Digital Sundai Conversations – Raymond Alves

For Digital Sundai Conversations we interviewed Raymond Alves, former director of the Rockstart AI program. No video interview this time, since Raymond was driving his car while providing us with the coolest insights. We summarized our key findings for you:

Problem first, application second
Raymond: ‘Many AI startups use their technical knowledge and methodology to think of developing a certain application first, without clearly having in mind a real-world problem to which this idea can be applied. Successful startups think the other way around – identify a real-world problem or a market gap first, then start thinking of how AI can be part of the solution. Is the issue big enough to build an entire business around it? Is there any scaling potential? Explore the problem, then explore your options.

Gain experience to see the complete package
Establishing a startup without some years of working in the field is possible, but chances are you lack important knowledge on how to identify a specific market gap, business need or how to build a business anyway. Besides, gaining work experience first can also provide you with the needed insights on both operational, financial or legal procedures. Knowing all ins and outs of the problem on a detailed level ensures you have a complete overview, so you start building on an application with an exact goal in mind.’

Algorithms don’t determine success – mentality does
‘An impressive algorithm is nice, but the team’s entrepreneurial mentality really makes the difference. How well do you handle setbacks? How easily can you identify whether core capabilities can be applied in different contexts as well? In other words: how resilient and flexible are you? Make sure you have the right people on board or business partners close to you to handle every challenge your team might be facing.

Besides, paying attention to the wrong criteria for predicting success can be a pitfall. A promising revenue model and a well thought through marketing campaign are good assets to have in place, but they are often overrated. The most important criteria: growth. How can you continuously grow the amount of end users, and how often do they come back? That determines whether your startup has what it takes to be successful in the long term.’

And, last, but not least:

Want to work with AI? Check out China
‘It wasn’t until my visit to China at the end of 2018 that I witnessed how extremely fast China’s AI economy is developing. With technology and innovation being on top of mind of Chinese leaders, companies and individuals, neglecting the developments happening over there would be a naive thing to do. I am especially curious to see how China’s development in the telecommunication networks and their different approach of privacy and data will most likely influence the Western ways of doing business.’  

Want to find out more about China as an AI superpower? This book was the main motivator for Digital Sundai’s founder Robin Zondag to pay China a visit in early 2020!

AI superpowers book

Digital Sundai joins Google Cloud Partner Advantage Program

When Digital Sundai was founded in October 2019, joining the Google Cloud Partner Advantage program was something we planned to realise in our business journey. We are proud to announce that we have been able to reach this milestone within such a short period of time!

Digital Sundai is acknowledged to be a official Google Cloud Partner now, underlining our capability to help customers solve complex AI & Analytics topics to improve their business performance. The partnership provides us with even more tools, technical knowledge and access to the Google Cloud ecosystem to do so.

Bootstrapped digital transformation

Tomorrow delivered today is the tagline of Digital Sundai. At Digital Sundai we deeply believe that applying digital and AI technologies enables a far better performance of organizations. And we mean today’s digital technology, no need to wait on the even better technology of tomorrow. We also think few organizations and industries have already truly leveraged these benefits. 

At the moment the disruption of Covid-19 is accelerating the adoption of digital technology at unprecedented speed. Whether it is online shopping, remote digital education, call centres with all agents working from home, AI to detect Covid-19 from CT scans or apps to support governments to control the spread of the virus. Habits are changing and will not return fully to pre-Covid 19 days. At the same time the upcoming recession will make cost reduction a key theme for almost all organizations. This will severely limit the ability of organizations to invest in digital change. 

So here we have our dilemma. Society and markets will reward the digitally mature organizations more than ever, yet organizations will have less means to invest to become digitally mature. The answer in our view….bootstrapped digital transformation. 

Sounds good bootstrapped digital transformation but is it possible to realize digital change at low cost in existing organizations? Most research on why digital and AI are difficult to scale in organizations list several topics like strategic focus, lack of skilled resources, legacy technology, which will not change a lot in the coming period. The opportunity however lies in what most research list as key blockers;

  • Organizations are not bold enough when pursuing digital transformation
  • The way they are organized and their culture do not fit the digital age. 

Recently Covid-19 has forced organizations to be bold and to change ways-of-working to digital overnight, and in most cases successfully. The focus on the job to be done was clear, and the lack of resources nor legacy technology did stop organizations from realizing the changes. Also, compared to making these kinds of digital changes under normal business circumstances the costs were multiple times lower. 

Bootstrapped digital transformation keeps this momentum going, without the disruption that originally enabled it. Key for bootstrapped digital transformation:

    1. Top management has a clear focus for what areas to digitize. This directs the efforts of the organization.
    2. A little more action, a little less conversation please. Probably the most important and the hardest bit, especially without a disruptive event to drive it. Successful traditional organizations are in control at almost all times by a command-control way of organizing. Disadvantage is that this often requires a tremendous amount of alignment (i.e. conversation) between different silos to be able to make even small changes. A strong will to transform and the distribution of decision making authority to multi-disciplined agile digital teams will deliver digital change fast enough, good enough and cheap enough. 
    3. Use cloud technology unless. The only place where operations can be scaled fast without large CAPEX investments, access to all company data can be established, and innovative features are readily available. Probably more secure than homegrown solutions too.
    4. Mix own resources with external digital experts. When budgets are tight one cannot hire large external teams to do the job. This might be a blessing in disguise as digital requires a new way of working at organizations, which can only be sustained by its own employees. Gaps in expertise and methodology can be overcome by bringing in the right external digital experts and partners.      

Most organizations will have to accelerate their digital transformation capabilities, and most organizations will have less means to achieve this. Bootstrapped digital transformation might be a way out. This demanding fix combines a strong will to transform, distributed leadership, digital & AI technology, and the right team and right approach.

   

Podcast on AI for business

How about killing two birds with one stone?
Sit back, relax, listen to an episode of leadership insights by Future Processing and gain valuable insights on AI for business
AI
Michal Grela interviewed our Robin Zondag. They covered:
➡️ Why would you bother doing AI in the first place?
➡️ How to approach an AI project?
➡️ What skills should your team possess?
➡️ How to align business, IT and Data?
AI
You can find the links here ⬇️
 

The Benefits of failure

Fail fast, learn from your failures – encouraging people to take risks, move and learn fast is an essential part of any digital culture. It can be hard to do too. Therefore, some inspiration from J.K. Rowling in her classical ‘Benefits of Failure’ speech..

Chatbots serve customers better

Chatbots serve your customers better

In these customer centric times what better than a way to improve your customer service. In these P&L heydays what better than a way to reduce your cost. A chatbot is a great way to do both. Increasing customer satisfaction while at the same time reducing the cost of serving your customers. Please note this applies as much to ‘real’ external customers as to internal customers. Too good to be true? Let’s examine the technological developments, some real life experiences, perspectives on how to implement chatbots, and their bright future potential.

From zero to hero

People above 30 years old will remember Clippy the office assistant Microsoft included in Office for Windows from 1997 till 2003. Any user would try to silence Clippy as soon as possible as it was of absolutely no value. It would never come up with an answer for the problems one was experiencing. 

The days of Clippy are over and since a few years chatbots are becoming a very useful, always available and highly scalable tool to support users in getting what they need quickly and instantaneously.  

The biggest progress comes from AI and its ability to understand natural language. The user no longer has to describe his or her problem in exactly the same wording as the tool makers were expecting. AI is able to interpret the natural language pretty accurately and identify the intent of the user. Once the question is properly classified (“where can I find the print button….” e.g.) the chatbot feeds back the right answer from a knowledge base or executes an action (“buy this product…”). 

The world’s organizations are discovering the benefits of chatbots fast. Research&Markets.com reports ‘the chatbot market size is projected to grow from USD 2.6 billion in 2019 to USD 9.4 billion by 2024, at a CAGR of 29.7% during the forecast period’. Gartner says Artificial Intelligence (AI) will be a mainstream customer experience investment in the next couple of years. 47% of organizations will use chatbots for customer care and 40% will deploy virtual assistants.

Implementing chatbots

There is a lot of magic talk about self learning chatbots. Don’t be fooled, with today’s technology creating and improving a chatbot still requires a significant amount of manual work:

  • Defining how and where to use the chatbot in the customer journey
  • Defining the intents correctly
  • Designing and implementing the tone of voice of the chatbot
  • Creating or collecting the knowledge articles
  • Keeping the knowledge articles up-to-date
  • Integration the chatbot with backend systems to execute tasks (e.g. “reset my password”) 
  • Analyzing interactions and improving the answers of the chatbot

Although getting the chatbot conversations right is definitely possible today, it is not a given. Forrester predicts that even in 2020 four out of five chatbot-based customer interactions will continue to flunk the Turing Test. Spiceworks provides an overview of the most common errors. 

A solid implementation methodology, thorough testing, reasonable expectations, picking the right conversation cases, and an experienced partner are amongst the key success factors for implementing meaningful chatbot conversations. 

Advancing technology will make things easier. For example, improving chatbot conversations is increasingly supported by strong analytical tools and automatic improvement suggestions. Also, technology is now enabling the automatic creation of new intents by scraping websites or uploading (somewhat structured) pdfs. This is e.g. part of Google Cloud’s Contact Center AI product. More and more chatbot vendors are including predefined workflows and connectors to make it easier to integrate with backend systems for the execution of identified tasks. These kinds of features reduce chatbot implementation and maintenance effort.

The next frontier, Voice is becoming mainstream

Biggest advancement however is today’s fast increasing maturity of voice technology. Where traditional chatbots require users to type to interact with them, the new wave of chatbots (or perhaps better, Voicebots) are able to interact by voice. Most mobile phones already have an assistant like Siri or Google Assistant installed, and more and more households have Alexa or Google Home (25% of US households in 2019). While American English still performs best it is quickly followed by mainstream European and Chinese languages. 

Humans are very comfortable and skilled in using voice to interact with each other. Now computers can do this too. Today many organizations have implemented e-commerce, webcare and chatbots to make customer interaction easier but still operate large call centers with agents talking to customers. This is expensive for organizations and often little appreciated by customers (wait time and frequent hand-overs). Voice technology promises to speed things up for customers as they can be served immediately anytime of day no matter the call volumes, to lower costs for organizations and to augment quality of work for agents as voicebots are likely to deal with routine tasks first. So get it right, and the future is bright!

Great news: Google’s Contact Center AI now generally available

Google Cloud’s Contact Center AI is now generally available. That is fantastic news for everyone in customer service.

The power of AI made available to improve the performance of customer service . Promising to improve  at the same time customer satisfaction (getting the answer you are looking for faster and easier)  and reduce costs (automate some of the more mundane customer service tasks). Especially highly impactfull when combined with voice technology.

For more details please see https://cloud.google.com/blog/products/ai-machine-learning/contact-center-ai-now-ga

Getting the AI & Analytics team right

Getting the AI & ANALYTICS team right

For companies that weren’t born in the digital age transforming into a data-driven organization is a hell of a job. It requires the right technology, the right data, the right agile culture, the right business vision, the right transformation, the right AI methodology and the right AI & Analytics team. History confirms the complexity of this kind of challenge. It took about 30 years to move from steam driven factories to modern day electric engine factories. Most steam engine based companies did not survive this transformation. Let’s eat the elephant in pieces. In this blog we zoom in on one aspect, getting the AI & Analytics team right. 

Your AI & Analytics dream team

The AI & Analytics domain is evolving quickly. It now encompasses technical topics like extreme data, building data pipelines, creating algorithms, training AI models and  contextual understanding. Business topics are essential, like improving performance with specific use cases, building your data-driven organization, creating agility and embedding AI & Analytics in all of your business processes. On top of this we find social topics like compliancy, fairness and ethics have become condition sine qua non in this field.  

Inevitably therefore the right AI & analytics team is a multidisciplinary team. Yes, even a kind of multiskilled dream team in our vision. A team consisting of very different, complementary roles & capabilities, with open minded people.  Sounds terribly logical but what does this team actually look like and how to make it thrive?

Key roles

Prime objective of any AI & Analytics team is to create business value through predictions and insights. As technological possibilities are relatively new to mankind, no surprise that many organizations still need to find out what the best possible business outcome exactly looks like. And how to create this best. This is where subject matter experts, business managers, AI business consultants, AI experts and data engineers need to meet in a creative process. 

This process is typically driven by the AI business consultant, sometimes known as the analytics translator, making the different domains collaborate. A design thinking based approach delivers the best results in our experience. Combining AI technology, available data with products and services,  business processes, business models and human resources. The next step is to validate the concept and create its first value by building and shipping a Minimal Viable Product.

The data product needs to be fair, ethical and compliant with internal procedures and legislation like GDPR. This  is where the data privacy and security expert comes in. Advising and monitoring the team, and working with the data privacy officer, the security officer and the ethical board.

For the largest part of their time an AI & Analytics team is preparing, cleaning and analyzing the dataset to create the data product. The more mature organizations have implemented supporting tools (like e.g. Google’s AI hub, Data catalog and Kubeflow), organized their data and data governance, and made it as easy as possible to find and access data in a controlled manner. All this greatly reduces the time spent on preparational tasks.  

AI & Analytics team roles

                • AI business consultant
                • Data scientists
                • AI experts
                • Data engineers
                • Data privacy & security expert
                • Solution architect
                • SW programmer
                • UX designer
                • Success manager

Still in the AI & Analytics team it is advised to have specialists on board to execute these tasks, the data engineers. They excel at transforming, shaping, filtering, moving and connecting the data. Leaving space for the complex and unique statistical methods to the data scientists and the AI model training to the AI experts

Making the data product available at scale to targeted end-users and/or customers requires yet another set of capabilities in the team. On the technical side we need a solution architect to ensure the solution fits with the organization’s  IT landscape and delivers the intended performance. A SW programmer is needed to integrate the data product with the input data and to integrate the outcome with the applications used by the end-users and/or customers. To deliver a dataproduct that meets today’s ease of use standards a UX-designer is onboarded.  

Managing the success of your data product by clearly communicating with stakeholders and if needed providing training to stakeholders is an essential task. This is the task of the AI business consultant or a specialized success manager

How to make your AI & Analytics team thrive?

The right environment is needed to make your AI & Analytics team thrive. Sponsoring, mandate, subject matter expertise, business support, interpretation of findings, IT support, changed business decisions as a result of insights or predictions are key items typically situated outside of the team. 

Successful organizations create the right environment. In all the right environments you will find the following.  At the highest level a Board that provides the AI & Analytics function with guidance, support and mandate. On executional level a product owner who prioritizes to get the most important features of the data product right. Subject matter expertise is available to inject specific knowledge. The IT department supports the AI & Analytics team by providing data, tooling, IT support and integration into the existing IT landscape. You have implemented the agile way-of-working as the default standard. An innovation friendly culture, digitally skilled employees and most decision making low in the organization?…. check!   

All of the above enables your AI & Analytics team to really thrive. They are also the building blocks which you will find in any data-driven organization.

Digital Sundai, pleased to meet you!

Let me introduce you…Digital Sundai

October 1st, 2019 is the day Digital Sundai opened for business. Based out of Amsterdam this new company strives to create superior performing organizations through digital and AI. 

Our believes

    • We believe Digital & AI only succeed when technology & business are both done right in an integral manner
      • We bring experienced digital business competence 
      • and top AI & Analytics expertise
      • Executed through our agile digital culture and methodology
    • Digital Sundai is a networked enterprise which only works with top digital talent & top digital partners
    • We are an Open company and an integral part of the Digital community with relations and access to the latest Business & Tech start-ups, scale-ups academia, and established companies
    • Google Cloud is our preferred Technology ecosystem
    • We support the society we live in

Our contribution to your business

Digital Sundai offers AI solutions and consultancy to improve your business. Call us when:

  • Your sales, customer service or operations do not use AI yet. We will help you define how and how much your business can be improved 
  • You have ideas on how AI could improve your business but need to validate and sharpen those ideas and turn them into a robust roadmap
  • You have a clear AI use case and need to get it built, implemented and used in your business
  • You plan to use Google Cloud AI & analytics to realize your AI project
  • You would like to improve your capability to use AI and digital to improve your business

What we bring to the table

Digital Sundai brings AI solutions and accelerators, methodologies, and digital & AI expertise.  Our own or partner AI solutions & accelerators increase speed and de-risk the implementation of AI use case. Digital Sundai is committed to expanding the offered AI solutions and accelerators.

Born in the digital age Digital Sundai is designed as a modern consultancy. We use digital & AI as much for our own operation as for our customers. Key difference with traditional consultancies is the focus on our network. We believe that by applying a clear, rigid and  agile methodology we are able to integrate the best experts and partners in our projects, regardless whether they are employed by Digital Sundai or not. A typical project is staffed by Digital Sundai employees, first ring resources and partners, and flexible resources from digital platforms like UPWORK. Our skills are digital business, AI & analytics and data engineering on the Google Cloud platform.