In the dynamic world of software development, effective project management is the key to success, especially when it comes to managing team workload. It’s like orchestrating a symphony where every team member plays a crucial role. With the right project management tools and techniques, tasks flow seamlessly, deadlines are met, and creativity thrives. No more asking, “Hey, has anyone seen the project plan?” only to find it being used as a coffee coaster. These tools streamline communication, keep everyone on the same page, and ensure that every member’s workload is manageable. Ultimately, they empower teams to deliver top-notch software, on time and within budget—and with far fewer emergency pizza orders to keep everyone working late into the night.

ZipDo reports the following statistics:

Key aspects for project success

Before we start reviewing planning tools that can lead a project to success, let’s define what makes a project successful. Traditionally, a successful project is the one that is completed within the agreed timeline, budget, and scope. However, many managers add a crucial fourth criterion: ensuring all team members go home on time without overtime. Tools like project management software, collaboration platforms, and workload trackers can help keep projects on schedule while respecting work-life balance, creating the conditions for a truly successful outcome.

Here are the key areas in a project that require careful attention:

Project planning and tracking 

This involves setting the timeline, milestones, deliverables, and team allocation (who is needed, when, and how many). Additionally, any activities that could impact deadlines, like setting up an Apple TestFlight account for mobile app testing, should be accounted for. Once the project begins, continuous tracking and recalibrating of the plan is crucial, especially if requirements or priorities change.

Requirements management

Clear and consistent management of project requirements ensures that the final deliverables meet the stakeholder’s expectations. This involves gathering, documenting, and maintaining requirements throughout the project lifecycle. It is particularly important to maintain consistency and coherence during the change management process as the project progresses.

Scope management

This process defines the work to be completed within a given timeline, preventing scope creep and keeping the project within agreed-upon boundaries. It involves setting clear objectives for each sprint and identifying key deliverables. Cross-links between planned tasks and their corresponding requirements are crucial, ensuring focus and alignment with project goals. By tracking and adjusting the scope for each sprint, teams can manage expectations and deliver results on time.

Team workload management

Effective workload management is crucial to prevent burnout and ensure successful project execution, especially for remote development teams. It requires understanding each team member’s strengths, weaknesses, and current capacity. To accurately estimate team workload, it’s crucial to establish initial task estimates, track remaining work for each task, and maintain a regular work log where team members record their work hours. This helps identify who is overloaded or underloaded, allowing you to redistribute tasks and balance the workload.

Quality management

This area is about ensuring project deliverables meet the expected standards and requirements. In addition to software functionality and usability, it covers code quality and technical debt control. Tools like SonarQube and GitHub CodeQL automate code quality checks within the CI/CD pipeline, identifying issues such as code smells and vulnerabilities. To monitor quality in real time and address problems quickly, project managers should choose issue-tracking systems with dashboards and comprehensive reporting features.

Delivery management

This involves organizing the final handover of the project, ensuring it is delivered to the client or other stakeholders on time and in line with expectations. The delivery package should include release notes describing new features, bug fixes, and other changes, a Quality Assurance (QA) report that also presents the results of test case execution, and a report with key performance indicators (KPIs), such as schedule compliance, estimate accuracy, and budget variance. A key part of effective delivery management is using the right tools to reduce manual effort when creating release notes and other deliverables.

Project planning and workload management tools

To manage projects and software development teams efficiently, it’s essential to use tools that integrate seamlessly into your development environment. This minimizes manual work and mitigates potential errors resulting from switching between different systems. A cohesive ecosystem ensures that data flows smoothly, communication is streamlined, and all team members have a unified view of the project’s status, leading to more successful project outcomes. 

Below, we’re describing the tools that are best suited for managing software development projects.

Atlassian Stack

The Atlassian stack is renowned for its comprehensive project management solutions. The popular combination of Jira, Confluence, and Bitbucket caters to the diverse needs and requirements of most projects. A major advantage of this setup is its seamless integration, allowing smooth data transitions and transparent processes for the whole team. This makes it easier for project managers to oversee every step with minimal effort.

For those seeking an alternative to Jira, Trello looks a viable option. With its simplistic board-and-card system, Trello provides an easy way to visualize work in progress. However, its functionality is comparatively limited compared to Jira, particularly in terms of advanced reporting, complex workflow automation, and detailed project tracking. This simplicity makes Trello suitable for smaller teams, but for those in need of a more comprehensive project management, Jira is preferable.

From automating task assignments to orchestrating  workflow transitions based on specific triggers, Jira allows you to automate entire processes within the platform. Additionally, integration with other services ensures seamless data flow. Jira automation capabilities make project management more effortless, reducing manual labor and the risk of errors, thereby allowing project managers to focus on higher-level strategy and decision-making.

Here is how the Atlassian stack can be applied to the key areas of project management:

• Project planning and tracking: Jira excels in detailed iteration planning, featuring sprint tracking boards and reports (such as sprint velocity and team velocity), and Jira Roadmaps to visualize overarching project timelines. With Advanced Roadmaps, you can create comprehensive project plans that enable simultaneous management of multiple teams. However, it lacks features for automatic task rescheduling and resource leveling, which makes the process more manual and less automated. Trello, on the other hand, offers even fewer capabilities for planning and tracking project progress, but it works well for maintenance projects where you need to monitor open issues and prioritize their completion.
• Requirements management: Confluence serves as a robust solution for documentation tracking, including versioning and cross-references between wiki pages, ensuring requirements remain consistent and integrated.
• Scope management: Jira boards (SCRUM, Kanban, or custom configurations) and backlogs are effective for managing project and iteration scope. Trello, with its simplicity, is suitable for smaller projects, generally using the Kanban methodology.
• Team workload management: Jira provides features to compare planned workload with team capacity. This can be achieved through built-in tools, reports, and a wide range of community plugins.
• Quality management: Software quality control is a core aspect of Jira’s functionality. Integrating with code quality tools like SonarQube further enhances quality management by allowing for control of technical debt within the standard Jira workflow.
• Delivery management: Jira supports version and release management, including the automatic generation of release notes. Its close integration with Bitbucket or other CI/CD servers, like GitHub or GitLab, allows for automated delivery packages throughout the entire pipeline.

GitHub Stack

GitHub is a great choice for remote software development teams, enabling code collaboration, issue tracking, and task management from anywhere.

Here’s how GitHub can be used to address key areas of project management:

• Project planning and tracking: GitHub Projects is ideal for Kanban-based iteration planning, offering customizable boards for task tracking. It provides tools like issues, milestones, and Gantt charts to organize and visualize project timelines. However, GitHub doesn’t support automatic task scheduling, resource leveling, or budget tracking.

• Requirements management: GitHub Wiki allows you to document requirements and maintain consistency across the project. However, its functionality is more basic compared to Confluence.

• Scope management: GitHub Issues allows you to create, assign, and track tasks, using labels to categorize items and milestones to mark project stages. However, GitHub is mainly Kanban-focused and lacks advanced Scrum features like sprint planning, burndown charts, and sprint retrospectives. For Scrum-specific functionality, consider third-party integrations like ZenHub.

• Team workload management: GitHub does not offer built-in features for work logs, task time estimates, remaining time tracking, or timesheet generation. You can use GitHub Projects and GitHub Insights as  basic tools to manage workload by tracking task assignments and deadlines. To bridge the gaps in this functionality, one can apply to third-party integrations like ZenHub, WakaTime, or Clockify.

• Quality management: GitHub isn’t primarily designed for quality assurance (QA), but you can create a specific issue type called “Bug” to track open bugs in your software. Although GitHub doesn’t offer built-in QA reports, you can generate custom ones using third-party tools like Microsoft Power BI. Additionally, GitHub CodeQL can be used alongside SonarQube to enhance static analysis and improve security vulnerability detection.

• Delivery management: GitHub Actions can automate the delivery process, from building to deploying software. GitHub Releases lets you manage versions, create release notes, and distribute software to stakeholders, offering a complete solution for automated delivery and release management.

Here’s how GitHub can be used to address key areas of project management:

• Project planning and tracking: GitHub Projects is ideal for Kanban-based iteration planning, offering customizable boards for task tracking. It provides tools like issues, milestones, and Gantt charts to organize and visualize project timelines. However, GitHub doesn’t support automatic task scheduling, resource leveling, or budget tracking.
• Requirements management: GitHub Wiki allows you to document requirements and maintain consistency across the project. However, its functionality is more basic compared to Confluence.
• Scope management: GitHub Issues allows you to create, assign, and track tasks, using labels to categorize items and milestones to mark project stages. However, GitHub is mainly Kanban-focused and lacks advanced Scrum features like sprint planning, burndown charts, and sprint retrospectives. For Scrum-specific functionality, consider third-party integrations like ZenHub.
• Team workload management: GitHub does not offer built-in features for work logs, task time estimates, remaining time tracking, or timesheet generation. You can use GitHub Projects and GitHub Insights as  basic tools to manage workload by tracking task assignments and deadlines. To bridge the gaps in this functionality, one can apply to third-party integrations like ZenHub, WakaTime, or Clockify.
• Quality management: GitHub isn’t primarily designed for quality assurance (QA), but you can create a specific issue type called “Bug” to track open bugs in your software. Although GitHub doesn’t offer built-in QA reports, you can generate custom ones using third-party tools like Microsoft Power BI. Additionally, GitHub CodeQL can be used alongside SonarQube to enhance static analysis and improve security vulnerability detection.
• Delivery management: GitHub Actions can automate the delivery process, from building to deploying software. GitHub Releases lets you manage versions, create release notes, and distribute software to stakeholders, offering a complete solution for automated delivery and release management.

GitLab Stack

GitLab stands as another software development platform that fosters collaboration among remote engineers and cross-functional project teams. While being similar to GitHub, GitLab distinguishes through an array of itself built-in features, making it a more comprehensive tool for advanced project planning, scope management, team workload management, and quality management.

Here’s how GitLab covers each of the considered project management areas:

• Project planning and tracking: Like GitHub, GitLab focuses more on iteration planning than on comprehensive project planning and tracking. It also lacks resource leveling and automatic task scheduling. However, GitLab Issues offers robust tools for visualizing tasks and tracking project milestones, while the Roadmaps feature enables you to plan project timelines and track task dependencies.
• Requirements management: within GitLab lies a dedicated Requirements Management functionality, allowing users to create, track, and link requirements throughout the project lifecycle. Unlike GitHub Wiki, which provides basic documentation features, GitLab’s requirements management is structured and designed for detailed traceability. This feature enables you to establish relationships between requirements and issues, ensuring consistency and alignment across the project’s various stages.
• Scope management: GitLab Issues functions similarly to GitHub, but it includes Epics and Roadmaps, offering more extensive scope management for both projects and programs. With GitLab’s issue-tracking system and Agile boards (including Scrum and Kanban), you can define epics, stories, and tasks, set dependencies, and manage priorities. The Milestones feature allows you to set project phases and track progress toward key goals.
• Team workload management: GitLab provides team workload management tools, offering built-in reports, work-in-progress (WIP) tracking, and the ability to assign tasks to specific team members with set deadlines to manage capacity. Its Roadmaps and Epic features enable planning across multiple teams, while advanced analytics, burndown charts, and workload distribution reports ensure balanced workloads and help prevent overload.
• Quality management: GitLab’s Code Quality feature automatically scans code for issues, providing real-time feedback to developers. Additionally, GitLab supports integration with diverse code quality tools like SonarQube. GitLab presents an array of built-in QA reports showcasing detailed results from automated tests, code coverage reports to track the percentage of code covered by tests, security reports, and performance testing capabilities.

Delivery management: With GitLab’s CI/CD, you can set up automated pipelines to build, test, and deploy software. GitLab empowers users to define environments for different stages of deployment, such as staging and production, and configure rollbacks and automated deployment rules. GitLab’s Release feature supports version management and the generation of release notes, enabling seamless delivery and deployment processes.

YouTrack

YouTrack is a JetBrains tool that offers a variety of features to help manage software development teams and projects. Here’s how YouTrack can cover specific project needs:

• Project planning and tracking: By harnessing YouTrack’s capabilities, you can create multiple sprints, each with customizable start and end dates, allowing you to plan ahead and structure your project timeline. You can set deadlines, prioritize issues, and track project progress with various built-in reports and analytics. The platform offers burndown charts to monitor sprint progress and Gantt charts for visualizing project timelines and dependencies. Like the tools mentioned above, YouTrack doesn’t support automatic task scheduling or resource leveling.
• Requirements management: YouTrack’s Knowledge Base fosters users to create wiki-style articles to document project requirements, providing a platform for creating, organizing, and sharing project-related content. With features like issue linking, collaborative editing, and version control, you can link requirements to tasks, collect stakeholder feedback, and maintain a comprehensive record of requirement changes throughout the project lifecycle.
• Scope management: YouTrack provides flexibility through its Agile boards, supporting both Kanban and Scrum, with customizable workflows to manage tasks, user stories, sprints, and more. The Backlog feature allows you to prioritize issues and organize work for efficient planning and execution.
• Team workload management: YouTrack helps review team capacity with features like task tracking, work-in-progress (WIP) limits, and various visualizations. The built-in timesheet feature assists team members in logging work hours, providing insights into task time allocation. You can also create custom workflows to automate task assignments for even work distribution.
• Quality management: YouTrack facilitates quality management through customizable issue tracking, integration with code analysis tools like SonarQube, and a variety of QA reports, including the Matrix report. The Matrix report provides a visual representation of the relationship between different issues, such as issue criticality by epic, allowing you to analyze trends, assess risks, and prioritize tasks. This feature offers a tabular view for comparing metrics and gaining deeper insights into project dynamics.
• Delivery management: YouTrack can integrate with CI/CD tools like JetBrains’ TeamCity, GitHub, or GitLab, enabling automated delivery processes by connecting with CI/CD pipelines to streamline builds and deployments. While it doesn’t offer a built-in release note report or a dedicated release management feature, YouTrack allows you to automate tasks with custom workflows, and its customizable reports provide insights into delivery timelines and key milestones.

Microsoft Project

All the tools mentioned earlier lack the capacity to track an entire project plan, manage budgets, and calculate the required number of resources for each project stage. This is where Microsoft Project comes in. It’s a robust solution for detailed project planning, resource management, and scheduling. Supporting Gantt charts, critical path analysis, and automatic task scheduling, Microsoft Project is perfect for planning and tracking project releases. Its standout feature is resource leveling, which automatically balances workloads to prevent overallocation. It’s also excellent for detailed budget planning, setting milestones, and managing team capacity throughout various project stages. However, it might not be the best option for granular planning of smaller iterations. Despite integrating with other Microsoft tools like Teams and SharePoint, it offers fewer collaboration features compared to platforms like Atlassian and GitLab. Thus, Microsoft Project can most effectively be used as a complementary tool alongside other project management platforms.

So, which tools ensure project success?

With so many tools available, choosing the right one for your project can be challenging. The best choice often depends on your project’s specific needs. The technology stack or target deployment platform can significantly impact which project management tools are most suitable. 

The need to fix the budget, stick to tight deadlines, or maintain flexibility through agile methodologies are all crucial factors to consider when choosing the right tools. Monitoring team workload is also essential, as this is critical to project success. Since business leaders often juggle multiple responsibilities, they usually have little time to manage the complexities of software development. That’s where our expert team comes in. With our support, you can focus on what truly matters: growing your business.

References

1. Essential Software Project Failure Statistics In 2024. ZipDo, 2024. 
2. Advanced planning guide in Jira. Atlassian, 2024. 
3. GitHub Docs: Customizing the roadmap layout. GitHub, 2024. 
4. GitLab Docs: Plan and track work. GitLab, 2024. 
5. You plan your way with YouTrack. JetBrains, 2024. 
6. Microsoft Project Online Feature Details. Microsoft, 2024. 

Timspark has been acknowledged as a trusted Research Partner by GoodFirms, an innovative B2B research and reviews company.

As a Research Partner with GoodFirms, Timspark contributes its expertise and knowledge, sharing valuable industry insights and contributing to the platform’s comprehensive evaluations. This partnership further strengthens Timspark’s reputation as a trusted software development and technology solutions authority.

Our recent collaboration resulted in the publication of a research article titled “Transforming Ideas into Applications.” We are grateful for this opportunity to share our knowledge and contribute to GoodFirms’ comprehensive evaluations.

“We are privileged to be recognized as a Research Partner by GoodFirms,” remarked Hanna Strashynskaya, Chief Marketing Officer at Timspark. “Timspark has consistently showcased its commitment to providing top-notch software development services to clients worldwide. Both clients and industry experts have acknowledged our dedication to quality, innovation, and customer satisfaction, and this brand-new acknowledgment reaffirms our dedication to helping businesses succeed. We look forward to continuing our collaboration with GoodFirms and contributing our insights to benefit the B2B community.”

About GoodFirms

GoodFirms is a pioneering B2B exploration and evaluations firm that meticulously surveys the market to pinpoint business service providers and link them with those seeking services. Known as the most reliable source for the B2B market, GoodFirms boasts top-tier expertise with collaborators worldwide.

Some may be concerned about the presence of AI in our lives, and others may be too enthusiastic about its capabilities, but one thing is for sure: artificial intelligence can greatly facilitate us in taking on a number of routine tasks. That means we’ve got more time to get creative and focus on what truly matters — the things artificial intelligence does not cover yet.

Are custom AI solutions really popular?

To stay in trend, modern applications incorporate AI-based functionality to varying degrees. Implementing custom AI development is not only beneficial for successful marketing campaigns, but also for taking software to a higher standard of quality, and therefore increasing competitiveness, even when designing products with saturated markets.

According to an Accenture study, each industry’s profit share is expected to increase by 2035 when comparing a baseline without AI to expected profits with AI. The top 8 included the following industries:

• Education: 84%
• Accommodation and food services: 74%
• Construction: 71%
• Wholesale and retail: 59%
• Healthcare: 55%
• Agriculture, forestry, and fishing: 53%
• Social services: 46%
• Transportation & Storage: 44%

At the same time, it is clear that developing a custom AI software from scratch entails significant costs and, notably, time. Training and calibration of AI modules are time-consuming tasks that demand highly qualified specialists. How can you overcome this challenge? By integrating off-the-shelf AI software into your solution you can speed up your product’s time to market.

Using ready-to-use AI solutions: pros and cons

Which types of artificial intelligence software to use to speed up your own product development? Ready-made AI solutions can be divided into the following groups:

• AIaaS (or AI as a Service) are ready-to-use AI products and services with limited customization capabilities. This kind of software usually has built-in ML and DL algorithms, which can be changed only by its vendor. A prime example of AIaaS is ChatGPT.
• AI PaaS (or AI Platform as a Service) is a set of AI and ML services for developing, training, and deploying artificial intelligence applications. Such AI development platforms provide users with the infrastructure and tools to build custom AI software. AWS AI and Azure AI services are the best examples of AI PaaS. 
• The most scarce group of ready-to-use AI solutions are standalone applications. This is usually open source software that you can integrate into your own product and improve as needed. A good example of such a solution is H2O.ai, a comprehensive open source GenAI platform built for isolated, on-premises or cloud VPC deployments.

Off-the-shelf AI solutions range from commercial products to free applications, they may offer low-code AI tools to improve their functionality, or remain entirely closed to changes. Integrating such third-party software into your own product entails both pros and cons.

Pros

• Included support. Vendors of AI solutions usually provide support, that is, they release updates with new functionality and bug fixes.
• Speeded development. You can reduce the development time of your product by integrating an off-the-shelf AI module.
• Savings. The cost of integrating off-the-shelf solutions, including initial licensing fees, is typically lower than developing such a solution from scratch.
• No need to hire AI software developers. With an out-of-the-box AI solution, you don’t need to hire expensive AI programmers, just integrate that ready-made software into your product using a public API. Besides, some  solutions provide the ability to customize them using low-code/no-code AI platforms.
• Predefined architecture and data format. You don’t need to design the architecture of custom AI software or invent the data format needed to train the AI module, since ready-made software has rather clear requirements for input parameters. 

Cons

• Changes due to the AI solution update. Any update to a third-party system may require changes to your product, especially if it is an AI as a service and you are unable to keep using its older version. In the latter case, you will have to update your product too, otherwise it may simply stop working.
• Implementation constraints. Any integration may impose certain restrictions on your software and its architecture. That is, you may lose the flexibility and openness of the system. If, for some reason, the ready-made AI solution does not fully cover the functional and non-functional requirements, then you will have to change the scope of the project in order to integrate the selected AI-based module.
• Increases in license fees. With the growing popularity of your product, the cost of licenses for using a third-party AI solution may grow. Moreover, some vendors use a non-linear royalty calculation method, setting the price based on the revenue from the final product. In this case, it may be reasonable to consider abandoning integration in favor of your own AI software development, which at this stage should be easier, given you already have a client base and accumulated historical data necessary for training and calibrating the AI module.
• Specific skills to configure a 3rd party AI solution. One disadvantage of low-code/no-code AI platforms is the need to learn how to configure this ready-made software, with no guarantee that your team will use this knowledge in future work. Moreover, many off-the-shelf solutions have poor documentation, and the manufacturers sometimes implement significant changes causing the necessity to retrain the team.
• Vendor dependency. When using off-the-shelf AI products, you are completely dependent on a third-party supplier, so it is important to use solutions from vendors that have proven their reputation and sustainability in the market.

Despite the cons described above, the benefits of integrating third-party AI solutions outweigh. The main thing is to choose the right software provider.

AI software development: 6 basic steps to integrate ready-made modules

Whether your product is already under maintenance or you are just at the very beginning of developing your idea, implementing AI modules into your solution is a prudent step. This strategic move will strengthen your position in the market and provide users with enhanced and valuable functionality. 

There are many areas where artificial intelligence can be applied: from a recommendation engine built on the analysis of user behavior to a fraud detection module that increases the security of your solution, to all kinds of image and speech recognition modules, and so on. To determine the most suitable AI solution for each specific case and seamlessly integrate it into a product with minimal risks, it is recommended to follow the next steps:

1. Business requirements analysis

At this step, requirements are prioritized based on several key parameters: demand for functionality from end users, projected business profit, impact on other parts of the system. The requirements are then roughly estimated considering the complexity of their implementation. Additionally, it makes sense to analyze the time commitment required from the end users for  certain system features. For example, if you have a chat room dedicated to salesperson-customer communication, it might be reasonable to consider using a chatbot for implementing some of the salesperson’s work. To succeed at this step, it is critical to engage an experienced business analyst who is familiar not only with the required business domain, but also with the different AI platforms and solutions that can bring value to this domain.

2. Data preparation

Once the requirements are analyzed and appropriate AI solutions are proposed, data engineers get to work to determine what specific data needs to be prepared for the chosen AI modules. Any ready-made artificial intelligence solution requires input parameters in a specific format. The data engineer compares the required format with the data that the solution operates on, and then suggests a way to collect and transfer this data to a ready-to-use AI module. If the chosen AI solution needs improvement and it is open source, then it makes sense to involve data scientists and perhaps ML engineers who will jointly calibrate the AI software so that it has a sufficient level of accuracy.

3. Proof of Concept (PoC)

This step is necessary to mitigate the risks associated with the integration of third-party AI modules. Advertising materials are not usually comprehensive, and even developer’s documentation may be incomplete, if not outdated. To understand whether the selected ready-to-use AI solution covers our business requirements and is free from hidden limitations that may affect the product, validate the selected solution using a PoC. A solutions architect or R&D engineer creates small programs that test various aspects of the integration, performance, and security of selected third-party software. Skipping this step is fraught with potential monetary losses due to extension of development deadlines, and also with reputational risks if the inconsistency of the chosen AI solution is discovered in production.

4. Infrastructure setup

Once you have decided which ready-made solutions to use, start preparing the infrastructure for seamless integration of your product with the selected software. This could be infrastructure for deploying a third-party AI module if it is standalone, or infrastructure with the ability to integrate with an AI as a service. Since most AI-based solutions consume quite a large amount of resources, it is also recommended to check whether the selected third-party software meets the requirements of green IT (ICT sustainability). To perform this activity you will need DevOps. The PoC from the previous step can be reused not only to set up the necessary infrastructure, but also to conduct load and security testing. In addition, you can engage AQA specialists to automate functional and integration tests to ensure that the off-the-shelf AI solution is seamlessly integrated into the product, and also to reduce the risk that a third-party software may be changed without notice and thereby impact your development.

5. General development flow

At this step, the development life cycle is no different from conventional custom software development. Depending on the chosen methodology (Agile or Waterfall), the product is developed considering that some of its features are replaced by integration with third-party AI solutions. The only addition to the standard development cycle is constant training and calibration that AI modules require to keep the results they produce at the required accuracy level.

6. Maintenance

Congratulations, you’re at the production stage! However, you can’t just sit back and collect fees from the users. Any application requires maintenance, especially if there are integrations with third-party solutions. Changes to the software with which you have integrated may require changes to your product. In addition to bug fixing and new features implementation, you will need to calibrate your AI-based modules throughout the lifecycle of the application, whether you use a third-party software or have custom AI solutions.

Do you need to automate document management and at the same time you have various sources of structured and unstructured data, including in paper form? Nanonets, a specialized artificial intelligence platform, may be a good choice. This platform streamlines workflows by allowing users to easily automate tasks without writing a single line of code. Nanonets offers both ready-made solutions for common document types and the ability to flexibly customize your own models using no-code AI tools. 

Top 5 low-code AI solutions

We won’t list well-known AI services from Google, Amazon, and Azure here, nor will we touch on high-profile AI chatbots like ChatGPT or Gemini. Alternatively, we’ll look at ready-made AI software that can give you a good start in developing your product. Here are our TOP 5 low-code AI solutions.

1. Viso Suite Platform software

Should computer vision functionality be a part of your upcoming product? Viso Suite may be the right choice for this. The solution is designed for development teams and allows building, deploying, scaling and securing AI-based applications. The Viso platform provides a low-code visual editor. With its help, you can quickly create applications, integrate existing cameras, and take advantage of the latest algorithms (such as YOLOv7).

2. Salesforce Einstein software

If you are looking for a CRM (customer relationship management) module with the ability to build predictive models, having natural language processing and computer vision features, then it makes sense to consider Salesforce Einstein. It is an AI analytics platform that does not require any model management or data preparation. However, if you need customization, Einstein 1 Studio provides a set of low-code AI tools. With its help, you can configure an AI conversational assistant Einstein Copilot, create custom AI actions with the help of Copilot Builder, set up custom prompts using a Prompt Builder, and build their own predictive AI models with Model Builder.

3. Nanonets

4. Observe.AI software

If you want to transcribe calls in real time, Observe.AI is worth a look. This AI-powered solution provides not only speech recognition capabilities, but also a Real-Time AI product suite designed to improve agent-customer interactions and productivity through behavioral learning, dynamic prompts, smart scripts and on-the-fly supervisor assistance. Real-Time AI is one of the best no-code platforms to easily deploy a custom agent interface and personalize the real-time experience for each agent or team.

5. C3 AI software

Are you looking for a more versatile tool for AI software development? Perhaps the C3.ai platform will suit you. This software is available as AI as a Service (AIaaS) and is used to build custom AI solutions and accelerate digital transformation in general.
You can integrate C3 AI into your software as a turnkey solution, taking advantage of one or more pre-built AI applications that address the mission-critical needs of enterprises in manufacturing, financial services, government, utilities, energy management, fraud detection, money laundering, etc.

Additionally, the C3.ai Platform provides an integrated family of deep-code (via Visual Studio), low-code, and no-code (using the C3 AI Ex Machina interface) AI development tools available in a rich and collaborative environment.

What to choose: implement or integrate AI solutions?

So, the ultimate question is, what is the best AI platform for your case? With expertise in both AI and software development, Timspark specialists can advise you on all the steps required to create your future product. Our DevGreenOps will provide you with the best options for deploying such projects to reduce resource consumption and meet green agenda, while experienced data engineers will help you improve the accuracy of your AI software.

References

1. 150+ Artificial Intelligence Statistics You Need to Know in 2024 – Who is Using It & How? Technopedia, 2024. 

2. On-premise, airgapped and cloud VPC. H2O.ai, 2024.
3. Low-Code For Ai Vision. Viso.ai, 2024.
4. YOLOv7: A Powerful Object Detection Algorithm (2024 Guide). Viso.ai, 2024.
5. Salesforce Launches Einstein 1 Studio: Low-Code AI Tools for Customizing Einstein Copilot and Embedding AI into Any CRM App. Salesforce, Inc., 2024.
6.  No-Code Automation: Revolutionizing Business Processes Without Coding. Nanonets, 2023.
7. Observe.AI Launches Real-Time AI to Help Contact Centers Drive Sales and Retain Customers With End-to-End Conversation Intelligence. Business Wire, 2024.
8. This is Enterprise AI. C3.ai, Inc., 2024. 

In the online store world, choosing the best CMS software for eCommerce stands among the most impactful decisions. Two big contenders are WordPress and Drupal. With WordPress powering 43.1% of the internet and 21% of them using WooCommerce, a great share of eCommerce revenue comes from WordPress sites.

As for Drupal, in the first quarter of 2024, Drupal online stores grew by 19% compared to the previous quarter. Yearly, they surged by a remarkable 161% in the same period.

It’s obvious that they both have many features for eCommerce, but which is best, WordPress or Drupal? Let’s take a closer look at WordPress and Drupal for online stores.

Understanding WordPress and Drupal

On the other hand, Drupal is famous for its scalability and flexibility. Though it might be harder to learn than WordPress, Drupal is excellent for large-scale projects. Developers appreciate its strong structure and customization options, making it ideal for tailored solutions.

Thus, the platforms are widely used outside the eCommerce industry. But here’s why they make online stores specifically so powerful.

WordPress vs Drupal for eCommerce

WordPress has lots of eCommerce plugins, and WooCommerce is the most popular one. It works seamlessly with WordPress and gives users a feature-packed platform to create and run online stores. You can customize your store, manage inventory, handle payments, and track sales, all using the familiar WordPress interface.

Another plugin, Easy Digital Downloads, designed for selling digital products, provides a lightweight yet powerful solution for eCommerce on WordPress. It enables users to sell digital goods like software, ebooks, and music effortlessly, with features for managing downloads, customer accounts, and sales reports.

Drupal doesn’t have as many dedicated eCommerce plugins as WordPress, but it makes up for it with flexibility and scalability. Modules like Drupal Commerce let users build fully customized solutions. With Drupal Commerce, you have precise control over every part of your online store. It is perfect for businesses with special requirements or complex processes.

While not as widely used as Drupal Commerce, Ubercart remains a viable option for eCommerce on Drupal. It offers features such as catalog management, shopping cart, and order processing, providing a straightforward solution for setting up online stores on the Drupal platform.

The choice between WordPress or Drupal for eCommerce hinges on specific business requirements and technical expertise. While WordPress offers unparalleled ease of use and a vast ecosystem of plugins, Drupal excels in flexibility and customization.

Regardless of the platform chosen, Drupal and WordPress both provide advanced modules to meet the demands of the online store.

Choosing the Best eCommerce CMS

WordPress vs Drupal CMS for eCommerce

Additionally, we recommend diving in the ‘WordPress vs. Drupal Guide’ prepared and compiled by Designrush, where our CTO, Alex Velesnitski shares his opinion on these two platforms.

Success in eCommerce hinges not just on the Drupal vs WordPress choice itself but also on factors like marketing, customer service, and product quality. By carefully evaluating your options and selecting the right eCommerce CMS for your needs, you’ll be well-positioned to thrive in the competitive online marketplace.

Feel stuck about what CMS web development services you need? Don’t hesitate to reach out. At Timspark, we’ve launched global eCommerce projects powered by both systems and can totally suggest the best option for your case.

At Timspark, we specialize in software development and digital transformation consulting. To ensure we’re always at the cutting edge, we actively participate in IT events. This experience makes us well-versed in discerning which events are worth attending and how they can benefit businesses.

Drawing upon our experience and expertise, we have compiled a guide to tech events of Q2 2024. This ready-to-use list is a valuable resource for businesses looking to make informed decisions and plan strategically.

Each event featured in the list has been carefully chosen according to its potential to deliver valuable insights and connections. It is not limited to a mere list but also includes reasons and recommendations why attending them might benefit your business. Whether it’s the chance to connect with industry leaders, learn directly from experts, or explore new tools and technologies for innovating within your organization, you will be able to browse all of them and eventually pick the most suitable events that could present unique opportunities for your growth and progress.

Start mapping out your schedule now by downloading the guide from our website. It takes a moment of your time to fill in the form, and you will get the guide asap. And hopefully, see you at the upcoming events! 

Timspark proudly announces its recognition as the second-place winner in the prestigious AI Development Companies List by DesignRush.

With a comprehensive suite of services, including AI Development, Timspark has established itself as a go-to partner for businesses seeking scalable and efficient software solutions.

Hanna Strashynskaya, Chief Marketing Officer at Timspark, expressed her excitement and gratitude for the recognition, stating:

‘We are thrilled to be recognized by DesignRush as one of the top AI development companies. This achievement is a testament to the hard work and dedication of our talented team, as well as the trust and support of our clients. At Timspark, we remain committed to pushing the boundaries of technology and delivering unparalleled value to our clients.’

As Timspark continues to innovate and lead in AI development, this recognition from DesignRush fosters our commitment to excellence and client success.