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We recently compiled a list of the Worst Cloud Stocks To Buy According to Short Sellers. In this article, we will look at where UiPath Inc. (NYSE:PATH) stands against the worst cloud stocks to buy according to short sellers. Cloud computing refers to the use of remote servers, typically accessed ...

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UiPath

uipath

Halloween. It’s spooky season, so let’s bludgeon a few old stack vendors to death — Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, UIPath…they’re The Walking Dead, and they know it. Back in 2010–2012 I wrote a fair bit about business process automation over on my old blog, BPM Redux, and something I was fascinated with was the concept of self-assembling or dynamic processes, created on the fly to handle a task using artificial intelligence without the need for drawing a process map or dicking around designing another set of screens. The problem I have with ‘business rules’ being an inherent part of some BPMS is that they start to constrain process decision making and flexibility, but having a set of AI routines that not only adapts on the fly (or JIT) but also learn and potentially create a brand new process instance out of evolving from previous instances… Fast forward to today and it might finally become a reality. I looked at what some of the larger stack and cloud vendors are doing today, and they all look so desperate — throwing Generative AI into their software stacks to appear relevant and forward-thinking. The trouble is, Gen-AI renders them pointless. Every. Single. One. Let’s take Pegasystems for example (disclosure: I used to work for them years ago). They have Generative AI as part of their software solutions now but it’s still rooted in the past, forcing enterprise users to not only continue using Pega but also to continually think about building workflows with it — it doesn’t matter that it’s “low-code” pseudo-development or that you can instantly draw new process steps using the magic of AI, you’re still stuck with using Pega. The same goes for all the rest — whether it be Salesforce, IFS, SAP; whether it’s ERP, CRM, RPA — what they all potentially understand and won’t tell customers is that you have all the data already, both structured and unstructured, and a Gen-AI layer is all you need on top. Think about the following example — a customer complaint. A customer emails in a complaint moaning about one of your products or services and expecting an action in response. Typically, the email will come from a contact form with all the required details to identify the customer or client, the product in question and what they expect done. If not, then it’s freeform unstructured data with a dollop of misery and anger. Normally, it falls into a defined process that an agent has been trained to perform, with clear steps already mapped out. If not, then it’s treated as an exception and passed to someone higher up the chain to make a decision on. All that takes time to create, and develop into someone’s expensive SaaS solution that looks like a dog’s dinner on the screen. Invariably, a few years later there will be a multi-million dollar “digital transformation” program sold by a consultancy deck to eke a few tweaks and savings out of this godforsaken process to justify the cost. But what if all this didn’t need to be done anymore? What if training a Large Language Model on your existing processes, rules, customer data, product or service information was all you needed to do? Going down this train of thought leads to a very different outcome. The LLM reads and understands the content of the email, pulling out key information like customer details, the product or service mentioned, and the specific issue being raised. In the case of a telephone conversation then we’ve already seen some Gen-AI solutions transcribe in real-time an interaction and then offer up advice, a response, or an answer to a question. So, already there is no need for parsing of data to another system to fill out a screen for an agent to look at. Based on this information, the LLM figures out the priority of the complaint, sees if the issue is common or recurring, and decides if any specific business rules or past actions apply. In some cases there may well be sentiment analysis done on the language of the complaint to determine just how important this is, how urgent or how annoyed the customer is. Things can get funky from here. Based on the type of complaint, the LLM creates a workflow to resolve the issue on the fly. This could involve updating customer data, processing a refund, or sending the complaint to a specialised team but it’s all done without a front end system we’ve all grown accustomed to loathe. With JIT (Just In Time) capabilities, the LLM can generate code on the spot to do necessary backend tasks. For example, if a refund is needed, the LLM creates the code to process it and updates the backend customer database with the details. Since the LLM has been trained on business rules, it makes sure all actions follow company policies — checking if the customer is eligible for a refund before processing it for example. The results of each complaint handling process are used to train the LLM further. If a complaint is resolved successfully, the LLM learns from the actions taken, improving future responses and workflows. Over time, the LLM can find recurring issues, improve existing workflows, and even suggest changes to business rules or policies based on patterns in customer complaints. The point here is that (a) you don’t need to define what the business process is any more than you do with task definition, nobody cares about static processes anymore when you can dynamically generate and execute them, (b) the LLM generates code and minimal screen UI to complete the task and have the agent involved but after that’s done the code and the UI is deleted, it’s an artefact that is no longer required because it was created to handle an interaction that is personal to that customer, and (c) humans are still in the loop, the efficiency gains are not in reducing headcount but removing the expensive excuse of paying for ancient software and unnecessary transformation programs based on old methods that are always initiated by some incoming c-suite with a relationship with a favourite vendor. It’s not as simplistic and current Gen-AI solutions aren’t geared up for this (yet) but this should be enough to start to question why you need a huge ERP implementation, or why an expensive CRM is required at all if you can just have a conversation with the customer data directly. Remember Master Data Management? Enterprises spent millions and years on making sure all their backend databases were fit for purpose, clean, deduped, they’re in potentially good shape and structured enough for a single Gen-AI layer on top and nothing else. Imagine not having to worry about integrating one behemoth frankenstack vendor to another, having to sync data sources, transform the data, orchestrate processes between them…it’s a pretty tantalising prospect and one that none of the aforementioned vendors really want you to know about. Major vendors offering Gen-AI wallpaper on top of the cracked and creaking software they push are slowly dying, the benefits they offer will eventually run dry when stacked up against using artificial intelligence as the alternative on its own. In a way Altman was right, it’s a new type of operating system, in this case it’s a business operating system and one that doesn’t need the bloatware. I’ve maybe sat on these ideas for too long now, I might go and start building again. I mean, depending on who you read the global workflow automation market size was valued at $16.41 billion in 2021 and is projected to reach $34.4 billion by 2030, growing at a CAGR of 9.71%. Not small numbers. Who’s with me? Halloween. It’s spooky season, so let’s bludgeon a few old stack vendors to death — Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, UIPath …they’re The Walking Dead, and they know it. Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, UIPath The Walking Dead, Back in 2010–2012 I wrote a fair bit about business process automation over on my old blog, BPM Redux , and something I was fascinated with was the concept of self-assembling or dynamic processes , created on the fly to handle a task using artificial intelligence without the need for drawing a process map or dicking around designing another set of screens. BPM Redux BPM Redux self-assembling or dynamic processes The problem I have with ‘business rules’ being an inherent part of some BPMS is that they start to constrain process decision making and flexibility, but having a set of AI routines that not only adapts on the fly (or JIT) but also learn and potentially create a brand new process instance out of evolving from previous instances… The problem I have with ‘business rules’ being an inherent part of some BPMS is that they start to constrain process decision making and flexibility, but having a set of AI routines that not only adapts on the fly (or JIT) but also learn and potentially create a brand new process instance out of evolving from previous instances… The problem I have with ‘business rules’ being an inherent part of some BPMS is that they start to constrain process decision making and flexibility, but having a set of AI routines that not only adapts on the fly (or JIT) but also learn and potentially create a brand new process instance out of evolving from previous instances… Fast forward to today and it might finally become a reality. Fast forward to today and it might finally become a reality. I looked at what some of the larger stack and cloud vendors are doing today, and they all look so desperate — throwing Generative AI into their software stacks to appear relevant and forward-thinking. The trouble is, Gen-AI renders them pointless. Every. Single. One. Every. Single. One. Let’s take Pegasystems for example (disclosure: I used to work for them years ago). They have Generative AI as part of their software solutions now but it’s still rooted in the past, forcing enterprise users to not only continue using Pega but also to continually think about building workflows with it — it doesn’t matter that it’s “low-code” pseudo-development or that you can instantly draw new process steps using the magic of AI, you’re still stuck with using Pega. Pegasystems Pegasystems The same goes for all the rest — whether it be Salesforce, IFS, SAP; whether it’s ERP, CRM, RPA — what they all potentially understand and won’t tell customers is that you have all the data already, both structured and unstructured, and a Gen-AI layer is all you need on top. you have all the data already, both structured and unstructured, and a Gen-AI layer is all you need on top. Think about the following example — a customer complaint. A customer emails in a complaint moaning about one of your products or services and expecting an action in response. Typically, the email will come from a contact form with all the required details to identify the customer or client, the product in question and what they expect done. If not, then it’s freeform unstructured data with a dollop of misery and anger. Normally, it falls into a defined process that an agent has been trained to perform, with clear steps already mapped out. If not, then it’s treated as an exception and passed to someone higher up the chain to make a decision on. All that takes time to create, and develop into someone’s expensive SaaS solution that looks like a dog’s dinner on the screen. Invariably, a few years later there will be a multi-million dollar “digital transformation” program sold by a consultancy deck to eke a few tweaks and savings out of this godforsaken process to justify the cost. But what if all this didn’t need to be done anymore? But what if all this didn’t need to be done anymore? What if training a Large Language Model on your existing processes, rules, customer data, product or service information was all you needed to do? Going down this train of thought leads to a very different outcome. The LLM reads and understands the content of the email, pulling out key information like customer details, the product or service mentioned, and the specific issue being raised. In the case of a telephone conversation then we’ve already seen some Gen-AI solutions transcribe in real-time an interaction and then offer up advice, a response, or an answer to a question. So, already there is no need for parsing of data to another system to fill out a screen for an agent to look at. Based on this information, the LLM figures out the priority of the complaint, sees if the issue is common or recurring, and decides if any specific business rules or past actions apply. In some cases there may well be sentiment analysis done on the language of the complaint to determine just how important this is, how urgent or how annoyed the customer is. Things can get funky from here. Things can get funky from here. Based on the type of complaint, the LLM creates a workflow to resolve the issue on the fly. This could involve updating customer data, processing a refund, or sending the complaint to a specialised team but it’s all done without a front end system we’ve all grown accustomed to loathe. With JIT (Just In Time) capabilities, the LLM can generate code on the spot to do necessary backend tasks. For example, if a refund is needed, the LLM creates the code to process it and updates the backend customer database with the details. Since the LLM has been trained on business rules, it makes sure all actions follow company policies — checking if the customer is eligible for a refund before processing it for example. The results of each complaint handling process are used to train the LLM further. If a complaint is resolved successfully, the LLM learns from the actions taken, improving future responses and workflows. Over time, the LLM can find recurring issues, improve existing workflows, and even suggest changes to business rules or policies based on patterns in customer complaints. The point here is that (a) you don’t need to define what the business process is any more than you do with task definition, nobody cares about static processes anymore when you can dynamically generate and execute them, (b) the LLM generates code and minimal screen UI to complete the task and have the agent involved but after that’s done the code and the UI is deleted, it’s an artefact that is no longer required because it was created to handle an interaction that is personal to that customer, and (c) humans are still in the loop, the efficiency gains are not in reducing headcount but removing the expensive excuse of paying for ancient software and unnecessary transformation programs based on old methods that are always initiated by some incoming c-suite with a relationship with a favourite vendor. (a) you don’t need to define what the business process is any more than you do with task definition, nobody cares about static processes anymore when you can dynamically generate and execute them, (b) the LLM generates code and minimal screen UI to complete the task and have the agent involved but after that’s done the code and the UI is deleted, it’s an artefact that is no longer required because it was created to handle an interaction that is personal to that customer, and (c) humans are still in the loop, the efficiency gains are not in reducing headcount but removing the expensive excuse of paying for ancient software and unnecessary transformation programs based on old methods It’s not as simplistic and current Gen-AI solutions aren’t geared up for this ( yet ) but this should be enough to start to question why you need a huge ERP implementation, or why an expensive CRM is required at all if you can just have a conversation with the customer data directly. yet Remember Master Data Management? Enterprises spent millions and years on making sure all their backend databases were fit for purpose, clean, deduped, they’re in potentially good shape and structured enough for a single Gen-AI layer on top and nothing else. Imagine not having to worry about integrating one behemoth frankenstack vendor to another, having to sync data sources, transform the data, orchestrate processes between them…it’s a pretty tantalising prospect and one that none of the aforementioned vendors really want you to know about. Imagine not having to worry about integrating one behemoth frankenstack vendor to another, having to sync data sources, transform the data, orchestrate processes between them…it’s a pretty tantalising prospect and one that none of the aforementioned vendors really want you to know about. Major vendors offering Gen-AI wallpaper on top of the cracked and creaking software they push are slowly dying, the benefits they offer will eventually run dry when stacked up against using artificial intelligence as the alternative on its own. In a way Altman was right, it’s a new type of operating system, in this case it’s a business operating system and one that doesn’t need the bloatware. In a way Altman was right, it’s a new type of operating system, in this case it’s a business operating system and one that doesn’t need the bloatware. I’ve maybe sat on these ideas for too long now, I might go and start building again. I mean, depending on who you read the global workflow automation market size was valued at $16.41 billion in 2021 and is projected to reach $34.4 billion by 2030, growing at a CAGR of 9.71%. Not small numbers. Who’s with me? Who’s with me?

Generative AI Will Kill Old Stack Vendors. Let It.

This practical tutorial provides an introduction to leading Robotic Process Automation (RPA) platform and demonstrates how it can be integrated with the open source no-code/low-code application platform. There are 3 parts: UiPath Joget Part 1: Get Started with UiPath Part 2: Get Started with Joget Part 3: Integrate UiPath with Joget For background information, please read . Robotic Process Automation: A Quick Introduction and How it Complements Low-Code Application Platforms Part 1: Get Started with UiPath UiPath Overview is one of the leading RPA vendors, having been recognized as a leader in . UiPath “The Forrester Wave™: Robotic Process Automation, Q2 2018” report UiPath consists of 3 main components: UiPath Component Architecture : UI tool to visually design the process to automate UiPath Studio : Web application that manages the creation, monitoring, and deployment of all robots and processes UiPath Orchestrator : Runs processes that were built in UiPath Studio. Execution agent that is installed and executed in the actual machine. UiPath Robot So in a nutshell, this is how it works: Visually design a process using UiPath Studio in a developer PC, Publish/deploy the process to UiPath Orchestrator to be managed, scheduled and monitored. The actual process itself is executed by the UiPath Robots installed in the respective machines. UiPath Concepts and Terms Here are some of the basic concepts and terms in UiPath that you should get familiar with for the tutorial later: : Represents the actual machine the robot executes in Machine : An environment is a grouping of Robots, that is used to deploy processes Environment : A UiPath Studio project that is published Package : A process represents the association between a package and an environment. Each time a package is deployed to an environment, it is automatically distributed to all machines that belong to that environment. Process : A job is the execution of a process on one or multiple Robots. Job There are other concepts for more advanced usage that will not be used in the tutorial, such as: : A process library describes a system of activities that are reusable for sharing Library : Enables jobs to be executed in a preplanned manner Schedule : Usually represent shared variables or credentials that can be used in different projects. Asset : A place to store multiple types of data, such as invoice information or customer details. Queue Step 1: Sign Up for UiPath Cloud Platform Let’s sign up for an account at . There is a Community plan to get started for free, so we’ll to register. https://www.uipath.com/platform-trial Choose Community Sign up using a social account or an email, and upon registration you will be taken to a dashboard at . https://platform.uipath.com A represents a deployment in the company. A default service is already created e.g. DemoDefault. service Select the service name to open the web application. UiPath Orchestrator Step 2: Install UiPath Studio and UiPath Robot Download the installer (UiPathStudioSetup.exe) from the , and install it on the target computer. More information on UiPath Studio is available at the . UiPath Studio Resource Center UiPath Studio Guide After installing UiPathStudioSetup.exe, start from the Windows Start menu and activate it. You can start free by activating the Community Edition. UiPath Studio Step 3: Launch UiPath Robot and Obtain Machine Name In the Windows Start menu, search for and start it UiPath Robot Click on the icon to open the window, and copy the . Gear Orchestrator Settings Machine Name Step 4: Obtain Machine Key Back in the , create a new under the Machines menu. UiPath Orchestrator Machine : Make sure that the name matches the shown in the earlier. IMPORTANT Machine Name UiPath Orchestrator Settings View the selected and copy the . Machine Machine Key Step 5: Connect UiPath Robot to the Machine In the click on the icon to open the window. UiPath Robot, Gear Orchestrator Settings Fill in the ( for the UiPath Cloud Platform) and the copied in the previous step, then click on . Orchestrator URL https://platform.uipath.com Machine Key Connect Once connected, you should see “ ” at the bottom of the window. Status: Robot unavailable Hover over that message, and you should see the reason “ ” This message means that the machine connection is valid, so now it’s time to create a robot. If you see a different reason then there might be an error in your configuration so you will want to double check your settings. Robot is not available. Step 6: Create a Robot In the under the menu, click on the icon on the top right. UiPath Orchestrator Robots Plus Choose “standard robot that works on a single standard machine”. Assign the robot to the machine created previously. Set a “Name”, and fill in the actual Windows login for the target machine (where the is installed) under and . UiPath Robot Domain\Username Password Step 7: Add Robot to Environment Next, we’ll need to add the created to the . Select the tab at the top of the menu and select the option. Robot Environment Environments Robots Manage Select the previously created robot and click . UPDATE At this point, the robot should be successfully connected to the machine. Check the and the status should show “ ” UiPath Robot Orchestrator Settings Connected, licensed Step 8: Start a Job Now that the robot is deployed, it’s time to start a job to test it. Under , click on the icon on the top right. Jobs Play Select the pre-existing and the previously robot, then click on . Demo Process Start In the , it will show “ ”. Once the package has been installed, it will show “ ” which means the process has started. In this demo process, a Hello popup dialog will appear. UiPath Robot Installing package… Job started processing The robot has successfully started the job. You can monitor the robots and jobs in the menu in the . Monitoring UiPath Orchestrator Step 9: Prepare for UiPath Integration In June 2019, UiPath introduced a new mechanism for . There are a number of steps required to retrieve some required information for integration: consuming its Cloud APIs 1. Get Access and ID Tokens 1.1 Generate Code Challenge and Code Verifier In a web browser, visit and run the following code: https://repl.it/languages/nodejs function base64URLEncode(str) {
 return str.toString('base64')
 .replace(/\+/g, '-')
 .replace(/\//g, '_')
 .replace(/=/g, '');
}
 
function sha256(buffer) {
 return crypto.createHash('sha256').update(buffer).digest();
}
 
console.log("Generating challenge and Verifier");
var cryptoResult = crypto.randomBytes(32);
var verifier = base64URLEncode(cryptoResult);
var challenge = base64URLEncode(sha256(verifier));
console.log("Code challenge: " + challenge)
console.log("Code verifier: " + verifier); Take note of the and values in the output e.g. code challenge code verifier Generating challenge and Verifier
Code challenge: RzYlHiiGzPGgOLaRQJYftZ1mmc3sCbeicZVRftTmC-A
Code verifier: YVMnLczXQgJ9dwzV7MlMWEjGyAia4nXvTZzU4UVrAPE 1.2 Get Authorization Code Replace the into the URL below, and visit the updated URL in a browser. code challenge https://account.uipath.com/authorize?response_type=code&nonce=b0f368cbc59c6b99ccc8e9b66a30b4a6&state=47441df4d0f0a89da08d43b6dfdc4be2&code_challenge=[code_challenge]&code_challenge_method=S256&scope=openid+profile+offline_access+email &audience=https%3A%2F%2Forchestrator.cloud.uipath.com&client_id=5v7PmPJL6FOGu6RB8I1Y4adLBhIwovQN&redirect_uri=https%3A%2F%2Faccount.uipath.com%2Fmobile The browser should redirect to https://account.uipath.com/mobile?code=[authorization_code]&state=47441df4d0f0a89da08d43b6dfdc4be2 Copy the in the URL. authorization_code 1.3 Get Refresh Token Using any API testing tool (e.g. ), submit a request to Postman POST with the body below, replacing the values for and accordingly. https://account.uipath.com/oauth/token [authorization_code] [code_verifier] {
 "grant_type": "authorization_code",
 "code": "[authorization_code]",
 "redirect_uri": "https://account.uipath.com/mobile",
 "code_verifier": "[code_verifier]",
 "client_id": "5v7PmPJL6FOGu6RB8I1Y4adLBhIwovQN"
} Copy the value of the from the response: refresh_token {
 "access_token": "eyJ0eX...",
 "refresh_token": "CBZcOD6vrP2FQ9qa8fuqDdfoEwnVPuR2Kp...",
 "id_token": "eyJ0eX...",
 "scope": "openid profile email offline_access",
 "expires_in": 86400,
 "token_type": "Bearer"
} 1.4 Get Access Token and ID using the Refresh Token Using any API testing tool, submit a request to POST with the body below, replacing the values for accordingly. https://account.uipath.com/oauth/token [refresh_token] {
 "grant_type": "refresh_token",
 "client_id": "5v7PmPJL6FOGu6RB8I1Y4adLBhIwovQN",
 "refresh_token": "CBZcOD6vrP2FQ9qa8fuqDdfoEwnVPuR2Kpz-hitbTAIzG"
} Copy the values for and from the response. access_token id_token {
 "access_token": "eyJ0eX...",
 "id_token": "eyJ0eX...",
 "scope": "openid profile email offline_access",
 "expires_in": 86400,
 "token_type": "Bearer"
} 2. Get UiPath Account, Service and Process Identifiers 2.1 Get the Account Logical Name Using any API testing tool, submit a request to POST with a request header https://platform.uipath.com/cloudrpa/api/getAccountsForUser Authorization: Bearer [id_token] Copy the value for from the response: accountLogicalName {
 "userEmail": "demo@domain.com",
 "accounts": [
 {
 "accountName": "Demo",
 "accountLogicalName": "Demo"
 }
 ]
} 2.2 Get Service Instance Logical Name Using any API testing tool, submit a request to POST with a request header https://platform.uipath.com/cloudrpa/api/account/ /getAllServiceInstances [accountLogicalName] Authorization: Bearer [id_token] Copy the value for from the response: serviceInstanceLogicalName [
 {
 "serviceInstanceName": "DemoDefault",
 "serviceInstanceLogicalName": "DemoDefaultzous50676",
 "serviceType": "ORCHESTRATOR",
 "serviceUrl": "https://platform-community.azurewebsites.net"
 }
] 2.3 Get Release Key for the Desired Process We will now need to retrieve the Release Key, a unique identifier for a desired process. Using any API testing tool, submit a request to GET with a request header https://platform.uipath.com/odata/Releases Authorization: Bearer [id_token] Copy the value for from the response**:** Key {
 "@odata.context": "https://platform.uipath.com/odata/$metadata#Releases",
 "@odata.count": 1,
 "value": [
 {
 "Key": "b27c7363-459c-4520-bae5-660d4a1d3813",
 "ProcessKey": "Demo_Process",
 "ProcessVersion": "1.0.21",
 "IsLatestVersion": false,
 "IsProcessDeleted": false,
 "Description": "Demo Process",
 "Name": "Demo Process",
 "EnvironmentId": 98069,
 "EnvironmentName": "Demo Environment",
 "InputArguments": null,
 "QueueDefinitionId": null,
 "QueueDefinitionName": null,
 "Id": 120897,
 "Arguments": {
 "Input": null,
 "Output": null
 },
 "ProcessSettings": null
 }
 ]
} 2.4 Test Orchestrator API Call to Start a Job At this point, make sure you have the following critical information at hand: (Refresh Token) refresh_token (Access Token) access_token (Service Instance Logical Name) serviceInstanceLogicalName (Release Key) Key Let’s try making an API call to start a job for the process. Using any API testing tool, submit a request to POST with 2 request headers https://platform.uipath.com/odata/Jobs/UiPath.Server.Configuration.OData.StartJobs Authorization: Bearer [access_token]
X-UIPATH-TenantName: [serviceInstanceLogicalName] and the body: { 
 "startInfo":
 { "ReleaseKey": "b27c7363-459c-4520-bae5-660d4a1d3813",
 "Strategy": "All",
 "RobotIds": [ ],
 "JobsCount": 0,
 "Source": "Manual" 
 } 
} If successful, the response will be as follows, with the status of the Job shown in the attribute: State {
 "@odata.context": "https://platform.uipath.com/odata/$metadata#Jobs",
 "value": [
 {
 "Key": "e811bd29-26fb-4cc3-af91-7fd6308ca643",
 "StartTime": null,
 "EndTime": null,
 "State": "Pending",
 "Source": "Manual",
 "SourceType": "Manual",
 "BatchExecutionKey": "382e7a96-a5b9-4343-b258-26a80cf87f80",
 "Info": null,
 "CreationTime": "2019-06-12T08:02:03.2595408Z",
 "StartingScheduleId": null,
 "ReleaseName": "Demo Process",
 "Type": "Unattended",
 "InputArguments": null,
 "OutputArguments": null,
 "HostMachineName": null,
 "HasMediaRecorded": false,
 "Id": 10169582
 }
 ]
} Part 2: Get Started with Joget Joget Overview is an open source no-code/low-code application platform for faster, simpler digital transformation. Joget combines the best of business process automation, workflow management and rapid application development in a simple, flexible and open platform. Visual and web-based, it empowers non-coders to instantly build and maintain apps anytime, anywhere. Joget Joget Platform Architecture Sign Up for Joget Workflow On-Demand If you already have your own installation of the Joget platform, you can use it and skip this step. Otherwise, you can sign up for , a hosted version of the Joget platform that allows you to quickly get started. Joget Workflow On-Demand Visit and click on to register a new account. https://cloud.joget.com Sign Up Free Once you have successfully verified your email, you will be linked to your own running installation of Joget Workflow where you’ll be able to . visually build a full app in 30 minutes without coding Part 3: Integrate UiPath with Joget How to Integrate with UiPath UiPath provides the for integration. Orchestrator API The most commonly used function is , which should satisfy a majority of use cases. We will use this function in the following integration tutorial. Starting a Job Design a Joget Process to Start a UiPath Job Since the is a API with data in format, we can use the Joget to invoke the API. UiPath Orchestrator API REST JSON JSON Tool Before we start, ensure that you have the following critical UiPath information at hand (obtained from the previous tutorial): Prepare for UiPath Integration (Refresh Token) refresh_token (Service Instance Logical Name) serviceInstanceLogicalName (Release Key) Key Step 1. Design New App First, let’s design a new app by clicking on in the Joget . Design New App App Center Fill in desired values for the and , and click . App ID App Name Save Step 2. Design New Process Click on the menu, then on the button to launch the . Processes Design Process Process Builder Design a simple process containing 2 tools, as shown below. Click on the pencil icon after hovering over the process name along the top. Enter a suitable process name and create 3 workflow variables to store the response values from UiPath API call: edit status id_token access_token Click on . Deploy Step 3. Configure First Tool to Get UiPath Access Token Once the process has been deployed, close the Process Builder. In the process view page, select the tab. Map Tools to Plugins Click on for the first tool and select , then key in the following configuration: Configure Plugin JSON Tool JSON URL: https://account.uipath.com/oauth/token Call Type: POST POST Method: Custom JSON Payload Custom JSON Payload: {
"grant_type": "refresh_token",
"client_id": "5v7PmPJL6FOGu6RB8I1Y4adLBhIwovQN",
"refresh_token": "[refresh_token]"
} Under , map the variables to store the tokens in the matching workflow variables i.e. Store to Workflow Variable access_token: access_token id_token: id_token Click on to save. Submit Step 4. Configure Second Tool to Start Job Using the Access Token Click on for the second tool and select , then key in the following configuration: Configure Plugin JSON Tool JSON URL: https://platform.uipath.com/odata/Jobs/UiPath.Server.Configuration.OData.StartJobs Call Type: POST POST Method: Custom JSON Payload Custom JSON Payload (Replace with the actual Release Key): [Key] {
"startInfo":
{ "ReleaseKey": "[Key]",
"Strategy": "All",
"RobotIds": [ ],
"JobsCount": 0,
"Source": "Manual"
}
} Request Headers (Replace [ ] with the actual Service Instance Logical Name: serviceInstanceLogicalName Authorization: Bearer #variable.access_token# X-UIPATH-TenantName: [serviceInstanceLogicalName] Under , map the status variable to the attribute in the response JSON i.e. Store to Workflow Variable State status: value[0].State That’s it. The Joget process has been configured to invoke the to start a job. UiPath Orchestrator API Step 5. Run the Process Now, let’s test the process. Click on the button at the top, then again in the confirmation dialog. Run Process Run Process Once the process has started, the 2 tools will execute as configured. To view the results of the process, navigate to -> . Monitor Completed Processes Select the process instance and you will see the 2 tools executed. Click on each activity to view the values of the workflow variables which obtained the results of the calls. Orchestrator API If you are running Joget on-premise and have access to the system logs, you will be able to see the tool requests and responses if the debugging option is enabled. If you are using , you can stream the logs directly in the web browser. Joget DX Back at the and , you would also be able to monitor the execution of the job. UiPath Orchestrator UiPath Robot Conclusion In this article we covered a tutorial on getting started with UiPath and Joget, followed by integration between them. As demonstrated in the tutorial, a lot of the work required for integration is in preparing the authentication mechanism to the UiPath Orchestrator API. Once the required integration keys are available, the configuration in Joget to invoke the UiPath API itself is pretty simple. To learn more about each of the platforms, visit and . UiPath Joget <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

Robotic Process Automation and Low-Code: A Practical Tutorial on Integrating UiPath with Joget

Process mining isn't a novel concept. Since its emergence in 2011, process mining has been undergoing continuous improvement as leading providers like and consistently refine and expand their suite of tools. Celonis UiPath The process mining market is expected to reach , so understanding both the direct and hidden benefits of the methodology is crucial for enterprises aiming for a competitive edge. Therefore, it makes sense to break down the value and purpose of this technology, while outlining the key steps for achieving the most rewarding results. by 2028 $12.1 billion The definition of process mining Process mining involves extracting and utilizing data to explore and improve enterprise workflows. The reason why it is important is directly connected to the process most common reason for failed digital transformations— , as noted by Organizations resist change when they can’t see the entire journey clearly and thus fail to align with leaders' thoughts. lack of vision and direction Deloitte. Business leaders may not provide specifics, resulting in unclear transformation goals, undefined timeframes, and a lack of transparency for employee motivation within updated workflows. Low visibility arises from the abundance of enterprise data and processes requiring detailed sorting and examination. However, manual handling is time-consuming, error-prone, and leads to blind spots rather than insights. Due to this, process mining becomes a game-changer by collecting log event data, transforming it into a graph that visualizes a specific enterprise process and its cycle. By using process mining technology, executives and decision-makers can and gain insights on potential areas of improvement. view process performance in different scenarios Process mining value Process mining offers new opportunities across the enterprise and brings efficiency to various departments Marketing and sales productivity boost Process mining allows breaking down sales processes, identifying weak points. It also delivers insights into lead and client interactions. Monitors sales process: visualizing processes and interactions for KPI tracking. Designs agile sales process: defining success criteria for a flexible process. Increases conversion rates: identifying and addresses stages limiting conversion. Optimizes customer experience: defining activities impacting customer choices. Improves marketing strategies: evaluating lead responses to marketing campaigns. Compliance and auditing facilitation According to an EY survey, process mining can optimize control-related activities, including auditing and regulatory compliance, in about of cases with: 19% Accelerated issue discovery: uncovering the reasons causing operational issues through detailed workflow visualization. Transparent financial reporting: analyzing event logs for financial transactions. Improved financial control testing: identifying control problems and non-compliance issues for consistent auditing instead of periodic assessments. Cost reduction: reducing manual tasks and optimizing employee time management. How to get max value from process mining Over 12 years, process mining has evolved from a concept to a core technology embraced by vendors like Celonis and UiPath. As new approaches emerge, the landscape of implementing process mining technology Despite this, executives and decision-makers should focus on the following essentials. will see changes. Form a team Process mining was once a collective effort, involving data gathering and employee surveys for . Now, it focuses on engaged personas instead of employees. actionable visualizations Executive: sets process mining project goals and secures funding and resources. Process owner: is responsible for end-to-end process execution and productivity, defining desired processes, and monitoring. Project manager: handles all aspects of the process mining task, selects technology, manages team communication, and reports progress. Business analyst: analyzes process mining data, identifies opportunities and risks, proposes solutions, and monitors process changes. Data engineer: extracts, transforms, and merges log event data for creating process graphs. BI engineer: connects data with users, ensures a smooth user experience, and facilitates data visualization for process graphs. Infrastructure engineer: ensures proper integration of the process mining solution within the enterprise structure, considering security and compliance. Check data system health For any data-driven process to succeed, having relevant, validated, and healthy data is essential. To ensure fruitful process mining outcomes, executives and project managers must verify the availability of necessary event data and sufficient data history. Identify the need A successful process transformation begins by recognizing a clear need. To avoid blind spots, business leaders should thoroughly investigate the issue prompting the idea for process automation, considering various levels. The depth of research directly impacts the final results and process mining goals. Establish KPIs After understanding enterprise needs, executives and stakeholders move on to the crucial step of defining primary key performance indicators. It involves specifying data sources for insights extraction, considering data dimensions (region, supplier, etc.), and estimating costs and expected outcomes. Develop process maps For successful transformation, creating detailed and multilayered process maps is essential. These maps visually represent the process, facilitating conformance checks to find deviations, process violations, and bottlenecks. Act on insights Contrary to the belief that process mining is only for insight-gathering, it can also contribute to immediate improvements. After identifying opportunities , it's beneficial to address issues promptly instead of postponing for later stages or automation. through process graphs and checks For example, process owners and analysts can streamline processes before implementing intelligent automation, ensuring a smoother transformation. Addressing issues upfront enhances the success of subsequent automation efforts. How to secure consistent growth Intelligent automation is a continuous process that constantly demands insights and awareness of enterprise operations. To improve process mining productivity, there are several important guidelines for business leaders to follow. Decision-makers should regularly update their knowledge of process mining and intelligence trends, connecting with peers and consulting digital partners for valuable insights. Stay updated on trends. Promoting innovation understanding among employees reduces resistance to change. Leaders should consistently assess employees' perceptions of transformation, ensuring friction-free initiatives with clear, trackable results. Foster innovation culture. Maintaining data health by preventing bottlenecks and silos. Investing in advanced data validation methods, and constantly updating and improving security measures for smooth integration across systems. Prioritize data hygiene.

How to Achieve Optimal ROI Through Process Mining

In the rapidly evolving world of information technology, a new trend is reshaping how we develop apps or software programs - the rise of /no-code platforms. low-code According to , Forrester The low-code market will increase to $21.2 billion by 2022, up from $3.8 billion in 2017. These platforms empower non-technical individuals to create applications without needing to write code, leading to ease of software development. So, what does this mean for traditional programming? The Low-Code/No-Code Revolution Low-code/no-code platforms are revolutionizing the way we think about coding. By leveraging visual interfaces with drag-and-drop features, these platforms allow users to build applications through graphical user interfaces and configuration, rather than traditional hand-coded programming. The benefits of this approach are significant. For one, it democratizes the process of software development, making it accessible to a broader range of people, regardless of their technical skillset. It also speeds up the development process, as building applications can be done at a much faster rate when compared to traditional methods. In fact, according to a survey by OutSystems, 41% of IT professionals believe that low-code tools can free up developers' time by 10-50%. Popular platforms like Salesforce, Zoho Creator, Microsoft PowerApps, Google AppMaker, UiPath, and many more are leading the charge in this space, providing powerful tools that enable rapid application development with minimal coding. Impact on Traditional Programming While it's true that low-code/no-code platforms can speed up application development and lower barriers to entry, they cannot replace the need for traditional programming in complex and customized software solutions. Despite the rise of these platforms, the demand for skilled programmers remains high. According to the , U.S. Bureau of Labor Statistics Employment of software developers is projected to grow 22 percent from 2019 to 2029, a much faster rate than the average for all occupations. This is because low-code/no-code solutions often lack the flexibility and granular control that hand-coding offers. For complex, bespoke applications that require intricate functionalities, traditional coding is still the go-to method. To illustrate, Let's consider the case of a retail company that used a low-code platform to develop its inventory management system. Despite having a small IT team with limited coding expertise, the company was able to build a functional, efficient system quickly using a low-code platform. However, as the company grew and its operations became more complex, it found that the low-code solution could not accommodate all of its needs. The company had to hire a team of skilled programmers to build custom functionalities into the system, demonstrating that while low-code/no-code solutions can be powerful, they have their limitations. This is where ies like ours having the experience that lets us best utilize the combination of low-code/no-code solutions with traditional programming comes as a savior. software development compan The Future of Low-Code/No-Code and Traditional Programming Looking ahead, the rise of low-code/no-code platforms is likely to shape the future of the programming landscape significantly. As artificial intelligence continues to advance, we may see more automation in code writing, making low-code/no-code platforms even more accessible and powerful. However, this doesn't spell the end for traditional programming. Instead, it highlights the need for programmers to adapt their skills and learn to work alongside these platforms. In the future, we might see more collaboration between coders and non-coders, leading to a more inclusive, diverse tech industry. Conclusion In conclusion, the rise of low-code/no-code platforms is undoubtedly shaking up the world of software development. But rather than replacing traditional programming, it's more accurate to say that it's expanding the toolkit available to developers. As we move forward, striking the right balance between these new tools and traditional coding methods will be key to driving innovation and inclusivity in the tech sector.

How the Rise of Low-Code/No-Code Impacts Traditional Programming

The Power industry, spanning from nuclear to renewable energy sources such as solar, wind, hydro, and conventional fuels, holds a vital role in shaping the world's future. In an era of rapid global advancement, the Power sector is embracing major digital transformation, mirroring trends in other industries. At the heart of this transformation lies Intelligent Automation (IA), a fusion of Robotic Process Automation ( ) and Artificial Intelligence (AI). RPA IA's power extends to managing critical IT functions within the Power Industry among the various other capabilities. This includes strengthening operational efficiency, strengthening resilience against disruptions, and beyond. This article delves into the role of IA in reshaping the Power sector, spotlighting its significance in the management of Critical IT Applications. Demystifying Intelligent Automation Let’s start by understanding what IA is. The core of lies in the fusion of RPA and AI. RPA involves deploying software robots to execute rule-based tasks, mimicking human interactions with software interfaces. AI, on the other hand, focuses on data analysis, enriching decision-making processes, and facilitating complex problem-solving. Noteworthy tools like UiPath, Automation Anywhere, Blue Prism, and Microsoft Power Automate seamlessly combine RPA with AI-driven functionalities to implement Intelligent Automation solutions. Intelligent Automation 5 Instances of IA Application for Efficient Critical IT Operations Integrating IA into critical IT operations within the Power sector improves industry advancement by helping streamline processes, optimizing resource utilization, and avoiding inefficiencies. Eventually, IA plays a pivotal role in the management of Critical IT Applications. Here are 5 such instances where Intelligent Automation facilitates the efficient management of Critical IT Applications - 1. Redefining IT Application Operations  Management - Secure Automation IA is redefining the Power Industry's IT landscape by orchestrating the management of Critical IT Applications. This involves automated seamless processes, automated workflows, and powerful and quick decision-making capabilities. For example, IA's ability to facilitate automated software updates, patches, and optimizations for Critical IT Applications using RPA bots and strengthening the capabilities using AI. This reduces the need for manual interventions, powering operational efficiency while empowering IT professionals to concentrate on strategic tasks. Also, IA can remotely monitor the network activity of critical applications, promptly identifying software-based network threats. IA enhances cybersecurity while being powered by AI-powered cybersecurity measures in parallel leveraging software RPA bots to enact corrective actions. 2. Enhancing Grid Health IT Monitoring IA holds a pivotal role in monitoring the health of power grids. By collecting data from Critical IT Applications linked to Power sensors and monitoring devices, IA aids in real-time data analysis. AI algorithms monitor this data to detect anomalies, forecast plausible failures, and pinpoint high-demand areas in Critical IT Applications. When issues emerge, IA, through the deployment of RPA bots, reacts promptly by rerouting power flow or dispatching maintenance teams. This proactive stance ensures an uninterrupted power supply, amplifying the reliability and efficiency of energy distribution. 3. Data-Informed Decision-Making Enhanced Data Analytics highlights IA's impact on the Power Industry. AI-driven systems skillfully analyze vast datasets originating from Critical IT Applications linked with power grids, equipment performance, and maintenance records. Imagine a scenario where AI carefully monitors transformer health, gathering real-time data on parameters like temperature and load fluctuations. Predictive analysis detects subtle anomalies, empowering RPA-assisted maintenance teams to proactively schedule automatic repairs. This enhances operational efficiency, brings down downtime, and maintains a steady power supply. 4. Remote Management - In Normalcy and Crisis times During instances of voltage fluctuations, IA's remote monitoring capabilities swiftly detect irregularities, prompting alerts. AI pinpoints the origin of the issue. RPA is then deployed to rapidly re-calibrate voltage levels in affected regions, guaranteeing an uninterrupted power supply. Furthermore, IA significantly enhances emergency response mechanisms within Critical IT Applications. AI quickly identifies areas demanding immediate attention, while RPA bots optimize power source allocation, prioritizing vital facilities during crises. This coordination, enabled by IA, re-imagines emergency protocols in the Power Industry’s Critical Applications 5. Streamlined Documentation and Reporting Another way IA benefits the Power Industry is by streamlining documentation and IT reporting associated with IT applications. IA can automatically generate reports, compile data, and create summaries from various IT systems. This reduces manual effort and ensures accurate and timely reporting, aiding in decision-making and compliance. In Conclusion The above-mentioned examples are just a handful of examples of how IA enhances efficiency, fortifies reliability, and optimizes IT application management within the power industry. The embrace of IA is thus not merely an option but a necessity in today’s world. As the Power Industry embraces Intelligent Automation, the path to sustainable energy shines bright!

Intelligent Automation for Efficient Management of Critical IT Applications in the Power Industry

132 Stories To Learn About Robotics

The continued fallout of the Covid-19 pandemic and widespread economic downturns may have punctuated much of the early 2020s, but thanks to innovations in the world of , we may soon be set for the unlikely return of the ‘Roaring 20s’. generative AI At least, these are the thoughts of leading economist Ed Yardeni, of Yardeni Research, who to subscribers that generative AI "may be the event that re-launches the Roaring 2020s." wrote in a May newsletter It may be hard to anticipate the emergence of a Wall Street boom period given the economic backdrop that investors have been contending with. As inflation rates continue to cause issues in the cost of living for global populations, the prospect of a recession still lingers. Despite this, it’s important to note that indexes like the S&P 500 have been remarkably quick to recapture their significant losses in the wake of 2022’s tech stock sell-offs. In 2023 alone, we’ve seen the S&P 500 grow more than 18% off the back of innovations in generative AI and their ability to capture the imagination of investors. Could the cautious optimism delivered by generative AI so far in 2023 be sustainable? And is it possible for investors to buy into the market despite significant trading volumes entering the market? Let’s take a deeper look at the AI phenomenon and how it may develop: Generative AI Gears up for a Strong Second-Quarter The S&P 500 isn’t the only tech-heavy index that’s experienced a strong resurgence amidst 2023’s generative AI boom. The Nasdaq composite has grown since the beginning of the year and is showing little signs of abating. almost 35% “Heading into the second half of 2023, we see a much broader tech rally ahead as investors further digest the ramifications of this $800bn AI spending wave on the horizon and what this means for the software, chip, hardware, and tech ecosystem over the next year,” , managing director at Wedbush Securities. explained Dan Ives “We view this as a ‘1995 internet moment’... not a ‘1999 dot bubble moment’. We estimate for 2024 that AI could comprise up to 8% to 10% of overall IT budgets v 1% in 2023,” Ives added. This strongly indicates that the generative AI boom that we witnessed in early 2023 is just the beginning, and that seismic price rallies are sustainable enough to transcend the perception that they exist in a bubble. Crucially, leading tech firms like , , and are working to realize the potential of this surge in investor interest in AI. Microsoft, for instance, has introduced a $30 subscription service focused on uniting generative AI with Microsoft 365 services. Microsoft Alphabet Adobe The general feeling among financial experts is that generative AI could also be the tide that raises all boats. Maxim Manturov, head of investment advice at Freedom Finance Europe, suggests that may even be capable of reviving interest in ailing metaverse projects. generative AI “Coupled with AI, the metaverse could become a major force in the coming years. It could revolutionize the way we learn, work, and play,” Manturov notes. “However, it's important to remember that the meta is still in the early stages of development. It is too early to say for sure what the future holds for the metaverse.” So, how can investors embrace the age of generative AI? Are leading AI-facing stocks overvalued in comparison to small-cap opportunities? While it’s impossible to anticipate which firms will come out of the generative AI boom thriving, it’s certainly worth exploring small-cap options with the potential to grow exponentially should the ‘Roaring 20s’ truly come to fruition. Notable small-cap AI stocks include: 1. UiPath (NYSE: PATH) When looking at UiPath’s 2023 performance, you’d be forgiven for thinking that it’s another example of a wildly successful generative AI stock. However, zooming out a little further shows that the stock is still over 78% underwater and steadily recapturing ground based on the intelligent adoption of new AI technologies. Recently, UiPath announced an and tools offering more support for Amazon’s Falcon Large Language Model (LLM), OpenAI, Azure OpenAI, and Google’s Vertex connector. expanded suite of generative AI technologies These prospects, coupled with UiPath’s relatively modest market cap of just under $10 billion illustrate a forward-focused firm with plenty of room for growth. 2. Domo Inc. (NASDAQ: DOMO) With a market capitalization of around $600 million, Domo is certainly a stock that’s yet to be fully swept away by the generative AI force. Operating as a cloud software company delivering business intelligence tools and data visualization solutions, Domo’s AI Service Layer helps to simplify the process of managing, deploying, and enhancing AI and machine learning models, as well as Large Language Models (LLMs) for data experiences. Offering generative AI and natural language processing for text generation and SQL queries to audio transcribing and text translating, Domo may prove to be a leading resource in B2B generative AI services. 3. Lemonade Inc. (NYSE: LMND) Lemonade is a digital insurance company that utilizes AI and behavioral economics to offer a vast range of insurance services for customers in the US. In a May shareholder letter, the firm announced that it anticipates leveraging generative AI to enhance . With strong roots in artificial intelligence services, Lemonade’s $1.5 billion market cap may have room for growth as economic headwinds weaken. as many as 100 of its business processes For investors looking for small-cap market opportunities in generative AI, sufficient research must be conducted before adding a new firm as a long hold. The market is still in its fledgling phase, and new challengers are likely to emerge at a rapid pace. With this in mind, it’s fair to expect volatility throughout generative AI stocks over the coming years. However, likely, the stocks that emerge as market leaders in their respective sectors will see significant growth as the AI boom continues to win new supporters.

Could Wall Street’s ‘Roaring 20s’ be Powered by Undervalued Generative AI Stocks?

ওয়াল স্ট্রিটের 'ররিং 20' কি কম মূল্যহীন জেনারেটিভ এআই স্টক দ্বারা চালিত হতে পারে?

Könnten die „Roaring 20s“ der Wall Street durch unterbewertete generative KI-Aktien angetrieben werden?

¿Podrían los 'Roaring 20s' de Wall Street estar impulsados por acciones generativas de IA infravaloradas?

Les "Roaring 20s" de Wall Street pourraient-elles être alimentées par des actions d'IA générative sous-évaluées ?

क्या वॉल स्ट्रीट का 'रोअरिंग 20' अंडरवैल्यूड जेनरेटिव एआई स्टॉक्स द्वारा संचालित हो सकता है?

ウォール街の「激動の20年代」は、過小評価されている生成型AI株によって支えられる可能性があるだろうか？

월스트리트의 '광란의 20대'는 저평가된 생성 AI 주식에 의해 구동될 수 있습니까?

Os '20 anos loucos' de Wall Street poderiam ser impulsionados por ações geradoras de IA subvalorizadas?

Wall Street'in 'Kükreyen 20'leri', Değeri Düşük Üretken Yapay Zeka Hisse Senetlerinden Güç Alabilir mi?

'Những năm 20 bùng nổ' của Phố Wall có thể được hỗ trợ bởi các cổ phiếu AI sáng tạo bị định giá thấp không?

华尔街的“咆哮的20年代”能否由被低估的生成型人工智能股票推动？

FinTech, or financial technology, is a booming industry that uses cutting-edge technologies like artificial intelligence (AI), blockchain, data analytics and cloud computing to transform and improve financial processes and services. FinTech companies offer innovative solutions for various financial needs, such as payments, lending, investing, insurance, wealth management and more. They challenge the traditional financial institutions and create new markets and opportunities. However, FinTech companies also face significant challenges in terms of cost optimization, which is essential for their profitability and sustainability in a highly competitive environment. Cost optimization means strategically controlling and reducing expenses, improving financial efficiency and maximizing the return on investment. Inefficient operations can result in high costs, unhappy customers, low competitiveness and slow economic growth. In this article, we will examine the main types of costs that FinTech companies have to cope with, the strategies they can employ to optimize them and the future trends that will influence the cost optimization landscape. The Cost Structure of FinTech Companies FinTech companies have different types of costs depending on their business model, target market, product portfolio and technology stack. According to Toptal, some of the : main cost categories are These are the costs of running the essential backstage operations that keep the main business functions smooth and secure, such as handling transactions, assisting customers, updating systems, stopping fraudsters and managing risks. These costs are crucial for ensuring cost-effectiveness and customer satisfaction. Operational costs can vary depending on the volume and complexity of transactions, the quality and availability of customer service, the reliability and security of systems and the effectiveness of fraud and risk management. For instance, Revolut, a digital banking platform that delivers banking, payments, investing, budgeting and crypto services, has operational costs associated with handling millions of transactions per day, offering round-the-clock customer support in various languages, sustaining its cloud-based architecture and adhering to anti-money laundering rules. Operational Costs: These are the expenses related to the infrastructure that enables the delivery of FinTech services, such as cloud platforms, software licenses, hardware devices, cybersecurity tools and network connectivity. These costs are often high for FinTech companies as they invest heavily in innovation and security to provide seamless, reliable and secure financial services. Technology costs can vary depending on the type and scale of technology used, the level of customization and integration required, the frequency of updates and maintenance needed and the degree of protection against cyberattacks. For example, Stripe, a global payment platform that enables online businesses to accept and manage payments, has technology costs related to using cloud platforms such as AWS, software licenses such as Ruby on Rails, hardware devices such as card readers, cybersecurity tools such as Stripe Radar and network connectivity such as internet service providers. Technology Costs: These include salaries, benefits, training and recruitment expenses related to hiring and managing skilled talent. This type of costs is a key driver for FinTech companies as they need to attract and retain finance and technology experts who can develop, implement and maintain cutting-edge solutions. Human resource costs can vary depending on the number and quality of employees hired, the level of training and development provided, the turnover rate and retention rate of employees and the compensation structure and incentives offered. For instance, Acorns, an investment app that helps users save and invest their spare change, has human resource costs related to hiring and managing over 200 employees, providing online courses and training programs, offering referral bonuses and stock options and maintaining a low turnover rate. Human Resource Costs: These significant expenses are related to complying with the legal and regulatory requirements of the jurisdictions where FinTech companies operate. Regulatory costs include compliance monitoring systems, legal and regulatory filings, audits, fines and penalties. FinTech companies need to adhere to complex and evolving regulations that aim to protect consumers, ensure financial stability and prevent money laundering and terrorism financing. Regulatory costs can change based on the range and difficulty of regulations, the regularity and rigor of compliance audits performed, the quantity and harshness of fines or sanctions enforced and the assets and proficiency required to comply with regulations. As an example, to obey the insurance laws and regulations in different states, Coalition, an insurance app that provides cyber insurance and security tools for businesses, faces regulatory costs. These costs include using compliance monitoring systems such as Plaid, filing legal and regulatory reports with authorities, undergoing audits by regulators and insurers, adhering to regulations and dodging fines or penalties for non-compliance. Regulatory Costs: These are the less obvious expenses that can accumulate over time and impact the bottom line. Hidden costs include customer acquisition costs, system downtime costs, inefficiency costs, opportunity costs and reputation costs. FinTech companies need to be aware of these hidden costs and take measures to avoid or minimize them. Hidden costs can vary depending on the effectiveness of marketing strategies used to acquire customers, the availability and reliability of systems and services, the level of operational efficiency and productivity, the potential revenue lost due to missed opportunities and the impact of negative feedback or reviews on brand image and customer loyalty. Hidden Costs: For example, ZestFinance, a lending app that uses machine learning to provide fair and transparent credit, has hidden costs related to acquiring customers through online channels such as Google Ads or Facebook Ads, experiencing system downtime due to technical issues or cyberattacks, losing efficiency due to manual processes or errors, missing opportunities due to limited market reach or product range and damaging reputation due to customer complaints or lawsuits. The Cost Optimization Strategies for FinTech Companies FinTech companies have a range of options to fine-tune their costs and boost their bottom line. Some of the popular strategies are: These technologies can help FinTech companies improve their efficiency, enhance their customer experience and reduce their labor costs by automating repetitive tasks, streamlining workflows, providing personalized recommendations, detecting anomalies and optimizing decisions. For instance, FinTech companies can use automation to speed up transaction processing, AI to provide tailored financial advice and machine learning to prevent fraud losses. A recent example of a FinTech company that leverages automation, AI and machine learning is SpdLoad, a FinTech app development company that uses these technologies to create custom FinTech solutions for its clients. SpdLoad uses automation to reduce development time and costs, AI to provide data-driven insights and machine learning to improve user experience and security. Leverage automation, AI and machine learning: Lean principles are a set of practices that aim to streamline processes, eliminate waste, increase efficiency and deliver value to customers. By applying lean principles, FinTech companies can identify and remove non-value-added activities, reduce errors and defects, simplify complexity and increase agility. They can use lean principles to reduce paperwork, minimize rework, standardize procedures and empower employees. A recent example of a FinTech company that implements lean principles is Revolut, a digital banking platform that offers banking, payments, investing, budgeting and crypto services. Revolut applies lean principles to simplify its processes, automate its customer verification, outsource its card issuance, use cloud computing to scale its infrastructure and use data analytics to monitor its performance. Implement lean principles: FinTech startups can reduce expenses by outsourcing non-essential services to external providers. Specialized firms that focus on specific functions can offer accounting, legal, marketing or IT support at lower costs and/or higher quality. Outsourcing allows FinTechs to access top-tier capabilities without having to build in-house teams for non-core activities. This allows them to concentrate resources on their core products and innovations that drive value for customers. A recent example of a FinTech company that outsources non-core support activities is Square, a financial services company that provides payment solutions, point-of-sale systems, e-commerce platforms and business tools for small businesses. Square outsources some of its accounting, legal, marketing and IT services to external providers. For instance, Square uses Stripe as its payment processor, AWS as its cloud provider, Zendesk as its customer service platform and Mailchimp as its email marketing tool. Outsource non-core support activities: RegTech is a subset of FinTech that focuses on using technology to automate compliance processes. FinTech companies can adopt RegTech solutions to reduce their regulatory costs by automating data collection, reporting, validation, analysis and audit. RegTech solutions can also help FinTech companies stay updated with regulatory changes and avoid fines or penalties. FinTech companies can utilize RegTech solutions to monitor transactions for suspicious activity, generate reports for regulators and verify customer identity. An example of a FinTech company that adopts RegTech solutions is Coalition, an insurance app that provides cyber insurance and security tools for businesses. Coalition uses RegTech solutions to automate its compliance processes, such as data collection, reporting, validation, analysis and audit. Coalition also uses RegTech solutions to stay updated with regulatory changes and avoid fines or penalties. For example, the company uses Plaid to verify customer identity, Stripe Radar to monitor transactions for fraud and AWS Config to audit its cloud infrastructure. Adopt RegTech solutions: Using effective HR management practices, FinTech companies can optimize their human resource costs. These practices include remote work arrangements that can save on office space and travel expenses, continuous training programs that can improve employee skills and performance, strategic hiring processes that can find the best talent for the job and performance-based incentives that can motivate and reward employees. These practices can help FinTechs boost their productivity, enhance their employee engagement and retention and reduce their turnover and recruitment costs. A recent example of a FinTech company that implements effective HR management practices is Acorns, an investment app that helps users save and invest their spare change. Acorns adopts various HR management practices that enhance its employees’ productivity and satisfaction. For example, Acorns allows its employees to work remotely from anywhere, saving them time and money on commuting and office space. Acorns also provides its employees with online courses and workshops to improve their skills and performance. Moreover, Acorns uses referrals, assessments and interviews to find and hire the best talent for each job. Furthermore, Acorns rewards its employees with stock options, bonuses and recognition based on their achievements and contributions. Effective HR management: FinTech companies can form partnerships with other entities to share resources, technology, expertise and customer bases, reducing their costs and increasing their reach. They can partner with banks, telecom operators, retailers or other FinTechs to offer complementary services, access new markets or leverage existing infrastructure. ZestFinance, a lending app that leverages machine learning to offer credit that is transparent and fair, is a recent example of a FinTech company that collaborates with other entities. ZestFinance forms partnerships with banks, telecom operators, retailers and other FinTech entities to share resources, technology, expertise and customer bases, reducing its costs and increasing its reach. For example, ZestFinance partners with Baidu to offer credit in China, with Ford Credit to offer car loans in the US, with JD.com to offer e-commerce loans in China and with LendUp to offer payday loans in the US. Partnerships: Cloud computing is a model of delivering IT services over the internet, where users only pay for the resources they use. FinTech companies can use cloud computing to avoid upfront infrastructure investments and reduce their technology costs by scaling up or down their resources according to their demand. Cloud computing also offers benefits such as flexibility, reliability, security and innovation. Finch, a consumer finance app that combines checking and investing in one account, is a recent example of a FinTech company that benefits from cloud computing. Cloud computing helps Finch to host its applications and data on the cloud, access software and hardware resources on demand, protect its systems from cyber attacks and try out new technologies. Finch uses AWS as its cloud provider, which provides it benefits such as scalability, reliability, security and innovation. Use cloud computing: Open-source software provides invaluable benefits to FinTech companies looking to optimize costs. By leveraging freely available open-source applications, startups gain access to innovative tools that can be customized, integrated, and shared. This promotes seamless compatibility across systems and enables transparency and adaptability. By tapping into these resources, FinTechs can focus their efforts on developing their core solutions rather than building foundational platforms from scratch. A recent example of a FinTech company that employs open-source software is Stripe, a global payment platform that enables online businesses to accept and manage payments. Stripe uses open-source software to build its platforms, integrate with other systems, access new features and customize its solutions. Stripe uses open-source software such as Ruby on Rails, React, PostgreSQL, Redis and Kubernetes. Leverage open-source software: Software robots or digital workers can copy human actions and do tasks automatically. This technology is called robotic process automation (RPA). FinTech companies can automate boring, fixed and low-value tasks such as typing data, matching data, checking data and approving data by using RPA. This can help them spend less on labor and work better. For example, paying bills, opening accounts, scoring credit and helping customers are some of the tasks that FinTech companies can automate with RPA. Coalition is one of FinTech companies that uses RPA. Coalition uses RPA to automate invoice processing, account opening, credit scoring and customer service. Coalition uses RPA tools such as UiPath, Automation Anywhere and Blue Prism. Implement RPA: Data analytics transforms raw data into valuable insights that can guide decision making. It involves collecting, processing, analyzing and visualizing data to reveal patterns, trends, opportunities and risks. FinTech companies can leverage data analytics to enhance their performance and profitability by optimizing their resource allocation, minimizing their wasteful spending and maximizing their return on investment. For instance, Acorns, an investment app that helps users save and invest their spare change, uses data analytics to fine-tune its pricing strategies, target its marketing campaigns, improve its customer retention and manage risks. Acorns uses data analytics tools such as Google Analytics, Mixpanel, Amplitude and Segment. Use data analytics: Future Trends The cost optimization landscape for FinTech companies will continue to evolve as new technologies, regulations, customer expectations and competitive forces emerge. Some of the future trends that will shape the cost optimization landscape are: These technologies will enable FinTech companies to further improve their efficiency, enhance their customer experience and reduce their costs by automating more tasks, streamlining more workflows, securing more transactions and enabling more flexible work arrangements. Greater adoption of AI, automation, blockchain and remote work: FinTech companies will need to adopt sustainability practices to reduce their environmental impact, comply with green regulations and meet the growing demand for sustainable finance. Sustainability practices can also help FinTech companies optimize their costs by reducing their energy consumption, waste generation and carbon footprint. Greater focus on sustainability practices: Constant innovation is crucial to create new value propositions, offer new solutions and differentiate themselves from the competition. Innovation can also help FinTech companies optimize their costs by creating new revenue streams, improving customer loyalty and enhancing operational efficiency. Greater innovation and differentiation: Conclusion FinTechs experience fierce competition and need to optimize costs to succeed. They can cut expenses and grow profits by adopting technological innovations, simplifying operations, following regulations, and creating novel business models. They can gain a cost advantage and a profitability surge by innovating quicker than competitors. Cost optimization through operational excellence and forward-thinking strategy is the key to becoming a market leader in FinTech. Sources FinTech App Development in 2023: Cost Breakdown [Examples] - SpdLoad Methodologies for How to Value a Fintech Startup | Toptal® Cost to Develop a FinTech App in 2023 [Major Cost Drivers] - MindK.com Ten Business Cost Optimization Ideas - Gartner What Is Cost Optimization? 8 Best Practices To Use ASAP - CloudZero

How to Cut Costs and Boost Profits in the FinTech Sector

Autonomous Agents and the Next Managerial Revolution

স্বায়ত্তশাসিত এজেন্ট এবং পরবর্তী ব্যবস্থাপক বিপ্লব

Autonome Agenten und die nächste Managerrevolution

Agentes Autónomos y la Próxima Revolución Gerencial

Les agents autonomes et la prochaine révolution managériale

स्वायत्त एजेंट और अगली प्रबंधकीय क्रांति

自律型エージェントと次の経営革命

자율 에이전트와 차세대 경영 혁명

Agentes Autônomos e a Próxima Revolução Gerencial

Otonom Aracılar ve Sonraki Yönetim Devrimi

Đại lý tự trị và cuộc cách mạng quản lý tiếp theo

自主代理和下一次管理革命

The American Hospital Association reports that hospital expenses from 2019 to 2022, while Medicare reimbursements remained far behind, growing by a mere 7.5%. increased by 17.5% Given this unfortunate situation, healthcare organizations need to take it upon themselves to reduce costs without overloading their medical and administrative staff, who already have too much on their plate. One way to cut down expenses is by deploying a hospital facility management solution that will improve both internal operations and patient care. offer different types of ready-made and custom tools that can address your unique needs. And healthcare organizations seem to love it. The global healthcare facility management market is forecast to after it was valued at $6 billion in 2021. Hospital software development companies reach $8.3 billion by 2026 This article explains what hospital facility management is, presents seven exciting ways in which technology can improve your operations, and gives a brief guide on how to proceed with deployment. What tasks comprise hospital facility management in 2023? Health facilities management aims to sustain an environment that helps staff members perform at their best while improving patient experience. The concept comprises a wide range of tasks, including managing equipment, taking care of employees, making sure the facilities are clean and safe, and overseeing the budget. In many healthcare organizations, facility managers are also responsible for public relations, marketing, and compliance. Basically, this discipline is responsible for all non-clinical activities performed at healthcare organizations. Here is a list of the key tasks associated with hospital facility management: Maintaining patient comfort and privacy Simplifying navigation for staff and visitors Coordinating the use of equipment Taking over equipment purchase, installation, and maintenance Building renovations, which includes selecting contractors and monitoring their work Taking measures to enforce hospital’s safety and security, including periodically assessing the building for fire hazards and such Placing protocols for emergency situations, like hazardous waste release and spreading infections Ensuring compliance with the governmental and local regulations, such as Centers for Disease Control and Prevention policies Handling finances, including putting forward the operations budget and adhering to it, prioritizing investments, negotiating service agreements Marketing the hospital and its services Crafting strategies to improve patient and employee experience Let us investigate how technology helps enhance or fully automate these processes, lessening the burden of administrative work for healthcare organizations. 7 ways technology is transforming healthcare facility management as we know it Here are seven major examples of how hospital facility management technology can support your healthcare organization. Transforming asset management Enabling predictive maintenance Optimizing energy consumption Streamlining process automation Facilitating healthcare data analytics Improving patient care Ensuring compliance with standards and policies Transforming asset management Technology can help hospital facility managers handle both staff and equipment effectively. Below you will find several examples of hospitals that leveraged cutting-edge technologies, such as the Internet of Things and artificial intelligence, to streamline their operations. Equipment management The NutrsingTime.net survey reported that nurses tend to looking for lost equipment; in 16% of the cases, they fail to locate it. Hospitals can embed different types of trackers, such as GPS and Bluetooth tags, into defibrillators, medical carts, and any other piece of equipment to retrieve information on the equipment’s whereabouts from healthcare IT systems in real time. waste around 40 hours per month As an added benefit, this practice will also reduce theft. Research shows that during their lifecycle, which averages a loss of $3000 per item. 10%-20% of medical assets are stolen There are several off-the-shelf facility management solutions for hospitals that can help you with equipment tracking. One example is . The company built a medical inventory and device monitoring solution that tracks equipment’s location, displays its condition and where it was deployed, and schedules maintenance when needed. Hospital managers can add as many users as they want while enabling role-based access to the assets. Texas-based Asset Panda Staff management Healthcare facility management technology powered by can help evenly distribute workload among medical staff, taking into consideration their preferences and capabilities. AI solutions for hospitals A research team from the Massachusetts Institute of Technology built an AI robot that could assign care givers to different medical procedures and patients. This solution was tested at the labor-and-delivery ward. The staff and testified to its success in creating a more even workload than they could accomplish with a manual assignment. accepted 90% of the tool’s recommendations You can find more examples in article on our blog. how AI benefits the healthcare sector Enabling predictive maintenance Predictive maintenance is about relying on the Internet of Things (IoT) sensors and data analysis techniques to detect any anomalies in a piece of equipment before it starts malfunctioning. At the first signs of defects, the device’s manager can conduct maintenance at a convenient time to prevent a breakdown. offer sensors and the corresponding software to gather data on power consumption, temperature, and more. This data is analyzed , like excessive vibration, which might be caused by machine overload, but can also be an early indicator of a breakage. This approach to maintenance prevents unexpected device failures, which can result in poor patient outcomes, litigation, and reputational damage. Hospital IoT development companies to detect any anomalies A team of researchers in the United Arab Emirates developed an approach to IoT-powered predictive maintenance and tested it on a Vitros-Immunoassay analyzer at a local hospital. The primary cause of this device’s breakage is metering arm belt slippage when the belt is worn out. It’s possible to catch the belt’s early deterioration signs by analyzing vibration signals. So, the researchers collected vibration data and analyzed it with ML algorithms to detect any evidence of malfunctioning. This experiment resulted in . 25% savings in the device’s maintenance costs Optimizing energy consumption Unlike residential and commercial buildings, hospitals operate 24/7, consuming around 2.5 times more electricity than other establishments of the same size. Hospital facility management technology, such as , , and , can help significantly reduce the electricity bill in the following ways: AI data analytics cloud computing Data analytics can identify areas of wasted energy and recommend optimizations. Machine learning algorithms can be trained to predict energy demands based on historical usage data, building information, climate, and other relevant parameters. This allows hospitals to prepare in the case of spiking demand and maybe cut down on energy use in other areas for that time period. Some technologies can reduce energy consumption just by deploying them. For instance, studies show that organizations by moving their workload to the cloud instead of keeping it on premises. can achieve almost 80% of energy saving One example of applying hospital facilities management technology to energy consumption comes from the ENGIE x NTU Innovation Challenge in Singapore, where participants gained access to a local hospital to come up with an energy optimization solution over the course of eight months. The winners deployed multi-variant sensors to capture data on room occupancy parameters, such as carbon dioxide and humidity, analyze these using machine learning algorithms, and control air conditioning accordingly. The team succeeded in . reducing energy consumption by 20% Streamlining process automation Robotic process automation (RPA) . Here are some of the key applications of this technology in the context of hospital facility management: has many benefits in healthcare . RPA bots can assist staff in medical coding and preparing insurance claims. Streamlining billing and payments . RPA tools can monitor that data sharing across different healthcare facilities adheres to data privacy protocols and has strict access rights. Facilitating data sharing . This technology can monitor the location and usage of different medical tools. It’s even applied in organ tracking to facilitate organ transplantation procedures. Managing inventory . Bots can help spot the right candidates and guide them through the onboarding processes, taking the load off human resources. Assisting in recruitment and onboarding For example, US-based ApprioHelth deployed UiPath’s RPA solution enhanced with AI and computer vision to facilitate insurance claims handling. This tool automates data capturing and entering into the corresponding application form fields. It can even extract text from images. This solution . yielded a 96% reduction in insurance claims backlog Facilitating healthcare data analytics . This includes assisting in cancer treatment, mitigating hospitalization risks, helping patients with mental issues, and more. Big data has many applications in the medical field In the context of hospital facility management, big data analytics tools can assist with strategic planning as they enable managers to understand the needs and motivations of medical staff. Analytics can also predict when a senior staff member needs to be present to consult junior colleagues, and can help devise training and development plans, as it monitors employees’ performance. Data analytics tools can also help better allocate hospital facilities. For instance, Texas Children’s Hospital relied on an to identify available practice rooms and assign them to appointments. The tool processes appointment data to estimate room demands and finds empty rooms that meet these conditions. Thanks to this solution, the hospital could schedule 550 additional appointments, which added $86,000 to its revenue in merely six months. analytics-powered space visualization solution Improving patient experience Hospital facility management while taking the load off the medical staff and the facilities. Here is what it can do: technology can also enhance patient care Predicting how many patients to expect at a hospital on any given day, which helps with staffing and spares patients the wait Easing the administrative burden around doctor visits, as technology supports patients in appointment scheduling, sends reminders, and helps visitors navigate around the facilities instead of having to call someone asking for directions Post-discharge care, such as sending automated reminders of medication intake, distributing wellbeing surveys, and analyzing the results without overwhelming doctors and nurses Enabling virtual care and remote patient monitoring, as patients don’t need to be physically present at the hospital to receive consultations. Patients are happy to stay at home and the hospital isn’t overloaded. Even though telehealth solutions are not typically classified under the hospital facility management technology, one can argue that they improve patient care without putting an extra load on the clinic’s facilities. And they enable doctors to consult patients, who they otherwise wouldn’t reach. For instance, Aravind Eye Hospital located in India opened five IT-powered centers in rural areas to conduct telemedicine sessions with people who couldn’t reach the hospital. Thanks to this setup, over 90% of the patients received adequate care without commuting. Ensuring compliance with standards and policies Compliance is extremely important in the healthcare sector, and it’s one of a hospital facility manager’s responsibilities. Depending on your country of operations, there are different compliance regulations to abide by. In the US, the medical sector is governed by the Health Insurance Portability and Accountability Act (HIPAA). And in addition to the country-wide regulations, each hospital has its set of policies that the medical facility management solutions can also help enforce. Technology, such as , can increase compliance in the following ways: AI and its subtypes . Compliance laws tend to change rapidly, and hospitals can’t afford to stay behind. AI-powered solutions can monitor the regulations and report on any changes. Monitoring for updates in healthcare standards and regulations . Data privacy is a huge concern for medical institutions, and artificial intelligence tools can ensure that only authorized parties gain access to patient information. Controlling data access . AI can continuously monitor hospital software looking for . It can also monitor passwords and guide users to select options that are hard to hack. Preventing data breaches security vulnerabilities . There are ready-made credentialing management software solutions that can keep track of and verify staff qualifications, including their university degrees and any training they received afterwards. Streamline employee credentialing . This type of solution can help trace the number of visitors at any given time to remain within the limits imposed by the hospital and the government. Visitor tracking . Some healthcare software solutions can capture incidents/near incidents, analyze them to determine the cause, and monitor improvement. Incident management One example of incident management and reporting solution comes from . Headquartered in Houston, symplr uses technology to improve healthcare operations. The company offers a solution that enables hospitals to evaluate and mitigate operational and financial risks that would accompany failure to comply with changing regulations. The company claims that with the help of their software, managers will spend 45% less time monitoring compliance issues. symplr How to approach a hospital facility management overhaul The following steps can guide you through medical facility management technology deployment: Determine which organizational needs you want to address. Are you considering monitoring equipment, improving energy efficiency, enhancing compliance, or enabling predictive maintenance? Write down what you want to cover, prioritize this list, and choose the best suited technology accordingly. In addition to the use cases, consider other factors, such as scalability, ease-of-use, how well it will , and so on. integrate with your existing system Develop the overall strategy. Identify key stakeholders and determine how to engage them, allocate people and budget, determine how you will measure success. Conduct a pilot test. Experimenting with the technology will reveal: How do different systems work together, especially if it involves legacy equipment? Can your infrastructure handle the new load? Does the new solution incorporate all the corresponding business flows? Is every user comfortable with the new tools? Offer training and education. Hospital facility managers might not be familiar with this type of tools, and it’s better to offer formal training instead of expecting employees to learn independently. If they find the new tools hard to use, they will not be eager to adopt them. Come up with a . Facility management solutions for hospitals tend to generate vast amounts of data, especially when it involves IoT. You will need a system that can store and process all this information. data management strategy Also, depending on the technology you choose, you might encounter different challenges associated with its implementation. Our blog details , , and to help you sail through the transformation. AI implementation challenges RPA-related challenges issues associated with big data Still hesitating to proceed? As the technology becomes more powerful, it enables facility managers in hospitals to accomplish more with fewer resources and in shorter time. There is no more running around searching for equipment, wasting hours on trivial manual tasks, and getting fined for missing the latest regulations updates. If this is still happening at your practice, it might feel acceptable at the moment, but in the near future it will become difficult to compete with more tech-savvy facilities. You don’t have to take any radical steps now. You can start with one or two use cases, such as equipment tracking, and scale from there. If you want to work with a basic ready-made hospital facility management solution, we can help you integrate it into your workflows and customize it if needed. And if you are interested in deploying something more comprehensive, we can set you up with IoT sensors, build and train AI algorithms, develop data analytics and reporting tools that will give you a 360-degree view of your operations, and more. And, don’t hesitate to reach out if you have any questions. Considering streamlining your operations with hospital facility management tech? ! We will assist you in choosing the right technology, implementing the software, and integrating the final solution into your system. Get in touch Also published . here

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