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.

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.

Quantum computing is rapidly transitioning from theory to reality, using the principles of quantum mechanics to achieve computational power far beyond traditional computers. Imagine upgrading from a bicycle to a spaceship—quantum computers can solve complex problems at extraordinary speeds. However, this leap in computing power poses significant challenges, particularly for cybersecurity, which forms the backbone of data protection in our digital world.

The Quantum Revolution and its Impact on CyberSecurity

Today’s cybersecurity heavily relies on encryption,  converting data into secret codes to protect sensitive information like passwords, financial data, and emails. Modern encryption relies on complex mathematical problems that even the fastest supercomputers would take thousands of years to solve. However, quantum computers could change this model. Cryptography operates on the assumption that classical computers cannot break their codes. With their immense power, quantum computers may be able to crack these algorithms in hours or even minutes. This possibility is alarming, as it could make current encryption techniques obsolete, putting businesses, governments, and individuals at risk.

This post is the third in a series of four about estimating project hours. Part 1: Sandbagging & Lowballing Part 2: Dependencies & Creep Part 3: Assumptions & Uncertainty Part 4: The Emotional Conclusion

The emotional finale is here! Don’t worry, this isn’t about curling up in a ball and crying – we’ve already done that. This final installment of my series on project estimating is all about navigating the emotions of everyone involved and trying to avoid frustration.

If you’ve been following this blog series on project estimations, you’ve probably noticed one key theme: People. Estimating isn’t just a numbers game, it’s full of opinions and feelings. So, let’s dive into how emotions can sway our final estimates!

Partners or Opponents

There are many battle lines drawn when estimating larger projects.

• Leadership vs Sales Team
• Sales Team vs Project Team
• Agency vs Client
• Agency Bid vs Competing Bids
• Quality Focus vs Time/Financial Constraints
• Us vs Ourselves

It’s no wonder we all feel like we’re up against the ropes! Every round brings new threats – real or imagined. How will they react to the estimate? What will they consider an acceptable range?

To make matters worse, everyone involved brings their own personality into the ring. Some see negotiations as a game to be won. Others approach it as a collaboration toward shared goals. And then there’s the age-old playbook: start high, counter low, meet in the middle.

Planning the Attack with Empathy

Feeling pummeled while estimating? Tempted to throw in the towel? Don’t! The best estimates aren’t decided in the ring – they’re made by stepping back, planning, and understanding the perspectives of your partners.

Empathy is your secret weapon. It’s a tactical advantage. When you understand what motivates others, new paths emerge to meet eye to eye.

How do you wield empathy? By asking real questions. Don’t steer people to what you want, instead ask open-ended questions that encourage discussion. How does the budgeting process work? How will you report on the project? How do you handle unexpected changes? Even “this-or-that” questions can help: Do you prioritize on-time delivery or staying on-budget? Do you want quality, or just want to get it done? Let them be heard.

Studying the Playing Field

The good news? Things tend to get smoother over time. If you’ve gone a few rounds with the same group, you already know some of their preferences. But when it’s your first matchup, you’ve got to learn their style quickly.

With answers in hand, it’s time to plan your strategy. But check your ego – this still isn’t about you. It’s about finding the sweet spot where both sides feel like winners. Strategize for the win-win.

If they have a North Star, then determine what it takes to follow that journey. If budget is their weak point, consider ways to creatively trim without losing the project’s intent. If the timeline is the pressure point, then consider simplifying and phasing out the approach to deliver quick wins sooner.

Becoming a Champion

Victory isn’t about knocking your opponent out. It’s about both sides entering the ring as a team and excited to start. The client needs to feel understood, with clear expectations for the project. The agency needs confidence that it won’t constantly trade quality to remain profitable.

Things happen though. It’s inevitable. As in life, projects are imperfect. Things will go off-script. Partnerships are tested when hit hard by the unexpected. Were there contingency plans? Were changes handled properly?

True champions rise to the occasion. Even if the result is no longer ideal, your empathy and tactical questions can guide everyone toward the next best outcome.

Conclusion

Emotional tension almost always comes from a lack of communication. Expectations were not aligned and people felt unheard.

Everyone is different. Personalities will either mesh or clash, but recognizing this helps you bob and weave with precision.

Focus on partnership. Ask questions that foster understanding, and strategize to find a win for both sides. With empathy, clear communication, and a plan for the unexpected, you’ll look like a champion – even when things don’t go perfectly.

……

If you are looking for a sparring partner who can bring out the best in your team, reach out to your Perficient account manager or use our contact form to begin a conversation.

This post is the third in a series of four about estimating project hours. Part 1: Sandbagging & Lowballing Part 2: Dependencies & Creep Part 3: Assumptions & Uncertainty Part 4: The Emotional Conclusion

Healthcare leaders are engaging us in a variety of discussions to explore intelligent automation’s role for complex business challenges, ranging from efforts to enhance consumer trust and use artificial intelligence (AI) in effective ways, to navigating change that comes with prior authorization mandates. This series shares key insights coming from those discussions.  

As the saying goes, diamonds are made under pressure, and the most impactful opportunities are often those that challenge leaders the most. 

Prior Authorization, In a Nutshell

The CMS Prior Authorization mandate, which goes into effect on January 1, 2026, aims to reduce guesswork for healthcare consumers and the administrative burden on care teams, and to improve patient/member care by streamlining processes and enhancing the exchange of health information. 

Enabling prior authorization through API development is a good start; however, APIs are not a comprehensive solution. Rather, the introduction of multiple third-party APIs creates new processes and steps, often prompting manual follow-ups to track and connect data gathered from multiple sources. In addition, these new data points require new data models and methods to handle patient data.  

To address these inherent challenges, healthcare leaders are prioritizing investments in interoperability and automation technologies. 

Intelligent Automation Supports Prior Authorization and Business Efficiencies

True trust-enhancing transparency can be unlocked through intelligent automation. This is especially true as low-code, more-approachable AI, machine learning (ML) and Generative AI (GenAI) capabilities enter the mix. 

Intelligent automation connects digital process automation (DPA), robotic process automation (RPA), and artificial intelligence (AI) to deliver efficient and intelligent processes and align all aspects of your organization with the vision of constant process improvement, technological integration, and increasing consumer value. 

Although DPA, RPA and AI don’t make final decisions, they can streamline and leverage information, so the right decision gets made. Health insurers are always seeking access to actionable information about their members while adhering to data privacy laws and regulations. 

Getting to that actionable data requires multiple considerations: 

• Using best practices to assemble and curate the right data fields for any given use case 

• A continuous process of identifying and resolving issues in core systems 

• Appropriate environments in which to store data to maintain its integrity, security, and accessibility 

• Only then can you effectively enable specific sub-functions (i.e. functions that ingest the data then act or recommend actions) to happen accurately and on time 

Streamline and Optimize Prior Auth Processes

Every step in the prior authorization process has potential for improvement using intelligent automation. It can support, enhance, and accelerate based on rules engines, event logs, decision rules, and simple automations of high-volume processes. 

These intelligent tools streamline information sharing between payers and providers, reducing the need for repeated exchanges and guesswork, enhancing clinical review, and ensuring timely, accurate decisions. 

Intelligent automation rapidly optimizes the prior authorization workflows that occur at the edge of what can conveniently and cost-effectively be managed through APIs. AI and machine learning (ML) can assist required communications, reporting, and decision flows in many ways, including: 

• Orchestration: Automate the coordination of tasks and data flow between disparate systems and stakeholders. 

• Monitoring: Continuously track the status of prior authorization requests and flag any issues or delays. 

• Standardization: Ensure consistent repeatable workflows and processes across all systems to facilitate smoother information exchange. 

YOU MAY ALSO ENJOY: Evolving Healthcare: Generative AI Strategy for Payers and Providers 

Best Practices to Transform Prior Authorization Experiences

Intelligent automation enhances and overlays existing systems, helping to accelerate the prior authorization process with greater efficiency and generating insights into any recurring root causes in process breakdowns. 

As you’re approaching your prior authorization initiatives, we recommend the following transformation best practices: 

Transformation Tip #1: Cross-Functional Feedback

Maintaining cross-functional feedback is essential to identify and address pain points effectively. Automation allows for healthcare providers to quickly identify and communicate common pain points, such as inaccurate or incomplete record keeping, avoiding common pitfalls in the prior authorization process. 

Transformation Tip #2: Measurement and Tracking

Automated processes provide valuable insights for contracting, reporting requirements, and more. By measuring and tracking these processes, efficiency, effectiveness, and consumer experience are greatly impacted. This information can be used to improve upstream messaging to patients and members about prior authorizations.  

The overlay of technology not only increases operational efficiencies, but it also provides valuable insights that can be used to improve communication and support for consumers. 

Empowering Solutions for Healthcare

We partner with healthcare leaders to optimize prior authorization experiences and drive transparent, consistent engagement with consumers.  

Interested in learning more? In a recent webinar, our experts explored how better prior authorization experiences could enhance consumer trust in healthcare. 

Discover why we’ve been trusted by the 10 largest healthcare systems and 10 largest health insurers and are consistently recognized by Modern Healthcare as a leading healthcare consulting firm. Contact us today to explore how we can help you forge better experiences and improve outcomes.

With just a cryptocurrency wallet, cybercriminals can now execute complex cyberattacks without advanced technical knowledge or sophisticated software. This alarming trend is a byproduct of the growing popularity of cloud computing and the “as-a-service” model, where services like infrastructure, recovery, and cybersecurity are now accessible on demand. Known as “cybercrime-as-a-service” (CaaS), this model has modified cyberattacks by lowering barriers to entry, turning the digital world into a profitable and accessible cybercrime ecosystem.

What is Cybercrime-as-a-Service?

Cybercrime-as-a-service refers to a business model where organized crime syndicates and threat actors offer specialized hacking capabilities for sale. These services are available through dark web marketplaces, exclusive forums, and even encrypted messaging apps like Telegram. Vendors provide cyberattack tools and expertise to customers, who pay in cryptocurrency to preserve anonymity, creating a secure transaction system and enabling even novice hackers to carry out sophisticated attacks. This ecosystem has contributed over $1.6 billion in annual revenue to the global cybercrime market.

Types of Cybercrime-as-a-Service

Cybercrime-as-a-service encompasses a variety of criminal offerings, each targeting specific objectives:

1. Ransomware-as-a-Service (RaaS)
   RaaS is one of the most profitable CaaS segments, where attackers lease ransomware software to clients. The client executes an attack by encrypting data on target systems and demanding a ransom for decryption. Often, the “service provider” receives a percentage of the ransom, making this a lucrative model for cybercriminals.
2. Phishing-as-a-Service
   Phishing-as-a-Service (PhaaS) platforms offer ready-made phishing kits, targeting email, social media, or other communication channels. These kits typically come with templates, scripts, and customization options, enabling even non-technical users to launch sophisticated phishing campaigns that trick victims into revealing sensitive information.
3. DDoS-as-a-Service
   Distributed Denial of Service (DDoS)-as-a-Service allows individuals to hire attackers who overload a target’s network, effectively shutting down websites or services. This service is frequently used to harm businesses by disrupting their operations or to demand ransom payments.
4. Exploit-as-a-Service
   In Exploit-as-a-Service, vendors provide exploits that target specific software vulnerabilities. These services are typically marketed to attackers who want to breach particular networks or gain unauthorized access to secure systems, often for data theft or further exploitation.

The availability of these services has transformed the underground market into a virtual “one-stop shop” for digital crime, where criminals can easily acquire all the necessary resources.

Role of the Dark Web in Cybercrime-as-a-Service

The Dark Web, a hidden layer of the internet, enables users to operate anonymously and has become a hub for illegal activity. Cybercriminals use the Dark Web to connect with vendors, buy or sell stolen credentials, and procure hacking tools or services. This anonymity adds to the security of transactions, creating a low-risk, high-reward marketplace for would-be attackers.

Defending Against Cybercrime-as-a-Service

Unlike specific cyberattacks, CaaS represents a business model, complicating efforts to counteract it. To defend against this growing threat, organizations must strengthen their cybersecurity defenses with proactive and continuous monitoring. While reactive tools, like traditional antivirus software, may catch known threats, modern cybersecurity demands adaptive solutions.

Many companies now offer cybersecurity as a service, including IBM, Palo Alto Networks, Cisco Secure, Fortinet, and Trellix. These providers combine cutting-edge technology with human expertise to detect, monitor, and respond to cyber threats. Leveraging machine learning, threat intelligence, and expert analysts, cybersecurity services are now more efficient at identifying and neutralizing potential attacks early—often before they can cause any significant damage.

Conclusion

Cybercrime-as-a-service represents a dark shift in how cyberattacks are conducted, making hacking tools and expertise widely available to criminals of all levels. This calls for a proactive defense, as businesses and individuals are increasingly at risk. With comprehensive cybersecurity as a service solutions, organizations can stay vigilant, constantly improving defenses to keep their systems secure in a changing digital environment. By staying one step ahead of cybercriminals, we can begin to mitigate the impacts of this growing cybercrime economy.