One of the powers of Data Cloud is the way that it can unlock ‘trapped’ data.  Let’s say that you had a large set of data like VIN (Vehicle Identification Number) numbers that you did not want to put in your Sales Cloud because of row storage limits, but you really wanted to use that data as part of a new Agent you were building in Agentforce.   This is an example of where Data Cloud will give you a place to store those VIN numbers and make it very easy to query and leverage that VIN data through an Action tied to your Agent.

The amazing part of this is that this can all be done now with ‘No Code’.  We will leverage Flows in Sales Cloud and the ability of an Action in Agentforce to use that Data Cloud data retrieved through a Flow.

First a couple of demo screens from my Agent…

1. I want to ask my Agent for details about a specific VIN like Description, Make, Model and Year.  That data is stored in Data Cloud.  So I ‘prompt’ my Agent with this sentence:  ‘Can you get me the details for a VIN?’
   
2. My agent recognizes what I am asking for and gives me some properly formatted responses so I can ask for the details about my VIN number.
   
3. So now I type in my response with my VIN as it suggested.
   
4. Wow!  I got back the details about my VIN number that are stored in Data Cloud.  These details included a Description, Make, Model and Year.
   

Now, how did Agentforce do that?

It is really pretty amazing how much of the above demo is simply Agentforce doing ‘what it does’.  My extra part is adding a new Action to extend my Agent from Data Cloud.

So let’s go through those pieces…

1. We first need to get the data into Data Cloud into a ‘Data Model Object’.  It cannot just get to a ‘Data Lake Object’ (DLO), but has to also be mapped onto a ‘Data Model Object’ (DMO).   The ‘GetRecords’ in the Flow that we will use later cannot query a DLO, but only a DMO.  This will involve creating a Data Lake Object, a Data Model Object and the associated Data Stream for pulling the VIN data in.
   1. Here is one way to move data into Data Cloud from Sales Cloud using ‘No Code’, but with our use case above we would probably be using a noETL option to load such a large dataset into Data Cloud from something like Snowflake or Databricks.
   2. Here is my DMO and the associated VIN data being shown in the ‘Data Explorer’.
      
   3. Now the data is loaded in Data Cloud and is ready to be used.
2. We need to create a new Action with proper Instructions that is tied to our Flow that will get the data from Data Cloud.
3. Create a new ‘Start from Scratch’, ‘Autolaunched Flow (No Trigger)’.
   
4. We can build a Flow as simple as this to make the data available for our Agent.
   
5. The GetRecords can query our Data Cloud DMO that we created above by configuring it like so…
   
   
6. But where did that ‘VIN_Number’ variable come from?  We do need to create a variable in the Flow that is needed for ‘Input’ like this with the ‘Available for input’ being checked…
   
7. We will tie that Input variable to out Action later.
8. Now we will do a ‘Decision’ to make sure our ‘GetRecords’ actual found something…
   
9. Now I need to create some ‘Output’ variables to return this data back to my Action.  Make sure the ‘Available for Output’ checkbox gets selected for each variable.
   
   
10. Finally in an ‘Assignment’ set all of these Output variables…
    
11. I did try to work out some formatting issues as I was working through this.  My ‘Year’ kept coming back as 2,019 with a comma in it since it was a number.  So one solution I did was to create a formula in the flow to convert it to being text.   That seemed to work fine…
    
12. I also tried originally to have my Output variable be a custom object in Sales Cloud so that I did not have so many output variables.  That did not work well because when Agentforce was trying to show the output of an Object like that it needed a Salesforce ID to display it.  So I just went back to individual variables which worked well.
13. Go ahead and Save and Activate your Flow.
14. Now we need to tie this Flow to an Action in Agentforce.
15. In Setup go to ‘Agent Actions’ and choose ‘New Agent Action’.
    1. Choose ‘Flow’ for the ‘Reference Action Type’
    2. Choose your ‘Reference Action’ which is the new Flow you just created.
    3. Give it an understandable ‘Agent Action Label’ and ‘Agent Action API Name’
    4. Click on ‘Next’
       
    5. Now the ‘Input’ and ‘Output’ variables from our Flow come into play.  We need to configure our new ‘Agent Action’ with Instructions at the ‘Action’ level and also Instructions for each ‘Input’ and ‘Output’ item.   It can look like this…
       
       
       
    6. Make sure you select ‘Require Input’ and ‘Collect data from user’ for the Input Variable.
    7. Make sure you select ‘Show in conversation’ for the Output variable.
    8. Click on Finish
16. Now we need to add our new Action to the Agent.
    1. In Setup go to ‘Agents’
    2. In the drop-down at the end of the row for your ‘Agent Name’ click on ‘Open in Builder’
       
    3. Deactivate your Agent with the button in the top right corner.
       
    4. Then click on ‘New’, ‘New Topic’ under the Topic Details section because we have to add a new Topic.
    5. Fill out the ‘Create a Topic’ page…
       
       
    6. Click on Next
    7. Select your Flow and click on Finish
       
    8. Now we need to add the new Topic to the Agent.  Again make sure your Agent is de-activated.
    9. On the ‘Topic Details’ tab click on the ‘New’ drop-down and select ‘Add from Asset Library’
    10. Select the Topic you just created and click on ‘Finish’
    11. Do some testing in ‘Agent Builder’ to make sure your Agent is doing what you want it to do.
    12. Activate your Agent.

All of that above was ‘No Code’!

While all of that above was a good number of steps, there really is not a ton of work and there was ‘No Code’.  Agent Builder and building Actions and Topics is new, but it really is pretty simple configuration.  I think the hardest part will be learning how to write really great ‘Instructions’ for these new Topics and Actions so they are leveraged appropriately when the Agent runs.  The skill set related to being a ‘Prompt Engineer’ is become more important every day.

Leveraging Data Cloud data is not required for your first Agentforce implementation.  Data Cloud has to exist but you would not have to be pulling data out of Data Cloud on day 1.  The above solution could be just as well implemented by making an API call through Apex exposing the same VIN data.

Hopefully the above shows you that Salesforce has put together the pieces to make this easy for you to do.

If you are brainstorming about use cases for Agentforce, please read on with this blog post from my colleague Darshan Kukde!

If you want a demo of this in action or want to go deeper please reach out and connect!

As a Databricks Champion working for Perficient’s Data Solutions team, I spend most of my time installing and managing Databricks on Azure and AWS. The decision on which cloud provider to use is typically outside my scope since its already been made by the organization. However, there are occasions where the client is using both hyperscalers already or they have not yet moved to the cloud. It heloful in those situations to be able to advise the client on the advantages and disadvantages of one platform over another from a Databricks perspective. I’m aware that I am skipping over Google Cloud Platform, but tI want to focus on the questions I am actually asked rather than questions that could be asked. I am also not advocating for one cloud provider over another. I am limiting myself to the question of AWS versus Azure from a Databricks perspective.

Advantages of Databricks on Azure

Databricks is a first-party service on Azure, which means it enjoys deep integration with the Microsoft ecosystem. Identity management in Databricks is integrated with Azure Active Directory (AAD) authentication, which can save time and effort in an area that I have found can be difficult in large, regulated organizations. The same is true of the deep integration with networking, Private Links and Azure’s compliance frameworks. The value of this integration is amplified if the client also uses some combination of Azure Data Lake Storage (ADLS), Azure Synapse Analytics, or Power BI. The Databricks integration with these products on Azure is seamless. FinOps gets a boost in Azure for companies with an Azure Consumption Commitment (MACC) as Databricks’ costs can be applied against that number. On the topic of cost management, Azure spot VMs can be used in some situations to reduce cost. Azure Databricks and ADLS Gen2/Blob Storage are optimized for high throughput, which reduces latency and improves I/O performance.

Disadvantages of Databricks in Azure

Databricks and Azure are tightly integrated when you are staying within the Microsoft ecosystem. Azure Databricks uses Azure AD, role-based access control (RBAC), and network security groups (NSGs). These dependencies will require additional and sometime complex configurations areIf you want to use take a hybrid or multi-cloud approach. Some of these advanced networking configurations require enterprise licensing or additional manual configurations in the Azure Marketplace.

Advantages of Databricks on AWS

Azure is focused on seamless integration with Databricks under the assumption that the organization is a committed Microsoft shop. AWS takes the approach of providing more dials to tune in exchange for greater flexibility.  Additionally, AWS offers a broad selection of EC2 instance types, Spot Instance options, and scalable S3 storage, which can result in better cost and performance optimization. Finally, AWS has more instance types than Azure, including more options for GPU and memory-optimized workload. AWS has a more flexible spot pricing model than Azure. VPC Peering, Transit Gateway, and a more granular IAM security controls than Azure make AWS a stronger choice for organizations with advanced security requirement and/or organizations committed to multi-cloud or hybrid Databricks deployments. Many advanced features are released in AWS before Azure. Photon is a good example.

Disadvantages of Databricks in AWS

AWS charges for cross-region data transfers, and S3 read/write operations can become costly, especially for data-intensive workloads. This can result in higher networking costs. AWS also has weaker native BI Integration when you compare Tableau on AWS versus PowerBI on Azure.

Conclusion

Databricks is a strong cloud database on all the major cloud providers. If your organization has already has committed to a particular cloud provider, Databricks will work. However, I have been asked about the differences between AWS and Azure enough that I felt I wanted to get all of my thoughts down in one place. Also, I recommend a multi-cloud strategy for most of our client organizations for Disaster Recovery and Business Continuity purposes.

Contact us to discuss the pros and cons of your planned or proposed Databricks implementation so we can help you navigate the technical complexities that affect security, cost and BI integrations.

Generative AI. The phrase itself conjures images of intricate artwork, realistic text, and even code springing forth from the digital ether. It’s not just hype; generative AI is rapidly transforming industries, offering unprecedented potential for innovation and efficiency. Unlike traditional AI models that primarily classify or predict, generative AI creates new content, from images and text to music, code, and even 3D models. This capability is unlocking a wave of use cases across diverse sectors, promising to reshape how we work, create, and interact with the world around us. This blog delves into the transformative power of generative AI, exploring its applications across multiple industries, examining its implementation, weighing its pros and cons, and ultimately, assessing its profound impact on the future. 

What is Generative AI? 

At its core, generative AI leverages sophisticated machine learning models, often based on deep learning architectures like Generative Adversarial Networks (GANs) and transformers, to learn the underlying patterns and structures of input data. Once trained, these models can generate new data that shares similar characteristics with the training data. Think of it like an artist studying the works of the masters. After absorbing the techniques and styles, they can create original pieces that reflect those influences. Generative AI models operate in a similar fashion, learning from vast datasets to produce novel outputs. 

Use Cases Across Industries: 

Let’s explore the tangible impact of generative AI across several key industries: 

1. Healthcare:

• Drug Discovery: Generative AI can accelerate the drug discovery process by generating novel molecules with desired properties, predicting their efficacy, and optimizing their design. This can drastically reduce the time and cost associated with bringing new drugs to market. 

• Personalized Medicine: By analyzing patient data, generative AI can create personalized treatment plans, predict disease risk, and even generate customized prosthetics or implants. 

• Medical Imaging: Generative models can enhance medical images, improve diagnostic accuracy, and even generate synthetic data for training other AI models, addressing the challenge of limited labeled data. 

• Virtual Assistants: AI-powered chatbots can provide personalized health advice, answer patient queries, and even monitor patients remotely. 

2. Creative Industries (Art, Music, and Entertainment):

• Content Creation: Generative AI can create stunning visuals, write compelling stories, compose original music, and even generate realistic voiceovers. This opens up new avenues for artists, writers, musicians, and filmmakers. 

• Game Development: Generative AI can be used to create realistic game environments, generate character designs, and even develop dynamic storylines, enhancing the player experience. 

• Marketing and Advertising: AI-powered tools can generate personalized marketing content, create targeted ads, and even design unique product packaging. 

• Fashion Design: Generative AI can create new fashion designs, predict trends, and even personalize clothing recommendations. 

3. Manufacturing:

• Product Design: Generative design tools can explore numerous design options, optimizing for factors like performance, cost, and manufacturability. This can lead to innovative and more efficient products. 

• Predictive Maintenance: By analyzing sensor data, generative AI can predict equipment failures and generate optimal maintenance schedules, minimizing downtime and improving operational efficiency. 

• Quality Control: Generative models can be used to identify defects in manufactured products, improving quality control and reducing waste. 

• Supply Chain Optimization: AI-powered tools can analyze supply chain data, predict demand fluctuations, and optimize logistics, improving efficiency and reducing costs. 

4. Finance:

• Fraud Detection: Generative AI can be used to detect fraudulent transactions by identifying patterns and anomalies that are difficult for humans to spot. 

• Risk Management: AI models can assess financial risk, predict market trends, and generate personalized investment recommendations. 

• Algorithmic Trading: Generative AI can be used to develop sophisticated trading algorithms that can adapt to changing market conditions. 

• Customer Service: AI-powered chatbots can provide personalized financial advice, answer customer queries, and even help with account management. 

5. Software Development:

• Code Generation: Generative AI can assist developers by generating code snippets, automating repetitive tasks, and even creating entire programs. This can significantly increase developer productivity. 

• Bug Detection: AI models can be used to identify potential bugs in code, improving software quality and reducing development time. 

• Automated Testing: Generative AI can create test cases and generate realistic test data, simplifying the testing process. 

• Documentation Generation: AI can automatically generate documentation for code, making it easier for developers to understand and maintain software. 

Implementing Generative AI: 

Implementing generative AI is not simply a matter of plugging in a pre-trained model. It requires a strategic approach, encompassing data collection and preparation, model selection and training, and deployment and monitoring. 

• Data is King: Generative AI models thrive on data. The quality and quantity of training data are crucial for the model’s performance. Data collection, cleaning, and preprocessing are essential steps. 

• Model Selection: Choosing the right model architecture is critical. GANs, transformers, and variational autoencoders (VAEs) are just a few examples, each with its strengths and weaknesses. The choice depends on the specific application and the available data. 

• Training and Tuning: Training a generative model requires significant computational resources and expertise. Fine-tuning the model’s parameters is essential to achieve optimal performance. 

• Deployment and Monitoring: Once trained, the model needs to be deployed in a production environment. Continuous monitoring is essential to ensure the model’s performance and identify any potential issues. This often involves setting up feedback loops to refine the model over time. 

Pros of Generative AI: 

• Innovation and Creativity: Generative AI can unlock new levels of creativity and innovation, enabling the creation of novel products, services, and experiences. 

• Increased Efficiency: Automation through generative AI can streamline processes, reduce costs, and improve efficiency across various industries. 

• Personalization: Generative AI can personalize experiences, tailoring products, services, and content to individual needs and preferences. 

• Problem Solving: Generative AI can help solve complex problems by generating new solutions and exploring different possibilities. 

• Accelerated Development: In areas like drug discovery and software development, generative AI can significantly accelerate research and development cycles. 

Cons of Generative AI: 

• Bias and Fairness: Generative models can inherit biases from the training data, leading to unfair or discriminatory outputs. Addressing bias is a critical challenge. 

• Ethical Concerns: The ability of generative AI to create realistic fake content raises ethical concerns about misinformation, deepfakes, and intellectual property. 

• Computational Resources: Training large generative models requires significant computational resources, making it accessible primarily to organizations with substantial computing power. 

• Explainability: Understanding how a generative model arrives at a particular output can be challenging, making it difficult to interpret and trust the results. This lack of explainability can be a barrier to adoption in certain fields. 

• Job Displacement: As generative AI automates tasks, there are concerns about potential job displacement in certain industries. However, it’s also argued that it will create new job opportunities in other areas. 

Addressing the Challenges: 

While the challenges are real, they are not insurmountable. Researchers are actively working on addressing bias, improving explainability, and developing more efficient training methods. Ethical guidelines and regulations are also being developed to ensure the responsible use of generative AI. 

The Future of Generative AI: 

The future of generative AI is bright. As the technology continues to evolve, we can expect to see even more groundbreaking applications across industries. Generative AI is poised to revolutionize how we create, innovate, and interact with the world around us. We are only at the beginning of this transformative journey, and the potential is immense. Imagine personalized education tailored to each student’s learning style, on-demand creation of any product imaginable, or even AI-powered scientific breakthroughs that solve some of humanity’s greatest challenges. 

Conclusion: 

Generative AI is not just a technological marvel; it’s a powerful tool with the potential to reshape industries and redefine possibilities. While challenges remain, the benefits are undeniable. By understanding the capabilities and limitations of generative AI, we can harness its power to create a more innovative, efficient, and personalized future. As we move forward, it’s crucial to prioritize ethical considerations, address biases, and ensure that this powerful technology is used for the benefit of all. The generative AI revolution is underway, and its impact will continue to unfold in the years to come. It’s a space to watch closely, as it promises to transform the world as we know it. 

So, DeepSeek just dropped their latest AI models, and while it’s exciting, there are some cautions to consider. Because of the US export controls around advanced hardware, DeepSeek has been operating under a set of unique constraints that have forced them to get creative in their approach. This creativity seems to have yielded real progress in reducing the amount of hardware required for training high-end models in reasonable timeframes and for inferencing off those same models. If reality bears out the claims, this could be a sea change in the monetary and environmental costs of training and hosting LLMs.

In addition to the increased efficiency, DeepSeek’s R1 model is continuing to swell the innovation curve around reasoning models. Models that follow this emerging chain of thought paradigm in their responses, providing an explanation of their thinking first and then summarizing into an answer, are providing a step change in response quality. Especially when paired with RAG and a library of tools or actions in an agentic framework, baking this emerging pattern into the models instead of including it in the prompt is a serious innovation. We’re going to see even more open-source model vendors follow OpenAI and DeepSeek in this.

Key Considerations

One of the key factors in considering the adoption of DeepSeek models will be data residency requirements for your business. For now, self-managed private hosting is the only option for maintaining full US, EU, or UK data residency with these new DeepSeek models (the most common needs for our clients). The same export restrictions limiting the hardware available to DeepSeek have also prevented OpenAI from offering their full services with comprehensive Chinese data residency. This makes DeepSeek a compelling offering for businesses needing an option within China. It’s yet to be seen if the hyperscalers or other providers will offer DeepSeek models on their platforms (Before I managed to get his published, Microsoft made a move and is offering DeepSeek-R1 in Azure AI Foundry).  The good news is that the models are highly efficient, and self-image hosting is feasible and not overly expensive for inferencing with these models. The downside is managing provisioned capacity when workloads can be uneven, which is why pay-per-token models are often the most cost efficient.

We are expecting that these new models and the reduced prices associated with them will have serious downward pressure on per-token costs for other models hosted by the hyperscalers. We’ll be paying specific attention to Microsoft as they are continuing to diversify their offerings beyond OpenAI, especially with their decision to make DeepSeek-R1 available. We also expect to see US-based firms replicate DeepSeek’s successes, especially given that Hugging Face has already started work within their Open R1 project to take the research behind DeepSeek’s announcements and make it fully open source.

What to Do Now

This is a definite leap forward and progress in the direction of what we have long said is the destination—more and smaller models targeted at specific use cases. For now, when looking at our clients, we advise a healthy dose of “wait and see.” As has been the case for the last three years, this technology is evolving rapidly, and we expect there to be further developments in the near future from other vendors. Our perpetual reminder to our clients is that security and privacy always outweigh marginal cost savings in the long run.

The comprehensive FAQ from Stratechery is a great resource for more information.

Artificial Intelligence (AI) has exploded into the mainstream, largely through chatbots and agents powered by Large Language Models (LLMs). Users can now have real-time conversations with multimodal AI tools that understand your text, voice, and images – even documents! The progress has been mind blowing, and tech companies are racing to integrate AI features into their products.

AI features today are being released with obvious interfaces and promoted heavily. My prediction though is that the future of AI will increasingly lean toward hidden, unnoticeable improvements to our daily experiences.

Visible AI – Current State

In our haste to compete, most AI tools today share a similar experience: either a chatbot interface or a feature trigger. What started as fresh and magical is becoming repetitive and forced.

ChatGPT, Bard, Claude… They all share the same conversational interface, resembling many lackluster customer service chatbots. The great ones now offer multimodal capabilities like voice or video input, but the concept is the same – back-and-forth dialogue.

Meanwhile, operating systems, web browsers, word processors, and other apps are tacking on AI features. Typically, these are triggered through a cool new AI icon to generate, summarize, or improve your content.

Invisible Enhancements – Yesterday & Today

Machine Learning (ML), on the other hand, has typically been rolled out as behind-the-scenes improvements that exponentially raise user expectations. Most users don’t even realize what ML processes are at play! Nearly invisible algorithms have transformed industries.

Google revolutionized search with its deceptively simple interface – a single search box delivering surprisingly targeted results. YouTube and Netflix ushered in streaming video, but they gained more attention surrounding their advanced recommendation engines. No more wandering the aisles of the local video store and reading the back of DVD cases!

The banking industry’s automated fraud detection is another perfect example of unobtrusive features. Instead of combing through your bank statement, you are notified in real time that your bank card has been disabled and the funds returned.

AI Ubiquity – Future State

AI is not going away – it offers tremendous opportunities for both businesses and consumers. Like subscription services where businesses cut costs and increase revenue, while the consumers enjoy better experiences, convenience, and options.

However, as with subscription services (access vs ownership), there are trade-offs. AI introduces trust issues, ethical concerns, and bias. Even so, the benefits are likely to outweigh the downsides. AI will reduce cognitive load in your daily life and have a far more natural interaction with digital systems. With AI, exciting products and benefits will be introduced.

Industries like healthcare, finance, automotive, retail, and energy are already exploring AI applications. At first these will be noticeable additions, but over time, AI will become seamlessly integrated and nearly invisible.

Conclusion

There will be bumps along the way (we should learn from our past). Legal disputes and unethical practices are inevitable, but progress will continue. We’ll need to get through some of the bad to reap the benefits – in the same way that fire is crucial to society but can also be destructive – we learn from our mistakes and move forward. Human creativity and innovation have brought us this far, and now we will integrate AI to amplify our potential.

I’m excited to see what is yet to come! We humans get nervous about game-changing technologies, but history shows that we are adept at adding safeguards and correcting our course. I think we’re going to surprise ourselves.

……

If you are looking for a digital partner who is excited about the future of AI, reach out to your Perficient account manager or use our contact form to begin a conversation.

Measuring developer productivity has always been a hot topic in the tech world. From outdated metrics like lines of code written to the introduction of Generative AI (GenAI) tools, the conversation has continually evolved. Forrester’s report, Your Focus On Developer Productivity Is Killing You explores the complexities of developer productivity and its impact on innovation, offering insights into aligning metrics with business value and fostering long-term success.

Perficient’s Inclusion in Forrester’s Recent Report

Perficient is proud to announce that two of our experts, Jonathan Crockett and Elisha Goldman, were quoted in Forrester’s report, sharing their expertise on how organizations can rethink their approach to measuring and improving developer productivity. The report challenges traditional productivity metrics and emphasizes creating environments that encourage innovation and meaningful alignment with business objectives.

Jonathan Crockett, managing director of product development at Perficient, highlights the importance of a problem-solving mindset and its role in driving innovation. He stated, “We’re not order takers; we’re problem solvers, and we’re going to seek out how to solve those underlying problems. And that’s really the mindset that drives our teams forward with confidence and purpose.”

Elisha Goldman, delivery and product director at Perficient, focused on how understanding and leveraging metrics can drive success for clients. In the report, she shared, “At Perficient, folks are responsible for understanding what metrics are there, what their clients are looking for, and trying to figure out a way that they can (improve). How can we use that to think about longer-term roadmaps and visions?”

Addressing Developer Productivity in the Age of GenAI

The rise of Generative AI tools has sparked a renewed focus on developer productivity. While these tools hold immense potential, the report emphasizes the risks of relying on flawed or superficial metrics. As Jonathan and Elisha’s quotes suggest, the key to unlocking true innovation lies in aligning teams around meaningful goals and providing developers with the autonomy and clarity to solve real-world problems.

Organizations must look beyond surface-level measures and adopt strategies that prioritize collaboration, creativity, and long-term value. By doing so, they can enable their engineering teams to deliver impactful results that resonate with both business leaders and end users.

Learn More

Forrester’s report offers a fresh perspective on developer productivity, challenging conventional wisdom and highlighting innovative approaches to aligning business objectives with engineering efforts.

Discover how Perficient’s experts are helping organizations navigate the evolving landscape of developer productivity and innovation by reading the full report: Your Focus On Developer Productivity Is Killing You. Available to Forrester subscribers or for purchase.

Empower Your Engineering Teams with Perficient

Our team of experts is committed to guiding organizations toward meaningful transformation, helping them adopt forward-thinking strategies that align engineering efforts with business goals. Interested in learning more? Contact us to explore how we can help you achieve your digital transformation objectives today.

Introduction
The advent of generative AI (GenAI) marks a pivotal moment in the evolution of healthcare, particularly in the context of acute care settings. As hospitals and health systems grapple with the challenges of increasing complexity, rising costs, and the ever-present need to improve patient outcomes, GenAI emerges as a transformative force, offering unparalleled opportunities to revolutionize healthcare delivery. This blog post delves into the myriad ways in which GenAI can reshape acute care, from enhancing clinical decision-making and optimizing operations to elevating the patient experience and driving innovation.

The Landscape of GenAI in Acute Care
GenAI, a subset of artificial intelligence capable of dynamically generating novel content, insights, and solutions, has the potential to permeate every aspect of acute care delivery. From clinical decision support and real-time intelligence to operational excellence and resource optimization, GenAI can unlock previously unattainable levels of efficiency, accuracy, and personalization. By leveraging vast amounts of data, advanced algorithms, and continuous learning, GenAI systems can augment human expertise, streamline processes, and drive evidence-based practices.

Clinical Decision Support & Real-time Intelligence
One of the most promising applications of GenAI in acute care lies in its ability to revolutionize clinical decision-making. By integrating multimodal data streams, including electronic health records, medical imaging, lab results, and real-time patient monitoring, GenAI systems can provide clinicians with adaptive clinical pathways tailored to individual patient needs. These intelligent systems can dynamically adjust treatment protocols based on a patient’s unique characteristics, comorbidities, and response to interventions, ensuring optimal care delivery.

Moreover, GenAI can serve as a powerful tool for predictive crisis management, leveraging advanced analytics to identify early warning signs of patient deterioration. By continuously monitoring vital signs, lab results, and other critical indicators, GenAI systems can alert clinicians to potential adverse events before they occur, enabling proactive interventions and improved patient safety. Additionally, GenAI can generate intelligent order sets, taking into account patient-specific factors, hospital formularies, and evidence-based guidelines to streamline the ordering process and reduce variability in care.

Operational Excellence & Resource Optimization
Beyond clinical decision support, GenAI holds immense potential for optimizing hospital operations and resource allocation. Through dynamic staff scheduling, GenAI models can predict patient acuity levels and optimize staffing across departments, ensuring that the right personnel are in the right place at the right time. This not only enhances operational efficiency but also reduces burnout and improves staff satisfaction.

GenAI can also revolutionize supply chain management in acute care settings. By leveraging predictive analytics and historical usage patterns, GenAI systems can anticipate supply needs, optimize inventory levels, and prevent stockouts. This intelligent approach to supply chain management can lead to significant cost savings, reduced waste, and improved resource utilization.

Furthermore, GenAI can transform capacity planning and patient flow management. Through real-time bed management and patient flow optimization, GenAI systems can minimize wait times, reduce bottlenecks, and ensure the efficient allocation of resources. By leveraging natural language processing techniques, GenAI can extract insights from unstructured data, such as clinical notes and patient feedback, to identify opportunities for process improvement and enhance the overall patient experience.

Revenue Cycle Optimization & Payer Integration
The complexities of revenue cycle management and payer relations often pose significant challenges for acute care providers. GenAI can streamline these processes, driving efficiency and maximizing reimbursement. Through automated prior authorization, GenAI systems can predict authorization requirements and generate supporting documentation, reducing administrative burden and expediting the approval process.

Moreover, GenAI can revolutionize claims processing by identifying potential denials before submission and suggesting corrective actions. By analyzing historical claims data, GenAI models can detect patterns and anomalies, enabling proactive revenue leakage prevention. Additionally, GenAI can synthesize data from disparate sources to identify missed charges and documentation gaps, ensuring accurate and complete billing.

Enhancing the Patient Experience
At the heart of acute care delivery lies the patient experience. GenAI can play a pivotal role in elevating the patient journey, from personalized care navigation to multilingual communication and smart room technology. By leveraging GenAI-powered assistants, hospitals can guide patients through their stay, providing real-time information, answering questions, and offering support. These intelligent systems can adapt to individual patient preferences, cultural backgrounds, and language requirements, fostering a more inclusive and patient-centric environment.

GenAI can also transform the way patients interact with their physical surroundings. Through voice-enabled environmental controls and smart room technology, patients can effortlessly adjust lighting, temperature, and entertainment options, enhancing comfort and autonomy. Moreover, GenAI-powered patient assistance systems can anticipate patient needs, such as pain management or mobility support, and alert healthcare providers accordingly, ensuring timely and personalized care delivery.

Quality & Safety Enhancement
Ensuring the highest standards of quality and safety is a paramount concern in acute care settings. GenAI can serve as a powerful ally in this pursuit, offering advanced capabilities for adverse event prevention, clinical variation analysis, and infection control. By continuously monitoring patient data and identifying patterns indicative of potential safety risks, GenAI systems can alert clinicians to intervene proactively, mitigating harm and improving patient outcomes.

GenAI can also play a critical role in reducing unwarranted clinical variation, a significant contributor to suboptimal outcomes and increased costs. By analyzing vast amounts of clinical data, GenAI models can identify best practices, detect deviations from evidence-based guidelines, and provide real-time recommendations to standardize care delivery. This data-driven approach to quality improvement can lead to more consistent, high-quality care across the organization.

Furthermore, GenAI can revolutionize infection prevention and control efforts in acute care settings. By leveraging real-time surveillance data, GenAI systems can detect patterns indicative of potential outbreaks, identify high-risk patients, and recommend targeted interventions. This proactive approach to infection control can significantly reduce the incidence of hospital-acquired infections, improve patient safety, and optimize resource utilization.

Knowledge Management & Clinical Research
The exponential growth of medical knowledge presents both challenges and opportunities for acute care providers. GenAI can serve as a powerful tool for knowledge management and clinical research, enabling healthcare organizations to stay at the forefront of evidence-based practices. Through continuous literature synthesis, GenAI systems can analyze vast amounts of research data, identify emerging trends, and update clinical protocols accordingly. This real-time integration of new evidence into clinical decision-making can accelerate the adoption of best practices and improve patient outcomes.

Moreover, GenAI can facilitate real-world evidence generation by automating the analysis of treatment outcomes and identifying patterns across large patient populations. This data-driven approach to clinical research can uncover novel insights, inform quality improvement initiatives, and drive innovation in acute care delivery. Additionally, GenAI can streamline clinical trial matching by identifying eligible patients and predicting trial success, accelerating the development of new therapies and interventions.

Regulatory Compliance & Risk Management
Navigating the complex landscape of regulatory compliance and risk management is a critical challenge for acute care providers. GenAI can serve as a valuable tool in this regard, offering automated compliance monitoring, privacy protection, and audit preparation capabilities. By continuously tracking regulatory requirements and identifying potential violations, GenAI systems can help healthcare organizations maintain compliance and mitigate legal and financial risks.

Moreover, GenAI can play a crucial role in protecting patient privacy and ensuring secure data sharing. Through advanced anonymization techniques and synthetic data generation, GenAI systems can enable the safe and compliant use of patient data for research and quality improvement purposes. Additionally, GenAI can streamline audit preparation by continuously monitoring and documenting compliance activities, reducing the administrative burden on healthcare staff.

Interoperability & Data Integration
The success of GenAI in acute care settings hinges on the ability to seamlessly integrate data from disparate sources and systems. Interoperability and data integration are critical enablers of GenAI adoption, allowing for the free flow of information across the healthcare ecosystem. Through smart data harmonization, GenAI systems can standardize and integrate data from electronic health records, medical devices, and other sources, creating a comprehensive view of the patient journey.

Moreover, GenAI can facilitate the development of intelligent APIs that optimize data exchange between systems, enabling real-time access to critical information at the point of care. These context-aware interfaces can adapt to the specific needs of healthcare providers, presenting relevant data and insights in a user-friendly manner. Additionally, GenAI can play a vital role in modernizing legacy systems, bridging the gap between existing infrastructure and cutting-edge technologies.

Future-ready Infrastructure
As GenAI continues to evolve and mature, it is essential for acute care providers to invest in future-ready infrastructure that can support the growing demands of this transformative technology. This includes the adoption of edge computing, which enables real-time data processing and decision-making at the point of care. By distributing GenAI capabilities across the healthcare network, edge computing can reduce latency, improve responsiveness, and enhance the overall user experience.

Moreover, as quantum computing advances, it is crucial for healthcare organizations to explore quantum-ready algorithms that can leverage the immense computational power of these emerging technologies. By preparing for the next generation of computing capabilities, acute care providers can position themselves at the forefront of innovation and unlock new possibilities for GenAI-driven healthcare delivery.

Sustainable & Responsible Innovation
As the adoption of GenAI in acute care settings accelerates, it is imperative to prioritize sustainable and responsible innovation. This involves considering the environmental impact of GenAI implementations and embracing energy-efficient approaches to minimize the carbon footprint of healthcare delivery. Moreover, responsible innovation requires a strong commitment to ethical principles, ensuring that GenAI systems are transparent, accountable, and free from bias.

To achieve this, healthcare organizations must engage in multidisciplinary collaboration, bringing together clinicians, data scientists, ethicists, and patient advocates to guide the development and deployment of GenAI solutions. By fostering a culture of responsible innovation, acute care providers can harness the power of GenAI while upholding the highest standards of patient care and societal well-being.

Conclusion
The advent of generative AI marks a transformative moment in the evolution of acute care delivery. From enhancing clinical decision-making and optimizing operations to elevating the patient experience and driving innovation, GenAI holds immense potential to revolutionize healthcare. However, realizing this potential requires a strategic and collaborative approach, one that prioritizes data governance, workflow integration, ethical considerations, and continuous evaluation.

As healthcare leaders navigate this exciting new frontier, it is essential to engage proactively with GenAI technologies, shape their development, and harness their power to improve patient outcomes, reduce costs, and drive sustainable innovation. By embracing the transformative potential of GenAI, acute care providers can position themselves at the forefront of a new era in healthcare delivery, one that promises to transform the lives of patients, empower healthcare professionals, and redefine the boundaries of what is possible.

The journey towards unleashing the full potential of GenAI in acute care is just beginning, and the road ahead is filled with both challenges and opportunities. However, with a clear vision, unwavering commitment, and collaborative spirit, healthcare organizations can navigate this uncharted territory and emerge as leaders in the age of generative AI. The future of acute care is here, and it is time to embrace it with open arms, bold vision, and a steadfast dedication to improving the lives of those we serve.

References

1. Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048. https://doi.org/10.3389/bjbs.2024.14048
2. Jindal, J. A., Lungren, M. P., & Shah, N. H. (2024). Ensuring useful adoption of generative artificial intelligence in healthcare. Journal of the American Medical Informatics Association, 31(6), 1441-1444. https://doi.org/10.1093/jamia/ocae043
3. Lan, G., Xiao, S., Yang, J., Wen, J., & Xi, M. (2023). Generative AI-based data completeness augmentation algorithm for data-driven smart healthcare. IEEE Journal of Biomedical and Health Informatics. Advance online publication. https://doi.org/10.1109/JBHI.2023.3327485
4. Lee, C. C., & Low, M. Y. H. (2024). Using genAI in education: The case for critical thinking. Frontiers in Artificial Intelligence, 7, 1452131. https://doi.org/10.3389/frai.2024.1452131
5. Malhotra, K., Wiesenfeld, B., Major, V. J., Grover, H., Aphinyanaphongs, Y., Testa, P., & Austrian, J. S. (2025). Health system-wide access to generative artificial intelligence: The New York University Langone Health experience. Journal of the American Medical Informatics Association, 32(2), 268-274. https://doi.org/10.1093/jamia/ocae285
6. Prescott, M. R., Yeager, S., Ham, L., Rivera Saldana, C. D., Serrano, V., Narez, J., Paltin, D., Delgado, J., Moore, D. J., & Montoya, J. (2024). Comparing the efficacy and efficiency of human and generative AI: Qualitative thematic analyses. JMIR AI, 3, e54482. https://doi.org/10.2196/54482
7. Solaiman, B. (2024). Generative artificial intelligence (GenAI) and decision-making: Legal & ethical hurdles for implementation in mental health. International Journal of Law and Psychiatry, 97, 102028. https://doi.org/10.1016/j.ijlp.2024.102028
8. Wachter, R. M., & Brynjolfsson, E. (2024). Will generative artificial intelligence deliver on its promise in health care? JAMA, 331(1), 65-69. https://doi.org/10.1001/jama.2023.25054
9. Yim, D., Khuntia, J., Parameswaran, V., & Meyers, A. (2024a). Preliminary evidence of the use of generative AI in health care clinical services: Systematic narrative review. JMIR Medical Informatics, 12, e52073. https://doi.org/10.2196/52073
10. Yim, D., Khuntia, J., Parameswaran, V., & Meyers, A. (2024b). Preliminary evidence of the use of generative AI in health care clinical services: Systematic narrative review. JMIR Medical Informatics, 12, e52073. https://doi.org/10.2196/52073

In Informatica Intelligent Cloud Services (IICS), In-Out and Input Parameters provide flexibility in managing dynamic values for your mappings. This allows you to avoid hard-coding values directly into the mapping and instead configure them externally through parameter files, ensuring ease of maintenance, especially in production environments. Below, we’ll walk through the concepts and how to use these parameters effectively in your IICS mappings.

In-Out Parameters

1. Similar to Mapping Variables in Informatica PowerCenter:In-Out parameters in IICS function similarly to mapping parameters or variables in Informatica PowerCenter. These parameters allow you to define values that can be used across the entire mapping and changed externally without altering the mapping itself.
2. Frequently Updating Values: In scenarios where a field value needs to be updated multiple times, such as a Product Discount that changes yearly, quarterly, or daily, In-Out parameters can save time and reduce errors. Instead of hard-coding the discount value in the mapping, you can define an In-Out parameter and store the value in a parameter file.
3. For Example – Product Discount: If the Product Discount changes yearly, quarterly, or daily, you can create an In-Out parameter in your IICS mapping to store the discount value. Instead of updating the mapping each time the discount value changes, you only need to update the value in the parameter file.
4. Changing Parameter Values: Whenever the discount value needs to be updated, simply change it in the parameter file. This eliminates the need to modify and redeploy the mapping itself, saving time and effort.
5. Creating an In-Out Parameter: You can create an In-Out parameter in the mapping by specifying the parameter name and its value in the parameter file.
6. Configuring the Parameter File Path: In the Mapping Configuration Task (MCT), you can download the parameter file template. Provide the path and filename of the parameter file, and you can see the In-Out parameter definition in the MCT.
7. Download the Parameter File Template: You can download the parameter file template directly from the MCT by clicking on “Download Parameter File Template.” After downloading, place the file in the specified directory.
8. Defining Parameter Values: In the parameter file, define the values for your parameters. For example, if you’re setting a Discount value, your file could look like this:#USE_SECTIONS[INFORMATICA].[INOUT_PARAM].[m_test]$Product_Discount=10[Global]
9. Creating Multiple Parameters: You can create as many parameters as needed, using standard data types in the In-Out Parameters section. Common real-world parameters might include values like Product Category, Model, etc.

Input Parameters:

Input parameters are primarily used for parameterizing Source and Target Connections or objects. Here’s how to use input parameters effectively:

1. Create the Mapping First: Start by designing your mapping logic, completing field mappings, and validating the mapping. Once the mapping is ready, configure the input parameters.
2. Parameterizing Source and Target Connections: When parameterizing connections, create parameters for the source and target connections in the mapping. This ensures flexibility, especially when you need to change connection details without modifying the mapping itself. To create the Input parameter, go to the Parameter panel, click on Input Parameter, and create the Source and Target Parameter connections. Select the type as Connection, and choose the appropriate connection type (e.g., Oracle, SQL Server, Salesforce) from the drop-down menu.
3. Overriding Parameters at Runtime: If you select the “Allow Parameters to be Overridden at Runtime” option, IICS will use the values defined in the parameter file, overriding any hard-coded values in the mapping. This ensures that the runtime environment is always in sync with the latest configuration.
4. Configuring Source and Target Connection Parameters: Specify the values for your source and target connection parameters in the parameter file, which will be used during runtime to establish connections.
   For example:
   #USE_SECTIONS
   [INFORMATICA].[INOUT_PARAM].[m_test]$$Product_Discount=10$$SRC_Connection=$$TGT_Connection=[Global]

Conclusion

In-Out and Input Parameters in IICS offer a powerful way to create flexible, reusable, and easily configurable mappings. By parameterizing values like field values, Source and Target Connections, or Objects, you can maintain and update your mappings efficiently.

In a rapidly evolving digital landscape, staying ahead demands more than just cutting-edge tools; it requires a vision, strategy, and a commitment to excellence. In an exclusive interview for We Are Perficient, Matt Shields, Managing Director of Perficient’s Adobe practice, shared his expert insights on sales and delivery excellence, the transformative potential of Adobe technologies, and the collaborative power of Perficient’s global reach. 

With over 15 years of experience in the Adobe ecosystem, Matt has helped some of the world’s leading brands leverage digital transformations to gain a competitive edge. Here’s what he had to say about the future of sales and technology and how Perficient drives impact across industries. 

The Growing Influence of Adobe in a Content-Driven World 

As the demand for personalized content grows exponentially, Adobe is leading the charge in helping organizations connect with their customers in meaningful ways. According to Matt, the future lies in how companies deliver experiences tailored to each individual’s unique needs, preferences, and cultural contexts. 

“Understanding customers directly—whatever culture, geography, or language—is the key to creating meaningful engagement that drives business objectives,” he explained. 

Adobe’s innovative tools, such as generative AI capabilities within Adobe Experience Cloud, are empowering companies to produce dynamic, high-quality content at scale. This technology is not just about efficiency; it’s about fostering genuine, memorable connections between brands and their audiences. 

Perficient’s Role in Driving Digital Transformation 

Perficient’s partnership with Adobe amplifies the ability to meet the demands of a global market. With a focus on customer experience, Perficient delivers personalized solutions that go beyond traditional content management. 

“It’s no longer just about managing content. It’s about creating it at speed and scale while ensuring it reaches the right audience through the right channels,” Matt emphasized. 

What sets Perficient apart is its global presence and deep industry expertise. By leveraging teams from diverse regions, Perficient offers clients a wealth of knowledge, perspective, and technical capabilities that ensure every project is executed with precision and cultural relevance. 

The Role of Artificial Intelligence in Transforming Experiences 

Artificial Intelligence is reshaping the way businesses engage with customers, and Adobe is at the forefront of this transformation. Matt highlighted how Adobe’s AI tools, such as GenStudio for Performance Marketing, enable companies to generate relevant, impactful content faster and more effectively. 

“AI is a conversation we’re having with every customer,” he noted. “Whether it’s streamlining internal processes or enhancing customer interactions, AI is central to every industry’s digital transformation journey.”

Perficient’s ability to integrate AI within Adobe’s ecosystems ensures clients can harness these innovations to achieve their strategic goals. From content generation to customer engagement, AI is paving the way for smarter, more efficient processes. 

Industry Trends and Perficient’s Global Advantage 

When asked about emerging trends, Matt underscored the importance of creating memorable customer experiences. It’s not just about conversions; it’s about how well a brand resonates with its audience. 

“The question is: How do we connect effectively? Is it the right time, the right content, the right message?”

Perficient’s global footprint plays a crucial role in delivering these impactful experiences. By combining local insights with global expertise, Perficient ensures its clients stay ahead in an increasingly connected world. 

Transforming the Healthcare Sector with Adobe 

In healthcare, customer experience is synonymous with patient experience. Matt shared how Adobe solutions are enabling healthcare organizations to create patient-centric journeys that improve outcomes. 

“From follow-ups to treatment accessibility, driving a better patient experience can literally be a matter of life and death,” Matt explained. 

Perficient’s industry-specific expertise, combined with Adobe’s platform capabilities, is helping healthcare providers deliver care that is not only efficient but also empathetic and personalized. 

The Power of Partnership: Adobe and Perficient 

As a highly specialized Adobe Platinum Partner, Perficient works hand-in-hand with Adobe to deliver the latest technologies and innovations across industries. This partnership is rooted in shared expertise and a commitment to the client. 

“We collaborate daily with Adobe teams across healthcare, manufacturing, financial services, and more to ensure our clients get the best of both worlds—industry expertise and cutting-edge technology,” Matt said. 

Discover More 

Curious about how our partnership with Adobe is transforming industries? Learn how we leverage Adobe’s cutting-edge technologies to deliver personalized, impactful solutions for our clients here.

Augmented analytics—an approach that leverages machine learning (ML) and artificial intelligence (AI) to enhance data exploration and analysis—holds enormous potential for companies seeking to improve decision-making, boost operational efficiency, and secure a competitive advantage. Before examining the hurdles organizations often face when rolling out this technology, it’s important to understand the rewards associated with embracing augmented analytics.

Potential Advantages of Augmented Analytics

Organizations can reap several benefits by adopting augmented analytics:

• More Informed Decision-Making: Automation features and AI-driven insights enable quicker, more precise decisions.
• Reduced Bias: Augmented analytics can minimize the risk of biased outcomes through automated data processing and less human intervention.
• Accelerated Insight Generation: By expediting data analysis, these tools help teams respond with greater agility to market shifts.
• Heightened Accuracy: AI algorithms often identify patterns or outliers that human analysts might overlook, resulting in more precise insights.
• Operational Efficiency: Routine data handling is automated, freeing analysts to tackle higher-level strategic work.
• Enhanced Data Literacy: By making data more transparent, augmented analytics tools can foster better understanding and usability across the organization.
• Democratization of Insights: With easier access to analytic capabilities, more employees can participate in data-driven decision-making, promoting a culture of widespread data usage.

Having outlined these potential gains, we can now concentrate on the barriers that may arise during the implementation phase.

Common Implementation Challenges

Although augmented analytics offers significant advantages, organizations commonly encounter challenges in three broad categories: technological, organizational, and data-related.

Technological Challenges

• Integration with Legacy Systems: Merging augmented analytics platforms with existing tools and older infrastructures can be complex. Organizations might need to manage compatibility issues, enable smooth data transfers, and migrate legacy databases into newer environments.
• Scalability Concerns: Because augmented analytics thrives on large volumes of data, some companies struggle to secure adequate infrastructure and computing power to handle increasing data complexity. Adopting scalable cloud-based solutions or upgrading hardware may be required.
• Performance Constraints: Factors such as the amount of data, the complexity of models, and algorithmic efficiency all influence performance. Achieving optimal results depends on careful model tuning, database optimization, and potentially distributed computing.
• Accuracy and Contextual Relevance: If the insights generated do not align with the specific business scenario or are simply inaccurate, stakeholder trust may deteriorate. Thus, selecting suitable algorithms, rigorously validating data, and monitoring model outputs are essential.

Organizational Challenges

• Change Resistance: Employees might be wary of new technologies or feel unprepared to become “citizen data scientists.” Effective strategies to overcome this include transparent communication, thorough training, and fostering an environment where experimentation is encouraged.
• Cultural Realignment: A shift in corporate culture toward data-informed decision-making often requires breaking down silos, encouraging collaboration, and advocating for data-driven approaches.
• Job Security Fears: Automation can cause anxiety about job displacement. Alleviating these worries involves emphasizing how augmented analytics can empower staff with new competencies rather than eliminating their roles.
• “Black Box” Syndrome: Some augmented analytics solutions lack transparency regarding how their outputs are generated. Offering interpretable explanations and visualizations that clarify AI-driven outcomes helps address doubts.
• Complexity and User Adoption: Many augmented analytics platforms can be intricate, and users may need guidance to interpret analyses. Designing intuitive interfaces, providing relevant training, and offering ongoing user support are critical.

Data-Related Challenges

• Reliance on Data Quality: Inaccuracies or inconsistencies in input data undermine the reliability of an augmented analytics tool’s results. Organizations should invest in robust data governance and quality assurance to maintain trust in the platform.
• Data Bias: Any biases embedded in training datasets can lead to skewed outputs and, in turn, unfair or discriminatory outcomes. Companies must be vigilant in spotting and countering bias during both data preparation and model evaluation.
• Privacy and Security Risks: Because augmented analytics platforms often handle large quantities of sensitive data, stringent data governance and security measures—including compliance with relevant regulations—are essential.

Strategies for Overcoming Implementation Roadblocks

Addressing these challenges calls for a comprehensive approach that covers technical, organizational, and data-related dimensions.

Technological Strategies

• Gradual Rollout: Launch a pilot project targeting a specific, high-value use case to gain experience and demonstrate the viability of augmented analytics on a smaller scale.
• Choosing Compatible Solutions: Focus on tools that align well with existing infrastructures, offer robust security, and can scale to accommodate future growth.
• Upgrading Infrastructure: Evaluate whether computing power, storage solutions, and network capabilities are sufficient. In many cases, cloud-based solutions offer the scalability needed to handle larger datasets efficiently.

Organizational Strategies

• Build a Data-Focused Culture: Enhance collaboration and promote knowledge sharing to support data-driven decision-making. Training initiatives, cross-departmental collaboration, and visible leadership commitment to data initiatives play a critical role.
• Comprehensive Training: Develop programs to improve data literacy at different organizational levels. Focus on analytical methods, hands-on tool usage, and interpretation of outcomes.
• Proactive Change Management: Address worries about evolving job roles by highlighting how augmented analytics can open up professional development opportunities.
• Encourage Transparency: Opt for systems that explain how results are produced to instill confidence in the insights. Visual explanations and active participation from domain experts help solidify trust.
• Identify and Resolve User Pain Points: Conduct user research to understand existing workflow challenges and tailor augmented analytics solutions to real-world needs.
• Continuous Improvement: Maintain feedback loops by collecting user input and monitoring model performance, adjusting processes or algorithms as needed.
• Data Sharing Across Teams: Reduce silos by promoting inter-departmental data sharing. This leads to more comprehensive analyses and fosters a collaborative culture.

Data-Related Strategies

• Data Quality Improvements: Formalize data governance protocols—like cleansing, validation, and enrichment—to ensure the underlying data is accurate and dependable.
• Prioritize Data Security: Put robust encryption, access controls, and anonymization measures in place to protect sensitive information. These steps also ensure adherence to data privacy laws and regulations.

Best Practices for Data Modeling in Augmented Analytics

An effective data model is vital to a successful augmented analytics rollout. Consider these guidelines:

1. Start with Simplicity: Begin with a lean data model to minimize potential errors and validate initial outcomes. Scale up complexity once trust in the process grows.
2. Design for Adaptability: Because business goals and data sources evolve, data models should be designed with future modifications in mind.
3. Maintain a Data Dictionary: Store updated metadata to clarify each element’s purpose and structure, ensuring consistency across datasets.
4. Enforce Data Quality Standards: Integrate data freshness and integrity checks to uphold reliability.
5. Leverage Various Modeling Approaches: Evaluate techniques—like star schemas, materialized tables, or views—to optimize for performance and ease of access.

Types of Augmented Analytics Solutions

A wide range of augmented analytics platforms is available, each with unique strengths:

• Microsoft Power BI: Through its Copilot feature, Power BI integrates seamlessly with Microsoft’s ecosystem, offering AI-powered insights and natural language interactions.
• Tableau: Tableau’s Pulse and Agent Force features support natural language queries and automated insight generation. Its user-friendly interface appeals to both technical and non-technical audiences.
• Oracle Analytics Cloud: Delivers a robust suite of analytics capabilities—from automated data discovery to ML-driven predictions.
• IBM Cognos Analytics: Incorporates AI for automated data preparation, natural language querying, and in-depth pattern discovery.
• MicroStrategy: Embeds AI and ML functionalities, including automated insights and user-friendly data exploration.

Selecting the best platform depends on organizational priorities, infrastructure environments, and budgeting.

Financial Considerations

Organizations adopting augmented analytics should budget for both one-time and recurring expenses:

• Initial Outlay: These costs typically include software licensing, hardware or cloud service upgrades, data migration, and consulting or training fees.
• Ongoing Costs: Upkeep expenses may encompass software support, subscription fees for cloud hosting, data storage costs, and continuous staff development.

Carefully comparing these investments with anticipated returns helps shape a viable rollout plan.

Essential Skills and Resources

Implementing augmented analytics effectively requires a mix of competencies and personnel:

• Data Literacy: End users should have a baseline understanding of data concepts and analysis methods.
• Analytical Thinking: Although AI tools automate much of the process, human insight remains essential to interpret results and make strategic decisions.
• Domain Knowledge: Familiarity with the relevant business sector ensures data is interpreted within the proper context.
• Data Management Expertise: Professionals who can handle data governance, protect data quality, and ensure strong security measures are crucial.

Key roles include data scientists to develop and maintain ML models, data analysts to translate findings into actionable recommendations, IT professionals to manage infrastructure, and training facilitators to promote ongoing skill development.

Conclusion

Augmented analytics can revolutionize how organizations glean insights and strengthen their market position. Yet, achieving these benefits calls for a methodical plan that anticipates common difficulties and fosters a supportive, data-oriented environment. Organizations that understand and address these challenges early are more likely to see successful outcomes and long-term value.

Key actions for successful augmented analytics implementations:

1. Pilot with a Targeted Use Case: Demonstrate value on a smaller scale before wider adoption.
2. Select Appropriate Tools: Ensure compatibility with existing systems, future scalability, and robust data protection.
3. Emphasize Data Integrity: High-quality data is foundational to dependable insights.
4. Nurture a Data-Centric Culture: Encourage the organization to base decisions on analysis and evidence.
5. Provide Ongoing Training: Equip users with the knowledge they need to navigate tools effectively.
6. Manage Change Proactively: Address concerns around job security and clarify how the technology benefits employees.
7. Strengthen Privacy and Security Measures: Safeguard sensitive information and comply with privacy regulations.

By methodically planning for these considerations, organizations can better navigate the inevitable challenges and unleash the full value of augmented analytics.

In 2025, the medical device industry trends are not just shaping the future—they’re redefining the present. As technology advances at an unprecedented pace, regulatory landscapes evolve, and patient expectations rise, the industry stands at a pivotal juncture. Embracing these trends offers a pathway to innovation, market expansion, and enhanced patient outcomes. However, success requires strategic foresight to navigate challenges in compliance, operational efficiency, and trust-building.

Explore the key trends shaping 2025, uncovering data-driven insights and actionable strategies to seize opportunities and maintain a competitive edge in this rapidly evolving industry.

MedTech Trend #1: Artificial Intelligence (AI) Integration

AI is revolutionizing the medical device industry by addressing inefficiencies in diagnostics, streamlining regulatory approvals, and enabling highly personalized experiences and patient care. These advancements tackle critical challenges such as the growing demand for precision medicine and operational efficiency. However, the industry faces unique challenges that many other sectors don’t encounter. Strict regulations around HIPAA, PHI, and PII create significant barriers, making it difficult to adopt off-the-shelf AI solutions from fields like commerce or digital experience. These regulations demand that AI be specifically tailored to ensure data privacy, security, and compliance, limiting the utility of plug-and-play approaches seen in other industries.

Recommended Approach: AI implementation brings value to every stage of the product lifecycle. However, by considering AI as a standalone strategy across your organization, you’ll miss the true potential that a holistic strategy can provide. Instead, consider it as a powerful enabler of broader business objectives. In the design phase, predictive analytics identify unmet market needs and guide the development of innovative, consumer-relevant product features. During regulatory submissions, AI-powered compliance tools streamline the process by reducing review times and ensuring adherence to complex guidelines, accelerating time-to-market. In the post-market phase, machine learning models enhance device monitoring by predicting failures, optimizing performance, and enhancing reliability, safety, and care plan adherence. In each phase, a well-formed strategy aligns key business priorities with organizational capabilities – people, technology, and processes – to create a cohesive framework.

Related: Outpace the Competition with Smart Predictions

MedTech Trend #2: Building Consumer Trust in AI-Enabled Devices

Consumer trust remains a significant consideration to the adoption of medical devices, especially those that are AI-enabled. Patients need confidence that their data is secure. Meanwhile, providers require assurance that these technologies are reliable and inclusively designed and tested to enhance care delivery for all populations. For healthcare leaders, building trust is not optional—it’s essential. Successfully addressing these concerns translates into higher adoption rates, stronger provider relationships, and expanded market share. Transparency and engagement are critical to creating a trusted brand that resonates with all stakeholders.

Recommended Approach: Building trust requires a multi-faceted strategy rooted in transparency, education, and clinician advocacy and centered on a core tenet: know your audience. Clear communication about how a device and AI operate, the benefits, and safeguards in place to mitigate AI bias and protect patient data can significantly alleviate skepticism. Drive understanding and adoption by deeply understanding your audience personas and journeys, then tailor experiences around those insights. Demystify AI and support better health decisions by educating through interactive webinars, videos, and other preferred modes, speaking the language of your patients and your providers. Adhering to robust data security standards, such as GDPR and HIPAA, and transparently communicating these measures to stakeholders reinforce confidence.

You May Enjoy: Your Playbook for Building Trust in Artificial Intelligence for Medical Devices

MedTech Trend #3: Regulatory Evolution

Evolving regulatory frameworks, including updated FDA guidelines, underscore the critical importance of cybersecurity and proactive risk management of medical devices, particularly those that are AI-enabled. These developments reflect a growing emphasis on protecting patient safety and ensuring data integrity in today’s interconnected healthcare landscape. For medical device leaders, embedding compliance into the innovation process is crucial for building stakeholder trust and positioning their organizations as reliable partners in a competitive market.

Recommended Approach: Adopt a compliance-first mindset, collaborating across regulatory, IT, and R&D teams to ensure a unified approach to evolving standards. By integrating cybersecurity protocols into the earliest stages of product design and regulatory documentation, your organization can proactively address vulnerabilities and innovate confidently while protecting patient trust and ensuring market viability.

See Also: Innovate Medical Device Software Quickly and Compliantly

MedTech Trend #4: Direct to Consumer Wearables and Devices

The demand for wearable medical devices is rapidly increasing as patients play a more active role in managing their well-being and seek real-time health monitoring tools that seamlessly integrate into their daily lives. In turn, these devices enable proactive disease management and generate valuable data for providers, facilitating personalized, data-driven care beyond the traditional care setting. Concurrently, providers are expanding into digitally connected services, such as telemedicine, remote patient monitoring, and personalized care plans, enabling patients to manage their health in more convenient and accessible settings. For medical device leaders, the wearable market presents significant growth potential, provided usability, privacy, and interoperability challenges are addressed.

Recommended Approach: To maximize the potential of wearable devices, organizations must prioritize user-friendly designs that integrate easily into patients’ routines, encouraging adoption and compliance. Interoperability is also critical. Ensuring wearables integrate seamlessly with electronic health records (EHRs) enhances their value for patients and providers by enabling coordinated, data-driven care. By overcoming these challenges, wearable devices can become indispensable tools in modern healthcare, supporting long-term adoption and loyalty while driving better health outcomes.

Strategic Position: Meet Customers Where They Are

MedTech Trend #5: Collaborative Innovation for Growth

Mergers, acquisitions, and partnerships are reshaping the medical device industry, enabling organizations to scale operations, accelerate innovation, and expand into new markets. These collaborative efforts address the rising costs of R&D while meeting the demand for advanced technologies, helping companies remain competitive in a fast-evolving landscape. Strategic partnerships offer access to cutting-edge technologies and fresh perspectives, shortening product development cycles and facilitating faster market entry. For medical device leaders, these alliances are essential for addressing unmet market needs, navigating healthcare complexities, and driving long-term growth.

Recommended Approach: Organizations should pursue partnerships that align with their strategic goals and operational strengths. Evaluating compatibility across technology, culture, and objectives is essential for fostering productive relationships. Robust integration plans for mergers minimize disruptions and maximize synergies, ensuring a seamless transition. Partnering with startups, academic institutions, or tech firms provides opportunities to access disruptive innovations such as AI-powered diagnostics and next-generation wearables. These collaborations position companies as leaders in innovation, allowing them to efficiently meet market demands and deliver transformative healthcare solutions. Strategic collaboration is no longer optional—it is a necessity for maintaining a competitive edge in the medical device industry.

See More: Drive Business Velocity and Growth

Expert Digital Healthcare Consulting Services for the Medical Device Industry: Innovate, Modernize, Lead

The medical device industry is at a pivotal moment, with groundbreaking advancements in AI, evolving regulatory landscapes, and a growing emphasis on consumer-centric healthcare reshaping how organizations innovate, operate, and deliver value. These trends are not just reshaping the industry but also creating new opportunities to lead through innovation, operational efficiency, and patient-focused solutions.

We combine strategy, industry expertise, and cutting-edge technology to empower medical device companies to adapt, thrive, and lead in this rapidly evolving environment:

• Business Transformation: Activate strategies that align clinical innovation with business objectives for transformative healthcare solutions.
• Modernization: Leverage advanced technologies like AI and machine learning to drive innovation, regulatory compliance, and operational excellence.
• Data Analytics: Harness enterprise data to generate actionable insights, enabling precision medicine, device reliability, and market leadership.
• Consumer Experience: Build trust, transparency, and engagement with AI-enabled devices and wearable technologies to elevate the patient journey.

Our partnerships with leading technology providers, recognition from top industry analysts, and consistent ranking by Modern Healthcare as one of the largest healthcare consulting firms demonstrate our expertise and commitment to results.

Discover why the top medical device manufacturers and healthcare organizations trust us to deliver measurable outcomes. Explore our expertise in the medical device industry and contact us to learn how we can help you lead in this new era of healthcare innovation.

As private health insurers weather industry headwinds, strategic transformation priorities remain firmly centered on operations and patient-centric experiences that accelerate efficiencies. Outcomes-driven leaders recognize the value of aligning key business needs with people, technology, and processes. 

Leading Digital Transformation for U.S. Payers 

We are proud to announce the recent recognition of Perficient’s digital services for healthcare payers by an industry-leading advisory firm. This highlights the value that our expert, global teams bring to the largest U.S. health insurers as industry leaders work to improve operations, efficiency, and effectiveness.  

Now more than ever, insurers can accelerate a shift from traditional cost management to proactive health enablement. The most effective payers are integrating technologies to modernize operations, streamline experiences, and not only unlock reliable data, but elevate insights and experiences with AI and advanced, integrated analytics. AI can be harnessed to offer hyper-personalized benefit plans, predictive risk analytics, and real-time insights that not only manage costs but also enhance member experience and engagement. 

You May Enjoy: Current Digital Trends in Healthcare 

We believe our inclusion in a leading study of digital health services showcases our dedication to easing consumer journeys, ensuring integrated data is reliable and secure, and modernizing the enterprise so it can accelerate progress toward key business priorities. We are committed to helping healthcare leaders stay competitive with our award-winning, tailored solutions.  

“This acknowledgment underscores our commitment to helping healthcare leaders optimize workflows, uncover insights, innovate care experiences, and strengthen consumer trust.”– Brent Teiken, General Manager, Healthcare + Life Sciences

Our healthcare experts guide and drive a shared understanding with clients. This insight is especially vital as leaders seek solutions to highly complex business challenges that rely on protected data and span a complex healthcare ecosystem. Our technology experts further ensure that solutions are not only implemented correctly but can scale as consumer expectations and business needs evolve.  

Success In Action: Enabling Better Insight Into Key Patient Data Using GenAI 

Elevate Health and Business Outcomes With Our Expertise 

We help health insurers navigate intense technological and regulatory requirements while controlling costs and improving the user experience to support and delight members. 

• Business Transformation: Transform strategy into action: reduce costs, increase quality, and improve member experiences. 
• Modernization: Maximize technology to drive innovative, digital-first care solutions in automation, AI, and cloud. 
• Data + Analytics: Provide governed, accessible, and trusted data to drive insight and engagement for members, providers, and groups. 
• Consumer Experience: Create personalized, value added, and measurable experiences across multiple channels for all constituents. 

Explore our healthcare expertise and contact us to discover why we have been trusted by the 10 largest U.S. health insurers, including 25 BCBS-affiliated insurers, and are consistently recognized by Modern Healthcare as one of the largest healthcare consulting firms.