Liitos Solutions oy

Predictive, Generative or Agentic AI? How to Choose the Right Approach

Suvi Lindholm — Mon, 03 Aug 2026 05:32:31 +0000

Generative AI, predictive AI, agentic AI…

How do you know which one to use and when? And when is good ol’ automation still enough?

The terminology can make AI sound more complicated than it needs to be. Different technologies are often discussed as competing alternatives, even though they solve different parts of a process.

The better question is not:

Which AI is the most advanced?

It is:

What does the process actually need to do?

Let’s walk through the differences using one familiar business process: invoice handling.

Start with the process, not the technology

Choosing technology based on what is currently trending is an easy way to make a simple process unnecessarily complicated.

Some tasks require a prediction. Others require new content, a predefined action or a system that can work towards a goal across several steps.

In many cases, the best result comes from combining different technologies as building blocks rather than trying to make one tool handle the entire process.

Before looking at the different approaches, there is one important distinction to make.

When traditional automation is enough

Traditional automation works well when the process follows clear and repeatable rules.

An invoice arrives. The system checks whether the required fields are present, matches the supplier, sends the invoice to the correct approval workflow and updates the ERP after approval.

The rules are known in advance. The same conditions should lead to the same predefined action.

There is no uncertainty to interpret and no decision to improvise.

In this situation, AI may add unnecessary complexity. Reliable automation is often the better choice because it is predictable, controlled and easier to maintain.

Good ol’ automation is not outdated. It remains the foundation of many business-critical processes.

When predictive AI is useful

Predictive AI becomes useful when the process needs to recognise patterns or estimate what is likely to happen.

In invoice processing, the question may no longer be:

Does this invoice meet our validation rules?

Instead, it may be:

Does something about this invoice look unusual?

Predictive AI can compare an invoice with historical data and identify patterns that may indicate a duplicate, an incorrect amount or another anomaly.

It does not necessarily resolve the issue. Its role is to create an insight:

This invoice may need attention.

That insight can then trigger a review, an automated workflow or another AI-supported step.

Predictive AI is a good fit when the main need is identifying risk, probability, patterns or anomalies from existing data.

What generative AI adds

Once an unusual invoice has been identified, someone still needs to understand what appears to be wrong.

This is where generative AI can help.

It can summarise the situation, explain how the invoice differs from previous ones and draft a message asking the supplier or internal approver for missing information.

The output is content:

Here is what seems unusual, and here is a possible response.

Generative AI is especially useful when people need help interpreting, summarising or communicating information.

However, creating a message is not the same as making sure the issue gets resolved. Someone or something still needs to decide what happens next, send the message and follow the case through the process.

When the process needs agentic AI

Agentic AI goes beyond generating an answer.

It works towards a defined goal and can decide which action to take next within the boundaries it has been given.

In our invoice example, the goal could be:

Resolve the invoice exception.

An AI agent could gather information from different systems, check the purchase order, identify the correct approver, request missing information and start the appropriate workflow.

If no one responds, it could follow up. When the required information arrives, it could evaluate the situation again and continue the process.

The output is not only an insight or a piece of content.

The output is progress towards a goal.

That greater autonomy also creates new requirements. The agent needs access to the right systems and data, clear permissions, reliable integrations and boundaries that define what it is allowed to do.

Without those foundations, an agent may understand the task but still be unable to complete it safely or reliably.

One invoice, four different jobs

The same invoice process can therefore use several different building blocks:

Approach	Main purpose	Invoice example
Traditional automation	Execute predefined rules	Validate fields, route the invoice and update the ERP
Predictive AI	Identify patterns or risks	Flag an invoice that looks unusual
Generative AI	Create or explain content	Summarise the issue and draft a message
Agentic AI	Work towards a goal	Investigate and move the exception towards resolution

Predictive AI spots the possible issue.

Generative AI helps explain it.

Agentic AI moves the case forward.

Traditional automation keeps the predictable parts of the process running reliably underneath it all.

The technologies do not need to compete with each other. Their value comes from using each one for the job it handles best.

How to choose the right approach

Before choosing a tool, look at the process and ask what kind of output is actually needed.

When the task follows clear and repeatable rules, use traditional automation.

When the process needs to recognise a pattern, assess a risk or estimate what may happen, predictive AI may be appropriate.

When someone needs a summary, explanation, draft or other new content, generative AI can help.

When the process has a goal that requires several decisions and actions across systems, agentic AI may be worth considering.

The answer may also be a combination.

A predictive model can identify the issue, generative AI can explain it, an agent can coordinate the resolution and traditional automation can handle the reliable system updates.

The most advanced option is not always the best one

The goal should not be to use as much AI as possible.

It should be to build a process that works.

Sometimes that requires agentic AI. Sometimes it requires several different technologies working together.

And sometimes good ol’ automation is still exactly what the process needs.

What does your process actually need: an insight, content, a predefined action or progress towards a goal?

AI Is in Production. But Is It Operational?

Suvi Lindholm — Tue, 28 Jul 2026 06:55:58 +0000

AI is entering daily work faster than operating models are adapting. The next question is whether the business foundation is ready to support it.

AI adoption is no longer the most interesting question. At least not on its own.

According to Tieto’s Nordic AI Survey 2026, organisation-wide AI use in production has grown from 7% to 31% in one year. That is a significant shift. Companies are no longer only discussing AI in strategy meetings or testing it in isolated pilots. More of it is finding its way into daily work.

But another figure tells a more complicated story: only 4% of organisations say AI is a critical part of their core infrastructure and business operations.

So yes, AI is moving into production. But if the process around it still depends on manual checks, copied data or someone exporting a file from one system to another, how much has the operating model really changed?

Using AI Is Not the Same as Changing the Process

This is where the AI conversation becomes less polished.

Using AI to summarise documents, draft messages, search internal information or support employees can already create real value. These are useful applications, and many teams are seeing practical benefits from them.

However, using AI inside an actual business process is a different challenge.

In that environment, the AI may need to access the right customer data, know which system is the source of truth, trigger an approval, update the CRM, check an ERP status, notify the right person and leave a clear record of what happened.

That is usually where the gaps become visible.

Pilots Work Differently from Daily Operations

A pilot can work well when the data has been prepared in advance and the scenario is carefully controlled. The same use case becomes harder when it needs to run every day across several systems, with missing fields, exceptions, unclear ownership and business rules that still live partly in someone’s head.

Many companies are now entering exactly this phase. They have tested AI, seen the potential and moved some use cases into production. The process layer around those use cases is still catching up.

Sometimes the systems are not connected in the right way. Sometimes the data looks reliable until it needs to be used automatically. Sometimes nobody is completely sure who owns the workflow when something fails.

And sometimes the automation works perfectly in one narrow scenario, but becomes fragile as soon as it touches real operations.

Smarter Tools Do Not Automatically Create Better Operations

Without the right foundations, AI can remain surprisingly close to the surface.

The tools become smarter, but the underlying process does not necessarily become more reliable.

That is why the next question may not be:

How do we use more AI?

A more useful question is:

What needs to be connected, trusted and controlled before this AI use case can safely support daily operations?

This is where integrations, automation and data foundations stop being purely technical topics. They become the difference between an AI experiment and an AI-supported process that the business can actually rely on.

Start with One AI Use Case

A practical next step is to choose one AI use case and map what it would need around it before it could operate every day.

Which systems would it need to access? Which data would it depend on, and where does that data come from? Who approves the action? Who owns the exception? How would the team know if the process failed halfway through?

That is often where the real AI readiness discussion begins.

Not with the model, but with the process around it.

When Integration Tools Multiply, Governance Gets Harder

Suvi Lindholm — Mon, 20 Jul 2026 06:00:41 +0000

Most integration problems do not begin with the first workflow. The first workflow is usually fine.

Someone wants to move rows from Excel into a CRM. Sales needs a simple approval flow. Finance wants to stop copying the same customer data between systems every Friday afternoon.

The requirement is clear, the platform handles it and the first result looks exactly as expected.

The problem begins later, when one workflow becomes five, then twenty, then a collection of automations built by different people in different tools.

Each workflow may still work on its own. Together, they create a different question.

The question is no longer:

Can we automate this?

It becomes:

Can we still understand, govern and maintain it when it scales?

That is where the difference between an automation tool and an orchestration platform starts to matter.

The first workflow rarely shows the full requirement

Integration tools are often compared through features: connectors, API support, approval steps, workflow logic, trigger options and on-premise connectivity.

These capabilities matter. A platform still needs to support the use case.

But feature lists do not always reveal what the solution will require once it becomes part of daily operations.

Consider a familiar Microsoft-based scenario. A team wants to move data from Excel into Dynamics CRM. Both tools belong to the same ecosystem, the users already know them and the first version appears straightforward.

A simple synchronisation may fit comfortably inside Power Automate. Then the Excel file grows from 20 rows to 2,000.

The team needs batch processing. Failed rows must be retried without running the whole file again. Someone asks for an API because Excel was never intended to be the permanent source. Logs need to remain available long enough to explain why one customer value changed several months ago.

The solution may gradually expand to include Power Automate, Azure Data Factory, API Management, Functions, storage accounts, custom logging and a monitoring dashboard.

There is nothing inherently wrong with this architecture. These services can be excellent when used for the right purpose.

But the operating model is no longer simple.

What started as a small automation has quietly become part of the company’s operational infrastructure.

The build cost is only part of the decision

The first integration may be inexpensive to create. The environment required to operate it may not be.

As the solution expands, IT has more places to configure, secure and monitor. Permission models become more distributed. Licensing discussions multiply. Documentation needs to cover not only the workflow, but also the services around it.

Ownership becomes harder to see as well.

The business user may own the original need. A developer may own an Azure function added later. An administrator manages permissions elsewhere, while the data team introduces another component because the process now supports reporting.

Each person understands their own part. The challenge is understanding the complete process from beginning to end.

That becomes especially visible when the person who built the original workflow changes role or leaves the company. The automation may still be running, but the knowledge around it may not be.

Usability is also a governance issue

Usability is often discussed as a matter of convenience: a clearer interface, a shorter learning curve or a tool that business users can adopt without extensive technical training.

In integration work, usability also affects governance.

When developers, business users, administrators and integration owners work inside one shared platform, the organisation has a better chance of maintaining one shared operating model.

A platform does not remove the need for roles, permissions, naming conventions or development standards. It cannot fix unclear ownership by itself.

But it can make good ownership easier to practise.

When tooling is fragmented, each role tends to move towards a different environment. A business user builds the first workflow in Power Automate. A developer extends it in Azure when edge cases appear. An administrator manages access somewhere else, and the data team adds another service when the flow becomes part of reporting.

The integration owner must then work out where the process starts, which system transforms the data, where failures are recorded, which component retries them and where the process finally ends.

This is more than an untidy architecture diagram. It determines how the company responds when something goes wrong.

What happens when the process fails?

Suppose a process fails at 02:17.

If 47 rows succeed and three fail, where is that visible? Can the failed rows be replayed safely? If a customer record receives the wrong payment term, did the problem originate in the source file, the mapping, the API response or a retry using old data?

These questions are not glamorous, but they determine whether an integration remains manageable after the first wave of adoption.

A usable integration platform reduces the distance between building, running, monitoring and improving a workflow. It does not remove the need for sound architecture, but it reduces the number of places in which that architecture has to be understood and maintained.

Governance is rarely missed on day one

Governance is difficult to appreciate when there is only one workflow. Its absence becomes visible later.

It appears when low-code adoption spreads across departments without shared standards. Nobody knows which workflows are business-critical and which ones were built for a one-off report last April.

Two automations update the same field using slightly different logic. Logs have already expired by the time someone asks what happened. A workflow fails during payroll, invoicing, onboarding or order handling.

These processes may look routine, but that is exactly why they matter. They are expected to work without drawing attention to themselves.

In Microsoft environments, governance can be built through Power Platform administration, policies, dashboards, reporting and continuous follow-up. Azure Integration Services can support robust architectures, but monitoring, logging, backup, security and ownership must be designed across several services.

Many organisations do this well.

But the operating model has to be designed and then maintained after the original project team has moved on.

That second part is often underestimated.

One Excel file shows where the complexity hides

Return to the Excel-to-Dynamics example.

The initial requirement sounds simple: read the rows, map the fields and update the corresponding CRM records.

Then the file arrives.

The column names have changed because someone exported the data from a different view. One date column contains real dates alongside values such as “TBC”. Some customer identifiers should contain leading zeros, but Excel has removed them.

Three rows refer to customers that do not exist in Dynamics. One contains a character that breaks a downstream validation rule. The same file is sometimes uploaded twice.

The business does not want the entire process to stop because of a few bad rows. IT still needs to know which rows failed, why they failed and whether they can be processed again without creating duplicates.

The real requirement is no longer:

Move data from Excel into Dynamics.

It is:

Move valid data safely, isolate invalid records, prevent duplicate processing, preserve enough history for investigation and make failures visible to the right people.

That wording is less impressive. It is also much closer to the work that needs to be done.

Fragmented and unified approaches solve different problems

In a fragmented architecture, the solution may expand as the requirements become clearer. Batch processing introduces one component. API exposure introduces another. Logging, retries, exception handling and monitoring add more.

This may be the right solution, especially when the organisation already has strong Azure engineering capabilities and clear ownership across the environment.

But someone still needs to keep the full picture together.

In a more unified orchestration platform, the workflow, transformations, errors, job history and monitoring remain closer to one operating model.

That does not make the solution perfect. No platform protects a team from unclear naming, rushed delivery, missing documentation or weak ownership.

The difference is in the baseline.

When someone asks why yesterday’s customer data did not update, the investigation should not begin with:

We need to check three systems and find the consultant who originally built it.

The issue should be visible enough that the team can begin solving it.

Power Automate, Azure or Workato?

The comparison should not be reduced to:

Which tool has the most features?

A better question is:

What kind of integration operating model are we trying to build?

For isolated personal productivity automations, Power Automate may be entirely sufficient. A small approval flow does not always need to be treated like enterprise integration architecture.

If an organisation has a strong Azure team, clear ownership and established practices for monitoring, logging, backup and security, Azure Integration Services can be a powerful choice.

But there is a trade-off.

The more the integration landscape depends on multiple tools, the more the operating model has to compensate through stronger ownership, better documentation, clearer standards and disciplined governance across services.

When the goal is to scale business processes across systems, teams and use cases without turning every integration into a multi-service architecture exercise, Workato’s unified model becomes more relevant.

It brings integration, automation, monitoring, job history, connectors, recipes and governance closer together in one environment.

The main output is not simply another working automation. It is a more manageable integration environment.

The successful workflow may create the bigger risk

The biggest integration risk is not always that the first workflow fails. A failed workflow is usually noticed, investigated and corrected.

The more difficult risk is that the first workflow succeeds and spreads.

Other teams copy the approach. Small exceptions become permanent logic. Temporary tools take on business-critical responsibilities, and useful automations accumulate faster than the company’s ability to govern them.

Integration architecture rarely becomes complex overnight. It becomes complex gradually, through solutions that work well enough that nobody wants to replace them.

That is why CIOs and IT leaders should evaluate integration platforms based on more than what can be built.

They should also ask whether the organisation can monitor it, understand what happened, secure it consistently and maintain it when the original developer is no longer available.

The first workflow proves that automation is possible.

What happens after the twentieth workflow shows whether the integration strategy is working.

From process automation to agent orchestration

K1vast1NapsahTaa — Mon, 09 Dec 2024 12:37:40 +0000

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.

Harri Vähäkangas

Preiskari Solutions Oy

harri.vahakangas@preiskari.fi

mob: 0400 600 648

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336 5453

About hyperautomation

K1vast1NapsahTaa — Tue, 29 Oct 2024 12:26:07 +0000

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:

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.
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:

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.
Scalability of automation: Organizations want to scale automation beyond individual processes, and hyperautomation enables this by providing comprehensive integrations between different technologies.
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.

Harri Vähäkangas

Preiskari Solutions Oy

harri.vahakangas@preiskari.fi

mob: 0400 600 648

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336 5453

Information retrieval and AI

K1vast1NapsahTaa — Thu, 05 Sep 2024 10:24:51 +0000

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::

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.
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.
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.
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.
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.
Automated assessment and correction: AI systems can automatically detect and correct data errors or inconsistencies, improving the reliability of data.
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.
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:

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.
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.
Updates and management: Paid services may also offer regular updates and better-managed content, which can improve both accuracy and relevance.
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::

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.
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.
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.
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.
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.
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.
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.

Harri Vähäkangas

Preiskari Solutions Oy

harri.vahakangas@preiskari.fi

mob: 0400 600 648

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336 5453

About integration platform – Workato

K1vast1NapsahTaa — Wed, 22 May 2024 16:04:55 +0000

The most common challenges companies face in integration projects often involve complexity, slow implementation, and high costs. Many organizations struggle with system compatibility, real-time data synchronization, and gaining reliable insights from their data.

These issues can reduce operational efficiency and hinder seamless business processes. They may also lead to unreliable information and limited visibility into business performance, making decision-making more difficult.

Workato is an integration and automation platform that addresses these challenges. It enables businesses to seamlessly connect systems and applications, automate workflows, and synchronize data in real time—without the need for complex code.

Workato offers scalable and customizable integration solutions tailored to each organization’s needs. This reduces implementation time and costs while improving control, visibility, and efficiency across processes.

In short, Workato provides an easy-to-use, powerful way to manage integrations and automate business operations—enhancing transparency, decision-making, and competitiveness.

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.

Harri Vähäkangas

Preiskari Solutions Oy

harri.vahakangas@preiskari.fi

mob: 0400 600 648

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336 5453

What benefits can iPaaS solutions bring to ICT operations and business?

K1vast1NapsahTaa — Fri, 12 Apr 2024 15:55:33 +0000

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.

Harri Vähäkangas

Preiskari Solutions Oy

harri.vahakangas@preiskari.fi

mob: 0400 600 648

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336 5453

Why consider integration solutions? What do modern integration solutions enable?

K1vast1NapsahTaa — Tue, 02 Apr 2024 16:18:19 +0000

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.

Harri Vähäkangas

Preiskari Solutions Oy

harri.vahakangas@preiskari.fi

mob: 0400 600 648

Jari Jokimäki

Liitos Solutions Oy

jari.jokimaki@liitos.io

mob: 050 336 5453

Liitos Solutions oy

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

About hyperautomation

Analysts’ view on hyperautomation

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

Why is Workato key to hyperautomation?

Information retrieval and AI

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

Solutions to the challenges listed above

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

Documented opportunities and benefits of using AI in information retrieval

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

About integration platform – Workato

What benefits can iPaaS solutions bring to ICT operations and business?

Why consider integration solutions? What do modern integration solutions enable?

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