Automated Processes: Toward an AI-Native Enterprise

What you’ll learn:

• The advantages and disadvantages of automation
• How to turn a spreadsheet into an application
• A primer on hyperautomation

Our businesses have come a long way since the days of purely manual processes. After all, how much paper are you pushing in your job these days?

In this ongoing series about preparing your business for the AI revolution, we’ve covered how manual processes have served us well and, in some cases (spreadsheets) still have their place. But these processes come with limitations and risks. As we climb the Process Maturity Ladder (PML), we transition from manual processes to automated processes.

Automation is crucial in our journey toward artificial intelligence, which promises to transform businesses and industries. This article will delve deeper into automated processes, discussing their advantages and disadvantages while exploring multicloud composite processes and applications.

Advantages of Automation

There are numerous benefits to automating processes. One of the most significant is increased efficiency. Automation allows businesses to complete tasks faster and more accurately, leading to cost savings and increased productivity.

Furthermore, data and process automation can free up employees’ time, allowing them to focus on higher-value tasks that require human judgment, creativity, or empathy.

Another positive is the reduction of errors. Humans are a common cause of mistakes in manual processes. By automating processes, businesses can reduce the likelihood of errors and improve the overall quality of their operations.

Automation can also lead to better scalability. As a business grows, manual processes may become increasingly difficult to manage. Automated processes scale up more efficiently, allowing businesses to handle increased workloads and continue to grow.

Finally, automation provides a solid foundation for the adoption of artificial intelligence. By automating processes, businesses can collect and analyze data more effectively, which is essential for training AI algorithms and implementing machine learning techniques.

Disadvantages of Automation

Despite its many advantages, automation also has drawbacks. One of the main issues is the initial cost of implementing automated systems. While the long-term benefits often outweigh these costs, businesses may need help to justify the upfront investment.

Another is the potential loss of jobs. As automation becomes more prevalent, some workers may find their roles becoming obsolete. Businesses must balance the benefits of automation and the impact on their workforce. That’s especially true as we gauge the potential impact of AI.

Additionally, automated systems can be susceptible to failures or vulnerabilities. While automation reduces human error, it can also introduce new risks, such as cyberattacks or technical glitches. Businesses must take steps to ensure the security and reliability of their automated processes.

We recommend automating as many of your processes as possible. But regardless of whether you choose to automate a process or not, it’s important to have your data in a digital format so it can be used to feed data-hungry AI models and systems.

It’s also paramount to document and maintain your processes every step of the way — because even the simplest of processes can become complex over time.

Conway’s Law and Automating Complexity Across Clouds

If enterprises are complex, it’s probably not a stretch to assume that business processes probably are, too. After all, Conway’s Law tells us that an organization’s structure directly influences its systems and processes. And the more complex the enterprise, the more complicated the processes, applications, and services. While automation can make complex systems run efficiently, it can also create layers of abstraction that make errors in the system difficult to track.

For example, complex organizations often rely on various applications and services hosted across multiple cloud platforms. This approach is known as multicloud computing. Multicloud composite processes and applications involve integrating and orchestrating services from multiple cloud platforms and on-premises endpoints.

To successfully implement multicloud composite processes and applications, businesses need modern, cloud-based middleware that can easily connect the hybrid (on-premises and cloud-native) endpoints of applications and services. This middleware acts as a conduit between various applications and services, enabling them to communicate and share data seamlessly.

Modern,cloud-based middleware gives businesses the flexibility and agility to adapt to changing business requirements and use new technologies. It also helps reduce the complexity of managing and integrating applications and services across multiple cloud or on-premises platforms. That makes it easier for businesses to focus on innovation and growth.

A critical aspect of multicloud composite processes is the use of exposing or abstracting processes or data through APIs. For the uninitiated, API is the acronym for application programming interface. APIs can either expose or abstract a set of protocols, processes, and functions that allow different software to communicate and share data with each other.

Exposing APIs enables data to be used in another process, application, or service. This is often done by providing an endpoint – a connection like a URL – along with what the expected behavior or requested data output. By abstracting APIs, the details of the routines and processes are hidden from a consumer. Consumers get the specific data they need from an API “call,” but the provider limits the access and can hide the process details. This combination of exposing or abstracting the API is meant to provide expected outcomes, whether one seeks data or function.

APIs enable different software applications to interact, allowing businesses to create new and innovative services by combining data from existing applications and services. Robust integration and automation platforms allow users to create new APIs or expose existing ones so that other callers (human or machine) can use these abstractions to provide a meaningful value with a data pipeline. Furthermore, the access may (and should) be governed by an API gateway or API management (APIM) platform or module. This way, you can institute rules over who has access to your APIs, what APIs they can access, and how often they can access them within a period of time.

By making processes and data available through APIs, businesses can automate and integrate their operations more efficiently, improving efficiency and collaboration. APIs also facilitate the development of new applications and services because developers can build on existing functionality without starting from scratch.

How to Automate a Spreadsheet

… and empower data.

Our previous article on manual processes focused on converting a simple spreadsheet to a database. Recall that spreadsheets are great to use individually, but poor to use for enterprise information systems. Let’s revisit that example to dive deeper into how a spreadsheet can be transformed into an endpoint where other automated processes can easily access data. There are several ways to automate a spreadsheet:

1. Expose the spreadsheet as a database
2. Migrate the spreadsheet into a database
3. Turn the spreadsheet into a full-blown application

1. Using Spreadsheets as Databases

Turning the spreadsheet into an endpoint means it becomes a database of sorts (but not a database in the formal sense.) Businesses can expose spreadsheet data to other applications and services using tools such as Google Sheets API or Microsoft Excel API. This enables other automated processes to read, write, or update the data in the spreadsheet without manual intervention.

For example, an inventory management system could automatically update the stock levels in a spreadsheet whenever a new order is placed, or a customer relationship management (CRM) system could automatically update a spreadsheet with new customer information. Data is accessible directly to and from the spreadsheet as if it were an enterprise information system of record. There is a low barrier to entry for humans to collaborate because we work with spreadsheets all of the time and are cost-effective, too.

Happy days, right? Wrong!

A spreadsheet being directly exposed to other applications as if it were an enterprise system of record is a bad idea – even worse if it’s automated. Unlike enterprise-class databases, spreadsheets are not built with data integrity, speed, resiliency, scalability, and security.

I won’t go into the litany of downsides, but a simple web search will reveal many reasons you don’t want your spreadsheet to become a database. If the data is critical to the business, build a database, create a logical schema, and migrate the data from the spreadsheet to a database.

2. Migrating Spreadsheets to Databases

Migrating your spreadsheet into a database is a far more pragmatic way to make it an enterprise-grade system of record. Rather than directly exposing the spreadsheet’s APIs to other applications, map the spreadsheet’s structure into an actual database. With data persisted in an enterprise-class ledger, each transaction – meaning a recorded exchange of information between two or more systems – is written to a ledger. Whether your spreadsheet morphs into a traditional relational document, key:value, or any other type of database, the idea is data written to or retrieved from this database is enterprise-grade.

Once this information is stored, only imagination, governance, security, time, and resources become barriers to the art of the possible.

How you implement a conversion of a spreadsheet to a database will vary. Some processes may be simple, like mapping the headers of a spreadsheet to form the schema of one or more database table columns. Others will take a lot of thought, energy, time, and resources that require multiple stakeholder involvement.

In every case, you’ll have to look at the current (as-is) process of exchanging data and the desired (to-be) process. You must carefully consider how the data interacts with other apps, especially when the process is automated. Then you’ll need to look at the gaps between each and make decisions about trade-offs of how you’ll get from your current to future state.

Migration of a spreadsheet to a database is paramount to automating processes. Infusing intelligence and everything you do going forward when creating, exchanging, and updating data and schemas is critical. Here are seven key takeaways:

1. Analyze and prepare the data:

• • Review the spreadsheet data to ensure it’s well-structured and consistent.
  • Identify discrepancies, missing values, or duplicates, and clean the data as needed.
  • Determine the appropriate data types and formats for each column in the spreadsheet, taking into account the requirements of the target database.

2. Choose the right database platform:

• • Evaluate various database platforms based on scalability, performance, security, and ease of integration with third-party systems.
  • Consider your organization’s infrastructure, resources, and expertise when selecting a database platform.

3. Design the database schema:

• • Create a schema that represents the structure and relationships of the spreadsheet data.
  • Define tables, columns, primary keys, foreign keys, and indexes, ensuring they align with the data types and formats identified in Step 1.
  • Consider any future data requirements and the need for normalization or denormalization to optimize performance and maintainability.

4. Import the data:

• • Export the spreadsheet data to an intermediate format, such as CSV, XML, or JSON.
  • Use database-specific tools, import wizards, or custom scripts to import the data from the intermediate format into the target database.
  • Verify the data has been imported correctly by running queries and checks against the database.

5. Configure access for third-party systems:

• • Set up appropriate user accounts, roles, and permissions within the database to control access from third-party systems.
  • Implement any necessary authentication and authorization mechanisms to ensure secure access.
  • Provide connection strings, APIs, or other interfaces for third-party systems to interact with the database.

6. Test and validate the migration:

• • Conduct thorough testing to ensure the migrated data is accurate, consistent, and accessible by the intended third-party systems.
  • Verify the database’s performance, stability, and security in its new environment.
  • Address any issues or concerns that arise during testing and validation, making adjustments as needed.

7. Monitor and maintain the database:

• • Establish ongoing monitoring and maintenance processes to ensure the database remains performant, secure, and up-to-date.
  • Implement backup and recovery strategies to safeguard the data.
  • Stay informed about updates and improvements to the chosen database platform, applying them as needed to optimize the system.

3. From Database to Applications and Endless Automated Possibilities

The world is your oyster with your information persisting in an enterprise-grade database.

Imagine if you could provide your trading partners with an application that allowed them to easily upload data in any format (including a spreadsheet, but also a PDF and many other types of documents)? What if this application could parse the file, check it for any security threats, and look for any changes to its structure – the metadata – or data contained within it since the last upload? How about providing a web interface, a dashboard, unique logins, and APIs that store the data and make the data accessible in ways that you didn’t think possible?

Well, this is not only possible, but it’s less complex, less time-consuming, and more affordable today than ever. With data safely in a database, many things can be done with it. Here are ten ways that you can automate processes because your data is in a database:

1. APIs: Application Programming Interfaces (APIs) provide a structured way to interact with data and services, allowing automated processes to access and manipulate data stored in a database.
2. Direct Database Queries: Automated processes can access data by executing SQL (Structured Query Language) or other query languages directly against the database.
3. Database Triggers: Predefined actions that automatically execute in response to specific database events. Automated processes can leverage triggers to act on data changes or events occurring in the database.
4. Data Transformation: Middleware components like message queues, data integration platforms, or ETL (Extract, Transform, Load) tools connect to databases and move data between systems. Automated processes can use these middleware components to access and manipulate data in a database.
5. Data Export/Import: Automated processes can export data from a database to a file format (e.g., CSV, JSON, XML) and then import it into another system or application for processing.
6. SaaS Application: As an alternative to a database, spreadsheet headers can be mapped to object fields of a SaaS application. The SaaS application is a system of record, and it has a persistent data store–which is an embedded database.
7. Webhooks/ Listeners: Webhooks are user-defined HTTP callbacks that allow automated processes to be notified when specific events or data changes occur in a database. These processes then access the relevant data for further processing.
8. Embedded Databases: Some applications embed databases directly within the application, providing seamless data access to automated processes without needing external connections or APIs. This approach is common in mobile and desktop applications.
9. Remote Procedure Calls (RPC): These allow automated processes to call functions or procedures on remote systems, which can then access and manipulate data stored in a database. This enables more direct and efficient interaction with the data.
10. Data Streaming: Technologies such as Apache Kafka, Amazon Kinesis, Pulsar, Solace, and more move data in real time between databases and automated processes. This approach is beneficial for event-driven architectures and real-time analytics.

Modern, cloud-based integration and automation platforms like Boomi can interact with and automate the flow of data residing in a database. Whether you want to create simple interfaces to allow endpoints to write or retrieve data from the database or build a fancy homegrown application with many bells and whistles, having data within a database puts you in a place where you can automate.

Automation as a Foundation for Artificial Intelligence

Automation is a crucial step in the journey toward artificial intelligence. AI promises to revolutionize businesses by enabling them to make smarter decisions, improve customer experiences, and optimize their operations. However, achieving these benefits requires a solid foundation of automated processes.

Automated processes enable businesses to collect and analyze vast amounts of data, which is essential for training AI algorithms and implementing machine learning techniques. By automating processes, businesses can ensure that their data is accurate, complete, and up-to-date – making it a valuable resource for AI systems.

Additionally, automation helps businesses to transition from rule-based processes to more advanced AI techniques. (Spoiler alert: We’ll address these intelligent processes in greater detail in the next article in this series.)

As processes become more automated, businesses can start incorporating machine learning algorithms to identify patterns and make predictions based on historical data. This allows businesses to move beyond simple automation and leverage the full power of artificial intelligence.

Hyperautomation: Toward an AI-Native Enterprise

As we delve deeper into the world of automation, it’s important to touch upon an emerging concept that takes automation to new heights – hyperautomation. A combination of multiple advanced technologies, hyperautomation aims to automate entire end-to-end processes rather than just individual tasks. By integrating AI, machine learning, robotic process automation (RPA), intelligent document processing (IDP), and intelligent business process management (iBPM), hyperautomation enables businesses to become more agile, efficient, and resilient.

While this article primarily focuses on the transition from manual to automated processes, it’s worth noting that the journey doesn’t end there. Our exploration of the Process Maturity Ladder will next spend time addressing hyperautomation through the lens of intelligent processes, diving into its potential benefits, challenges, and real-world applications in greater detail.

Automation is a vital component of the journey toward artificial intelligence. By automating processes, businesses can increase efficiency, reduce errors, and lay the groundwork for more advanced AI applications. At the same time, businesses must be aware of the potential disadvantages of automation and take steps to mitigate these risks. Through the use of multicloud composite processes and applications; modern, cloud-based middleware; and APIs, businesses can harness the full potential of automation and embark on a path toward true digital transformation toward an AI-native enterprise.

Stay tuned for part four of this blog series, in which we will address intelligent processes.

Check out the earlier posts in this series:

• The End of Business as Usual: Preparing for the AI Revolution Requires a Thoughtful Strategy
• Manual Processes and the End of the Legacy Enterprise

To find out how Boomi can help you start automating your manual processes, contact us anytime!