases from our customer community. We also added to the templates integrations we offer and currently, we’re integrated with 60+ third-party tools and channels. In 2022 we’ll grow the list even more.

If you have any suggestions for tools to integrate with, we’d love to hear from you: support@certainly.io  

Covid…  

I have to mention Covid and acknowledge how it kept impacting our lives, both privately and at work during 2021. Kudos to all of us for coping and getting things done despite it.  

A peek into 2022 

However, much we felt we accomplished in 2021, we have only just gotten started. We’re already working hard to make 2022 an unforgettable year. 

In this write-up, I will not dive too much into 2022 – that we will do at another time soon. For now, I just want to touch on some of the major topics for us in 2022 for driving value to you, and why. 

Zero-party data & Personalization

Covid has accelerated ecommerce penetration forward several years. Omnichannel brands are prioritizing growing the online share of total revenue and online-only DTC brands are similarly growing fast online. 

The frontrunners look to hyper-personalization as customer demand increases and capturing their audiences’ real-time intents, motivations, and preferences at scale allows them to create a truly personalized experience for each visitor. A user experience that drives revenue and turns one-time buyers into brand loyalists. 

While competition is increasing Apple/Facebook/Google’s efforts in protecting consumer’s data, (or war on advertising $$$ depending on what you believe), is restricting the use of third-party data, cookies, and IDFAs to target and understand visitors. 

This means that zero-party data becomes king and queen.  

What is zero-party data and why is it important? 

Targeting is especially important for brands with products that cater to specific audiences. With fewer opportunities to use third-party data (e.g. targeting your audience on Facebook/Google) and first-party data (e.g. what visitors are doing on your website), you must increasingly rely on data collected in real-time and directly from the visitor to convert them and get the ROI you are looking for. 

That is what zero-party data is. Zero-party data is that which a visitor intentionally and proactively shares with a brand. It can include purchase intentions, shopping session context, and similar. 

With zero-party data, you tap directly into the mind of the visitor, what she/he/they are looking for and what is important in that exact purchase moment. As mentioned earlier, when you pair zero-party data with technology that enables unlimited, concurrent conversations, you can tailor the experience to each purchase moment. 

That is where Certainly comes in. 

Enabling our customers to collect and use zero-party data in real-time conversations to drive sales and consumer insights is one of our main areas of focus in 2022.  

It’s not easy adopting a new product.

Most of our customers see immediate and great value in our product. That is a good starting point, but we know that our type of technology is still new to most people, and we, therefore, must work on enabling the market and helping each customer see value fast and continuously. 
 
Just like us, we know you already have your workday more than full of things to do. Now we’re asking you to take a bet on us and adopt a product that is most likely new to you. This is a BIG ASK and even though we successfully show the value to you, you still must carve out time to get familiar with our product, use it continuously, and allocate resources (time, money, people). 

Therefore, we focus relentlessly on making it faster to see the value and manage your bots continuously. It’s that simple. Keep improving the product experience to increase the value it brings to our customers, and enable the market to understand why, and how to use the product.  

More actionable insights & a new product version.

As part of this dedication, we will invest even more in how we provide you with actionable insights; in automated ways in the platform, via 1:1 dedicated support, via newsletters, webinars, and in the form of benchmark reports, best practices, and more. 

We will also launch a new version of our product in 2022. A version that is based on all the inputs and learnings from the past years being one of the frontrunners within Conversational AI. Stay tuned.  

Thank you for 2021 – here’s to a great 2022 together. 

Before ending this write-up, I want to thank you, dear customer and partner, for the time, resources, and valuable inputs you have provided in 2021. Let’s make 2022 a very successful year together. 

P.S. Remember life is a marathon with sprints – make sure to have fun while running it. 

Henrik  

The post Certainly CEO’s 2021 year in review (& a sneak-peek into 2022) appeared first on Certainly.

Certainly – one of the best bots around – has always produced the former.

But with significant investment, including from the founder of Nordic ecommerce giant Whiteaway, the Copenhagen-based business can now boast the latter as well.

Certainly as one of the chatbot companies revolutionizing ecommerce

Picture this. You’ve recently purchased some clothes from an ecommerce store, and you have a query.

You do what most of us would and check the brand’s website, but you’re unable to find an answer.

Upon calling customer support – which closes soon – you discover that you’re 28th in the queue to speak to a customer success representative.

It could take days to get a response to an email.

What do you do?

The solution: Conversational AI, or a chatbot.

Many businesses with a presence online are realizing the importance of optimizing their customer experience so that valued clients are not put in this situation.

And they are increasingly turning to chatbot companies like Certainly to do this.

With its 24/7 customer support, conversational commerce capabilities, and advanced machine learning, Certainly is streamlining customer service costs while enhancing customer satisfaction.

On the path to growth: big investment for Certainly’s ultimate bot

After a 2.5 million DKK investment from the company’s first venture round, Certainly is ready to further expand.

“We could see that because the interest was so great, we would be able to grow faster,” said Henrik Fabrin, CEO and co-founder of Certainly.

“Chatbots are new to many people, so our customers need some assistance.

“Otherwise, we risk a situation like in 2008, when every business wanted an app, but no one was sure how and why their customers should use it,” Fabrin finished.

Fabrin was a successful founder of several other companies before starting Certainly.

As a result, he had the available funds to invest in chatbot technology.

Certainly’s employees spent one year focussing on development and deployment, which Fabrin paid for out of his own pocket.

Despite increasing sales, the Co-Founder felt the time was right to get an investor on board.

“We had not been pitching ourselves, and we actually turned down quite a few potential investors,” he said.

“However, we ended up saying yes to Esben and Thomas because they are exceptionally talented, have solid networks, and they also happen to be lovely people!”

The names Esben and Thomas refer, respectively, to Esben Gadsbøll – who founded the Whiteaway group and is now a full-time angel investor – and Thomas Black-Petersen, who sold his IT company Inspari in 2016.

Certainly’s practical approach provides value for investors

“I had seen several chatbots, but I did not find the technology particularly exciting. It was not the right time for me to invest in it,” Gadsbøll said.

“However, Certainly has a completely different, practical approach and could provide value now. I could see that they had a lot of enthusiastic customers,” he continued.

Gadsbøll invests with a sharp focus on e-commerce; an industry he is intimately familiar with through his role as co-founder of Whiteaway.

Recent chatbot growth has shown us that chatbots are useful for a variety of purposes.

Gadsbøll also sees a prominent place for chat media in future online shopping experiences.

“Being able to manage 75% of customer inquiries with a chatbot, and only require people for that last 25% is super interesting.

“Once you have enough orders, it becomes expensive to have customer service employees,” he said.

Chatbot companies are not a new phenomenon

Back in 2005, Ikea launched the chatbot Anna, who could answer basic questions about things like opening hours.

But after ten years of service, she was retired, as customers were not overly satisfied with the service.

Today, the idea of automated answers has seen a great renaissance.

This is perhaps in part because of Amazon’s Alexa service and the rapid development of artificial intelligence. 

The way to build a bot is by training the machine with a lot of data from the real world.

That world can be very different from company to company – for example, a bank has very little in common with an ecommerce store selling underwear.

“Artificial intelligence and machine learning are all very nice, but you need a lot of data before it gets really interesting,” said Esben Gadsbøll.

“I hear many people say that ‘In about two or three years, we’ll have enough data.’ But then where do they get that data from?”

Creating one of the best bots available

To Henrik Fabrin and his colleagues in Certainly, it’s about getting started at the customer end quickly to create effective solutions for customer service.

“We usually start by inputting the 50 or 100 most common questions.

“However, it’s a journey with most companies. You can’t get answers to all types of questions, but you don’t need that,” the founder said.

On one ecommerce store, the chatbot may handle a customer who wants to return an item and send the package labels.

Once the request becomes more complicated than the coding can manage, a human takes over.

Outside of opening hours, you can order a phone call or a chat with a human the next day.

Besides customer service, an automated chat can also be used to purchase items.

This is particularly essential in Certainly’s home market of Denmark, where Facebook Messenger is extremely popular.

“In Denmark, it is the app where people spend the most time talking. As a brand, being able to sell through Messenger is interesting, because that’s where the consumers are,” says Henrik Fabrin.

An exciting future with Conversational Commerce

Conversational AI platforms are resolving inefficiencies within the customer service sector today.

It will be fascinating to see how this further develops.

Chatbot companies like Certainly offer conversational commerce capabilities, where customers receive a more personalized experience when shopping through zero-party data.

It allows for navigation around UIs, upselling, product suggestions, customer satisfaction scores (CSAT) of up to 96%, and ultimately increased revenue.

Considering the lack of coding experience needed to build a bot with Certainly, there’s clearly a compelling case for the continued success and growth of the business.

Article written by Farnaz Aref

The post Significant investment for one of the most innovative chatbot companies appeared first on Certainly.

The authorities are being bombarded with questions from people concerned about the health and financial effect of the Coronavirus, and the result is very, very long waiting times, uncertainty from citizens and unbearable stress on the hard-working people in those organizations.   

We want to do our part in helping people get faster, reliable answers and for organizations and companies to minimize the impact this has on their support teams. We provide AI-based solutions and know from experience that companies around the world, who are using our AI in their customer support today, are witnessing how AI is a win-win for everyone, both the end-users and employees. By using AI to answer a large percentage of the questions they get, the authorities, organizations and companies free up valuable time for their support teams, so they can spend more time on other questions that require a human touch. 

Our aim is to give our contribution to the fight against Coronavirus – but we need YOUR help. 

A chatbot for coronavirus

The challenge with getting the AI ready to help answer questions on Coronavirus is that the dataset it needs to be trained on is non-existing. The dataset in this case would be a variety of examples of Coronavirus-related questions in different languages. The dataset will allow the chatbot for coronavirus to understand user questions and provide information accordingly.

To solve this, we are now making public our proprietary AI trainer, which makes it easy to contribute with sentences. You can help by adding Coronavirus-related questions in your local language. Anyone who wants to contribute can do so, in any language. 

What will we do with that data? We will make sure to compile the data, refine it, train the AI and build the world’s first and largest AI dataset dedicated to Coronavirus. 

AI Trainer – creating the dataset

The online AI trainer will guide you through a series of typical Coronavirus questions. It will, for example, say “You want to know what to do if you are sick”. Then you will type in a question or sentence, in your language and in your own way of asking it. For example, you would maybe input sentences like “what should I do if I am sick?” or “what to do if I feel sick?”. 

As you type in your questions, you will help create a varied dataset so that the chatbot for coronavirus will be able to understand more and more variations of any of these questions. People will ask the same question in many different ways, so it’s best for the AI to have a large pool of phrases.

How you can contribute, today

There are two ways you can make an impact. 

• As an individual: 

All you have to do is to type in different questions. The data that anyone contributes with will be 100% anonymous. Once that is done, you can try out the AI and see how well it understands a Corona-related question. The more people contribute, the more the AI will improve – so it is really the sum of all our individual contributions that will make a difference.  

• Share with others: 

We encourage you to share the AI trainer with your colleagues, family and friends so that they can also contribute and share. AI technology is a powerful tool; let’s use it to do something good. 

What will the dataset help solve, now and later?

• In the short term: 

The dataset we will create during this time can help anyone who is currently building and using chatbots and voice bots to make it easier for end-users to ask questions in their native language and for the bot to understand the questions and provide answers. 

The dataset will be open sourced, free to use, and made available as a paid, plug-and-play version. Both are usable to all individuals, companies, organizations and universities who want to build solutions that help with Coronavirus. The data is also being used for the chatbot for coronavirus ‘Coronabot’ we have launched as an example of how the dataset can be used. 

• In the long term: 

The dataset can be re-used for other diseases. By replacing “Coronavirus” with any other known disease, virus or infection, future solutions will be able to provide information about those too.

This type of technology is fast to implement, and it would help health organizations, public authorities, doctors, and private companies respond much quicker and be proactive to any new local or international health issue. Providing instant answers to questions coming from citizens, employees and customers is crucial to avoid uncertainty and stress on both ends: those who ask the questions, and those who struggle to answer all of them, in call centers and customer support.

We are starting with the most asked questions on Coronavirus, but we will expand the topics into, for example, employment, insurance, travel, etc related to the Coronavirus. That means the dataset will expand over time and will be customizable to any context or industry. The dataset can easily become company-specific, to answer questions that apply to a specific company and their employees only.  

Having a public source of reliable information will also help combat misinformation. This chatbot for coronavirus, and any bot created with a similar purpose, can provide fast and reliable answers sourced from the authorities and companies people trust, without having to seek information in countless different places.  

The dataset can be used in any language and can be used together in both text- and voice-based bots or communication systems.

Thank you, 

Henrik Fabrin CEO & Co-founder + the entire Certainly team. 

The post Help create the world’s largest multi-language AI chatbot for Coronavirus appeared first on Certainly.

After successfully releasing Danish and Norwegian BERT models, Certainly is ready with a model for Swedish, the Scandinavian language with the most speakers.

Certainly’s Swedish BERT model has been trained on a staggering 25 GB of raw text data. This is more than ten times more data than the previous largest Swedish BERT model.

As with the Danish and Norwegian models, it’s freely downloadable from Certainly’s GitHub profile here: Nordic BERT.  

Certainly’s ambition is that the model will contribute to the Swedish Natural Language Processing community in the same manner that the Danish and Norwegian BERT models have been contributing in Denmark and Norway.

The hope is that Swedish data scientists will share their findings.

How Is Swedish Language Different?

Swedish has a different set of characters than Danish and Norwegian. Apart from the usual English letters, Swedish uses the vowels Å, Ä, and Ö.

More than 10 million people speak Swedish, almost as many as Danish and Norwegian combined. Certainly is in the process of running a more in-depth analysis of the data for the different languages.

They believe that careful analysis will allow them to improve the quality of the training data. 

As an example of Certainly’s current findings, consider the peculiarity of Lsjbot, a Swedish Wikipedia bot that skews automatically gathered datasets in Swedish.

What Is Lsjbot?

Lsjbot is an automated article-creation program that mostly writes articles for Swedish Wikipedia.  Take a look at the following chart of Wikipedia articles in different languages:

Do you notice something strange? Despite only having 16 million speakers in the southern part of the Philippines, Cebuano is the second most popular language.

The third most represented language by article count is Swedish. So what’s going on?  

It turns out that most of the Swedish articles were contributed by the Swedish physicist Sverker Johansson.

Or rather, Lsjbot, an automatic robot that Sverker Johansson created. The robot reads data from a database and automatically writes and publishes articles.

The articles are automatically generated, so they all incorporate similar expressions.  

For example, an article might display information about an animal, and feature the sentence “The average adult [Giraffe] is [4.6m-6.1m] tall and weighs [800kg]. Its diet mainly consists of [leaves, seeds, and fruit]”.

Lsjbot then replaces the variables within the sentence for different animals.

While this might be great for filling out a Wikipedia page, it isn’t beneficial for training Natural Language Processing algorithms.

The algorithm favors particular expressions because there is a high volume of similar sentences.

In turn, it skews the model and impacts performance negatively.  

As to why is Cebuano the second most represented language, here’s a hint: Sverker Johansson’s wife is from the Philippines…

What Can You Expect in the Future From Certainly?

Besides training a Finnish BERT model, Certainly is going to work on running a more detailed analysis of the data for different languages.

By publishing high-quality data sets, Certainly hope to get data scientists from all over Europe to contribute to their efforts in improving Natural Language Processing for all European languages.

Article written by: Jens Dahl Møllerhøj

The post Certainly Releases Swedish BERT Model, Completing the Scandinavian Trio appeared first on Certainly.

Certainly’s open-source Danish BERT Model has sparked quite a bit of interest. Danish newspaper Børsen wrote an article about it, and many Danish data scientists have been involved in discussions about it on GitHub.  

Many of our customers here at Certainly are also running experiments and are using the models for different projects.

Today, Certainly’s data science team has released the first-ever BERT model trained on Norwegian language data.

The most important aim of this is to help data scientists in Norway build state-of-the-art Natural Language Processing solutions.

We encourage Norwegian data scientists and managers to reach out to us, just as the Danish community did. 

Today, we are also releasing an improved version of the Danish model. You can find both the updated Danish BERT model and the new Norwegian BERT model in the same GitHub repository.

Why Release a Norwegian BERT Model?

The Norwegian language is only spoken in Norway, where there are approximately 4.6 million native speakers. 

Like Danish, this means that the language is often overlooked for Natural Language Processing tools. 

By open-sourcing a Norwegian BERT model, we hope to help the community build their own Natural Language Processing solutions.  

Conversational AI from Certainly supports Norwegian out of the box.

By using our prebuilt intents for Norwegian, it’s easy to build a personalized, state-of-the-art chatbot.

How Do We Train the Models?

We train BERT models on a new kind of computer chip called a TPU, short for Tensor Processing Unit.

This kind of chip is excellent at “Tensor” operations, which is perfect for training Deep Neural Networks. 

The same way that “Vector” means a list of numbers, and “Matrix” means a rectangle of numbers, a “Tensor” is just a fancy word for a box of numbers. 

A 1-dimensional tensor is a vector, a 2-dimensional tensor is a matrix and anything with more dimensions, such as a box, is a tensor. 

Renting Google’s TPUs – which is the only way to access them – costs a lot of money.  

In short, TPUs are expensive to use, so it is important to make the algorithms run as fast as possible to decrease cost. 

Where Does the Training Data Come From?

We use text fetched from the internet to train our BERT models.  

The non-profit organization, Common Crawl, periodically gathers huge amounts of data from the internet.

By automatically detecting the language of the text, we can create a data set of Norwegian data. 

Because it takes a lot of time to read through the vast amounts of data, consequently we have run our algorithms on multiple computers at once.

And also ensure that our algorithms are extremely fast!

What Are We Going to Do Next?

Now that we have released a Norwegian model, we are going to target other Nordic languages, including Swedish and Finnish. 

However, since NLP research is progressing so rapidly, it is becoming increasingly challenging to maintain a repository of models that are up-to-date with state-of-the-art research.

That is why we have decided to pick a different strategy.

Rather than releasing more European models, we are going to release our data sets formatted for training new BERT models in many different languages.

Importantly, we hope that we can get the European NLP community to help us train models that are up-to-date with state-of-the-art General Purpose Language Models. 

Please share this article and remember to check the blog regularly for updates on our new Model Zoo initiative! 

Article written by Jens Dahl Møllerhøj

The post Certainly Releases the First-Ever Norwegian Bert Model appeared first on Certainly.

The Certainly BERT model has been fed with a staggering 1.6 billion Danish words and it is also available as open source.

Google’s BERT model is one of the most well-known machine learning models for text analysis.

The search giant has released English, Chinese and multilingual models. But now, Certainly are the first to release an extensive open-sourced Danish BERT model.

Academics from Copenhagen University and Alexandra Institute have stated that the language model performs better than models by Google, Zalando, and Stanford University.

The conclusions of the study can be found in DaNE: A Named Entity Resource for Danish

The code is downloadable for free from Github.

Language models are constantly evolving

Certainly has built upon Google’s BERT model because many Danish private companies, institutions, or NGOs in need of AI in Danish could greatly benefit from it.

This makes Certainly one of the very few companies in Denmark to improve and support Danish AI by publishing open-source code.

This is no small thing. Firstly, this next step in the development of the Danish BERT model is highly useful for the whole Danish AI and machine learning ecosystem.

Secondly, it provides inspiration to the whole industry by democratizing AI and making new updates publicly available, for everyone.

“Certainly might be the single company in Denmark lifting the community with open source code,” said Jacob Knobel, CEO of AI consultancy from Datapult and a Forbes 30 under 30 in 2016.

“It is both important and inspirational for the industry,” he finished.

What is a BERT model?

BERT is an acronym for “Bidirectional Encoder Representations from Transformers”. It is a deep neural network that is useful for Natural Language Processing (NLP).

The network has learned about Danish grammar and semantics by reading vast amounts of Danish text.

How much text has the Danish BERT model read?

When working with AI language models, part of the challenge is to collect huge amounts of text.

This is needed to make an extensive model.

Certainly has managed to overcome the obstacle by turning the model into a massive bookworm.

Certainly’s Danish BERT model has read 1.6 billion words, equivalent to more than 30,000 novels.

Although this might sound like a lot, the model could have read even more, but it is difficult to find much more publicly available Danish text.

What can you use the Danish BERT model for?

The general language understanding capabilities of the model are useful for text analysis pipelines.

The model reads texts and returns vectors, which are points in a coordinate system.

The shorter the distance between the points returned by two different texts is, the more equivalent their meaning is.

You can therefore use the vectors to figure out if different pieces of text are related.

By combining the model’s general language understanding with, for example, data and knowledge of the positivity and negativity of the texts, the BERT model can help with sentiment analysis, entity extraction, and all the other disciplines in Natural Language Processing.

The Danish BERT model is useful for sentiment analysis in Danish.

For instance, it can analyze different prejudices in a text, define the purpose of a text, context, and point out relevant words.

This is useful to multiple industries such as e-commerce, finance, the tech industry, and the public sector.

Why is it so important to Denmark?

At Certainly, we believe that it is crucial for countries with ‘smaller’ languages, or less widespread languages, to secure themselves and make sure that they have a part in the global economy.

One of the ways of doing this is by using and taking advantage of the endless opportunities that come with Artificial Intelligence.

Furthermore, we think that it is important that it is not only up to the big international players to determine where, when and how Danish organisations can benefit from these technological achievements.

This would pose the risk for Denmark of being left behind in the AI race.

“It’s vitally important for people in Denmark to have access to the benefits that language technology has brought to the English-speaking world,” said Leon Derczynski, PhD, an Associate Professor in Natural Language Processing at the IT University of Copenhagen.

“Seeing game-changing advances like Danish BERT come from the commercial sector, through Certainly, is a hugely positive sign.

“It clearly puts the company ahead of the curve in today’s Danish AI,” he finished.

Work has since been completed on BERT models for

Why do we need a Danish BERT model?

Google has released a multilingual BERT model, but it is trained in more than a hundred different languages.

Danish text, therefore, only constitutes 1% of the total amount of data.

The model has a vocabulary of 120,000 words, suffixes, and prefixes.

It divides rare words so that “Inconsequential”, for example, becomes “In-”, “-con-” and “-sequential”. These kind of word divisions occur among all the different languages.

Google’s model therefore has room for about 1200 Danish words. In comparison, Certainly’s model has a vocabulary of 32,000 Danish words.

How does the model learn from text?

Firstly, it reads a sentence, e.g. “I like Chinese food, especially spring rolls.”.

Then, it hides some of the words from itself: “I like [HIDDEN] food, especially spring rolls.”

Next, it tries to guess the hidden word. If it guesses wrong, it adjusts so that it gets better the next time.

If it guesses correctly, then the model knows that it has understood the meaning of the text. In the example, the model learns that spring rolls are associated with Chinese cuisine.

Afterwards, the model would read the next sentence in the text, for example: “That’s why I often do my grocery shopping in the Asian supermarket”.

The model also reads a random sentence from another book: “At 7 o’clock, Jane Doe ate dinner”.

The model then tries to figure out which of the two sentences is the correct one that would logically follow the first sentence: “I like Chinese food, especially spring rolls.”.

How can we use the BERT model?

In line with our mission at Certainly to develop and make Danish AI publicly available, it only made sense that the Danish Certainly BERT model would be open source.

This means that others can further develop it and use it to improve their products and services as well as produce new solutions.

The model and the instructions for data scientists and engineers are available for free on Github. We hope that you will support Danish AI by sharing the link in your organization.

If your organization needs something industry-specific and you don’t have the time, ability, or resources to build it yourself, we can set it up for you on our platform.

Just get in touch with us at hello@certainly.io.

Follow our blog to keep an eye on the latest AI news, Conversational Chatbot best practices, and much more.

Since this blog was published, Certainly have completed work on BERT models for Swedish and Norwegian, with more to follow.

The post Certainly has trained the most advanced Danish BERT model to date appeared first on Certainly.

The only logical way for Denmark to not only compete but also to win the AI race is to make sure the AI products we market can be adopted already today by the current workforce in the companies.

That’s the opinion of the Founder and CEO of the Danish AI-tech startup Certainly, Henrik Fabrin.

Since change in any industry happens faster, the ever shortage of programmers, the fast-growing cloud sector and Software-as-a-Service being the primary way of selling B2B software, most companies and organizations choose to buy pre-made AI-solutions, as opposed to developing them from scratch.

Companies don’t have the desire or human- or capital resources to design, develop and maintain their proprietary solutions to how they run their business, so they turn to the internet and try to find suitable products there.

At the same time, the decision-making process of what products to adopt have moved from C-level to people working with the products to solve particular problems.

That could be customer service or e-commerce teams.

Denmark’s AI race – AI to adopt right away

Another factor is time. There are 1.7 million small and medium-sized businesses in the EU with between 10-249 employees, 44,000 with 250+ employees and thousands of public organizations and municipalities.

There is an estimated shortage of 20,000 to 80,000 people with deep analytical skills by 2030 in Denmark alone.

This shortage is similar in most other European countries.

At the same time, you have nations like the US and China (and nation-like companies like Facebook, Google, Amazon) putting more public and private money into developing and commercializing AI products in a day than what we have set aside for the next five years.

The only logical way for us to not only compete – but also win the AI race – is to make sure the products we market can be adopted today by the current workforce in the companies.

In this case, the market of approximately 1.8 million EU based small, medium and larger companies and a large number of organizations in the public sector.

How not to win the AI race

If you live in that reality, you will also realize that this changes how you – as a creator of products that companies use to improve how they run their business – want to think about how you design and make them available to the market.

We can build the best products in the world, but if no-one knows about them and they are not quickly and fast distributed in the market and adopted by the current workforce in companies of today how will we win tomorrow? We. Will. Not.

AI for everyone

We will do that by ensuring the products that we create and sell are easy to adopt now so that we have the upper hand.

The upper hand being, it is much easier for a person working at a company to grow the usage of the current product she is using than to find a replacement product.

It is much easier for a company to keep an existing customer than to find the next one.

The above given, the companies out there need products they can subscribe to and enable their current workforce to use them immediately to solve their problems of today and to trust they can solve the problems of tomorrow.

At the same time, they have to be able to adjust the product over time to tailor it to their particular needs.

That is, providing products that may include advanced AI/Machine Learning and other modern tricks but can be understood, purchased, launched, maintained and moulded by the hands of the line of business people to keep improving how they run their business.

Not only by developers or data scientists, but the people in customer service, sales, operations, etc.

Only tools change – not jobs

This will, over time, require a more in-depth technical understanding of how AI works or how APIs work, but at an incremental pace.

Upgrading of the current workforce’s skills should focus on deepening their general understanding of new technology such as AI but without having to re-educate them to becoming data scientists or data engineers.

In other words: Your job function will stay, but the tools you use will change. And now it is AI-based tools.

If we want to win, then we have to understand this battlefield. We will not win the race because of ethics in AI or by ensuring more data scientists in the companies.

We will earn if we make sure the product not only solves the companies’ problems but is also quickly adopted by the current workforce in the companies and organizations.

Hot topics like ethics in AI may be part of the unique selling points, but the easy-of-adoption will win the deal.

Article written by Henrik Fabrin

The post Yes, we can win the AI-race with the current workforce appeared first on Certainly.