This blog offers a comprehensive guide to setting up Continuous Integration (CI) in Azure DevOps to automate the integration of SharePoint Framework (SPFx) code by leveraging Azure DevOps pipelines. This process aims to streamline development workflows, improve code quality, and ensure quicker code validation before deployment without any manual processing.

Continuous Integration (CI) is the process of automating the build and testing of code when a developer commits changes to source control. Commit to source control triggers an automated build that grabs the latest code from version control, builds it, and runs tests on it (if configured).

Prerequisite for Building CI pipeline for SPFx in Azure DevOps

To set up Continuous Integration (CI) for SPFx in Azure DevOps, ensure you have the following things already setup:

• An Azure DevOps account with required access
• Your SharePoint Framework (SPFx) project should be stored in a Git repository
• Ensure the repository includes the necessary package.json, gulpfile.js, and other configuration files required to build and bundle your SPFx solution

Implementation

To implement CI, we must create a new Pipeline in Azure DevOps. Building a pipeline includes the following major steps:

• Create a build definition
• Install NodeJS
• Restore npm packages
• Build the solution
• Package the solution
• Prepare the Artifacts
• Publish the Artifacts

Create a Build Definition

Build definition contains the definition and configuration for the build. Follow the below steps to create a new build definition.

• Login to Visual Studio Online (Azure DevOps)
• Select your project to set up a build definition.
• From the left navigation, click Pipelines > Builds.
• Click “New pipeline” > Click on “Use the classic editor”.
• Select “Azure Repos Git” > Select Team Project > Select Repository > Select branch for CI implementation.

• Under “Select a template”, select “Empty Pipeline”.

• The build definition has a default agent. We can add multiple tasks to the agent to define our build.

In this case, in agent specification, I have used Windows-2022, but you can also choose “Windows-latest” based on the environment in which you want to run your build.

Install NodeJS

• On the default agent, click the + sign.
• Search for “Node”.
• Add Node.js tool installer.

• Make sure you specify 10.x in the Version Spec field. If your project is based on SharePoint Framework 1.7.1 or earlier, use version 8.x.

Restore npm Packages

SharePoint Framework solution uses third-party npm packages. We need to restore those before starting the build process.

• Add npm task.
• Verify if the command is set to install.

Build the Solution

Build the SPFx solution to minify the required assets to upload to CDN.

• Add gulp task.
• Set Gulp file path to gulpfile.js.
• Set Gulp task as a bundle.
• Set Gulp arguments to –ship.

Note: Ensure the gulp task has the “–warnoff” command and “–ship” to avoid build failure in a production environment. Refer to the Configuration section below for details.

Package the Solution

The next step is to combine the assets into a package.

• Add gulp task.
• Set Gulp file path to gulpfile.js.
• Set Gulp task as package-solution.
• Set Gulp arguments to –ship.

Prepare the Artifacts

Azure DevOps build does not retain any files. The “.sppkg” file created from the above step needs to be copied to the staging directory to be published to the release pipeline.

• Add “Copy Files” task.
• Set “Source Folder” to $(Build.Repository.LocalPath)/sharepoint/solution.
• Set “Contents” to *.sppkg.
• Set target folder to $(Build.ArtifactStagingDirectory)/drop.

Publish the Artifacts

Instruct Azure DevOps to keep the files after build execution.

• Add the “Publish Build Artifacts” task.
• Set “Path to publish” to $(Build.ArtifactStagingDirectory)/drop.
• Set “Artifact name” to drop.

Configuration

During bundling and packaging of your SharePoint Framework solution, you could see two types of messages:

• Warnings
• Errors

When running a DEBUG build, both messages do not cause the process to fail by a stderr (or standard error). But in the PRODUCTION build, you would get the following type of error output:

This might be an issue in your automated build/release pipelines. For instance, when you automatically bundle and package your solution on Azure DevOps, there is no way to tell that it should continue when warnings occur. The only option you have is to “continue” on error.

To restrict this, we can add a “warnoff” command in the build process, which won’t cause the build process to fail. For this, make the following changes in gulpfile.js.

// Retrieve the current build config and check if there is a `warnoff` flag set
const crntConfig = build.getConfig();
const warningLevel = crntConfig.args["warnoff"];
// Extend the SPFx build rig, and overwrite the `shouldWarningsFailBuild` property
if (warningLevel) {
    class CustomSPWebBuildRig extends build.SPWebBuildRig {
        setupSharedConfig() {
            build.log("IMPORTANT: Warnings will not fail the build.")
            build.mergeConfig({
                shouldWarningsFailBuild: false
            });
            super.setupSharedConfig();
        }
    }
    build.rig = newCustomSPWebBuildRig();
}
build.initialize(gulp)

Conclusion

Setting up a Continuous Integration (CI) pipeline for SPFx in Azure DevOps automates the process of building, testing, and bundling your SPFx solutions whenever any code changes occur. This pipeline will eventually reduce the need for manual intervention and guarantee that the latest code is thoroughly validated before deployment.

This blog provides a detailed explanation of the technical approach for implementing Continuous Deployment (CD) processes within Azure DevOps. It focuses on automating the deployment of solutions to SharePoint environments. This approach not only speeds up the release cycle but also enhances reliability, minimizes errors, and ensures that updates are deployed quickly and effectively.

Continuous Deployment (CD) takes validated code packages from the build process and deploys them into a staging or production environment. Developers can track successful deployments and narrow issues to specific package versions.

Prerequisite for building CD for SPFx in Azure DevOps

To set up Continuous Deployment(CI) for SPFx in Azure DevOps, ensure you have the following things already setup:

• An Azure DevOps account with required access
• CI pipeline for building the required package file .sppkg for deployment
• Required access to App Catalog for deploying to SharePoint Online

Implementation

We need to create a new Release in Azure DevOps to implement CD. It requires the following steps:

• Creating the Release Definition
• Link the Build Artifact
• Create the Environment
• Install NodeJS
• Install Office 365 CLI
• Connect to App Catalog
• Add Solution Package to App Catalog
• Deploy the App
• Set Environment Variables

Creating the Release Definition

• Login to Visual Studio Online (Azure DevOps)
• Select your project to set up a build definition.
• From the left navigation, click Pipelines > Releases.
• Click the “+ New” button > click “New Release Pipeline”.

• Select template > Empty job > Apply.

Linking the Build Artifact

• Click on Add an artifact.
• Select Project, Source, etc.

Note: Give a meaningful name to “Source alias” and note it down. This name will be used in upcoming steps.

Create the Environment

• Under Stages, click “Stage 1”.
• Name your environment.

Installing NodeJS

• Go to the “Tasks” tab
• The task configuration window will appear the same as in the build definition.
• On the default agent, click + sign.
• Search for “Node”.
• Add Node.js tool installer.
• Specify 10.x in the Version Spec field. If your project is based on SharePoint Framework 1.7.1 or earlier, use version 8.x.

Install Office 365 CLI

Office 365 Common Language Interface (CLI) is an open-source project from the OfficeDev PnP Community.

• Add npm task.
• Under “Command,” select custom.
• In the “Command and Arguments,” type install -g @pnp/office365-cli.

Set Environment Variables

Before connecting to SharePoint, we can define some variables used at multiple steps in the deployment process. So, define the process variables in the “Variables” tab below.

• Click the Variables tab.
• Under Pipeline variables, add the variables below.

Connect to App Catalog

We need to authenticate against our tenant’s app catalog.

• Add the “Command Line” task.
• In the “Script” field, type in the below command:

o365 spo login https://$(tenant).sharepoint.com/$(catalogsite) --authType password --userName $(username) --password $(password)

Add Solution Package to App Catalog

Now, we need to upload the solution package to the app catalog.

• Add “Command Line” task.
• In the “Script” field, type in the below command:

o365 spo app add -p $(System.DefaultWorkingDirectory)/<Source alias>/drop/ webparts.sppkg --overwrite

Note: “Source alias” is the alias name set up during the “Link the Build Artifact” step.

Deploy the App Catalog

Finally, we must deploy the app .sppkg file to the App Catalog to make it available to all site collections within the tenant.

• Add “Command Line” task.
• In the “Script” field, type in the below command.

o365 spo app deploy --name webparts.sppkg --appCatalogUrl https://$(tenant).sharepoint.com/$(catalogsite)

Conclusion

Setting up a Continuous Deployment (CD) for SPFx in Azure DevOps automates the process of solution package deployment to the App Catalog in the SharePoint environment. This process will enable developers to focus on ensuring a seamless and consistent delivery process, accelerate iterations, and maintain a more agile and adaptable development environment.

Consumer behavior is not just shaping online business operations—it’s fundamentally changing the digital marketplace. This paradigm shift is forcing companies to adapt or be left behind. Here are the key trends that will redefining the digital landscape in 2025:

The AI Revolution: From Convenience to Necessity

Artificial Intelligence will be the cornerstone of modern consumer interactions. AI-driven experiences will be ever-present, fundamentally altering the consumer decision-making process. This shift is driven by a growing consumer appetite for instant gratification and frictionless interactions.

AI-powered solutions, like advanced chatbots and sophisticated virtual assistants, are evolving from convenience to essential components of the customer journey. These technologies are not just responding to queries; they’re anticipating needs, personalizing interactions, and streamlining the path to purchase.

Hyper-Personalization: The New Battlefield for Consumer Loyalty

Personalization will go beyond being just another marketing tactic—it will be the primary differentiator in a crowded marketplace. AI and data analytics are enabling a level of personalization that borders on clairvoyant, with brands able to predict and fulfill consumer needs before they’re even articulated.

This trend is not just about tailored product recommendations; it’s about creating bespoke customer experiences across all touchpoints. The demand for personalization will reshape business models, forcing companies to prioritize data-driven insights and adaptive marketing strategies.

Social Commerce: The Convergence of Social Media and E-commerce

The rise of social commerce represents a continuing shift in consumer behavior, blurring the lines between social interaction and commercial transactions. This trend is particularly pronounced among younger demographics, with 53% of consumers aged 26-35 influenced to make purchases through social media ads.

Social platforms are no longer just tools for connecting with friends and family; they’re becoming fully integrated marketplaces. This evolution is driven by consumers’ desire for seamless experiences and the increasing time spent on these platforms. Brands that fail to establish a strong social commerce presence risk becoming invisible to a significant portion of their target audience.

In addition, the influence of social proof—reviews, influencer endorsements, and user-generated content—has become increasingly important. In this new landscape, a brand’s reputation is shaped in real-time through social interactions, making community management and social listening critical components of any digital strategy.

As we move towards 2025, these trends will intensify, creating a digital ecosystem where AI, personalization, and social commerce are inextricably linked. Businesses that can harness these forces will thrive.

In this episode of What If? So What?, Jim talks with Jennifer Baker, former CTO of Synovus. She shares her journey from unexpected career opportunities to becoming a trailblazing technology leader in the insurance and banking sectors, culminating in significant strides in product development, customer experience, and digital transformation.

Beyond her professional accolades, Jennifer passionately advocates for education and empowerment of women and children through her nonprofit work. Her story is a masterclass in continuous learning, adaptability, and the crafting of a career full of diverse experiences, which she likens to assembling “puzzle pieces.”

Step into the realm of fostering inclusive workplaces, especially in tech, where women are alarmingly underrepresented. Hear Jennifer’s insights on the critical need for mentorship and sponsorship to support women in technology, especially in a post-pandemic world.

As a self-described “accidental CTO,” Jennifer shares her journey embracing unforeseen opportunities and adapting in a rapidly changing digital world. Her experiences offer inspiration and practical lessons for aspiring tech leaders, underscoring the importance of intentional inclusion to spark innovation and ensure diverse representation.

Listen now on your favorite podcast platform or visit our website.

Subscribe Where You Listen

Apple | Spotify | Amazon | Overcast

Meet our Guest

Jennifer Baker, Strategic Technology & Business Transformation Leader

Jennifer has more than 20 years of experience and a proven track record of driving transformative change in diverse industries, including fintech, retail, banking, insurance, technology, and consumer products. A former chief technology officer, her expertise lies in spearheading comprehensive initiatives that enhance scalability, reduce technical debt, and streamline operations, resulting in substantial cost savings and revenue growth.

Jennifer is passionate about empowering women in science, technology, engineering, and math (STEM), and actively serves on the board of directors for both DataScan, a fintech company in the automotive sector, and Women in Technology in the Atlanta metropolitan area.

Connect with Jennifer

Meet the Host

Jim Hertzfeld is Area Vice President, Strategy for Perficient.

For over two decades, he has worked with clients to convert market insights into real-world digital products and customer experiences that actually grow their business. More than just a strategist, Jim is a pragmatic rebel known for challenging the conventional and turning grand visions into actionable steps. His candid demeanor, sprinkled with a dose of cynical optimism, shapes a narrative that challenges and inspires listeners.

Connect with Jim:

LinkedIn | Perficient

A strong and successful search engine optimization (SEO) strategy is essential in the extremely competitive world of e-commerce today. You can increase the visibility, draw in more visitors, and raise conversion rates with the correct tools and strategies. Product information management (PIM) is a crucial tool for accomplishing these objectives.

What is PIM?

PIM provides a central repository for product information, ensuring that information is accurate, consistent, and up-to-date. This allows businesses to streamline the management of product data, such as descriptions, images, specifications, and other key information related to their products. Having this organized and easily accessible information can be extremely beneficial to businesses looking to improve their customer service, increase sales, and ultimately enhance their SEO performance.

By using PIM, businesses can save time and resources by reducing manual work, increasing accuracy, and eliminating redundant data entry. A PIM system can also help with managing different versions of product descriptions, images, and other data fields in different languages and currencies. This allows businesses to quickly launch products into new markets and keep them updated across multiple channels.

How can PIM help improve your SEO?

Product Information Management (PIM) systems are designed to help businesses store, manage, and distribute product information in an efficient and organized manner. It has become a popular tool for businesses looking to improve their SEO rankings.

PIM can help improve your SEO rankings in several ways:

1. High-quality Content: PIM can help ensure that product information is accurate, complete, and consistent, which can lead to better on-page optimization and search engine visibility.
2. Enhanced Product Descriptions: PIM enables the creation of detailed and optimized product descriptions, which can help improve the relevance and quality of content for search engines.
3. Better Keyword Targeting: PIM can provide insights into which keywords are most relevant for each product, enabling e-commerce websites to better target those keywords in their product pages and other content.
4. Improved Taxonomy: Taxonomy helps to improve the customer experience by making it easier for customers to find what they are looking for, and to compare products based on relevant attributes. In addition, a well-structured taxonomy can also help to improve search engine optimization (SEO) by increasing the relevance of search results, which can drive more traffic to a company’s website.
5. Cross-Channel Distribution – PIM systems also make it easy to distribute your product information across multiple channels. This helps increase the visibility of your product pages and will help improve your SEO rankings.
6. Faster and More Efficient SEO Updates – PIM can also help make SEO updates faster and more efficient. With PIM, you can quickly and easily make changes to your product information, which can then be automatically updated across all of your sales channels. This saves time and reduces the risk of errors, making it easier to optimize your product pages for search engines. With PIM, you can keep your website up-to-date with the latest product information and take advantage of new SEO opportunities as they arise.
7. Asset Management – Asset management in a Product Information Management (PIM) system refers to the process of organizing and managing digital assets, such as images, videos, and other multimedia files, associated with a product. This includes storing, categorizing, and versioning these assets to ensure that they are easily accessible and up-to-date. We can also attach metadata to digital assets to help improve the search.

This can lead to improved organic search traffic and more conversions for your business but business always questions how do I know the optimization we were doing in PIM is helping us, One way to identify is utilizing Digital Self analytics.

inriver’s digital self-analytics tool, Evaluate, significantly enhances SEO optimization in several ways:

1. Content Compliance: Evaluate ensures that your product information is accurate and consistent across all channels, which is crucial for SEO. Accurate data helps search engines understand your products better, improving visibility.
2. Keyword Optimization: The tool tracks keyword performance and helps you optimize product listings for better search rankings. This includes monitoring keyword search and share-of-shelf.
3. Real-Time Insights: Evaluate provides real-time insights into how your products are performing on the digital shelf. This includes monitoring product search rankings, competitor pricing, and stock levels, allowing you to make data-driven decisions to improve SEO.
4. Engagement Intelligence: By analyzing customer interactions and engagement with your product listings, Evaluate helps you understand what works and what doesn’t. This information is vital for refining your SEO strategy to attract more traffic and improve conversions.
5. Automated Monitoring: The tool uses smart automation to constantly monitor your products, providing actionable insights that help you stay ahead of the competition and ensure your product information is always optimized for search engines.

Using inriver Evaluate, you can take control of your digital shelf, drive revenue growth, and enhance your SEO efforts with precise, actionable data.

By following these recommendations, you can make sure that you get the most out of your PIM system and improve your SEO performance. PIM can help you stay ahead of the competition in the e-commerce space. So if you’re looking to improve your SEO performance and reach more customers, it’s time to invest in PIM. For more information on this, contact our experts today.

Amazon Q was front and center at AWS re:Invent last week.  Q Developer is emerging as required tooling for development teams focused on custom development, cloud-native services, and the wide range of legacy modernizations, stack conversions and migrations required of engineers.  Q Developer is evolving beyond “just” code generation and is timing its maturity well alongside the rise of agentic workflows with dedicated agents playing specific roles within a process… a familiar metaphor for enterprise developers.

The Promise of Productivity

Amazon Q Developer makes coders more effective by tackling repetitive and time-consuming tasks. Whether it’s writing new code, refactoring legacy systems, or updating dependencies, Q brings automation and intelligence to the daily work experience:

• Code generation including creation of full classes based off natural language comments
• Transformation legacy code into other programming languages
• AI-fueled analysis of existing codebases
• Discovery and remediation of dependencies and outdated libraries
• Automation of unit tests and system documentation
• Consistency of development standards across teams

Real Impacts Ahead

As these tools quickly evolve, the way in which enterprises, product teams and their delivery partners approach development must now transform along with them.  This reminds me of a favorite analogy, focused on the invention of the spreadsheet:

The story goes that it would take weeks of manual analysis to calculate even minor changes to manufacturing formulas, and providers would compute those projections on paper, and return days or weeks later with the results.  With the rise of the spreadsheet, those calculations were completed nearly instantly – and transformed business in two interesting ways:  First, the immediate availability of new information made curiosity and innovation much more attainable.  And second, those spreadsheet-fueled service providers (and their customers) had to rethink how they were planning, estimating and delivering services considering this revolutionary technology.  (Planet Money Discussion)

This certainly rings a bell with the emergence of GenAI and agentic frameworks and their impacts on software engineering.  The days ahead will see a pivot in how deliverables are estimated, teams are formed, and the roles humans play across coding, testing, code reviews, documentation and project management.  What remains consistent will be the importance of trusted and transparent relationships and a common understanding of expectations around outcomes and value provided by investment in software development.

The Q Experience

Q Developer integrates with multiple IDEs to provide both interactive and asynchronous actions. It works with leading identity providers for authentication and provides an administrative console to manage user access and assess developer usage, productivity metrics and per-user subscription costs.

The sessions and speakers did an excellent job addressing the most common concerns: Safety, Security and Ownership.  Customer code is not used to train models using the Pro Tier but requires opt-out using Free version.  Foundation models are updated on a regular basis.  And most importantly: you own the generated code, although with that, the same level of responsibility and ownership falls to you for testing & validation – just like traditional development outputs.

The Amazon Q Dashboard provides visibility to user activity, metrics on lines of code generated, and even the percentage of Q-generated code accepted by developers, which provides administrators a clear, real-world view of ROI on these intelligent tooling investments.

Lessons Learned

Experts and early adopters at re:Invent shared invaluable lessons for making the most of Amazon Q:

• Set guardrails and develop an acceptable use policy to clarify expectations for all team members
• Plan a thorough developer onboarding process to maximize adoption and minimize the unnecessary costs of underutilization
• Start small and evangelize the benefits unique to your organization
• Expect developers to become more effective Prompt Engineers over time
• Expect hidden productivity gains like less context-switching, code research, etc.

The Path Forward

Amazon Q is more than just another developer tool—it’s a gateway to accelerating workflows, reducing repetitive tasks, and focusing talent on higher-value work. By leveraging AI to enhance coding, automate infrastructure, and modernize apps, Q enables product teams to be faster, smarter, and more productive.

As this space continues to evolve, the opportunities to optimize development processes are real – and will have a huge impact from here on out.  The way we plan, execute and measure software engineering is about to change significantly.

The healthcare industry stands at a transformative crossroads with generative AI (GenAI) poised to revolutionize care delivery, operational efficiency, and patient outcomes. Recent MIT Technology Review research indicates that while 88% of organizations are using or experimenting with GenAI, healthcare organizations face unique challenges in implementation.

Let’s explore a comprehensive approach to successful GenAI adoption in healthcare.

Find Your Starting Point: A Strategic Approach to GenAI Implementation

The journey to GenAI adoption requires careful consideration of three key dimensions: organizational readiness, use case prioritization, and infrastructure capabilities.

Organizational Readiness Assessment

Begin by evaluating your organization’s current state across several critical domains:

• Data Infrastructure: Assess your organization’s ability to handle both structured clinical data (EHR records, lab results) and unstructured data (clinical notes, imaging reports). MIT’s research shows that only 22% of organizations consider their data foundations “very ready” for GenAI applications, making this assessment crucial.
• Technical Capabilities: Evaluate your existing technology stack, including cloud infrastructure, data processing capabilities, and integration frameworks. Healthcare organizations with modern data architectures, particularly those utilizing lakehouse architectures, show 74% higher success rates in AI implementation.
• Talent and Skills: Map current capabilities against future needs, considering both technical skills (AI/ML expertise, data engineering) and healthcare-specific domain knowledge.

Use Case Prioritization

Successful healthcare organizations typically begin with use cases that offer clear value while managing risk:

1. Administrative Efficiency

• Clinical documentation improvement and coding
• Prior authorization automation
• Claims processing optimization
• Appointment scheduling and management

These use cases typically show ROI within 6-12 months while building organizational confidence.

2. Clinical Support Applications

• Clinical decision support enhancement
• Medical image analysis
• Patient risk stratification
• Treatment planning assistance

These applications require more rigorous validation but can deliver significant impact on care quality.

3. Patient Experience Enhancement

• Personalized communication
• Care navigation support
• Remote monitoring integration
• Preventive care engagement

These initiatives often demonstrate immediate patient satisfaction improvements while building toward longer-term health outcomes.

Critical Success Factors for Healthcare GenAI Implementation

Data Foundation Excellence | Establish robust data management practices that address:

• Data quality and standardization
• Integration across clinical and operational systems
• Privacy and security compliance
• Real-time data accessibility

MIT’s research indicates that organizations with strong data foundations are three times more likely to achieve successful AI outcomes.

Governance Framework | Develop comprehensive governance structures that address the following:

• Clinical validation protocols
• Model transparency requirements
• Regulatory compliance (HIPAA, HITECH, FDA)
• Ethical AI use guidelines
• Bias monitoring and mitigation
• Ongoing performance monitoring

Change Management and Culture | Success requires careful attention to:

• Clinician engagement and buy-in
• Workflow integration
• Training and education
• Clear communication of benefits and limitations
• Continuous feedback loops

Overcoming Implementation Barriers

Technical Challenges

• Legacy System Integration: Implement modern data architectures that can bridge old and new systems while maintaining data integrity.
• Data Quality Issues: Establish automated data quality monitoring and improvement processes.
• Security Requirements: Deploy healthcare-specific security frameworks that address both AI and traditional healthcare compliance needs.

Organizational Challenges

• Skill Gaps: Develop a hybrid talent strategy combining internal development with strategic partnerships.
• Resource Constraints: Start with high-ROI use cases to build momentum and justify further investment.
• Change Resistance: Focus on clinician-centered design and clear demonstration of value.

Moving Forward: Building a Sustainable GenAI Program

Long-term success requires:

• Systematic Scaling Approach. Start with pilot programs that demonstrate clear value. Build reusable components and frameworks. Establish centers of excellence to share learning. And create clear metrics for success.
• Innovation Management. Maintain awareness of emerging capabilities. Foster partnerships with technology providers. Engage in healthcare-specific AI research. Build internal innovation capabilities.
• Continuous Improvement. Regularly assess model performance. Capture stakeholder feedback on an ongoing basis. Continuously train and educate your teams. Uphold ongoing governance reviews and updates.

The Path Forward

Healthcare organizations have a unique opportunity to leverage GenAI to transform care delivery while improving operational efficiency. Success requires a balanced approach that combines innovation with the industry’s traditional emphasis on safety and quality.

MIT’s research shows that organizations taking a systematic approach to GenAI implementation, focusing on strong data foundations and clear governance frameworks, achieve 53% better outcomes than those pursuing ad hoc implementation strategies.

For healthcare executives, the message is clear. While the journey to GenAI adoption presents significant challenges, the potential benefits make it an essential strategic priority.

The key is to start with well-defined use cases, ensure robust data foundations, and maintain unwavering focus on patient safety and care quality.

By following this comprehensive approach, healthcare organizations can build sustainable GenAI programs that deliver meaningful value to all stakeholders while maintaining the high standards of care that the industry demands.

Combining technical expertise with deep healthcare knowledge, we guide healthcare leaders through the complexities of AI implementation, delivering measurable outcomes.

We are trusted by leading technology partners, mentioned by analysts, and Modern Healthcare consistently ranks us as one of the largest healthcare consulting firms.

Discover why we have been trusted by the 10 largest health insurers in the U.S. Explore our healthcare expertise and contact us to learn more.

References

1. Hex Technologies. (2024). The multi-modal revolution for data teams [White paper]. https://hex.tech
2. MIT Technology Review Insights. (2021). Building a high-performance data and AI organization. https://www.technologyreview.com/insights
3. MIT Technology Review Insights. (2023). Laying the foundation for data- and AI-led growth: A global study of C-suite executives, chief architects, and data scientists. MIT Technology Review.
4. MIT Technology Review Insights. (2024a). The CTO’s guide to building AI agents. https://www.technologyreview.com/insights
5. MIT Technology Review Insights. (2024b). Data strategies for AI leaders. https://www.technologyreview.com/insights
6. MIT xPRO. (2024). AI strategy and leadership program: Reimagine leadership with AI and data strategy [Program brochure]. Massachusetts Institute of Technology.

At AWS re:Invent last week, Amazon made one thing clear: it’s setting the table for the future of AI. With high-performance cloud primitives and the model flexibility of Bedrock, AWS is equipping customers to build intelligent, scalable solutions with connected enterprise data. This isn’t just about technology—it’s about creating an adaptable framework for AI innovation:

Cloud Primitives: Building the Foundations for AI

Generative AI demands robust infrastructure, and Amazon is doubling down on its core infrastructure to meet the scale and complexity of these market needs across foundational components:

1. Compute:
   • Graviton Processors: AWS-native, ARM-based processors offering high performance with lower energy consumption.
   • Advanced Compute Instances: P6 instances with NVIDIA Blackwell GPUs, delivering up to 2.5x faster GenAI compute speeds.
2. Storage Solutions:
   • S3 Table Buckets: Optimized for Iceberg tables and Parquet files, supporting scalable and efficient data lake operations critical to intelligent solutions.
3. Databases at Scale:
   • Amazon Aurora: Multi-region, low-latency relational databases with strong consistency to keep up with massive and complex data demands.
4. Machine Learning Accelerators:
   • Trainium2: Specialized chip architecture ideal for training and deploying complex models with improved price performance and efficiency.
   • Trainium2 UltraServers: Connected clusters of Trn2 servers with NeuronLink interconnect for massive scale and compute power for training and inference for the world’s largest models – with continued partnership with companies like Anthropic.

 Amazon Bedrock: Flexible AI Model Access

Infrastructure provides the baseline requirements for enterprise AI, setting the table for business outcome-focused innovation.  Enter Amazon Bedrock, a platform designed to make AI accessible, flexible, and enterprise-ready. With Bedrock, organizations gain access to a diverse array of foundation models ready for custom tailoring and integration with enterprise data sources:

• Model Diversity: Access 100+ top models through the Bedrock Marketplace, guiding model availability and awareness across business use cases.
• Customizability: Fine-tune models using organizational data, enabling personalized AI solutions.
• Enterprise Connectivity: Kendra GenAI Index supports ML-based intelligent search across enterprise solutions and unstructured data, with natural language queries across 40+ enterprise sources.
• Intelligent Routing: Dynamic routing of requests to the most appropriate foundation model to optimize response quality and efficiency.
• Nova Models: New foundation models offer industry-leading price performance (Micro, Lite, Pro & Premier) along with specialized versions for images (Canvas) and video (Reel).

 Guidance for Effective AI Adoption

As important as technology is, it’s critical to understand success with AI is much more than deploying the right model.  It’s about how your organization approaches its challenges and adapts to implement impactful solutions.  I took away a few key points from my conversations and learnings last week:

1. Start Small, Solve Real Problems: Don’t try to solve everything at once. Focus on specific, lower risk use cases to build early momentum.
2. Data is King: Your AI is only as smart as the data it’s fed, so “choose its diet wisely”.  Invest in data preparation, as 80% of AI effort is related to data management.
3. Empower Experimentation: AI innovation and learning thrives when teams can experiment and iterate with decision-making autonomy while focused on business outcomes.
4. Focus on Outcomes: Work backward from the problem you’re solving, not the specific technology you’re using.  “Fall in love with the problem, not the technology.”
5. Measure and Adapt: Continuously monitor model accuracy, retrieval-augmented generation (RAG) precision, response times, and user feedback to fine-tune performance.
6. Invest in People and Culture: AI adoption requires change management. Success lies in building an organizational culture that embraces new processes, tools and workflows.
7. Build for Trust: Incorporate contextual and toxicity guardrails, monitoring, decision transparency, and governance to ensure your AI systems are ethical and reliable.

Key Takeaways and Lessons Learned

Amazon’s AI strategy reflects the broader industry shift toward flexibility, adaptability, and scale. Here are the top insights I took away from their positioning:

• Model Flexibility is Essential: Businesses benefit most when they can choose and customize the right model for the job. Centralizing the operational framework, not one specific model, is key to long-term success.
• AI Must Be Part of Every Solution: From customer service to app modernization to business process automation, AI will be a non-negotiable component of digital transformation.
• Think Beyond Speed: It’s not just about deploying AI quickly—it’s about integrating it into a holistic solution that delivers real business value.
• Start with Managed Services: For many organizations, starting with a platform like Bedrock simplifies the journey, providing the right tools and support for scalable adoption.
• Prepare for Evolution: Most companies will start with one model but eventually move to another as their needs evolve and learning expands. Expect change – and build flexibility into your AI strategy.

The Future of AI with AWS

AWS isn’t just setting the table—it’s planning for an explosion of enterprises ready to embrace AI. By combining high-performance infrastructure, flexible model access through Bedrock, and simplified adoption experiences, Amazon is making its case as the leader in the AI revolution.

For organizations looking to integrate AI, now is the time to act. Start small, focus on real problems, and invest in the tools, people, and culture needed to scale. With cloud infrastructure and native AI platforms, the business possibilities are endless. It’s not just about AI—it’s about reimagining how your business operates in a world where intelligence is the new core of how businesses work.

Perficient Recognized in The Forrester Wave: Customer Experience Strategy Consulting Services, Q4 2024

Perficient is proud to be included as a “Contender” in The Forrester Wave: Customer Experience (CX) Strategy Consulting Services, Q4 2024 report. We were one of a set of only twelve organizations to be included in the report.

Forrester used extensive criteria to determine placement, including customer research, proprietary data offerings, and innovation.

To us, this placement shows our continued growth in CX Strategy Consulting services over the last year, as we previously were included among 31 organizations in The Forrester Customer Experience Strategy Consulting Services Landscape, Q2 2024 report.

We believe CX strategy capabilities and experience are at the heart of the report. With brands stretching across digital and physical properties, building an omnichannel customer experience can seem daunting. Partnering with an experienced consulting partner provides a strategic and custom approach, enabling organizations to implement digital transformation that activates and engages their customers at every touchpoint, while meeting and exceeding customer expectations.

Across all industries, customers expect positive omnichannel experiences, and brands that fall short of these expectations will not only miss out on current revenue, but also risk future sales due to negative perception and reputation challenges.

Digital Transformation Focused

Perficient believes its inclusion is a testament to our expertise leveraging digital capabilities to build seamless, personalized, and satisfying customer journeys.

According to the Forrester report, “Perficient is a good fit for organizations that want to center their CX strategy on a digital transformation.”

Our CX Strategy work empowers clients to make informed decisions about investing in and implementing solutions across both digital and non-digital channels. We also offer many types of services that are not specific to digital delivery. These include consulting on CX operations, governance, goal setting, team training and customer empathy development. These activities are designed to foster the growth and maturity of our clients’ organizations so they can serve their customers more effectively.

As the Forrester report mentions, “Reference customers praised Perficient’s flexibility and its willingness to be a true partner working alongside their employees.”

Perficient’s Strategic Partnership Approach

Our strategists employ a strategic formulation approach, Perficient’s Envision Framework, to help clients get to the future fast, using three cumulative phases: Insights, Ideas, and Investment. It’s how we help clients rapidly identify opportunities, define a customer-focused vision, and develop a prioritized roadmap to transform their business.

Do you know how ready your company is to create, deliver, and sustain exemplary customer experiences? Learn more about Perficient’s five-week CX IQ jumpstart that will help you highlight priorities, create strategic alignment, and guide decisions about where and how to improve CX.

Migrating to a cloud-delivered Contact Center as a Service (CCaaS) solution can revolutionize how your organization delivers customer service. However, this transition requires careful planning and execution to avoid disruptions. Assuming you have selected a CCaaS platform that aligns with your organizational needs, the following best practices outline key considerations for a seamless migration.

A successful migration to CCaaS not only enhances operational efficiency and scalability but also ensures a significant improvement in service delivery, directly impacting customer satisfaction and retention. Organizations should consider the risks of not embracing modern cloud-based customer care solutions, which can

include diminished customer service capabilities and potential costs due to outdated or inflexible systems. Moreover, organizations that delay this shift risk falling behind competitors who can adapt more quickly to market demands and customer needs. Thus, embarking on a well-planned migration journey is imperative for companies aiming to optimize their customer care operations and secure a competitive advantage in their respective markets.

1. Physical Infrastructure Migration

Understanding your current environment is critical for a successful transition. Start with a thorough site review to document the infrastructure and identify unique user requirements. Engage with call center managers, team leaders, and power users to uncover specific needs and configured features such as whisper settings, omnichannel components, call management, etc.

Factors such as bandwidth and latency are paramount for seamless operations. Evaluate your facility’s connectivity for both on-site and remote users, ensuring it aligns with the CCaaS product requirements. Fortunately, modern CCaaS solutions such as Amazon Connect, Twilio Flex and Five9 supply agent connectivity tools to verify that workers have sufficient resources to provide good customer service over various channels.

Additionally, document call treatments and station-specific configurations like call coverage paths. Legacy components requiring continued functionality should be cataloged to prepare for integration.

2. Change Management Planning

Change management is essential to mitigate risks and maximize adoption. A staged cutover strategy is recommended over a single-event migration, allowing for gradual testing and adjustments.

Develop a robust testing strategy to validate the platform’s performance under real-world conditions. Complement this with an organizational enablement strategy to train users and ensure they are comfortable with the new system. Adoption by your business units and users is one of the most critical factors which will determine the success of your CCaaS migration.

3. Operational Considerations

Operational continuity is vital during migration. Start by understanding the reporting requirements for business managers to ensure no loss of visibility into critical metrics. Additionally, review monitoring processes to maintain visibility into system performance post-migration.

4. Integration Planning

Integrating legacy infrastructure with the new CCaaS platform can present significant challenges. Document existing components, including FXO/FXS interfaces, Workforce Management solutions, FAX systems, wallboards, and specialty dialers. Verify that integrations comply with any regulatory requirements, such as HIPAA or FINRA.

Interactive Voice Response (IVR) systems often require specific integrations with local data sources or enterprise middleware. Assess these integrations to ensure call flows function as intended. For specialized applications, verify that they meet operational needs within the new environment.

5. Fault Tolerance and Disaster Recovery

Testing fault tolerance and disaster recovery capabilities are critical steps in any CCaaS migration. Develop and execute a failsafe testing plan to ensure resilience against both premise-level and carrier-level failures. It is important to align to your IT organization’s standards for recovery time objective (RTO) and business up-time expectations. Disaster recovery plans must reflect these measures and be tested to protect against potential downtime.

6. Scalability and Compliance

CCaaS solutions must scale with your business. Validate scalability by conducting load tests and documenting performance metrics. Compliance is equally important—ensure your migration adheres to industry standards like HIPAA, FedRAMP, or FINRA through thorough compliance testing and documentation.

Conclusion

A successful CCaaS migration hinges on meticulous planning, comprehensive testing, and strong change management. By following these best practices, you can minimize risks, ensure operational continuity, and set your organization up for long-term success with its new contact center platform. The result? An enhanced customer experience and a contact center infrastructure that grows with your business.

What are Legacy Systems? Why is Upgrading those Systems Required?

Upgrading means more than just making practical improvements to keep things running smoothly. It addresses immediate needs rather than chasing a perfect but impractical solution. The situation could spiral out of control if things don’t function properly in real-time.

One such incident happened on January 4, 2024, when South Africa’s Department of Home Affairs was taken offline nationwide due to a mainframe failure. In simple terms, Mainframe failures in such contexts are usually high-stakes issues because they impact the core infrastructure that supports vital public services. In South Africa, where the Department of Home Affairs handles a range of essential services such as passports, identity documents, and immigration control,  A mainframe failure can have widespread repercussions, leading to backlogs, delays, and potential administrative chaos. The South African Department of Home Affairs provides a clear example of a critical legacy system facing significant risks due to its outdated technology and operational challenges. 

Addressing these issues through modernization and digital transformation is crucial for improving service delivery and ensuring the system’s continued effectiveness and security. One cannot work on migrating the legacy system in one go, as the business and functional side of testing is a must. A planned and systematic approach is needed while upgrading the legacy system.

Question: What is the solution to avoid such a case?
Answer: Modernization of Legacy code.

Legacy code modernization is improving and updating outdated software systems to make them more maintainable, scalable, and compatible with modern technologies. Let’s understand this using Apigee (an API Management tool).

1. Scalability

Legacy system: Legacy systems were designed to provide solutions for the respected tasks but there was no scalability as records used to get limited within Infrastructure for improvements in business.
Apigee: Due to its easy scalability, centralized monitoring, and integration capabilities, Apigee helped the organization plan its approach to business improvements.

2. Security

Legacy system: One of the simplest methods for authenticating users in Legacy Systems was “Basic Authentication,” where the client sends a username and password in every HTTP request. This method was Vulnerable to man-in-the-middle (MITM) attacks if not combined with HTTPS. Credentials are exposed on each request.

Apigee: Using Apigee, the organization can quickly implement modern security features like OAuth, API key validation, rate limiting, and threat protection (e.g., bot detection) without changing the core logic of the APIs.

3. User and Developer Experience

Legacy system: The legacy API lacks good documentation, making it harder for external developers to integrate with it. Most systems tend to have a SOAP-based communication format.
Apigee: Apigee provides a built-in API portal, automatic API documentation, and testing tools, improving the overall developer experience and adoption of the APIs so that integration with other tools can be easy and seamless with modern standards.

There are now multiple ways to migrate data from legacy to modern systems, which are listed below.

1. Big Bang Migration
2. Phased Migration
3. Parallel Migration
4. Pilot Migration
5. Hybrid Migration
and more…

Although these things are known to the legacy system owners, they are very selective and picky when finalizing a migration plan. They are only aware of the short-term goal, i.e., to get the code up and running in production. Because when we are speaking of legacy systems, all there is left is code and a sigh of relief that it is still up and running.  For most systems, there is no documentation, code history, revisions, etc., and that’s why it could fail on a large scale if something goes wrong.

I have found some points that need to be ensured before finalizing the process of migrating from legacy systems to modern systems.

1. Research and Analysis

We need to understand the motives behind the development of the Legacy system since there is no or insufficient documentation.

In the study, we can plan to gather historical data to understand the system’s behavior. We need to dig deeper to find something that could help us understand the system better.

2. Team Management

After studying the system, we can estimate the team size and resource management. Such systems are way older when it comes to the tech on which they are running. So, it is hard to gather resources with such outdated skills. In that case, management can cross-skill existing resources into such technologies.

I believe adding the respective numbers of junior engineers would be best, as they would get exposure to challenges, which can help them improve their skills.

3. Tool to Capture Raw Logs

Analyzing the raw logs can talk more about the system, as this is the way communication is happening to complete each task requested by the system. By breaking down the data into layman’s language, understanding at what time requests are high by timestamps,

What parameter data consists of, and by such information, we can tell system behavior and plan properly.

4. Presentation of the Logs

Sometimes we may need to present the case study to high-level management before proceeding with the plan. So to simplify the presentation, we can use tools like Datadog and Splunk to get data in a format such as tabular, graphic, etc. so that other team members can understand.

5. Replicate the Architect with Proper Functionality

This is the most important part. End-to-end development is the only solution for smooth migration activity. We need to ensure standards here, such as maintaining core functionality, risk management, conveying data pattern changes to other associated clients, and ensuring user access, business processes, etc. The point 1 study can help us understand the behavior of systems to check on what modern tech we can land our migration on.

We can implement and plan using one of the migration methods I mentioned above in the blog.

6. End-to-end Testing

Once the legacy system is replicated to Modern Tech, we need to ensure that we have a User Acceptance Testing (UAT) environment to perform the system testing. It could be challenging if the respective legacy systems didn’t have a testing environment back in the day. We may need to call mock backend URLs to mock the behavior of services.

7. Before Moving to Production, do Pre-production Testing Properly

Only after successful UAT testing one can ensure the functionality and may think to move changes to production hassle-free. However, some points must be ensured, such as following standards and maintaining documentation. In standards, we need to ensure that no risk may lead to the failure of services in modern technology and that they are compatible properly.

In the documentation, we need to ensure that all service flows are appropriately documented and that testing is done according to the requirement gathering.

Legacy systems and their workings are among the most complex and time-consuming topics. But to make the job easier, one must put effort into it.

Understanding Unit Testing

Unit testing is a crucial aspect of software development, especially in complex applications like Android apps. It involves testing individual units of code, such as methods or classes, in isolation. This ensures the correctness of each component, leading to a more robust and reliable application.

Why Unit Testing in MVVM?

The Model-View-ViewModel (MVVM) architectural pattern is widely adopted in Android app development. It separates the application into three distinct layers:

• Model: Handles data logic and interacts with data sources.
• View: Responsible for the UI and user interactions.
• ViewModel: Acts as a bridge between the View and Model, providing data and handling UI logic.

Unit testing each layer in an MVVM architecture offers numerous benefits:

• Early Bug Detection: Identify and fix issues before they propagate to other parts of the app.
• Improved Code Quality: Write cleaner, more concise, and maintainable code.
• Accelerated Development: Refactor code and add new features with confidence.
• Enhanced Collaboration: Maintain consistent code quality across the team.

Setting Up the Environment

1. Android Studio: Ensure you have the latest version installed.
2. Testing Framework: Add the necessary testing framework to your app/build.gradle file:
   

   testImplementation 'junit:junit:4.13.2'
   androidTestImplementation 'androidx.test.ext:junit:1.1.5'
   androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'

3. Testing Library: Consider using a testing library like Mockito or MockK to create mock objects for testing dependencies.

Unit Testing ViewModels

1. Create a Test Class: Create a separate test class for each ViewModel you want to test.
2. Set Up Test Dependencies: Use dependency injection frameworks like Dagger Hilt or Koin to inject dependencies into your ViewModel. For testing, use mock objects to simulate the behavior of these dependencies.
3. Write Test Cases: Write comprehensive test cases covering various scenarios:

• Input Validation: Test how the ViewModel handles invalid input.
• Data Transformation: Test how the ViewModel transforms data from the Model.
• UI Updates: Test how the ViewModel updates the UI through LiveData or StateFlow.
• Error Handling: Test how the ViewModel handles errors and exceptions.

Example:

@RunWith(AndroidJUnit4::class)
class MyViewModelTest {

    @Test
    fun `should update the UI when data is fetched successfully`() {
        // ... (Arrange)
        val viewModel = MyViewModel(mockRepository)

        // ... (Act)
        viewModel.fetchData()

        // ... (Assert)
        viewModel.uiState.observeForever { uiState ->
            assertThat(uiState.isLoading).isFalse()
            assertThat(uiState.error).isNull()
            assertThat(uiState.data).isEqualTo(expectedData)
        }
    }
}

Unit Testing Repositories

1. Create Test Classes: Create separate test classes for each Repository class.
2. Set Up Test Dependencies: Use dependency injection to inject dependencies into your Repository. For testing, use mock objects to simulate the behavior of data sources like databases or network APIs.
3. Write Test Cases: Write test cases to cover:

• Data Fetching: Test how the Repository fetches data from remote or local sources.
• Data Storage: Test how the Repository stores and retrieves data.
• Data Manipulation: Test how the Repository processes and transforms data.
• Error Handling: Test how the Repository handles errors and exceptions.

Example:

@RunWith(AndroidJUnit4::class)
class MyRepositoryTest {

    @Test
    fun `should fetch data from remote source successfully`() {
        // ... (Arrange)
        val mockApi = mock(MyApi::class.java)
        val repository = MyRepository(mockApi)

        // ... (Act)
        repository.fetchData()

        // ... (Assert)
        verify(mockApi).fetchData()
    }
}

Implementing SonarQube

SonarQube is a powerful tool for code quality and security analysis. Here’s a detailed guide on how to integrate SonarQube with your Android project:

1. Set Up SonarQube Server:

• Install SonarQube Server: Download and install the SonarQube server on your machine or a server.
• Configure SonarQube: Configure the server with database settings, user authentication, and other necessary parameters.
• Start SonarQube Server: Start the SonarQube server.

2. Configure SonarQube Scanner:

• Install SonarQube Scanner: Download and install the SonarQube Scanner.
• Configure Scanner Properties: Create a sonar-scanner.properties file in your project’s root directory and configure the following properties:
  

  sonar.host.url=http://localhost:9000
  sonar.login=your_sonar_login
  sonar.password=your_sonar_password
  sonar.projectKey=my-android-project
  sonar.projectName=My Android Project
  sonar.sources=src/main/java
  sonar.java.binaries=build/intermediates/javac/release/classes

3. Integrate SonarQube with Your Build Process:

• Gradle: Add the SonarQube Gradle plugin to your build.gradle file:
  

  plugins {
      id 'org.sonarsource.scanner-gradle' version '3.3'
  }

  Configure the plugin with your SonarQube server URL and authentication token.

• Maven: Add the SonarQube Maven plugin to your pom.xml file. Configure the plugin with your SonarQube server URL and authentication token.

4. Run SonarQube Analysis:

• Execute the SonarQube analysis using the SonarQube Scanner. This can be done manually or integrated into your CI/CD pipeline.

5. Analyze the Results:

• Once the analysis is complete, you can view the results on the SonarQube dashboard. The dashboard provides insights into code quality, security vulnerabilities, and potential improvements.

Implementing Test Coverage with Bitrise

Test coverage measures the percentage of your code that is covered by tests. It’s a crucial metric to assess the quality of your test suite. Here’s how to measure test coverage with Bitrise:

1. Configure Code Coverage Tool: Add a code coverage tool like JaCoCo to your project. Configure it to generate coverage reports in a suitable format (e.g., XML).
2. Add Code Coverage Step to Bitrise Workflow: Add a step to your Bitrise Workflow to generate the code coverage report. This step should execute your tests and generate the report.
3. Upload Coverage Report to SonarQube: Add a step to upload the generated code coverage report to SonarQube. This will allow SonarQube to analyze the report and display the coverage metrics.

Best Practices for Unit Testing

• Write Clear and Concise Tests: Use descriptive names for test methods and variables.
• Test Edge Cases: Consider testing scenarios with invalid input, empty data, or network errors.
• Use a Testing Framework: A testing framework like JUnit provides a structured way to write and run tests.
• Leverage Mocking: Use mocking frameworks like Mockito or MockK to isolate units of code and control their behavior.
• Automate Testing: Integrate unit tests into your CI/CD pipeline to ensure code quality.
• Review and Refactor Tests: Regularly review and refactor your tests to keep them up-to-date and maintainable.

By following these guidelines and incorporating unit testing into your development process, you can significantly improve the quality and reliability of your Android apps.