Machine Learning (ML) may sound technical; however, once you break it down, it’s simply about teaching computers to learn from data—just like humans learn from experience.

What Is Machine Learning?

Machine Learning is a branch of artificial intelligence; in essence, it allows models to learn from data and make predictions or decisions without the need for explicit programming.

Every ML system involves two things:

• Input (Features)
• Output (Label)

With the right data and algorithms, ML systems can recognize patterns, make predictions, and automate tasks.

Types of Machine Learning

1.1 Supervised Learning

Supervised learning uses labeled data, meaning the correct answers are already known.

Definition

Training a model using data that already contains the correct output.

Examples

• Email spam detection
• Predicting house prices

Key Point

The model learns the mapping from input → output.

1.2 Unsupervised Learning

Unsupervised learning works with unlabeled data. No answers are provided—the model must find patterns by itself.

Definition

The model discovers hidden patterns or groups in the data.

Examples

• Customer segmentation
• Market basket analysis (bread buyers also buy butter)

Key Point

No predefined labels. The focus is on understanding data structure.

1.3 Reinforcement Learning

This type of learning works like training a pet—reward for good behavior, penalty for wrong actions.

Definition

The model learns by interacting with its environment and receiving rewards or penalties.

Examples

• Self-driving cars
• Game‑playing AI (Chess, Go)

Key Point

Learning happens through trial and error over time.

2. Core ML Concepts

2.1 Features

Input variables used to predict the outcome.

Examples:

• Age, income
• Pixel values in an image

2.2 Labels

The output or target value.

Examples:

• “Spam” or “Not Spam”
• Apple in an image

2.3 Datasets

When training a model, data is usually split into:

• Training Dataset
  Used to teach the model (e.g., 50% of data)
• Testing Dataset
  Used to check performance (the remaining 50%)
• Validation Dataset
  Fresh unseen data for final evaluation

2.4 Overfitting & Underfitting

Overfitting

The model learns the training data too well—even the noise.
Good performance on training data
✘ Poor performance on new data

Underfitting

The model fails to learn patterns.
Fast learning
✘ Poor accuracy on both training and new data

3. Common Machine Learning Algorithms

Below is a simple overview:

3.1 Regression

Used when predicting numerical values.

Examples

• Predicting sea level in meters
• Forecasting number of gift cards to be sold next month

Not an example:
Finding an apple in an image → That’s classification, not regression.

3.2 Classification

Used when predicting categories or labels.

Examples

• Identifying an apple in an image
• Predicting whether a loan will be repaid

3.3 Clustering

Used to group data based on similarity.
No labels are provided.

Examples

• Grouping customers by buying behavior
• Grouping news articles by topic

4. Model Evaluation Metrics

To measure the model’s performance, we use:

Basic Terms

• True Positive
• False Negative
• True Negative
• False Positive

Important Metrics

• Accuracy – How often the model is correct
• Precision – Of the predicted positives, how many were correct?
• Recall – How many actual positives were identified correctly?

These metrics ensure that the model is trustworthy and reliable.

Conclusion:

Machine learning may seem complex; however, once you understand the core concepts—features, labels, datasets, and algorithms—it quickly becomes a powerful tool for solving real‑world problems. Furthermore, whether you are predicting prices, classifying emails, grouping customers, or training self‑driving cars, ML is consistently present in the technology we use every day.

With foundational knowledge and clear understanding, anyone can begin their ML journey.

Additional Reading

• Understanding Common AI Workloads – Explained Simply / Blogs / Perficient
• What is Artificial Intelligence (AI)? / Blogs / Perficient
• What is Microsoft Copilot Studio? / Blogs / Perficient

1. Technical Foundations: From Prediction to Instruction

2. The Two Pillars of Effective Prompting

The evolution of AI is advancing at a rapid pace, progressing from Generative AI to AI agents, and from AI agents to Agentic AI.Many companies are developing their own AI tools, training The LLM specifically to enhance images, audio, video, and text communications with a human-like touch. However, the data used in these tools is often not protected, as it is directly incorporated into training the Large Language Models (LLMs).

Have you ever wondered how organizations can leverage LLMs while still keeping their data private? The key approach to achieve this enhancement is Retrieval-Augmented Generation (RAG).

Retrieval-Augmented Generation (RAG) is a framework where relevant information is retrieved from external sources (like private documents or live databases) and provided to the LLM as immediate context. While the LLM is not aware of the entire external dataset, it uses its reasoning capabilities to synthesize the specific retrieved snippets into a coherent, human-like response tailored to the user’s prompt.

How does RAG works?

Retrieval phase from document:

   1.External Documents

We manage extensive repositories comprising thousands of external PDF source documents to provide a deep knowledge base for our models.

2. The Chunking Process

To handle large-scale text, we break documents into smaller “chunks.” This is essential because Large Language Models (LLMs) have a finite context window and can only process a limited amount of text at one time.

Example: If a document contains 100 lines and each chunk has a capacity of 10 lines, the document is divided into 10 distinct chunks.

3. Chunk Overlap

To maintain continuity and preserve context between adjacent segments, we include overlapping lines in consecutive chunks. This ensures that no critical information is lost at the “seams” of a split.

Example: With a 1-line overlap, Chunk 1 covers lines 1–10, Chunk 2 covers lines 10–19, and Chunk 3 covers lines 19–28.

4. Embedding Process

Once chunked, the text is converted into Embeddings. During this process, each chunk is transformed into a Vector—a list of numerical values that represent the semantic meaning of the text.

• Example Vector: [0.12, -0.05, …, 0.78]

5. Indexing & Storage

The generated vectors are stored in specialized vector databases such as FAISS, Pinecone, or Chroma.

• Mapping: Each vector is stored alongside its corresponding text chunk.
• Efficiency: This mapping ensures high-speed retrieval, allowing the system to find the most relevant text based on vector similarity during a search operation.

The Augmentation Phase: Turning Data into Answers

Once your documents are indexed, the system follows a specific workflow to generate accurate, context-aware responses.

1. User Query & Embedding

The process begins when a user submits a prompt or question. This natural language input is immediately converted into a numerical vector using the same embedding model used during the indexing phase.

2. Vector Database Retrieval

The system performs a similarity search within the vector database (e.g., Pinecone or FAISS). It identifies and retrieves the top-ranked text chunks that are most mathematically relevant to the user’s specific question.

3. Prompt Augmentation

The retrieved “context” chunks are then combined with the user’s original question. This step is known as Augmentation. By adding this external data, we provide the LLM with the specific facts it needs to answer accurately without “hallucinating.”

4. Final Prompt Construction

The system constructs a final, comprehensive prompt to be sent to the LLM.

The Formula: > Final Prompt = [User Question] + [Retrieved Contextual Data]

Generation phase:

This is the final stage of the RAG (Retrieval-Augmented Generation) workflow.

Augmented prompt is fed into the LLM,LLM synthesizes the retrieved context to craft a precise, natural-sounding response. Thus  transforming thousands of pages of raw data into a single, highly relevant and accurate answer.

Application of RAG Industries

1. Healthcare & Life Sciences
2. Finance & Banking
3. Customer Support & eCommerce
4. Manufacturing & Engineering

Conclusion:

Retrieval-Augmented Generation (RAG) merges robust retrieval mechanisms with generative AI. This architecture provides a scalable, up-to-date framework for high-stakes applications like enterprise knowledge assistants and intelligent chatbots. By evolving standard RAG into Agentic RAG, we empower AI agents to move beyond passive retrieval, allowing them to reason, iterate, and orchestrate complex workflows. Together, these technologies form a definitive foundation for building precise, enterprise-ready AI systems.

A federated architecture for self-improving skills — from every employee’s laptop to the company brain.

Every enterprise has the same problem hiding in plain sight. Somewhere between the onboarding wiki that nobody reads, the Slack threads that disappear after a week, and the senior engineer who carries half the team’s knowledge in their head — institutional knowledge is dying. Not because companies don’t try to preserve it, but because the systems we’ve built to capture it are fundamentally passive. They wait for someone to write a doc. They wait for someone to search. They never learn on their own.

What if every employee’s computer had an AI agent that watched, learned, and guided — and every night, those agents pooled what they’d learned into something smarter than any of them alone?

The State of Enterprise AI Assistants: Smart But Shallow

Today’s enterprise AI tools — Google Agentspace, Microsoft Copilot, Moveworks, Atomicwork — follow the same pattern. A large language model sits in the cloud, connected to your company’s knowledge base. Employees ask questions, the model retrieves answers. It works. But it has three fundamental limitations.

First, all intelligence is centralized. The model only knows what’s been explicitly fed into the knowledge base. It doesn’t learn from the thousands of micro-interactions employees have daily — the workarounds they discover, the mistakes they make, the shortcuts they invent.

Second, there’s no feedback loop from the edge. When a new hire spends 40 minutes figuring out that the VPN must be connected before accessing the PTO portal, that hard-won knowledge dies in their browser history. The next new hire will spend the same 40 minutes. The system never improves from use.

Third, one model serves everyone the same way. A junior developer and a senior architect get the same answers, in the same depth, with the same assumptions about what they already know.

A Different Architecture: Agents That Learn at the Edge

Imagine a three-tier system where intelligence lives at every level — on the employee’s device, on the department server, and at the company core. Each tier runs a different class of model, owns a different scope of knowledge, and communicates on a defined rhythm.

Tier 1: The On-Device Agent (7B–14B Parameters)

Every employee’s workstation runs a small but capable language model — something in the 7B to 14B parameter range, like Llama 3 8B or Qwen 2.5 14B. This model is paired with two things that make it useful: skills and memory.

Skills are structured instructions — think of them as markdown playbooks that tell the agent how to guide the user through specific tasks. A “setup-dev-environment” skill walks a new developer through installing dependencies, configuring their IDE, and running the test suite. A “code-review-checklist” skill ensures PRs meet team standards. These aren’t hardcoded — they’re living documents that the agent reads and follows, and they can be updated without retraining the model.

Memory comes in two layers. Short-term memory captures the day’s interactions: what the user asked, where they got stuck, what worked, what corrections they made. This is append-only, timestamped, and stored locally. Long-term memory is a curated set of facts about the user — their role, expertise level, preferred tools, recurring tasks — that persists across sessions and personalizes every interaction.

The on-device agent is always available, even offline. It responds instantly because there’s no round-trip to a server. And critically, sensitive information — proprietary code, internal discussions, personal struggles — never leaves the machine during the workday.

Tier 2: The Department Server (40B Parameters)

Each department — Engineering, Operations, Sales — runs its own server with a more powerful model in the 40B parameter range. This server has three jobs.

Collecting learnings. On a configurable schedule — real-time, hourly, or nightly depending on the organization’s needs — each device pushes its short-term memory deltas to the department server. Not the raw conversation logs, but distilled learnings: “User discovered that the staging deploy requires flag --skip-cache after the recent infrastructure migration.” A privacy filter strips personally identifiable information before anything leaves the device.

Semantic merging. This is where the 40B model earns its keep. When Device A reports “Docker builds fail on M-series Macs without Rosetta” and Device B reports “ARM architecture causes container build errors on Apple Silicon,” the server recognizes these as the same insight expressed differently. It merges them into a single, authoritative entry in the department’s golden copy — the canonical knowledge base for that team.

Conflict resolution with authority. Not all learnings are equal. The system uses an authority model inspired by API authentication scopes. Each device agent carries a token encoding the user’s role and trust level. A junior developer’s correction gets queued for review. A senior engineer’s correction is auto-merged. A team lead can approve or reject queued items. This prevents the golden copy from being polluted by well-intentioned but incorrect contributions while ensuring high-confidence knowledge flows freely.

After merging, the department server pushes updated skills back to all devices. Tomorrow morning, when a new hire boots up, their agent already knows about the --skip-cache flag — because someone else discovered it yesterday.

Tier 3: The Company Master Server (70B Parameters)

At the top sits the most powerful model — 70B parameters — responsible for the company-wide knowledge layer. This server doesn’t communicate with individual devices. It only syncs with department servers, exchanging golden copies on a daily or weekly cadence.

The key constraint: departments don’t share raw learnings with each other. Engineering doesn’t see Sales’ objection-handling patterns; Sales doesn’t see Engineering’s debugging workflows. This is both a privacy boundary and a relevance filter — most departmental knowledge is only useful within that department.

But the master server can synthesize cross-cutting insights that no single department would discover alone. If Engineering’s golden copy contains “API response times increased 3x after the v2.4 release” and Sales’ golden copy contains “customer complaints about dashboard loading times spiked this week,” the 70B model connects the dots. It pushes a unified advisory to both departments: Engineering gets “customer-facing impact confirmed — prioritize the performance regression,” and Sales gets “engineering is aware of the dashboard slowdown — expected resolution timeline: 48 hours.”

The Daily Rhythm

The system operates on a natural cycle:

Morning. Department servers push updated skills to all devices. Each agent loads the latest golden copy fragments relevant to its user’s role. A new developer gets the freshly refined “setup-dev-environment” skill. A senior engineer gets the latest “production-incident-response” playbook with patterns learned from last week’s outage.

Workday. Each on-device agent guides its user, answers questions, and logs everything to short-term memory. When a user corrects the agent — “No, that’s wrong, you need to run migrations before starting the server” — the agent captures the correction with the user’s authority level.

Sync interval. Based on organizational preference, devices push their learnings to the department server. This could be real-time streaming for fast-moving teams, hourly batches for a balance of freshness and bandwidth, or nightly bulk uploads for organizations prioritizing minimal disruption.

Server processing. The department’s 40B model performs semantic merging — deduplicating, resolving conflicts, filtering PII, and distilling raw observations into authoritative skill updates. High-trust contributions go straight to the golden copy. Lower-trust contributions are queued for review.

Company sync. On a separate, slower cadence, department servers exchange golden copies with the company master. The 70B model looks for cross-departmental patterns and pushes synthesized insights back down.

The Interface: A Chatbot and Coding Agent on Every Machine

The three-tier architecture is the brain. But what the employee actually interacts with is a local chatbot and coding agent running on their machine — powered by the on-device model and grounded in the golden copy that was pushed down that morning.

This isn’t a generic AI assistant. It’s an agent that knows the company’s way of doing things, because the golden copy is the company’s accumulated, distilled operational knowledge. Every answer, every suggestion, every code change it proposes is informed by the patterns, standards, and hard-won lessons that the entire department has contributed to.

For Developers: A Coding Agent That Knows Your Codebase Standards

A developer opens their IDE and the on-device coding agent is available inline — similar to how tools like GitHub Copilot or Cursor work today, but backed by the department’s golden copy rather than a generic training corpus. When the developer writes a new API endpoint, the agent doesn’t just autocomplete syntax. It suggests the error handling pattern that the team standardized last quarter. It flags that the developer is about to use a deprecated internal library that three other engineers already migrated away from. It proposes the exact test structure that passed code review most consistently, based on patterns the department server distilled from hundreds of merged PRs.

If the developer asks “how do I connect to the staging database?” the agent doesn’t give a generic PostgreSQL tutorial. It gives the team’s specific connection string format, reminds them to use the read-only replica for queries, and mentions the VPN requirement — all because those details were learned by other developers’ agents, merged into the golden copy, and pushed down as part of this morning’s skill update.

For New Hires: A Conversational Onboarding Guide

A new operations hire opens the chatbot on day one and simply asks: “What should I do first?” The agent responds with a structured onboarding path tailored to their role — not from a static wiki, but from a living skill that has been refined by the struggles and discoveries of every previous new hire. It walks them through account setup, tool installation, and first tasks step by step, answering follow-up questions in context.

When the new hire asks a question the agent can’t answer confidently, it says so — and logs the gap. That gap becomes a learning signal: if three new hires in a row ask the same unanswered question, the department server flags it as a missing skill that needs to be authored by a senior team member. The system doesn’t just answer questions. It discovers which questions should have answers but don’t yet.

For Everyone: A Knowledge Q&A Layer

Beyond coding and onboarding, the chatbot serves as a universal knowledge interface. “What’s the process for requesting a new AWS account?” “Who owns the billing microservice?” “What changed in the deployment pipeline last week?” These questions get answered instantly from the golden copy, with the confidence that the answers reflect the department’s current, collectively validated understanding — not a stale Confluence page from 2023.

The agent can also proactively surface relevant knowledge. If it detects that a developer is working on the authentication module (based on file context), it might surface a note from the golden copy: “Reminder: the auth module has a known race condition under high concurrency. See the workaround documented after the January incident.” This isn’t the agent being clever — it’s the golden copy doing its job, putting the right knowledge in front of the right person at the right time.

Why On-Device Matters

Running a model on every employee’s machine isn’t just an architectural choice — it unlocks capabilities that cloud-only systems can’t match.

Privacy by design. Code, internal communications, and personal context never leave the device during work hours. Only distilled, anonymized learnings sync to the server. This matters enormously for regulated industries and for employee trust.

Zero-latency guidance. The agent responds in milliseconds, not seconds. For a developer in flow state, the difference between an instant inline suggestion and a 2-second cloud round-trip is the difference between staying focused and being interrupted.

Personalization without centralization. The on-device agent knows this user’s preferences, skill level, and work patterns. It adapts its explanations, adjusts its depth, and remembers past conversations — all locally, without the server needing to maintain per-user state.

Offline resilience. The agent works on airplanes, in server rooms with restricted connectivity, and during cloud outages. The skills it loaded that morning are sufficient for most guidance tasks.

The Federated Learning Parallel

This architecture mirrors a well-established pattern in machine learning: federated learning. Google uses it to improve phone keyboards — each device trains locally on your typing patterns, sends only model weight updates (not your texts) to a central server, and the server aggregates improvements that benefit all users.

The difference is that traditional federated learning operates on model weights — opaque numerical tensors. This system operates on natural-language skills and memories — human-readable markdown that can be version-controlled, audited, and manually edited. An engineering manager can open the golden copy, read every skill in plain English, and decide whether a particular learning should be promoted, revised, or rejected. This transparency is critical for enterprise adoption where auditability and human oversight are non-negotiable.

There’s also a conceptual parallel to knowledge distillation in ML research, where a large “teacher” model’s knowledge is compressed into a smaller “student” model for edge deployment. Here, the 70B company model’s synthesized insights are distilled into skill updates that the 7B device models can act on — not through weight transfer, but through updated natural-language instructions.

Concrete Scenarios

New Developer Onboarding (Week 1)

Monday morning. The developer’s laptop has a 7B model loaded with the Engineering department’s latest skills. The “new-hire-onboarding” skill activates automatically.

The agent walks through environment setup step by step. At step 4, the developer hits an error: node-gyp fails on their specific macOS version. They spend 15 minutes finding the fix on Stack Overflow and tell the agent: “I needed to install Xcode Command Line Tools first — add that as a prerequisite.”

The agent logs this to short-term memory with the user’s authority level (junior). At the next sync cycle, the department server receives this learning. Since three other new hires hit the same issue last month (already in the golden copy as a known friction point), the server’s 40B model upgrades the severity and adds the prerequisite to the onboarding skill.

Tuesday morning, the next new hire’s agent already includes: “Before proceeding, verify Xcode Command Line Tools are installed: xcode-select --install.”

Cross-Department Insight Discovery

The Engineering golden copy contains: “API latency P99 increased from 200ms to 800ms after deploying service mesh v3.2.”

The Sales golden copy contains: “Three enterprise prospects paused contract negotiations citing ‘platform performance concerns’ this quarter.”

Neither department connected these. During the weekly company sync, the master 70B model identifies the correlation and pushes an advisory to both: Engineering receives a business-impact escalation, and Sales receives a technical context update with an estimated resolution timeline sourced from Engineering’s incident tracking.

Open Questions and Honest Limitations

This architecture is a synthesis of existing building blocks — on-device models, skill-based agent systems, federated sync patterns, semantic merging — assembled in a way that doesn’t exist as a product today. Several hard problems remain.

Merge quality at scale. Semantic merging works well with 10 devices. With 500, the volume of daily learnings could overwhelm even a 40B model’s ability to meaningfully synthesize. Hierarchical sub-teams within departments — team leads running intermediate merges — may be necessary.

Skill drift. If the golden copy evolves continuously, skills from six months ago might be unrecognizable. Version control and the ability to diff skill changes over time are essential. Treating the golden copy as a git repository with commit history is one approach.

Model capability at the edge. A 7B model can follow instructions and log observations, but its reasoning is limited. It might misinterpret a user’s correction or log a false insight. The authority system mitigates this — low-trust contributions get reviewed — but it doesn’t eliminate the risk.

Adoption friction. Employees need to trust that their on-device agent isn’t surveillance. The system must be transparently opt-in for the learning cycle, with clear boundaries between what stays local and what syncs. The privacy filter must be verifiable, not just promised.

Hardware cost. Running a 7B model on every employee’s laptop requires machines with sufficient RAM and ideally a capable GPU. For many knowledge workers with modern laptops, this is already feasible. For organizations with aging hardware fleets, it may require phased rollout.

What Exists Today

The building blocks are real and available now:

• On-device models in the 7B–14B range run comfortably on Apple Silicon Macs and modern workstations using tools like Ollama, llama.cpp, and LM Studio.
• Skill-based agent frameworks — notably the AgentSkills open standard developed by Anthropic and adopted by multiple platforms — define exactly how to package instructions as markdown files that agents can discover and follow.
• Memory architectures with short-term daily logs and long-term curated knowledge are production-tested in platforms like OpenClaw, which uses MEMORY.md for persistent facts and memory/YYYY-MM-DD.md for daily context.
• Self-improving agent patterns exist in the wild — OpenClaw’s community has published skills that capture corrections and learnings automatically, and the Foundry plugin demonstrates a full observe-learn-write-deploy loop on a single device.
• Federated learning is a mature field in ML research, with frameworks like NVIDIA FLARE and Flower enabling distributed training across devices.
• Hierarchical multi-agent architectures — supervisor agents coordinating specialist agents across departments — are in production at companies like BASF (via Databricks) and documented extensively by Microsoft and Salesforce.

What nobody has assembled is the specific combination: on-device small models learning from daily use, syncing through department servers with semantic merging and authority-based trust, rolling up to a company-wide master that discovers cross-departmental patterns — all operating on human-readable, version-controllable, natural-language skills rather than opaque model weights.

The Bet

The bet is simple. Today’s enterprise AI is a library — it holds knowledge and waits for you to ask. The architecture described here is a living organism — it learns from every employee, improves overnight, and wakes up smarter each morning.

Every company already has the knowledge it needs to onboard faster, debug quicker, and operate more efficiently. That knowledge just lives in the wrong places: in people’s heads, in forgotten Slack threads, in tribal rituals passed from senior to junior. An on-device AI agent that captures this knowledge as it’s created — and a federated system that distills it into something the whole organization can benefit from — doesn’t require any breakthrough in AI capability. It requires assembling pieces that already exist into a system that nobody has built yet.

The pieces are on the table. Someone just needs to put them together.

This post explores a conceptual architecture for federated, on-device AI agents in enterprise settings. The building blocks referenced — AgentSkills, OpenClaw, federated learning frameworks — are real, production-available technologies. The specific three-tier system described is a proposed design, not an existing product.

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 

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

Large language models are powerful communicators but poor historians — they generate fluent answers without guaranteed grounding. Retrieval‑Augmented Generation (RAG) is the enterprise-ready pattern that remedies this: it pairs a retrieval layer that finds authoritative content with an LLM that synthesizes a response, producing answers you can trust and audit.

How RAG works — concise flow

• Index authoritative knowledge (manuals, SOPs, product specs, policies).
• Convert content to searchable artifacts (text chunks, vectors, or indexed documents).
• At query time, retrieve the most relevant passages and pass them to the LLM as context.
• The LLM generates a response conditioned on those passages and returns the answer with citations or source snippets.

RAG architectures — choose based on needs

• Vector-based RAG: semantic search via embeddings — best for unstructured content and paraphrased queries.
• Retriever‑Reader (search + synthesize): uses an external search engine for candidate retrieval and an LLM to synthesize — balances speed and interpretability.
• Hybrid (BM25 + embeddings): combines lexical and semantic signals for higher recall and precision.

Practical implementation checklist

• Curate sources: prioritize canonical documents and enforce access controls for sensitive data.
• Chunk and preprocess: split long documents into meaningful passages (200–1000 tokens) and normalize text.
• Select embeddings: evaluate cost vs. semantic fidelity for your chosen model.
• Tune retrieval: experiment with top‑k, score thresholds, and reranking to reduce noise.
• Prompt engineering: require source attribution and instruct the model to respond “I don’t know” when evidence is absent.
• Maintain pipeline: set reindex schedules or event-driven updates and monitor for stale content.

Risks and mitigations

• Stale or incorrect answers: mitigate by frequent reindexing and content versioning.
• Privacy and IP exposure: never index PII or sensitive IP without encryption, role-based access, and auditing.
• Hallucinated citations: enforce a “source_required” rule and validate citations against the index.
• Cost overruns: optimize by caching commonly used contexts, batching queries, and using smaller models for retrieval tasks.

High-value enterprise use cases

• Sales enablement: evidence-backed product comparisons and quoting guidance.
• Customer support: first-response automation that cites KB articles and escalates when required.
• Engineering knowledge: searchable design decisions, runbooks, and architecture notes.
• Compliance and audit: traceable answers linked to policy documents and evidence.

Metrics that matter

Measure accuracy (user-verified correctness), time-to-answer reduction, citation quality (authoritativeness of sources), user satisfaction, and escalation rate to humans. Use these to iterate on retrieval parameters, prompt rules, and content curation.

Example prompt template

“You are an assistant that must use only the provided sources. Answer concisely and cite the sources used. If the sources do not support an answer, respond: ‘I don’t know — consult [recommended source]’.”

Conclusion

RAG converts LLM fluency into enterprise-grade reliability by forcing answers to be evidence‑based, auditable, and applicable. It’s the practical pattern for organizations that need fast, helpful automation without fiction — think of it as giving your model a librarian and a bibliography.

Salesforce Marketing Cloud + AI is revolutionizing marketing by combining advanced artificial intelligence with marketing automation to create hyper-personalized, data-driven campaigns that adapt in real time to customer behaviors and preferences. This fusion drives engagement, conversions, and revenue growth like never before.

Key AI Features of Salesforce Marketing Cloud

• Agentforce: An autonomous AI agent that helps marketers create dynamic, scalable campaigns with effortless automation and real-time optimization. It streamlines content creation, segmentation, and journey management through simple prompts and AI insights. Learn more at the Salesforce official site.

• Einstein AI: Powers predictive analytics, customized content generation, send-time optimization, and smart audience segmentation, ensuring the right message reaches the right customer at the optimal time.

• Generative AI: Using Einstein GPT, marketers can automatically generate email copy, subject lines, images, and landing pages, enhancing productivity while maintaining brand consistency.

• Marketing Cloud Personalization: Provides real-time behavioral data and AI-driven recommendations to deliver tailored experiences that boost customer loyalty and conversion rates.

• Unified Data Cloud Integration: Seamlessly connects live customer data for dynamic segmentation and activation, eliminating data silos.

• Multi-Channel Orchestration: Integrates deeply with platforms like WhatsApp, Slack, and LinkedIn to deliver personalized campaigns across all customer touchpoints.

Latest Trends & 2025 Updates

• With advanced artificial intelligence, marketing teams benefit from systems that independently manage and adjust their campaigns for optimal results.

• Real-time customer journey adaptations powered by live data.

• Enhanced collaboration via AI integration with Slack and other platforms.

• Automated paid media optimization and budget control with minimal manual intervention.

For detailed insights on AI and marketing automation trends, see this industry report.

Benefits of Combining Salesforce Marketing Cloud + AI

• Increased campaign efficiency and ROI through automation and predictive analytics.

• Hyper-personalized customer engagement at scale.

• Reduced manual effort with AI-assisted content and segmentation.

• Better decision-making powered by unified data and AI-driven insights.

• Greater marketing agility and responsiveness in a changing landscape.

Related Posts

• • Mastering Customer Journey Mapping with Salesforce

  • Top AI Tools for Marketing in 2025

“Powerful technologies require equally powerful ethical guidance.” (Bostrom, N. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014).

The ethics of using artificial intelligence depend on how we apply its capabilities—either to enhance learning or to prevent irresponsible practices that may compromise academic integrity. In this blog, I share reflections, experiences, and insights about the impact of AI in our environment, analyzing its role as a creative tool in the hands of developers and as a challenge within the academic context.

Between industry and the classroom

As a Senior Developer, my professional trajectory has led me to delve deeply into the fascinating discipline of software architecture. Currently, I work as a Backend Developer specializing in Microsoft technologies, facing daily the challenges of building robust, scalable, and well-structured systems in the business world.

Alongside my role in the industry, I am privileged to serve as a university professor, teaching four courses. Three of them are fundamental parts of the software development lifecycle: Software Analysis and Design, Software Architecture, and Programming Techniques. This dual perspective—as both a professional and a teacher—has allowed me to observe the rapid changes that technology is generating both in daily development practice and in the formation of future engineers.

Exploring AI as an Accelerator in Software Development

One of the greatest challenges for those studying the software development lifecycle is transforming ideas and diagrams into functional, well-structured projects. I always encourage my students to use Artificial Intelligence as a tool for acceleration, not as a substitute.

For example, in the Software Analysis and Design course, we demonstrate how a BPMN 2.0 process diagram can serve as a starting point for modeling a system. We also work with class diagrams that reflect compositions and various design patterns. AI can intervene in this process in several ways:

• Code Generation from Models: With AI-based tools, it’s possible to automatically turn a well-built class diagram into the source code foundation needed to start a project, respecting the relationships and patterns defined during modeling.
• Rapid Project Architecture Setup: Using AI assistants, we can streamline the initial setup of a project by selecting the technology stack, creating folder structures, base files, and configurations according to best practices.
• Early Validation and Correction: AI can suggest improvements to proposed models, detect inconsistencies, foresee integration issues, and help adapt the design context even before coding begins.

This approach allows students to dedicate more time to understanding the logic behind each component and design principle, instead of spending hours on repetitive setup and basic coding tasks. The conscious and critical use of artificial intelligence strengthens their learning, provides them with more time to innovate, and helps prepare them for real-world industry challenges.

But Not Everything Is Perfect: The Challenges in Programming Techniques

However, not everything is as positive as it seems. In “Programming Techniques,” a course that represents students’ first real contact with application development, the impact of AI is different compared to more advanced subjects. In the past, the repetitive process of writing code—such as creating a simple constructor public Person(), a function public void printFullName() or practicing encapsulation in Java with methods like public void setName(String name) and public String getName()—kept the fundamental programming concepts fresh and clear while coding.

This repetition was not just mechanical; it reinforced their understanding of concepts like object construction, data encapsulation, and procedural logic. It also played a crucial role in developing a solid foundation that made it easier to understand more complex topics, such as design patterns, in future courses.

Nowadays, with the widespread availability and use of AI-based tools and code generators, students tend to skip these fundamental steps. Instead of internalizing these concepts through practice, they quickly generate code snippets without fully understanding their structure or purpose. As a result, the pillars of programming—such as abstraction, encapsulation, inheritance, and polymorphism—are not deeply absorbed, which can lead to confusion and mistakes later on.

Although AI offers the promise of accelerating development and reducing manual labor, it is important to remember that certain repetition and manual coding are essential for establishing a solid understanding of fundamental principles. Without this foundation, it becomes difficult for students to recognize bad practices, avoid common errors, and truly appreciate the architecture and design of robust software systems.

Reflection and Ethical Challenges in Using AI

Recently, I explained the concept of reflection in microservices to my Software Architecture students. To illustrate this, I used the following example: when implementing the Abstract Factory design pattern within a microservices architecture, the Reflection technique can be used to dynamically instantiate concrete classes at runtime. This allows the factory to decide which object to create based on external parameters, such as a message type or specific configuration received from another service. I consider this concept fundamental if we aim to design an architecture suitable for business models that require this level of flexibility.

However, during a classroom exercise where I provided a base code, I asked the students to correct an error that I had deliberately injected. The error consisted of an additional parameter in a constructor—a detail that did not cause compilation failures, but at runtime, it caused 2 out of 5 microservices that consumed the abstract factory via reflection to fail. From their perspective, this exercise may have seemed unnecessary, which led many to ask AI to fix the error.

As expected, the AI efficiently eliminated the error but overlooked a fundamental acceptance criterion: that parameter was necessary for the correct functioning of the solution. The task was not to remove the parameter but to add it in the Factory classes where it was missing. Out of 36 students, only 3 were able to explain and justify the changes they made. The rest did not even know what modifications the AI had implemented.

This experience highlights the double-edged nature of artificial intelligence in learning: it can provide quick solutions, but if the context or the criteria behind a problem are not understood, the correction can be superficial and jeopardize both the quality and the deep understanding of the code.

I haven’t limited this exercise to architecture examples alone. I have also conducted mock interviews, asking basic programming concepts. Surprisingly, even among final-year students who are already doing their internships, the success rate is alarmingly low: approximately 65% to 70% of the questions are answered incorrectly, which would automatically disqualify them in a real technical interview.

Conclusion

Artificial intelligence has become increasingly integrated into academia, yet its use does not always reflect a genuine desire to learn. For many students, AI has turned into a tool for simply getting through academic commitments, rather than an ally that fosters knowledge, creativity, and critical thinking. This trend presents clear risks: a loss of deep understanding, unreflective automation of tasks, and a lack of internalization of fundamental concepts—all crucial for professional growth in technological fields.

Various authors have analyzed the impact of AI on educational processes and emphasize the importance of promoting its ethical and constructive use. As Luckin et al. (2016) suggest, the key lies in integrating artificial intelligence as support for skill development rather than as a shortcut to avoid intellectual effort. Similarly, Selwyn (2019) explores the ethical and pedagogical challenges that arise when technology becomes a quick fix instead of a resource for deep learning.

References:

• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education. [Available at: https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed-Publication.pdf]
• Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.

Financial institutions are at a pivotal moment. As customer expectations evolve and AI reshapes digital engagement, leaders in marketing, CX, and IT must rethink how they deliver value.

Adobe’s report, “State of Customer Experience in Financial Services in an AI-Driven World,” reveals that only 36% of the customer journey is currently personalized, despite 74% of executives acknowledging rising customer expectations. With transformation already underway, financial leaders face five imperatives that demand immediate action to drive relevance, trust, and growth.

1. Make Personalization More Meaningful

Personalization has long been a strategic focus, but today’s consumers expect more than basic segmentation or name-based greetings. They want real-time, omnichannel interactions that align with their financial goals, life stages, and behaviors.

To meet this demand, financial institutions must evolve from reactive personalization to predictive, intent-driven engagement. This means leveraging AI to anticipate needs, orchestrate journeys, and deliver content that resonates with individual context.

Perficient Adobe-consulting principal Ross Monaghan explains, “We are still dealing with disparate data and slow progression into a customer 360 source of truth view to provide effective personalization at scale. What many firms are overlooking is that this isn’t just a data issue. We’re dealing with both a people and process issue where teams need to adjust their operational process of typical campaign waterfall execution to trigger-based and journey personalization.”

His point underscores that personalization challenges go beyond technology. They require cultural and operational shifts to enable real-time, AI-driven engagement.

2. Redesign the Operating Model Around the Customer

Legacy structures often silo marketing, IT, and operations, creating friction in delivering cohesive customer experiences. To compete in a digital-first world, financial institutions must reorient their operating models around the customer, not the org chart.

This shift requires cross-functional collaboration, agile workflows, and shared KPIs that align teams around customer outcomes. It also demands a culture that embraces experimentation and continuous improvement.

Only 3% of financial services firms are structured around the customer journey, though 19% say it should be the ideal.

3. Build Content for AI-Powered Search

As AI-powered search becomes a primary interface for information discovery, the way content is created and structured must change. Traditional SEO strategies are no longer enough.

Customers now expect intelligent, personalized answers over static search results. To stay visible and trusted, financial institutions must create structured, metadata-rich content that performs in AI-powered environments. Content must reflect experience-expertise-authoritativeness-trustworthiness principles and be both machine-readable and human-relevant. Success depends on building discovery journeys that work across AI interfaces while earning customer confidence in moments that matter.

4. Unify Data and Platforms for Scalable Intelligence

Disconnected data and fragmented platforms limit the ability to generate insights and act on them at scale. To unlock the full potential of AI and automation, financial institutions must unify their data ecosystems.

This means integrating customer, behavioral, transactional, and operational data into a single source of truth that’s accessible across teams and systems. It also involves modernizing MarTech and CX platforms to support real-time decisioning and personalization.

But Ross points out, “Many digital experience and marketing platforms still want to own all data, which is just not realistic, both in reality and cost. The firms that develop their customer source of truth (typically cloud-based data platforms) and signal to other experience or service platforms will be the quickest to marketing execution maturity and success.”

His insight emphasizes that success depends not only on technology integration but also on adopting a federated approach that accelerates marketing execution and operational maturity.

5. Embed Guardrails Into GenAI Execution

As financial institutions explore GenAI use cases, from content generation to customer service automation, governance must be built in from the start. Trust is non-negotiable in financial services, and GenAI introduces new risks around accuracy, bias, and compliance.

Embedding guardrails means establishing clear policies, human-in-the-loop review processes, and robust monitoring systems. It also requires collaboration between legal, compliance, marketing, and IT to ensure responsible innovation.

At Perficient, we use our PACE (Policies, Advocacy, Controls, Enablement) Framework to holistically design tailored operational AI programs that empower business and technical stakeholders to innovate with confidence while mitigating risks and upholding ethical standards.

The Time to Lead is Now

The future of financial services will be defined by how intelligently and responsibly institutions engage in real time. These five imperatives offer a blueprint for action, each one grounded in data, urgency, and opportunity. Leaders who move now will be best positioned to earn trust, drive growth, and lead in the AI-powered era.

Learn About Perficient and Adobe’s Partnership

Are you looking for a partner to help you transform and modernize your technology strategy? Perficient and Adobe bring together deep industry expertise and powerful experience technologies to help financial institutions unify data, orchestrate journeys, and deliver customer-centric experiences that build trust and drive growth.

Get in Touch With Our Experts

I think about the early ATMs now and then. No one knew the “right” way to use them. I imagine a customer in the 1970s standing there, card in hand, squinting at this unfamiliar machine and hoping it would give something back; trying to decide if it really dispensed cash…or just ate cards for sport. That quick panic when the machine pulled the card in is an early version of the same confusion customers feel today in digital banking.

People were not afraid of machines. They were afraid of not understanding what the machine was doing with their money.

Banks solved it by teaching people how to trust the process. They added clear instructions, trained staff to guide customers, and repeated the same steps until the unfamiliar felt intuitive. 

However, the stakes and complexity are much higher now, and AI for financial product transparency is becoming essential to an optimized banking UX.

Today’s banking customer must navigate automated underwriting, digital identity checks, algorithmic risk models, hybrid blockchain components, and disclosures written in a language most people never use. Meanwhile, the average person is still struggling with basic money concepts.

FINRA reports that only 37% of U.S. adults can answer four out of five financial literacy questions (FINRA Foundation, 2022).

Pew Research finds that only about half of Americans understand key concepts like inflation and interest (Pew Research Center, 2024).

Financial institutions are starting to realize that clarity is not a content task or a customer service perk. It is structural. It affects conversion, compliance, risk, and trust. It shapes the entire digital experience. And AI is accelerating the pressure to treat clarity as infrastructure.

When customers don’t understand, they don’t convert. When they feel unsure, they abandon the flow. 

How AI is Improving UX in Banking (And Why Institutions Need it Now)

Financial institutions often assume customers will “figure it out.” They will Google a term, reread a disclosure, or call support if something is unclear. In reality, most customers simply exit the flow.

The CFPB shows that lower financial literacy leads to more mistakes, higher confusion, and weaker decision-making (CFPB, 2019). And when that confusion arises during a digital journey, customers quietly leave without resolving their questions.

This means every abandoned application costs money. Every misinterpreted term creates operational drag. Every unclear disclosure becomes a compliance liability. Institutions consistently point to misunderstanding as a major driver of complaints, errors, and churn (Lusardi et al., 2020).

Sometimes it feels like the industry built the digital bank faster than it built the explanation for it.

Where AI Makes the Difference

Many discussions about AI in financial services focus on automation or chatbots, but the real opportunity lies in real-time clarity. Clarity that improves financial product transparency and streamlines customer experience without creating extra steps.

In-context Explanations That Improve Understanding

Research in educational psychology shows people learn best when information appears the moment they need it. Mayer (2019) demonstrates that in-context explanations significantly boost comprehension. Instead of leaving the app to search unfamiliar terms, customers receive a clear, human explanation on the spot.

Consistency Across Channels

Language in banking is surprisingly inconsistent. Apps, websites, advisors, and support teams all use slightly different terms. Capgemini identifies cross-channel inconsistency as a major cause of digital frustration (Capgemini, 2023). A unified AI knowledge layer solves this by standardizing definitions across the system.

Predictive Clarity Powered by Behavioral Insight

Patterns like hesitation, backtracking, rapid clicking, or form abandonment often signal confusion. Behavioral economists note these patterns can predict drop-off before it happens (Loibl et al., 2021). AI can flag these friction points and help institutions fix them.

24/7 Clarity, Not 9–5 Support

Accenture reports that most digital banking interactions now occur outside of business hours (Accenture, 2023). AI allows institutions to provide accurate, transparent explanations anytime, without relying solely on support teams.

At its core, AI doesn’t simplify financial products. It translates them.

What Strong AI-Powered Customer Experience Looks Like

Onboarding that Explains Itself

• Mortgage flows with one-sentence escrow definitions.
• Credit card applications with visual explanations of usage.
  
• Hybrid products that show exactly what blockchain is doing behind the scenes. The CFPB shows that simpler, clearer formats directly improve decision quality (CFPB, 2020).

A Unified Dictionary Across Channels

The Federal Reserve emphasizes the importance of consistent terminology to help consumers make informed decisions (Federal Reserve Board, 2021). Some institutions now maintain a centralized term library that powers their entire ecosystem, creating a cohesive experience instead of fragmented messaging.

Personalization Based on User Behavior

Educational nudges, simplified paths, multilingual explanations. Research shows these interventions boost customer confidence (Kozup & Hogarth, 2008). 

Transparent Explanations for Hybrid or Blockchain-backed Products

Customers adopt new technology faster when they understand the mechanics behind it (University of Cambridge, 2021). AI can make complex automation and decentralized components understandable.

The Urgent Responsibilities That Come With This

GenAI can mislead customers without strong data governance and oversight. Poor training data, inconsistent terminology, or unmonitored AI systems create clarity gaps. That’s a problem because those gaps can become compliance issues. The Financial Stability Oversight Council warns that unmanaged AI introduces systemic risk (FSOC, 2023). The CFPB also emphasizes the need for compliant, accurate AI-generated content (CFPB, 2024).

Customers are also increasingly wary of data usage and privacy. Pew Research shows growing fear around how financial institutions use personal data (Pew Research Center, 2023). Trust requires transparency.

Clarity without governance is not clarity. It’s noise.

And institutions cannot afford noise.

What Institutions Should Build Right Now

To make clarity foundational to customer experience, financial institutions need to invest in:

• Modern data pipelines to improve accuracy
• Consistent terminology and UX layers across channels
• Responsible AI frameworks with human oversight
• Cross-functional collaboration between compliance, design, product, and analytics
• Scalable architecture for automated and decentralized product components
• Human-plus-AI support models that enhance, not replace, advisors

When clarity becomes structural, trust becomes scalable.

Why This Moment Matters

I keep coming back to the ATM because it perfectly shows what happens when technology outruns customer understanding. The machine wasn’t the problem. The knowledge gap was. Financial services are reliving that moment today.

Customers cannot trust what they do not understand.

And institutions cannot scale what customers do not trust.

GenAI gives financial organizations a second chance to rebuild the clarity layer the industry has lacked for decades, and not as marketing. Clarity, in this new landscape, truly is infrastructure.

Related Reading

• The Personalization Gap Is Hurting Financial Services, Here’s How to Close It: A discussion of how personalization shortfalls are holding back financial services from meeting customer expectations. 

• Closing the Gap Between Expectations and Experience in Financial Services: Analysis of rising customer expectations and why legacy systems struggle to keep up. 

• Trust Is the New Currency in Financial Services and Customers Are Setting the Terms: A look at how trust, speed, transparency, and control are reshaping what customers expect from their institutions. 

References 

• Accenture. (2023). Banking top trends 2023. https://www.accenture.com
• Capgemini. (2023). World retail banking report 2023. https://www.capgemini.com
• Consumer Financial Protection Bureau. (2019). Financial well-being in America. https://www.consumerfinance.gov
• Consumer Financial Protection Bureau. (2020). Improving the clarity of mortgage disclosures. https://www.consumerfinance.gov
• Consumer Financial Protection Bureau. (2024). Supervisory highlights: Issue 30. https://www.consumerfinance.gov
• Federal Reserve Board. (2021). Consumers and mobile financial services. https://www.federalreserve.gov
• FINRA Investor Education Foundation. (2022). National financial capability study. https://www.finrafoundation.org
• Financial Stability Oversight Council. (2023). Annual report. https://home.treasury.gov
• Kozup, J., & Hogarth, J. (2008). Financial literacy, public policy, and consumers’ self-protection. Journal of Consumer Affairs, 42(2), 263–270.
• Loibl, C., Grinstein-Weiss, M., & Koeninger, J. (2021). Consumer financial behavior in digital environments. Journal of Economic Psychology, 87, 102438.
• Lusardi, A., Mitchell, O. S., & Oggero, N. (2020). The changing face of financial literacy. University of Pennsylvania, Wharton School.
• Mayer, R. (2019). The Cambridge handbook of multimedia learning. Cambridge University Press.
• Pew Research Center. (2023). Americans and data privacy. https://www.pewresearch.org
• Pew Research Center. (2024). Americans and financial knowledge. https://www.pewresearch.org
• University of Cambridge. (2021). Global blockchain benchmarking study. https://www.jbs.cam.ac.uk