We’re excited to announce that Perficient has officially attained SS&C Blue Prism Implementation Partner Certification at the Silver level. As we begin 2026, this achievement reflects our commitment to delivering world-class intelligent automation solutions and driving measurable value for our clients. 

What This Certification Means 

The SS&C Blue Prism Silver Implementation Partner Certification is a hallmark of quality, expertise, and consistency. It recognizes partners who meet rigorous standards across personnel, support, and delivery requirements. By earning this certification, Perficient has demonstrated its ability to implement intelligent RPA solutions that set a benchmark for customer success. 

About the SS&C Partner Program 

The SS&C Partner Program is designed to give customers access to the best partners and technology in the rapidly evolving world of intelligent automation and AI. As part of this program, Perficient joins a global ecosystem focused on helping businesses: 

• Deliver end-to-end transformation through full-stack automation. 

• Implement strategic governance tools for deployment success. 

• Expand into high-growth markets where demand for automation is accelerating. 

This recognition positions Perficient at the forefront of intelligent automation, enabling us to help clients streamline processes, reduce complexity, and unlock new efficiencies. 

A Testament to Teamwork and Vision 

This achievement underscores Perficient’s commitment to excellence and innovation in intelligent automation. It reflects not only the technical expertise required to meet SS&C Blue Prism’s rigorous standards but also our strategic focus on helping clients accelerate transformation.  

“This milestone reflects the hard work and dedication of our team,” said Mwandama Mutanuka, Vice President of AI Platforms. “We see this partnership as a key in our AI Platforms go-to-market this year.” 

Driving Intelligent Automation Forward 

As a Silver Implementation Partner, Perficient is proud to be part of SS&C Blue Prism’s 5-star rated Partner Program. This recognition strengthens our ability to help organizations become more intelligently connected through agentic automation, enabling businesses to scale responsibly and deliver meaningful outcomes. 

Learn More 

Explore how Perficient’s AI Automation expertise can help your organization embrace next-generation automation solutions. Visit https://www.perficient.com/contact to start your transformation journey. 

Subtitle: Side-by-side implementation of Secure AI on Android (Kotlin) and iOS (Swift).

In Part 1, we discussed why we need to move away from slow, cloud-dependent chatbots. Now, let’s look at how to build instant, on-device intelligence. While native code is powerful, managing two separate AI stacks can be overwhelming.

Before we jump into platform-specific code, we need to talk about the “Bridge” that connects them: Google ML Kit.

The Cross-Platform Solution: Google ML Kit

If you don’t want to maintain separate Core ML (iOS) and custom Android models, Google ML Kit is your best friend. It acts as a unified wrapper for on-device machine learning, supporting both Android and iOS.

It offers two massive advantages:

1. Turnkey Solutions: Instant APIs for Face Detection, Barcode Scanning, and Text Recognition that work identically on both platforms.
2. Custom Model Support: You can train a single TensorFlow Lite (.tflite) model and deploy it to both your Android and iOS apps using ML Kit’s custom model APIs.

For a deep dive on setting this up, bookmark the official ML Kit guide.

The Code: Side-by-Side Implementation

Below, we compare the implementation of two core features: Visual Intelligence (Generative AI) and Real-Time Inference (Computer Vision). You will see that despite the language differences, the architecture for the “One AI” future is remarkably similar.

Feature 1: The “Brain” (Generative AI & Inference)

On Android, we leverage Gemini Nano (via ML Kit’s Generative AI features). On iOS, we use a similar asynchronous pattern to feed data to the Neural Engine.

Android (Kotlin)

We check the model status and then run inference. The system manages the NPU access for us.

// GenAIImageDescriptionScreen.kt
val featureStatus = imageDescriber.checkFeatureStatus().await()

when (featureStatus) {
    FeatureStatus.AVAILABLE -> {
        // The model is ready on-device
        val request = ImageDescriptionRequest.builder(bitmap).build()
        val result = imageDescriber.runInference(request).await()
        onResult(result.description)
    }
    FeatureStatus.DOWNLOADABLE -> {
        // Silently download the model in the background
        imageDescriber.downloadFeature(callback).await()
    }
}

iOS (Swift)

We use an asynchronous loop to continuously pull frames and feed them to the Core ML model.

// DataModel.swift
func runModel() async {
    try! loadModel()
    
    while !Task.isCancelled {
        // Thread-safe access to the latest camera frame
        let image = lastImage.withLock({ $0 })
        
        if let pixelBuffer = image?.pixelBuffer {
            // Run inference on the Neural Engine
            try? await performInference(pixelBuffer)
        }
        // Yield to prevent UI freeze
        try? await Task.sleep(for: .milliseconds(50))
    }
}

Feature 2: The “Eyes” (Real-Time Vision)

For tasks like Face Detection or Object Tracking, speed is everything. We need 30+ frames per second to ensure the app feels responsive.

Android (Kotlin)

We use FaceDetection from ML Kit. The FaceAnalyzer runs on every frame, calculating probabilities for “liveness” (smiling, eyes open) instantly.

// FacialRecognitionScreen.kt
FaceInfo(
    confidence = 1.0f,
    // Detect micro-expressions for liveness check
    isSmiling = face.smilingProbability?.let { it > 0.5f } ?: false,
    eyesOpen = face.leftEyeOpenProbability?.let { left -> 
        face.rightEyeOpenProbability?.let { right ->
            left > 0.5f && right > 0.5f 
        }
    } ?: true
)

iOS (Swift)

We process the prediction result and update the UI immediately. Here, we even visualize the confidence level using color, providing instant feedback to the user.

// ViewfinderView.swift
private func updatePredictionLabel() {
    for result in prediction {
        // Dynamic feedback based on confidence
        let probability = result.probability
        let color = getColorForProbability(probability) // Red to Green transition
        
        let text = "\(result.label): \(String(format: "%.2f", probability))"
        // Update UI layer...
    }
}

Feature 3: Secure Document Scanning

Sometimes you just need a perfect scan without the cloud risk. Android provides a system-level intent that handles edge detection and perspective correction automatically.

Android (Kotlin)

// DocumentScanningScreen.kt
val options = GmsDocumentScannerOptions.Builder()
    .setGalleryImportAllowed(false) // Force live camera for security
    .setPageLimit(5)
    .setResultFormats(RESULT_FORMAT_PDF)
    .build()

scanner.getStartScanIntent(activity).addOnSuccessListener { intentSender ->
    scannerLauncher.launch(IntentSenderRequest.Builder(intentSender).build())
}

Conclusion: One Logic, Two Platforms

Whether you are writing Swift for an iPhone 17 pr0 or Kotlin for a medical Android tablet, the paradigm has shifted.

1. Capture locally.
2. Infer on the NPU.
3. React instantly.

By building this architecture now, you are preparing your codebase for Spring 2026, where on-device intelligence will likely become the standard across both ecosystems.

Reference: Google ML Kit Documentation

Subtitle: From Critical Medical Hardware to the Apple Ecosystem, the future of mobile intelligence is local, instant, and unified.

We are standing at a hardware tipping point. For the last decade, “AI” on mobile effectively meant one thing: sending data to the cloud and waiting for an answer. Especially for those chatbots, adding AI to an app meant integrating a slow, spinning loading indicator while data traveled to a server, waited in a queue, and eventually returned text. Users are tired of waiting. They are overwhelmed by generic bots that feel disconnected from the app they are actually using.

But as we move toward 2026, the script is flipping. Phone manufacturers are shipping devices with neural engines (NPUs) so powerful they rival the desktop GPUs of just a few years ago. This shift isn’t just about faster chatbots or smoother animations; it is reshaping critical industries like healthcare and unifying the mobile ecosystem under a single dominant model family: Google Gemini.

The Hardware Revolution: The “Brain” in Your Pocket

The defining trend of the 2025-2026 cycle is the explosion of Hardware Acceleration. Modern mobile processors—whether it’s the latest Snapdragons powering Android flagships or the A-series chips in iPhones—are no longer just Central Processing Units (CPUs). They are dedicated AI powerhouses capable of “always-on” generative tasks.

This hardware leap means we can now run massive models (like Gemini Nano) directly on the device. The benefits are immediate and transformative:

• Zero Latency: No network round-trips. The intelligence feels instantaneous.
• Total Privacy: Sensitive data never leaves the phone’s secure enclave.
• Offline Reliability: Intelligence works in elevators, basements, and airplanes.

The Critical Use Case: Android in Healthcare

Nowhere is this shift more vital than in the rapidly expanding world of Medical Devices. Android has quietly become the operating system of choice for specialized medical hardware, from handheld ultrasound scanners to patient vitals monitors.

Why is the edge critical here? Because medical environments are unforgiving. A doctor in a rural clinic or a paramedic in a speeding ambulance cannot rely on spotty 5G connections to process a patient’s vitals or analyze an X-ray.

• Privacy Compliance: Processing sensitive patient data (like facial analysis for pain detection) strictly on-device removes complex regulatory cloud compliance hurdles. The data stays with the patient.
• Reliability: An Android-based diagnostic tool must work instantly, 100% of the time, regardless of Wi-Fi status.
• Adoption: We are seeing a massive surge in smart, connected medical tools that rely on commodity Android hardware to deliver hospital-grade diagnostics at a fraction of the cost.

The “One AI” Future: Gemini on iOS & Android

Perhaps the most compelling reason to bet on Gemini is the upcoming unification of the mobile AI landscape. Reports indicate that Apple is partnering with Google to integrate Gemini models into iOS 18 and macOS Sequoia for complex reasoning tasks and summaries, a rollout expected to mature by Spring 2026.

While Apple will handle basic tasks with its own on-device models, it is leaning on Gemini’s superior reasoning for the “heavy lifting.” This creates a unique opportunity for developers:

• Unified Intelligence: Learning to engineer prompts and integrations for Gemini means you are effectively targeting the entire mobile market—both the Android medical devices and the premium iPhone user base.
• Cross-Platform Consistency: A feature built on Gemini’s logic will behave consistently whether it’s running on a Samsung Galaxy Tab in a hospital or an iPhone 17 in a consumer’s hand.
• Future-Proofing: With these updates expected shortly, building expertise in Gemini now puts us ahead of the curve when the feature goes mainstream across billions of Apple devices.

In Part 2, we will leave the strategy behind and dive into the code to see how we are already building this future today on iOS and Android.

The conversation around artificial intelligence (AI) in professional sectors, whether in law, finance, healthcare, or government, has reached a fever pitch. AI promises to boost productivity, reduce administrative burdens, and unlock new value across knowledge-based industries. Yet, for many organisations, the reality lags behind the rhetoric. Despite high levels of awareness and pilot projects aplenty, genuine, deep adoption of AI tools remains elusive.

As we stand on the brink of a new era in workplace technology, understanding the human factors that drive or block AI adoption is more critical than ever. The question is no longer if AI will reshape the workplace, but how and how deeply it will embed itself in the daily routines, decisions, and cultures of organisations.

The AI Adoption Gap

A striking paradox defines the current state of AI in the workplace. Surveys show that most professionals are familiar with generative AI, and organisations are investing heavily in pilots and proofs of concept. Yet, according to recent research, only a small minority of firms have moved beyond surface-level or “shallow” adoption to truly embed AI into core processes.

This “adoption gap” has tangible consequences:

• Missed Productivity Gains: Shallow use think drafting emails or summarizing documents, delivers only marginal improvements. The transformative potential of AI is realised only when it is integrated into complex, high-value workflows.
• Shadow IT Risks: Employees frequently use unauthorised or unapproved AI tools in the absence of clear guidelines, exposing organisations to compliance, security, and reputational risks.
• Stalled Innovation: Without deep adoption, firms risk falling behind competitors who are leveraging AI for strategic differentiation.

Bridging this gap requires more than technical solutions. It demands a Behavioral approach, one that recognizes the role of habits, heuristics, emotions, and social context in shaping how professionals embrace new technology.

Behavioral Science as the Missing Link

To accelerate meaningful AI adoption, organisations must look beyond binary metrics of use and instead understand the continuum of adoption, the barriers at each stage, and the Behavioral levers that can move individuals and teams deeper into productive engagement with AI.

1. Adoption Is a Continuum, Not a Toggle

AI adoption in professional settings is not a simple yes-or-no proposition. Instead, it unfolds along a spectrum:

• No Adoption: AI tools are ignored or avoided.
• Shallow Adoption: AI is used sporadically for low-stakes or auxiliary tasks.
• Deep Adoption: AI is fully integrated into core workflows, driving strategic gains in quality, innovation, and efficiency.

Implication: Organisations must diagnose where teams sit on this continuum and tailor interventions accordingly.

2. Motivation, Capability, and Trust: The Three Drivers of Adoption

Behavioral science identifies three essential ingredients for moving up the adoption ladder:

• Motivation: Do staff see a clear, relevant benefit to using AI?
• Capability: Do they feel able and confident to use AI effectively?
• Trust: Do they believe AI aligns with their values and professional standards?

Each driver comes with its own set of barriers and solutions:

• Motivation Barriers: Low salience of benefits, status quo bias, and “satisficing” (settling for good enough).
  • Solutions: Frame benefits in tangible terms, highlight quick wins, and use social proof and commitment devices.
• Capability Barriers: Friction in workflows, cognitive overload, and lack of operational readiness.
  • Solutions: Integrate AI seamlessly, reduce effort, and provide structured training and time for experimentation.
• Trust Barriers: Perceived threats to competence or identity, inconsistent signals, and doubts about AI’s legitimacy.
  • Solutions: Increase transparency, allow personalization, and celebrate early wins and responsible experimentation.

3. Small Design Choices Have Outsized Impact

Behavioral nudges like default settings, timely prompts, and visible endorsements from leaders can dramatically increase adoption. For example:

• Default AI notetakers in meetings can normalize use and reduce friction.
• Peer comparison and transparency about how AI works build trust and engagement.
• Showcasing successful use cases and creating AI “champions” can drive momentum across teams.

4. Context Matters: One Size Does Not Fit All

Adoption barriers and enablers vary by individual, role, sector, and task. For instance:

• High-stakes or identity-defining tasks (e.g., clinical diagnosis or legal decisions) require greater trust and clearer evidence of AI’s value.
• Adoption rates differ by gender, age, and professional background, highlighting the need for inclusive strategies.

5. From Shallow to Deep: The Real Value Is in Integration

The most significant gains come not from using AI more often, but from embedding it more deeply, redesigning workflows, updating performance metrics, and empowering employees to co-create new processes. Firms that achieve this see outsized returns in productivity, innovation, and employee satisfaction.

Charting a Roadmap for AI Adoption

The future of professional work will be shaped as much by behavioral insights as by technical breakthroughs. To unlock the full promise of AI, organisations must:

1. Assess Current Adoption: Map where teams are on the adoption continuum.
2. Diagnose Barriers: Identify motivational, capability, and trust-related obstacles.
3. Co-Design Interventions: Work with staff to develop tailored, behaviorally informed solutions.
4. Pilot, Measure, and Scale: Experiment, gather feedback, and iterate based on what works.
5. Celebrate and Learn: Share successes, acknowledge failures, and foster a culture of responsible AI experimentation.

Leaders committed to the AI-enabled future must move beyond hype and pilot projects. By applying behavioral science to the adoption challenge, professional firms can transform AI from a peripheral tool into a strategic asset—one that delivers on its promise for people, performance, and purpose.

For organisations seeking to accelerate their AI journey, the message is clear: start with behavior, and the technology will follow.

 Behavior+ AI Series

• Building A More Capable And Wiser AI

Based on the Adopt article from BIT.

Explore our AI services and capabilities at Perficient

What is MCP?

Model Context Protocol (MCP) is an open-source standard for integrating AI applications to external systems. With AI use cases getting traction more and more, it becomes evident that AI applications tend to connect to multiple data sources to provide intelligent and relevant responses.

Earlier AI systems interacted with users through Large language Models (LLM) that leveraged pre-trained datasets. Then, in larger organizations, business users work with AI applications/agents expect more relevant responses from enterprise dataset, from where Retrieval Augmented Generation (RAG) came into play.

Now, AI applications/agents are expected to produce more accurate responses leveraging latest data, that requires AI systems to interact with multiple data sources and fetch accurate information. When multi-system interactions are established, it requires the communication protocol to be more standardized and scalable. That is where MCP enables a standardized way to connect AI applications to external systems.

Architecture

Using MCP, AI applications can connect to data source (ex; local files, databases), tools and workflows – enabling them to access key information and perform tasks. In enterprises scenario, AI applications/agents can connect to multiple databases across organization, empowering users to analyze data using natural language chat.

Benefits of MCP

MCP serves a wide range of benefits

• Development: MCP reduces development time and complexity when building, or integrating with AI application/agent. It makes integrating MCP host with multiple MCP servers simple by leveraging built-in capability discovery feature.
• AI applications or agents: MCP provides access to an ecosystem of data sources, tools and apps which will enhance capabilities and improve the end-user experience.
• End-users: MCP results in more capable AI applications or agents which can access your data and take actions on user behalf when necessary.

MCP – Concepts

At the top level of MCP concepts, there are three entities,

• Participants
• Layers
• Data Layer Protocol

Participants

MCP follows a client-server architecture where an MCP host – an AI application like enterprise chatbot establishes connections to one or more MCP servers. The MCP host accomplishes this by creating a MCP client for each MCP server. Each MCP client maintains a dedicated connection with its MCP server.

The key participants of MCP architecture are:

• MCP Host: AI application that coordinates and manages one or more MCP clients
• MCP Client: A component that maintains a dedicated connection to an MCP server and obtains context from an MCP server for MCP host to interact
• MCP Server: A program that provides context to MCP clients (i.e. generate responses or perform actions on user behalf)

Layers

MCP consists of two layers:

• Data layer – Defines JSON-RPC based protocol for client-server communication including,
  • lifecycle management – initiate connection, capability discovery & negotiation, connection termination
  • Core primitives – enabling server features like tools for AI actions, resources for context data, prompt templates for client-server interaction and client features like ask client to sample from host LLM, log messages to client
  • Utility features – Additional capabilities like real-time notifications, track progress for long-running operations
• Transport Layer – Manages communication channels and authentication between clients and servers. It handles connection establishment, message framing and secure communication between MCP participants

Data Layer Protocol

The core part of MCP is defining the schema and semantics between MCP clients and MCP servers. It is the part of MCP that defines the ways developers can share context from MCP servers to MCP clients.

MCP uses JSON-RPC 2.0 as its underlying RPC protocol. Client and servers send requests to each other and respond accordingly. Notifications can be used when no response is required.

Life Cycle Management

MCP is a stateful protocol that requires lifecycle management. The purpose of lifecycle management is to negotiate the capabilities (i.e. functionalities) that both client and server support.

Primitives

Primitives define what clients and servers can offer each other. These primitives specify the types of contextual information that can be shared with AI applications and the range of actions that can be performed. MCP defines three core primitives that servers can expose:

• Tools: Executable functions that AI applications can invoke to perform actions (e.g., API calls, database queries)
• Resources: Data sources that provide contextual information to AI applications (e.g., file contents, API responses, database records)
• Prompts: Reusable templates that help structure interactions with language models (e.g., system prompts, few-shot examples)

Notifications

The protocol supports real-time notifications to enable dynamic updates between servers and clients. For example, when a server’s available tools change – such as when new functionalities are added or existing functionality is updated – the server can send tool update notifications to all its connected clients about these changes.

Security in Data Accessing

While AI applications communicate with multiple enterprise data sources thgrouch MCP and fetch real-time sensitive data like customer information, financial data to serve the users, data security becomes absolutely critical factor to be addressed.

MCP ensures secure access.

Authentication and Authorization

MCP implements server-side authentication where each MCP server validates who is making the request. The enterprise system controls access through:

• User-specific credentials – Each user connecting through MCP has their own authentication tokens
• Role-based access control (RBAC) – Users only access data that the role permits
• Session management – Time-limited sessions that expire automatically

Data Access Controls

MCP server acts as a security gateway that enforces the same access policies as direct system access:

• • Users can only query data that they are authroized to access
  • The server validates every request against permission rules
  • Sensitive information can be masked or filtered based on user privileges

Secure Communication

• • • Encrypted connections – All data transmissions uses TLS/HTTPS encryption
    • No data storage in AI – AI systems do not store the financial data it accesses; it only process it during the conversation session

Audit and Monitoring

MCP implementations in enterprise ecosystem should include:

• • • Complete audit logs – Every data access request is logged with user, timestamp and data accessed
    • Anomaly detection – Engage mechanisms that monitor unusual access patterns and trigger alerts
    • Compliance tracking – All interactions meet regulatory requirements like GDPR, PCI-DSS

Architecture Isolation

Enterprises typically deploy MCP using:

• • • Private network deployment – MCP servers stay within the enterprise secure firewall boundary
    • API gateway integration – Requests go through existing security infrastructure
    • No direct database access – MCP connects and access data through secure APIs, not direct access to database

The main idea is that MCP does not bypass existing security. It works within the same security as other enterprise applications, just showing a smarter interface.

MCP Implementation & Demonstration

In this section, I will demonstrate a simple use case where MCP client (Claude Desktop) interacts with “Finance Manager” MCP server that can fetch financial information from the database.

Financial data is maintained in Postgres database tables. MCP client (Claude Desktop app) will request information about customer account, MCP host will discover appropriate capability based on user prompt and invoke respective MCP tool function that can fetch data from the database table.

To make MCP client-server in action, there are three parts to be configured

• • • Backend Database
    • MCP server implementation
    • MCP server registration in MCP Host

Backend Database

Postgres table “accounts” maintains accounts data with below information, “transactions” table maintains the transaction performed on the accounts

MCP server implementation

FastMCP class implements MCP server components and creating an object of it initialize and enables access to those components to create enterprise MCP server capabilities.

The annotation “@mcp.tool()” defines the capability and the respective function will be recognized as MCP capability. These functions will be exposed to AI applications and will be invoked from MCP Host to perform designated actions.

In order to invoke MCP capabilities from client, MCP server should be up & running. In this example, there are two functions defined as MCP tool capabilities,

• • • get_account_details – The function accept account number as input parameter, query “accounts” table and returns account information
    • add_transaction – The function accepts account number and transaction amount as parameters, make entry into “transactions” table

MCP Server Registration in MCP Host

For AI applications to invoke MCP server capability, MCP server should be registered in MCP host at client end. For this demonstration, I am using Claude Desktop as MCP client from where I interact with MCP server.

First, MCP server is registered with MCP host in Claude Desktop as below,

Claude Desktop -> Settings -> Developer -> Local MCP Servers -> Click “Edit Config”

Open “claude_desktop_config” JSON file in Notepad. Add configurations in the JSON as below. The configurations define the path where MCP server implementation is located and instruct command to MCP host to run. Save the file and close.

Restart “Claude Desktop” application, go to Settings -> Developer -> Local MCP servers tab. The newly added MCP server (finance-manager) will be in running state as below,

Go to chat window in Claude Desktop. Issue a prompt to fetch details of an account in “accounts” table and review the response,

User Prompt: User issues a prompt to fetch details of an account.

MCP Discovery & Invoke: The client (Claude Desktop) processes the prompt, interacts with MCP host, automatically discover the relevant capability – get_account_details function in this case – without explicitly mention the function name and invoke the function with necessary parameter.

Response: MCP server process the request, fetch account details from the table and respond details to the client. The client formats the response and present it to the user.

Another example to add a transaction in the backend table for an account,

Here, “add_transaction” capability has been invoked to add a transaction record in “transactions” table. In the chat window, you could notice that what MCP function is being invoked along with request & response body.

The record has been successfully added into the table,

Impressive, isn’t it..!!

There are a wide range of use cases implementing MCP servers and integrate with enterprise AI systems that bring in intelligent layer to interact with enterprise data sources.

Here, you may also develop a thought that in what ways MCP (Model Context Protocol) is different from RAG (Retrieval Augmented Generation), as I did so. Based on my research, I just curated a comparison matrix of the features that would add more clarity,

The rapid advancement of artificial intelligence has sparked a broad spectrum of opinions across society, with strong arguments both supporting and opposing its implementation. On one side, many view AI-driven tools as transformative, bringing remarkable progress to sectors such as healthcare, education, and transportation, while also fueling innovation and research. On the other side, skeptics raise valid concerns about the reliability of AI-generated medical diagnoses and the safeguarding of sensitive patient information. Additional worries include potential job displacement, widened socioeconomic divides, the environmental impact caused by energy-intensive systems, and the accumulation of electronic waste—issues that question the long-term sustainability of these technologies.

Artificial intelligence undeniably continues to shape our society, emphasizing the urgency for individuals and organizations to establish ethical guidelines that encourage its responsible and transparent application. Here I share some key recommendations, to ensure AI is implemented conscientiously:

• Organizations should appoint dedicated teams to oversee AI development and usage. They must also outline clear policies that guarantee ethical and responsible practices
• It is crucial to design strategies for identifying and mitigating biases embedded in AI systems to prevent outcomes that could compromise human dignity or foster discrimination.
• Datasets utilized in AI training must be inclusive and representative of diverse populations, ensuring fairness across societal groups.
• Privacy and security measures should prioritize safeguarding data used by AI systems as well as data they generate.
• Transparency  in AI decision-making processes, operations, and applications.
• Organizations should implement tools that clearly and understandably explain how their AI systems operate and how they utilize them.
• Controls should be established to mediate or override critical decisions made by AI systems. Human oversight is vital for ensuring such decisions align with ethical principles.
• Compliance with relevant regulatory frameworks, such as the General Data Protection Regulation (GDPR), must be strictly maintained.

As the pace of AI innovation accelerates and new tools emerge, it is equally important to continuously refine ethical frameworks governing their function. This adaptability promotes sustained responsible usage, effectively addressing new challenges over time.

While challenges related to regulation and implementation remain significant, the opportunities created by artificial intelligence are boundless—offering immense potential to enrich society for the greater good.

References:

• No title. (s/f). Unesco.org. Recovered on Jan 7th, 2026 from https://www.unesco.org/es/artificial-intelligence/recommendation-ethics 
• García, L. P. (2025, enero 8). Interacción entre la ética y la Inteligencia Artificial. Labor Hospitalaria. https://www.laborhospitalaria.com/interaccion-entre-la-etica-y-la-inteligencia-artificial/ 
• Social, R., & De personas, H. (2024, diciembre 26). Riesgos éticos en el uso de la inteligencia artificial. Deloitte. https://www.deloitte.com/latam/es/services/risk-advisory/perspectives/riesgos-eticos-en-el-uso-de-la-inteligencia-artificial.html 
• Artificial, a. I. (s/f). Ética en la Inteligencia Artificial. Ibero.mx. Recovered on Jan 7th, 2026 from https://revistas.ibero.mx/ibero/uploads/volumenes/69/pdf/6-etica-en-la-inteligencia-artificial.pdf 

AI is reshaping industries, economies, and societies at an unprecedented pace. From powering everyday digital assistants to revolutionizing research and decision making, AI’s reach is expanding. However, as technology evolves, our understanding of what it means for AI to be truly intelligent also evolves. To build robust, adaptable, and trustworthy AI, we must look beyond technical achievements and draw from the insights of behavioral science.

Why Now?

Tasks that require more than just speed and size are now being assigned to AI. These tasks demand reasoning, flexibility, and judgment qualities traditionally associated with human cognition. As we uncover glitches, biases, and inefficiencies in even the most advanced AI, it’s clear that we need to learn from human thinking and feeling.

From Quick Responses to Wise Intelligence

Top-notch AI, particularly the large language models (LLMs), excel at delivering instant responses. But when it comes to the slower, reflective kind of thinking? They’re not so hot. That’s where we see the usual suspects like making things up, choking on the unfamiliar, and burning through resources like there’s no tomorrow.

For organizations deploying AI, these challenges have real implications:

• Reliability: Inconsistent results can erode trust and impede progress.
• Efficiency: Sometimes, we overthink the simple stuff or bail too soon on the tough jobs, and that’s just resources down the drain and chances missed.
• Risk Management: Without human oversight, AI can produce suboptimal results, biases, or damage to reputation.

To be a trusty sidekick, AI needs to level up and, at times, safely automate decisions in high-stakes areas, hitting the sweet spot of human cognition, fast and slow thinking.

The Behavioral Science Advantage

Through the application of behavioral science, we can create AI that’s not only fast but also wise.

1. Human-Like Reasoning Requires Metacognition

Relying on fast, automatic, and intuitive processing, most AI models today mirror the human brain’s “System 1.” However, robust decision-making also requires “System 2″reflective, deliberate, and analytical reasoning. The true advantage for AI lies in metacognition, the ability to think about its own thinking and choose the right mode for the task.

Consider the surgeon riddle, for example. LLMs can spot the punchline when it’s there, but metacognitive controls could help AI know when to take a shortcut and when to dig deeper.

2. Building a Metacognitive Controller

Envision a metacognitive controller as a savvy companion that always selects the perfect tool for the task. With a nod to behavioral science, we can craft AI that sizes up a problem, spots what it doesn’t know, and opts for the best strategy.

• Quick Fact Check: The controller sends simple queries to speedy, heuristic processors.
• Complex Tasks: It uses structured reasoning and formal checks for more challenging queries.
• Uncertainty: If it’s not sure, it’ll ask for more details or check with external sources.

This clever routing not only boosts accuracy but also saves us from knee-jerk errors and senseless waiting.

3. Resource Rationality: Smarter, Not Just Harder

Efficiency is key, especially when computing resources are limited. AI should focus on smart work, not just hard work.

A recent study, for example, showed that LLMs can sometimes “overthink” simple classification tasks, resulting in less human-like decisions and extended processing times. On the flip side, they may not invest enough effort in more demanding tasks. By embedding resource rationality, an explicit trade-off between expected accuracy and computational cost is made. AI can become more efficient and trustworthy.

4. Rewarding Wisdom, Not Just Outputs

Thanks to extensive human input, AI is trained to produce what we desire. But hey, behavioral science tells us to shake things up. We should be schooling AI in the art of wisdom, being humble, dealing with the unknown, listening to different voices, and knowing when to say, “You know what? You’re the expert here.”

Methods like Meta Reinforcement Learning (MRL) or Process Reward Models (PRM) can reward these metacognitive skills, encouraging AI to reason wisely, expressing uncertainty when justified, seeking other viewpoints, and challenging its own conclusions.

5. Neurosymbolic AI: Integrating Fast and Slow Thinking

The future of AI may lie in hybrid architectures that combine pattern-matching neural networks (System 1) with rule-based, symbolic systems (System 2). Behavioral science provides a blueprint for how these systems should work together, not as separate entities but as a spectrum with learning flowing both ways.

For example, human expertise involves refining slow, deliberate analyses into fast, intuitive responses. Neurosymbolic AI can use formal models to refine neural “hunches” and, conversely, guide symbolic engines toward promising paths, reducing search burdens and making logic-based reasoning more practical at scale.

As AI’s influence grows, it’s clear that we need to pair it with the wisdom of Behavioral science. We must move

Behavior + AI Series

• Cracking the Code on Real AI Adoption

Based on the Augment article from BIT.

Explore our AI services and capabilities at Perficient

The financial services industry is undergoing significant transformation, driven by the increasing adoption of artificial intelligence (AI) and data science. As financial institutions strive to stay competitive, they’re leveraging these technologies to improve customer experience, operational efficiency, and risk management. At Data Science Salon NYC, I had the opportunity to join industry experts in discussing the latest trends and innovations shaping our field. Here are four key takeaways from the event: 

AI Adoption Starts with Customer-Centric Use Cases 

Financial institutions are using AI to enhance customer experience through personalized services, and we’re seeing the most immediate impact in areas like call centers and knowledge retrieval. When we talk about saving time and effort, customer experience is an easy space where we can start thinking about answering questions faster.  

By putting the customer at the center and leveraging AI-driven analytics, financial institutions can gain deeper insights into customer behavior and preferences, enabling them to tailor services to meet specific needs. The key is starting with use cases that have clear, measurable impact on customer satisfaction and operational efficiency. 

Data Science Is About Business Outcomes, Not Just Technology 

One of the most important lessons we continue to emphasize: Data science is not just about algorithms and technology; it’s about business value. In our work with financial services and insurance clients, we’re constantly focused on driving tangible business results. 

When measuring success, we need to have open conversations because business leaders have very different definitions of success than technology leaders. Yes, latency is important, but at what point does that latency drive or impact revenue or costs? Ultimately, we need to put a dollar sign in front of it. Success boils down to two key metrics: 

Does it move the bottom line? 

Are people actually using it? 

Success is defined as whether everyone can use that tool and whether it’s simple to follow. In the end, it’s people who are driving the revenue. Financial institutions that invest in data science innovation with this business-first mindset are better positioned to stay ahead of the competition and drive real growth. 

AI Governance Isn’t a Yes or No Decision 

One of the biggest things we’re encouraging any enterprise to do as they think about AI governance is understanding that very few evaluations come down to a “yes” or “no” decision. Rather, we should strive to define the risk mitigations necessary to get a “yes.” Effective AI governance involves establishing clear frameworks that include: 

• Continuous monitoring and auditing of AI systems for bias and performance 

• Transparent AI explainability to build trust among stakeholders and regulators 

• Open dialogue about risk mitigation strategies 

We must make sure we’re building trust beyond the vendor level, but on each individual use case. By implementing thoughtful governance, financial institutions can manage risks while still innovating confidently. 

Adoption and Change Management Are Critical Success Factors 

The adoption question is crucial: Are people actually using it? We need to educate our teams on what we’re doing, why we’re doing these things, and how they can take advantage of it. 

One practice we always recommend is A/B testing. Many organizations don’t always A/B test the efficacy of the AI tool versus not having the AI tool. Instead of giving it to everyone at once, we’ve taken one area, split teams in half, and had one side do the work the traditional way while the other uses the new AI tool. This allows us to measure real impact and build confidence in the technology. 

AI-powered solutions are increasingly being used to detect and prevent financial crimes such as money laundering and fraud through predictive modeling and anomaly detection techniques. By leveraging these technologies thoughtfully (with proper governance, testing, and adoption strategies) financial institutions can reduce risk while improving regulatory compliance. 

Looking Ahead 

The key to success in AI and data science isn’t just adopting the latest technology, it’s ensuring that technology drives measurable business value, is governed responsibly, and is adopted by the people who need to use it. When we get those three elements right, that’s when we see transformational results in financial services. 

To learn more about Perficient’s AI capabilities in the financial services industry, visit https://www.perficient.com/industries/financial-services. For more AI insights, sign up for Perficient’s AI-First Newsletter.

At the Healthcare Interactive Conference (HCIC) last month, I got to talk to marketers who are very focused on results. They are also very focused on what will impact their marketing efforts and why. Every conversation came back to AI.

In my previous HCIC takeaway, I wrote about how AI is not a strategy—it’s a tool to solve real problems. Now I want to dig into a specific problem AI is creating for healthcare marketers: how we get found. We need to be thinking about all aspects of how AI can be used. In general, this breaks down into both impact and opportunity.

Impact: AI Search Is Transforming Healthcare Discovery

Several conference sessions alluded to this shift, but marketing experts Brittany Young and Gina Linville gave some deeper insight.

From a marketing perspective, the largest impact is one of being found. Think about how much time a typical hospital marketer puts into being found. I have had many conversations over the years about Search Engine Optimization (SEO) and the importance of having valuable content that the search engines view as unique and relevant.

AI impacts that in ways that are not at first obvious.

The New Reality of Patient Search

Think of how you typically use ChatGPT or how your search engine has evolved. AI now pulls the data and gives you a brief with information culled from multiple online sources. The good news is that the AI tool will typically reference a website it sources. The bad news is while AI typically credits source websites, patients get their answers without ever clicking through to your site.

The scale of this shift is staggering:

AI provides an overview for up to 84% of search queries when it comes to healthcare questions.

Healthcare leads nearly every sector in AI-powered search results—a trend that’s accelerating:

Strategic Response: Winning at AI Search in Healthcare

This shift demands a fundamental rethinking of content strategy. Two concepts are emerging as critical:

1) Answer Engine Optimization (AEO)

• Answer Engine Optimization (AEO) is the practice of structuring and optimizing content so that AI-powered systems, such as Google’s AI Overviews, ChatGPT, Perplexity AI, and voice assistants, can easily identify, extract, and cite it as a direct answer to user queries.

2) Generative Engine Optimization (GEO)

• Generative Engine Optimization (GEO) is a digital marketing technique designed to improve a brand’s visibility in results produced by generative artificial intelligence (GenAI) platforms. It involves adapting digital content and online presence to ensure that AI systems can accurately interpret, cite, and use the content when generating responses to user queries.

The imperative is clear: Organizations that don’t optimize for AI-powered discovery won’t just lose rankings—they’ll lose visibility entirely.

If you are not already thinking about how to orient your content to this then be aware that you will soon feel an impact.

Opportunity: Agentic AI and Productivity

On the flip side of the coin is the opportunity. While the impact above provides you with an opportunity provided you react appropriately, I want to focus on the productivity part of this. Specifically, think of what Agentic AI can do for your organization.

What Traditional Campaign Development Looks Like

Let me give you a few examples of common tasks and how long they typically take:

• Create a campaign brief: up to two weeks
• Create copy across multiple channels: 8-16 hours
• Create digital assets related to the campaign which fit your brand standards and work in each individual channel. Web site may allow for larger images. Paid search or paid social may have limited space: 40 hours
• Creation of the segment and pushing it to marketing automation tools: several hours

Now imagine specialized AI agents handling each component—not replacing human strategy and judgment, but accelerating execution while maintaining brand standards and compliance. Just getting one campaign going across multiple channels become a multi-person engagement over several weeks. While focused on that, you won’t focus on additional campaign or in honing your craft.

The AI Agent Team Your Marketing Organization Needs

The answer lies with Agentic AI. We believe that AI can cut down on the time necessary to complete these tasks and still keep humans in the loop. Here are a few examples of agents you might need in your organization:

Most importantly, this frees your marketing team to focus on what AI can’t do: strategic thinking, creative problem-solving, and understanding the nuanced needs of your community. 

The Path Forward: Integration, Not Replacement

The organizations winning in this new landscape aren’t choosing between human expertise and AI capabilities. They’re strategically integrating both.

Success requires more than technology. It needs an integrated approach:

1. Rethinking discoverability through AEO and GEO optimization
2. Deploying specialized AI agents for productivity acceleration
3. Maintaining human oversight for strategy, creativity, and judgment
4. Ensuring compliance at every step, particularly in heavily regulated healthcare
5. Measuring impact against business outcomes, not just operational metrics

Enabling Healthcare Organizations To Lead This Shift

HCIC reminded us that success in healthcare marketing isn’t about chasing technology for its own sake. As I shared in my first HCIC takeaway, AI is not a strategy—it’s a tool to solve real challenges that impact your organization’s ability to connect patients to care.

The search revolution is here. The productivity opportunity is real. The organizations that move quickly to optimize for AI-powered discovery while deploying strategic AI agents will gain a competitive advantage that compounds over time.

Start a conversation with our experts today.

Reflecting on this year’s InsureTech Connect Conference 2025 in Las Vegas, one theme stood out above all others: the insurance industry has crossed a threshold from AI experimentation to AI expectation. With over 9,000 attendees and hundreds of sessions, the world’s largest insurance innovation gathering became a reflection of where the industry stands—and where it’s heading.

What became clear: the carriers pulling ahead aren’t just experimenting with AI—they’re deploying it with intentional discipline. AI is no longer optional, and the leaders are anchoring every investment in measurable business outcomes.

The Shift Is Here: AI in Insurance Moves from Experimentation to Expectation

This transformation isn’t happening in isolation though. Each shift represents a fundamental change in how carriers approach, deploy, and govern AI—and together, they reveal why some insurers are pulling ahead while others struggle to move beyond proof-of-concept.

Here’s what’s driving the separation:

• Agentic AI architectures that move beyond monolithic models to modular, multi-agent systems capable of autonomous reasoning and coordination across claims, underwriting, and customer engagement. Traditional models aren’t just slow—they’re competitive liabilities that can’t deliver the coordinated intelligence modern underwriting demands.
• AI-first strategies that prioritize trust, ethics, and measurable outcomes—especially in underwriting, risk assessment, and customer experience.
• A growing emphasis on data readiness and governance. The brutal reality: carriers are drowning in data while starving for intelligence. Legacy architectures can’t support the velocity AI demands.

Success In Action: Automating Insurance Quotes with Agentic AI

Why Intent Matters: Purpose-Driven AI Delivers Measurable Results

What stood out most this year was the shift from “AI for AI’s sake” to AI with purpose. Working with insurance leaders across every sector, we’ve seen the industry recognize that without clear intent—whether it’s improving claims efficiency, enhancing customer loyalty, or enabling embedded insurance—AI initiatives risk becoming costly distractions.

Conversations with leaders at ITC and other industry events reinforced this urgency. Leaders consistently emphasize that purpose-driven AI must:

• Align with business outcomes. AI enables real-time decisions, sharpens risk modeling, and delivers personalized interactions at scale. The value is undeniable: new-agent success rates increase up to 20%, premium growth boosts by 15%, customer onboarding costs reduce up to 40%.

• Be ethically grounded. Trust is a competitive differentiator—AI governance isn’t compliance theater, it’s market positioning.

• Deliver tangible value to both insurers and policyholders. From underwriting to claims, AI enables real-time decisions, sharpens risk modeling, and delivers personalized interactions at scale. Generative AI accelerates content creation, enables smarter agent support, and transforms customer engagement. Together, these capabilities thrive on modern, cloud-native platforms designed for speed and scalability.

Learn More: Improving CSR Efficiency With a GenAI Assistant

Building the AI-Powered Future: How We’re Accelerating AI in Insurance

So, how do carriers actually build this future? That’s where strategic partnerships and proven frameworks become essential.

At Perficient, we’ve made this our focus. We help clients advance AI capabilities through virtual assistants, generative interfaces, agentic frameworks, and product development, enhancing team velocity by integrating AI team members.

Through our strategic partnerships with industry-leading technology innovators—including AWS, Microsoft, Salesforce, Adobe, and more— we accelerate insurance organizations’ ability to modernize infrastructure, integrate data, and deliver intelligent experiences. Together, we shatter boundaries so you have the AI-native solutions you need to boldly advance business.

But technology alone isn’t enough. We take it even further by ensuring responsible AI governance and ethical alignment with our PACE framework—Policies, Advocacy, Controls, and Enablement—to ensure AI is not only innovative, but also rooted in trust. This approach ensures AI is deployed with purpose, aligned to business goals, and embedded with safeguards that protect consumers and organizations.

Because every day your data architecture isn’t AI-ready is a day you’re subsidizing your competitors’ advantage.

You May Also Enjoy: 3 Ways Insurers Can Lead in the Age of AI

Ready to Lead? Partner with Perficient to Accelerate Your AI Transformation

Are you building your AI capabilities at the speed the market demands?

From insight to impact, our insurance expertise helps leaders modernize, personalize, and scale operations. We power AI-first transformation that enhances underwriting, streamlines claims, and builds lasting customer trust.

• Business Transformation: Activate strategy and innovation ​within the insurance ecosystem.​
• Modernization: Optimize technology to boost agility and ​efficiency across the value chain.​
• Data + Analytics: Power insights and accelerate ​underwriting and claims decision-making.​
• Customer Experience: Ease and personalize experiences ​for policyholders and producers.​

We are trusted by leading technology partners and consistently mentioned by analysts. Discover why we have been trusted by 13 of the 20 largest P&C firms and 11 of the 20 largest annuity carriers. Explore our insurance expertise and contact us to learn more.

As healthcare organizations accelerate toward value-based care, the ability to turn massive data volumes into actionable insights is no longer optional—it’s mission-critical.

In a recent webinar, Improve Healthcare Quality with Data + AI, experts from Databricks, Excellus BlueCross BlueShield, and Perficient shared how leading organizations are using unified data and AI to improve outcomes, enhance experiences, and reduce operational costs.

Below are the key themes and insights you need to know.

1. Build a Unified, AI-Ready Data Foundation

Fragmented data ecosystems are the biggest barrier to scaling AI. Claims, clinical records, social determinants of health (SDOH), and engagement data often live in silos. This creates inefficiencies and incomplete views of your consumers (e.g, members, patients, providers, brokers, etc.).

What leaders are doing:

• Unify all data sources—structured and unstructured—into a single, secure platform.
• Adopt open formats and governance by design (e.g., Unity Catalog) to ensure compliance and interoperability.
• Move beyond piecemeal integrations to an enterprise data strategy that supports real-time insights.

Why it matters: A unified foundation enables predictive models, personalized engagement, and operational efficiency—all essential for success in value-based care.

2. Shift from Reactive to Proactive Care

Healthcare is moving from anecdotal, reactive interactions to data-driven, proactive engagement. This evolution requires prioritizing interventions based on risk, cost, and consumer preferences.

Key capabilities:

• Predict risk and close gaps in care before they escalate.
• Use AI to prioritize next-best actions, balancing population-level insights with individual needs.
• Incorporate feedback loops to refine outreach strategies and improve satisfaction.

North Star: Deliver care that is timely, personalized, and measurable, improving both individual outcomes and population health.

3. Personalize Engagement at Scale

Consumers expect the “Amazon experience”—personalized, seamless, and proactive. Achieving this requires flexible activation strategies.

Best practices:

• Decouple message, channel, and recommendation for modular outreach.
• Use AI-driven segmentation to tailor interventions across email, SMS, phone, PCP coordination, and more.
• Continuously optimize based on response and engagement data.

Result: Higher quality scores, improved retention, and stronger consumer trust.

4. Operationalize AI for Measurable Impact

AI has moved beyond experimentation and is now delivering tangible ROI. Excellus BlueCross BlueShield’s AI-powered call summarization is a prime example:

• Reduced call handle time by one to two minutes, saving thousands of hours annually
• Improved audit quality scores from ~85% to 95–100%
• Achieved real-time summarization in under seven seconds, enhancing advocate productivity and member experience

Lesson: Start with high-impact workflows, not isolated tasks. Quick wins build confidence and pave the way for enterprise-scale transformation.

5. Scale Strategically—Treat AI as Business Transformation

Perficient emphasized that scaling AI is not a tech project—it’s a business transformation. Success depends on:

• Clear KPIs tied to business outcomes (e.g., CMS Stars, HEDIS measures)
• Governed, explainable, and continuously monitored data
• Change management and workforce enablement to drive adoption
• Modular, composable architecture for flexibility and speed

Pro tip: Begin with an MVP approach—prioritize workflows, prove value quickly, and expand iteratively.

Final Thought: Data and AI are Redefining Health Care Delivery

Healthcare leaders face mounting pressure to deliver better outcomes, lower costs, and exceptional experiences. The insights shared in this webinar make one thing clear: success starts with a unified, AI-ready data foundation and a strategic approach to scaling AI across workflows—not just isolated tasks.

Organizations that act now will be positioned to move from reactive care to proactive engagement, personalize experiences at scale, and unlock measurable ROI. The opportunity is here. How you act on it will define your competitive edge.

Ready to reimagine healthcare with data and AI?

If you’re exploring how to modernize care delivery and consumer engagement, start with a strategic assessment. Align your goals, evaluate your data readiness, and identify workflows that deliver the greatest business and health impact. That first step sets the stage for meaningful transformation, and it’s where the right partner can accelerate progress from strategy to measurable impact.

Our healthcare expertise equips leaders to modernize, personalize, and scale care. We drive resilient, AI-powered transformation to shape the experiences and engagement of healthcare consumers, streamline operations, and improve the cost, quality, and equity of care.

• Business Transformation: Activate strategy for transformative outcomes and health experiences.
• Modernization: Maximize technology to drive health innovation, efficiency, and interoperability.
• Data + Analytics: Power enterprise agility and accelerate healthcare insights.
• Consumer Experience: Connect, ease, and elevate impactful health journeys.

We understand that every organization is on a unique AI journey. Whether you’re starting from scratch, experimenting with pilots, or scaling AI across your enterprise, we meet you where you are. Our structured approach delivers value at every stage, helping you turn AI from an idea into a business advantage. Plus, as a Databricks Elite consulting partner, we build end-to-end solutions that empower you to drive more value from your data.

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

Watch the on-demand webinar now:

Nowadays, a person cannot live without some interaction with artificial intelligence, ranging from mobile apps to enterprise tools that use data and algorithms to help businesses make better decisions. What exactly are the main types of AI workloads? Let’s break them down in simple terms using real examples:

Natural Language Processing: How AI Understands Human Language

NLP is the name given to computers reading, understanding, and responding to human language.

Real-Life Examples

• Chatbots: Customer support bots reply to your queries instantly.
• Sentiment Analysis: AI shows brands whether posts on social media mention them positively or negatively.
• Language: Tools like Google Translate convert text between languages.

Computer Vision: Teaching Machines to See

With Computer Vision, machines can comprehend and interpret images and videos much like humans do.

Real-Life Examples

• Facial Recognition: Unlock your phone with your face.
• Object Detection: Self-driving cars identify pedestrians and traffic signs.
• Medical Imaging: This application enables doctors to detect diseases in X-rays or MRI scans using AI.

Predictive Models: AI Capable of Predicting the Future

Predictive models use historical data to predict future outcomes.

Real-Life Examples

• Sales Forecasting: Businesses predict monthly revenue.
• Fraud Detection: Banks detect suspicious transactions.
• Customer Churn Prediction: Companies predict which customers are likely to leave.

Conversational AI: Smart Chatbots & Virtual Assistants

Conversational AI is the technology behind systems that enable machines to have conversations with you in natural language.

Real-Life Examples

• Azure Bot Service: Customer support.
• Cortana: Virtual assistant provided by Microsoft.
• Customer Service Bots: You know, those helpful chat windows on websites.

Generative AI: Creating New Content with AI

Generative AI generates new text, images, or even code from learned patterns.

Real-life Examples

• GPT-4: can write blogs, answer questions, and even help with coding.
• DALL-E: Creates striking images out of textual prompts.
• Codex: Computer code from natural language instructions

Why Understanding AI Workloads Matters

Artificial Intelligence is no longer relegated to the pages of science fiction; it’s part of our daily lives. From Natural Language Processing powering chatbots to Computer Vision enabling facial recognition, and from Predictive Models forecasting trends to Generative AI creating new content, these workloads form the backbone of most modern AI applications.

A proper understanding of these key AI workloads will help businesses and individuals leverage AI to improve efficiency, enhance customer experience, and remain productive in a digitally evolving world. Whether you are a technology-savvy person, a business leader, or just an inquisitive mind about AI, knowing these basics gives you a clear picture of how AI is shaping the future.

Additional Reading

• What is Artificial Intelligence (AI)? / Blogs / Perficient
• What is Microsoft Copilot Studio? / Blogs / Perficient