Some time ago, my attention was drawn to an article in Helsingin Sanomat about the efficiency of different countries’ national economies and their ability to produce value for the national economy. The article showed that Finland’s ability to increase efficiency and grow its national economy has been lost for over 10 years. The fact stops me, at least.

I am not an economics expert. To put it simply, productivity increases when you can produce the same output in less time or you can cut down on the materials/costs needed to produce it. In other words, more with less.

Let the shoemaker keep his last, they say. So what could ICT / information management do to improve efficiency?

It is a pleasure to introduce Workato Genie! This artificial intelligence-based automation tool is revolutionizing the way companies integrate their applications. By solving complex integration challenges and automating repetitive tasks, Workato Genie enables teams to work more efficiently and accurately. Thanks to real-time snapshots and streamlined collaboration, organizations can scale their operations effortlessly and save costs.

In this article, you will find information about what Workato Genie is, the business challenges it solves, the benefits it offers to operations, and a few practical examples of areas where organizations can utilize Workato Genie.

Workato Genie leverages artificial intelligence to deliver intelligent automation solutions and seamlessly connect different applications, enabling businesses to create automated workflows without extensive coding knowledge, making it accessible to users of all technical levels.

Workato Genie helps businesses and services solve the following challenges:

1. Complex integration needs: Many organizations struggle with complex integrations across multiple applications and systems. Workato Genie simplifies this process and enables effortless integration.

2. Time-consuming manual tasks: Repetitive tasks typically consume organizational resources and reduce organizational productivity. Workato Genie automates these tasks, reducing time spent on manual data entry, among other things.

3. Lack of real-time visibility: Businesses often miss out on valuable insights due to data siloed between applications. Workato Genie facilitates real-time information flow, enabling better decision-making.

4. Limited technical expertise: Companies may lack technical staff to manage integrations. Workato Genie allows non-technical users to automate processes and democratize the use of automation tools and data.

When looking at organizations that have implemented Workato Genie, the following benefits can be noted:

1. Increased efficiency: By automating workflows, companies can streamline processes, which speeds up operations and improves productivity.

2. Cost savings: Automation reduces the need for manual work and helps organizations reduce operating costs.

3. Improved collaboration: Seamless integration of tools allows teams to collaborate more effectively and gain shared information and insights in real time.

4. Scalability: Workato Genie can easily scale as your organization grows and adapt to changing automation needs without significant investments in new technology.

5. Improved data accuracy: Automation minimizes human error, leading to more accurate data processing and reporting.

Finally, a few examples of Workato Genie implementations from different domains where Workato Genie has proven to be useful to the organization.

1.      Online store:

Implementation: Integrate platforms like Shopify or WooCommerce with inventory management systems (e.g. TradeGecko) and payment processors (e.g. Stripe) to automate order processing, inventory updates, and customer communications.

2.      Marketing automation:

Implementation: Integrates marketing tools like HubSpot or Marketo with CRM systems (e.g. Salesforce) to automate lead nurturing, campaign tracking, and customer follow-up. The end result is a seamless experience from marketing to sales (e.g. TradeGecko) and payment processors (e.g. Stripe) to automate order processing, inventory updates, and customer communications.

3.      Human resources:

Implementation: Automate the onboarding process by integrating HR software (e.g. Workday) with communication tools (e.g. Slack) and document management systems (e.g. DocuSign) to eliminate paperwork. The end result is a more efficient onboarding process for new employees and an improved customer experience.

4.      Finance and accounting sources:

Implementation: Integrate accounting software (e.g. QuickBooks) with expense management tools (e.g. Expensify) and payroll systems (e.g. Gusto). The end result is better automation and a more efficient process for expense reporting, invoicing, and payroll.

5.      Customer support:

Implementation: Integrates customer support platforms (e.g. Zendesk) with back-end systems (e.g. CRM or ERP solutions) to automate customer ticket routing, customer information updates, and follow-up notifications. The end result is more efficient customer support processes and an improved customer experience.

6.      Health care:

Implementation: Integrate electronic health records (EHR) and appointment scheduling tools and billing software with patient communications, appointment reminders, and payment processing to automate the process. The end result is a more efficient process.

7.      Project management:

Implementation: Automate updates and task assignments by integrating project management tools (e.g. Asana or Trello) with communication platforms (e.g. Microsoft Teams). The end result is a better picture of the situation for team members and status information between team members.

Workato Genie is useful across multiple industries, facilitating application integration and automating workflows that would otherwise be time-consuming and error-prone. By implementing Workato Genie, organizations can improve productivity, reduce operational burden, and ensure better information flow between systems. Workato Genie democratizes organizational operations by effectively utilizing the expertise of the entire organization. Each implementation can be tailored to meet the specific needs and goals of the organization or function.

If the article sparked your interest or you have any questions, please contact us and we can discuss the topic further. Let’s put Finland on the rise together, one organization at a time.

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336  5453

Analysts’ view on hyperautomation

Hyperautomation has been heavily featured in reports from many analyst houses, such as Gartner and Forrester. For Gartner, hyperautomation has been featured in their trends and reports, especially in the context of business transformation, and it has long been seen as a key part of the technological infrastructure of the future.

Gartner’s view on the future of hyperautomation:

• The Importance of Hyperautomation: Gartner has predicted that hyperautomation will be one of the key trends over the next few years as organizations look to reduce manual work and increase operational efficiency. Hyperautomation offers the opportunity to scale automation across the organization, accelerating digital transformation.
• 2024 and 2025 Trends: Gartner estimates that by 2025, 20% of all work in large enterprises will be automated, with hyperautomation emerging as a key driver for business optimization. This suggests that hyperautomation will be increasingly important in the future for both operational efficiency and strategic decision-making.

Gartner Magic Quadrant and Hyperautomation:

Although hyperautomation itself is not yet a categorized area in the Magic Quadrant, many related technologies and tools are included. For example, Robotic Process Automation (RPA) and intelligent business process management suites (iBPMS) are included in Gartner’s Magic Quadrant reports and are considered key components of the hyperautomation ecosystem.

Examples of Gartner Magic Quadrant reports:

1. RPA (Robotic Process Automation): Gartner publishes an annual Magic Quadrant report on RPA solutions. Market leaders such as UiPath, Automation Anywhere and Blue Prism are strongly represented. These tools often play a central role in hyper-automation implementations, and are combined with artificial intelligence and machine learning to expand the possibilities of automation.
2. Intelligent Business Process Management Suites (iBPMS): According to Gartner, iBPMS solutions such as Pega, Appian and IBM are significant tools for business process automation. These tools support hyperautomation by providing capabilities for workflow management, analytics, and AI-based decision-making.

Insights from Forrester and other analysts:

• Forrester: Forrester has also highlighted the importance of hyperautomation in accelerating digital business. According to Forrester, companies that adopt hyperautomation solutions can achieve significant benefits in cost savings, customer experience, and business agility.
• IDC: IDC has reported that hyperautomation will be one of the most critical technologies for companies looking to achieve the full potential of digital transformation by 2025.

Future prospects:

Analysts see the future of hyperautomation as a positive and growing area that will cover more and more business areas. The biggest development trends will be the following:

1. Adding intelligence to automation: The future of hyperautomation is closely tied to the expansion of artificial intelligence and machine learning capabilities, especially in automating decision-making.
2. Scalability of automation: Organizations want to scale automation beyond individual processes, and hyperautomation enables this by providing comprehensive integrations between different technologies.
3. User-friendliness and no-code/low-code solutions: In the future, hyperautomation will be more accessible and manageable even for non-technical users, on no-code/low-code platforms.

The importance of hyperautomation is growing rapidly, and analysts expect its integration into more complex processes to continue to evolve with new technologies.

Preiskari Solutions and Liitos Solutions together design, implement and further develop the technologies required and used by Hyperautomation.

Our offering includes Workato’s technologies, which are core technologies in Hyperautomation.

Workato is a very popular hyperautomation platform, and its solutions cover several critical business areas where automation aims to improve efficiency and integrate with different systems. Workato focuses specifically on workflow automation and integrations between different systems. Here are the main areas where Workato solutions are utilized in the context of hyperautomation:

1. Business process automation

• Example: Workato can automate complex workflows such as order processing, inventory management, and invoicing by connecting different business systems such as ERP, CRM, and accounting systems.
• Benefits: This reduces manual processes, speeds up operations, and eliminates errors. With hyperautomation, these processes can become self-learning and adaptive through artificial intelligence.

2. Customer service automation

• Example: Workato can automate customer service workflows by connecting support ticketing systems, chatbots, and CRM systems. Chatbots can handle common queries, while more complex queries are routed to customer service agents.
• Benefits: Customer service requests are processed faster, while customer service representatives can focus on more complex tasks.

3. HR process automation

• Example: Workato offers tools to automate HR processes such as recruitment, employee onboarding, and onboarding. For example, applications can be automatically integrated into different systems, and onboarding processes can be automatically initiated after selection.
• Benefits: This improves the employee experience and saves time for the HR team.

4. Marketing and sales automation

• Example: Workato can integrate a CRM system (like Salesforce) with marketing automation tools (like Marketo or HubSpot), enabling real-time data sharing and campaign optimization.
• Benefits: Marketing and sales teams can respond faster to changes in customer behavior, leading to better prospect management and increased sales.

5. Finance and accounting automation

• Example: Workato can automate financial management processes, such as invoice processing and revenue recognition, by integrating accounting systems (such as QuickBooks or NetSuite) with other business systems.
• Benefits: The quality of financial reports improves, while manual input work and related errors are reduced.

6. IT process automation

• Example: Workato allows IT departments to automate processes such as software deployment, device management, and employee access management to systems. For example, it can automatically synchronize user management across systems when a new employee joins the organization.
• Benefits: The burden on IT departments is reduced and employees can be provided with a faster and smoother user experience.

7. Integration of cloud services and SaaS systems

• Example: Workato plays a key role in cloud-based SaaS integration. It enables seamless integration of various SaaS services, such as Google Workspace, Slack, and Microsoft Teams, with different business systems.
• Benefits: This enables rapid deployment and allows organizations to easily scale their operations in the cloud.

8. Automation of analytics and reporting

• Example: Workato can automate data collection and report generation from various sources, connecting analytics tools (like Tableau or Power BI) to business systems. Data is automatically collected and analyzed, allowing companies to make better decisions.
• Benefits: This reduces manual reporting and ensures that the data on which decision-making is based is always up-to-date.

Why is Workato key to hyperautomation?

Workato focuses strongly on low-code/no-code platforms that enable the building of complex workflows and automations without in-depth programming skills. This makes hyperautomation more accessible to the entire organization, not just IT departments. In addition, its extensive integration capabilities between different systems and SaaS solutions make it an ideal platform for hyperautomation scalability and flexibility.

Workato therefore helps companies integrate and automate workflows across a wide range of business areas, and is a key tool in the hyperautomation ecosystem.

If you are interested in hyperautomation, please contact us, let’s build your path to implementing and developing Hyperautomation together.

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336  5453

Developing information retrieval using artificial intelligence is an interesting and current topic. In this article, we share the key problems and challenges that have been identified and reported.

The use of AI in information retrieval also opens up great opportunities. At the end of the article, we list some application areas where the use of AI in information retrieval is already being applied.

Below are the most commonly reported problems and challenges in using AI for information retrieval.

1. Reliability and accuracy

The results produced by AI in information retrieval are not always reliable. Algorithms can provide incorrect or misleading information, especially if the training data is incomplete or biased. Systems can also lead to incorrect conclusions if they are unable to understand context properly.

A critical attitude, expertise and processes are needed to analyze the results.

2. Quality of information

The performance of AI is particularly limited by the quality and diversity of available data. Poor quality or repetitive data can negatively impact search and analysis results.

3. Bias and distortions

AI systems can reflect problems in their training data, such as biases or inequities, which can lead to unequal or unfair outcomes.

4. Interpretation of results

Although artificial intelligence can produce results, their correct interpretation and application still requires expertise and critical thinking.

5. Data protection and ethical issues

The data used in information retrieval may contain personal information, which raises privacy issues. Similarly, the use of artificial intelligence may raise ethical concerns about the use of the data and its impacts.

Solutions to the challenges listed above

Improving and controlling data quality in the context of data retrieval can be achieved through a number of means, particularly with the help of artificial intelligence (AI). Here are some common strategies and methods::

1. Data preprocessing: AI can help clean and preprocess data before it is analyzed. Data normalization, handling missing values, and removing erroneous data are important steps to ensure high-quality data.
2. Contextual analysis: AI can analyze the context and meaning of data. This can help assess whether the information is reliable and relevant to a specific question or need.
3. Classification and labeling: Machine learning applications such as natural language processing (NLP) can be used to classify and label data, improving the accuracy of information retrieval. For example, automatically classifying documents or databases can help users find the information they need more easily.
4. Leveraging user data: AI can analyze users’ past searches and choices to improve future search results. Alumina recommendation systems can suggest information and sources that improve relevance.
5. Measuring and assessing quality: Artificial intelligence can help develop metrics to assess the quality of information. Various algorithms can assess how reliable, timely and relevant information is.
6. Automated assessment and correction: AI systems can automatically detect and correct data errors or inconsistencies, improving the reliability of data.
7. Network analysis: Analyzing social and other networks can provide additional information about the reliability of information sources. For example, the individuals or organizations responsible for sharing certain information can influence its quality.
8. Collecting feedback: Feedback from users can be valuable in improving the quality of information. AI can analyze user feedback and apply it to improve search results.

Using these approaches and artificial intelligence technologies can significantly improve the quality and efficiency of information retrieval.

The impact of the AI ​​solution used on the accuracy of the data – Free vs licensed solutions

When looking at information retrieval and the use of AI solutions on the market (free vs. licensed), there are differences when it comes to the accuracy of information retrieval. Below are a few considerations:

1. Accuracy: Free services: Free AI services may be limited in that their data sources may not be as reliable or of the same quality as paid services. Free services may also have more restrictions, which may affect their ability to process or produce timely information. Licensed services: These services, especially if they are from well-known and trusted providers, tend to provide more accurate and up-to-date information. They may use higher quality data and potentially have better algorithms that improve the accuracy of the information.
2. Relevance: Free services: Relevance can vary greatly for free services, as they may use generic or broad data sources that are not always relevant to the user’s specific needs. Licensed services: These may offer more opportunities for tailored information retrieval and provide more relevant information in user-defined contexts. They may also incorporate user feedback or other enhancements that increase relevance.
3. Updates and management: Paid services may also offer regular updates and better-managed content, which can improve both accuracy and relevance.
4. Expertise and support: Licensed services often offer customer support and expert service, which can help the user better understand and interpret the information.

In summary, while free AI services can be useful in certain contexts, licensed services often provide more accurate, timely, and relevant information, which can be critical, especially for professional or business use.

Documented opportunities and benefits of using AI in information retrieval

1. Efficiency and speed

Based on the reported analyses, it can be stated that artificial intelligence can significantly improve information retrieval by speeding up processes and enabling the analysis of large amounts of data in a short time.

2. Personalization

Artificial intelligence can customize information searches based on user preferences and behavior, providing users with more relevant information.

3. Understanding the context

The most advanced AI models, such as natural language processing (NLP), can better understand and analyze the context of questions, which improves the quality of search results and increases application areas.

4. Diversity and inclusion

Analyses have shown that artificial intelligence can help combine information from different sources, which can lead to more comprehensive and diverse insights on a topic.

5. Anticipation

Artificial intelligence can analyze trends and predict users’ information search needs, which can improve information availability and use.

Here are a few examples of practical benefits and applications of AI-powered data retrieval:

Here are some examples of current practices or applications where artificial intelligence is utilized in information retrieval::

1. Search engines: Google and other search engines use artificial intelligence to improve the relevance of search results. For example, Google’s BERT (Bidirectional Encoder Representations from Transformers) model better understands the meaning and context of search terms, resulting in more accurate search results.
2. Intelligent customer service: Many companies are using chatbots that leverage natural language processing (NLP) to quickly find and provide information to customers. These bots can interpret questions and search for information from large databases.
3. Optimizing information retrieval in scientific research: Artificial intelligence is used to retrieve and analyze research articles and publications. For example, PubMed and other scientific databases can use machine learning models to analyze and recommend relevant studies based on a user’s login.
4. Recommendation systems: Services like Netflix and Spotify use artificial intelligence to analyze user data and recommend content to users based on their preferences. This is based on data analysis and predicting user behavior.
5. Business data analytics: Many business data analytics solutions, such as Tableau and Power BI, make data discovery easier by using artificial intelligence to interpret and visualize data, allowing users to find information and insights faster.
6. Media literacy and fact-checking: Artificial intelligence has also begun to find applications in news and information verification. For example, automated fact-checking tools can analyze news sources and compare them with reliable sources of information.
7. AI-controlled F-16 fighter jet: The US Air Force has allowed the media to watch an air combat exercise between an AI-controlled F-16 fighter jet and a human-controlled fighter jet. According to the US Air Force, the AI ​​is developing so quickly in the exercises that it is already starting to defeat the human-controlled fighter jet.

These examples illustrate how widely artificial intelligence can be utilized in information retrieval in various fields and applications.

Information retrieval using AI enables and opens up new possibilities. When reviewing initial feedback, critical analysis and understanding of the underlying systems and processes are needed to understand the accuracy and relevance of the final results.

The benefits and application areas create new opportunities that we believe we will see more and more of. It is a good idea for companies and IT management to define, review and update their policies and practices in this regard.

Preiskari Advisor and Liitos Solutions together ensure the availability of the right information to their customers through modern integration solutions and services. We help our customers develop solutions and information that generate real, measurable value from information for business and services.

If the article raised any questions or you would like to discuss the topic further, please contact us.

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336  5453

Preiskari Solutions and Liitos Solutions offer analysis, design, supply, implementation, support and further development of the client’s integration solutions in ways and methodologies agreed with the client. You can contact us via the companies’ websites or Linkedin or by calling directly.

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336  5453

My first experience with cloud technology / technology as a service dates back to when I was in development work in the outsourcing business unit. The first materials and publications supporting cloud thinking can be found in 2006 – the presentation from the 2010 ATK-päivät can be found online Slide 1 (atk-paivat.fi).

Even though almost a generation has passed since that time, when I look at the content of the message, I find a lot of things that are still relevant to this day. The cloud is not only a technological transformation but also / and above all a new economic and operational way of producing IT services. I personally see the cloudization of things as an evolution, not a revolution.

One incident from those days with the CIO of a Finnish company stuck in my mind. The reason is that it taught me, at least, to think about the cloud as a more holistic thing than just technology or services. We had agreed with the CIO and the company he represented to migrate all X86-based, application-dedicated systems to capacity-based cloud services. The transformation went smoothly, the solution worked as expected, until I got a call a few months later.

The CIO had received an invoice that was significantly higher than the initial situation. As part of the project, he had been sold cost efficiency, but now it looked like the costs were going over budget – which was obviously not what either party intended.

When we started investigating the cause of the situation, the reason became clear. After the transformation, the company’s business units, which owned the application and process layers, had noticed how easy and fast it was to deploy a new cloud-based service architecture. Previously, the business received infrastructure within 6-10 weeks of ordering, now it is as fast as immediately – the SLO for critical services was a maximum of 2 hours from ordering to production. The ease and speed of the new service had opened the door to getting new applications up and running faster – this cumulated the growth in the total volume of infrastructure services used by the company and at the same time the growth in billing.

Cloudization of ICT business is a mainstream solution today – even to the point where some companies are using Cloud First thinking. If you want to build a dedicated infrastructure environment for an application, you have to go through an exception process to get it into production.

The Forrester report February 2024 begins with the title – Cloud costs are out of control. Below is a brief background, a few key ideas and figures to which the report refers, and finally a proposed solution to the situation.

• Research has shown that cloud costs continue to rise despite companies adopting cloud cost monitoring and optimization tools, such as CCMO (Cloud Cost Management and Optimization). 72% of Forrester respondents reported that cloud costs had increased above budget.
• When examining the issues that increase costs, the following 4 cost elements emerged as the most important: storage solutions, lack of integration strategy, overuse of network capacity, and cloud architecture choices.
• Of these, the lack of an integration strategy and the choices made in cloud architecture are highlighted in the Forrester report as the most important issues to investigate and fix.
• Forrester suggests using iPaaS integration solutions and modernization projects as a solution. Here are a few key takeaways from the solutions and why Forrester has reached its position.
• CCMO does not consider cloud costs in the early stages of the process. Therefore, solutions are needed that help proactively manage cloud costs earlier in the development process.
• According to Forrester, CCMO solutions lack the ability to proactively address cost optimization at the cloud architecture level, limiting the ability to leverage FinOps best practices to optimize and control costs.
• According to Forrester, iPaaS solutions offer an effective way to integrate and modernize the cloud by connecting applications, people, and data.
• Cloud architecture integration and modernization projects lead to more efficient communication between teams, better utilization of FinOps, and the ability to shift resources more to development and innovation, utilizing, for example, AI or machine learning solutions.

Preiskari Solutions (www.preiskari.fi) and Liitos Solutions (liitos.io) deliver integration solutions using  Workato technology. We enable the right information to exist in the right place at the right time with services and technology. Our solutions enable connecting different systems and applications so that data can be exchanged between them automatically and efficiently. Our solutions are iPaaS solutions.

If any of the themes in the article sparked your interest or questions, we would be happy to discuss the topic further.

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336  5453

Knowledge is power and when used correctly it helps to innovate, develop and conquer new markets and customers. The problem is the ways and history with which we have built and designed IT systems and technologies – they have traditionally been designed and built as their own system/application/data silos and islands.

There are solutions to solve the problem. What we are talking about in this article and what we represent at Preiskari Solutions Oy and Liitos Solutions Oy are cloud-native, low-code, Platform as a service (PaaS) integration systems.

Our solution breaks down information silos and islands. It enables a higher degree of automation in process workflows and enables real-time access to accurate, up-to-date information across applications. Our solution opens up opportunities for better data management and processing, for example through analytics and artificial intelligence.

When we are able to produce more advanced integration solutions, they in turn enable more efficient operations in IT functions. We are talking about democratizing things and doing things. In practice, people with non-IT backgrounds can also be better involved in developing and supporting things. This in turn increases the productivity of the organization, simplifies operations and reduces development costs.

Every customer and implementation is unique. Customer cases with benefits documented in the literature and verified by a third party can already be found.

 

Our operating model for mapping the above-mentioned issues, realizing them, and finding a suitable solution in customer relationships is as follows.

1) A consultative discussion is held with the customer to map the identified challenges.

2) A plan is built around the identified challenges. This phase forms the To-Be architecture. As part of the discussion, a tool is selected based on the customer’s needs.

3) We will pilot Integration / Automation. We will choose a common area to test with the company’s real data.

4) Integration / Automation implementation and testing is performed. The phase includes architectural work, development work and, if necessary, project work, maintenance and supervision.

We are happy to discuss, help and provide more information. We provide Workato integration solutions to our customers. You can open a discussion with us using the contact information below.

 

 

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336  5453