Three Connected Pillars of AI Modernization

Easily accessible from both the Back-Office and the EMMA Assistant, these hubs equip your field with the knowledge and assets it needs to replicate success.

Pillar 01

Software Development

Accelerate delivery with AI-assisted coding and smarter integrations

Pillar 02

Data Mining and Analytics

Accelerate delivery with AI-assisted coding and smarter integrations

Pillar 03

Customer Care Automation

Accelerate delivery with AI-assisted coding and smarter integrations

Software development and AI transformation

AI to Expedite Code Development

The Challenge

Feature delivery cycles averaging 10–12 weeks per release.
High defect rates in integration code and repeated boilerplate work in back‑end services.

AHM approach

AI coding assistants and GenAI workflows

AHM deploys AI coding assistants (e.g., Copilot‑style tools) in Acme’s IDEs, tuned on Acme’s codebase and patterns.
Developers describe features in natural language; the assistant generates code snippets, unit tests, and documentation for review.

AI‑Enhanced SDLC Practices

Introduces AI prompts for requirements clarification, architecture sketches, and test‑case generation to shorten design and QA timelines.
Uses AI to generate and refactor APIs and integration scripts, reducing manual integration coding effort.

Change management and guardrails

AHM defines coding standards, review checklists, and security rules the AI must follow, plus dashboards to monitor productivity and defect trends.

Illustrative Results

30–50% faster completion for many coding tasks, consistent with observed gains when using generative AI in development.
Noticeable reduction in boilerplate and integration bugs due to AI‑generated tests and standardized integration code.

Software development and AI transformation

Integrate AI for software integration

The Challenge

Multiple legacy systems (CRM, billing, support) with fragile point‑to‑point integrations.
Manual data handoffs between systems, causing delays and errors.

AHM approach

AI‑assisted integration blueprint

AHM uses AI to parse existing interfaces, logs, and documentation to map dependencies and detect integration pain points.
Proposes an event‑driven integration layer with standardized APIs and message schemas, co‑designed with AI tooling.

Smart integration services

Implements AI‑generated API stubs and transformation functions to mediate between legacy formats and modern microservices.
Adds anomaly detection on integration flows to flag unusual traffic or data mismatches.

Illustrative Results

Faster rollout of new integrations (weeks instead of months) by automating API code and documentation.
Improved reliability through earlier detection of integration failures and data quality issues.

Data mining and analytics

Corporate and internal data

The Challenge

Data dispersed across ERP, CRM, HR, and ticketing systems with inconsistent metrics and manual reporting.
Leadership lacks a trusted “single source of truth” for operational and financial performance.

AHM approach

Unified data foundation

AHM builds a centralized data platform (data lake or warehouse) and uses AI to profile, cleanse, and reconcile records (e.g., customers, contracts, cases).
Implements role‑based data access and a semantic layer so KPIs are consistent across departments.

AI‑enhanced KPI design and insight discovery

Uses ML to test which drivers (e.g., onboarding time, renewal cadence) correlate most with revenue growth and margin.
Helps leadership refine or create KPIs, an approach shown to increase financial benefits when AI is used to reshape measurement systems.

Self‑service analytics

Deploys AI‑powered analytics interfaces where managers ask natural‑language questions about performance (“Why did churn increase in Q3?”) and receive visual explanations.

Illustrative Results

Stronger strategic alignment as all units use the same, AI‑validated KPI definitions.
Faster decision cycles because leaders can explore scenarios and drivers directly rather than waiting for ad‑hoc reports.

Software development and AI transformation

Consumer and consultant KPI analytics

The Challenge

Limited visibility into individual customer journeys and consultant productivity.
KPIs focus on lagging indicators (e.g., total calls) rather than drivers (e.g., first‑contact resolution, sentiment).

AHM approach

Customer behavior and sentiment analytics

Mines interaction data (calls, chats, emails, web) using NLP to score sentiment, topics, and friction points.
Links these insights to revenue, churn, and NPS to create “customer journey” KPIs.

Consultant and agent performance analytics

Builds dashboards showing leading indicators per consultant: case mix, time to resolution, customer sentiment, and collaboration patterns.
Uses AI to flag coaching opportunities and best practices from high performers.

Illustrative Results

New AI‑driven KPIs reveal underexploited performance levers, similar to organizations that use AI to revisit KPI fundamentals.
Improved coaching programs and better matching of consultants to customer segments based on data‑driven insights.

Customer care AI

AI‑enabled call center engagement

The Challenge

High call volumes, long wait times, and inconsistent quality across agents.
Limited digital self‑service, most issues come through voice calls.

AHM approach

AI virtual agent and intelligent routing

AHM implements voice bots and chatbots using conversational AI to handle Tier‑1 requests (balance inquiries, status checks, basic troubleshooting).
The virtual agent authenticates customers, understands intents with NLP, accesses backend systems, and either resolves the issue or routes with full context to human agents.

Augmented human agents

Agents receive AI‑generated “next best action” suggestions, real‑time summaries, and knowledge article recommendations during live interactions.
AI summarizes calls and updates CRM notes automatically to reduce after‑call work.

Illustrative Results

A significant share of calls (e.g., 30–40%) handled entirely by virtual agents, mirroring other deployments where voice assistants resolve a large portion of inbound calls.
Reduced average handling time and improved CSAT due to faster routing and 24/7 availability.

Software development and AI transformation

End‑user issue resolution and automation

The Challenge

Users repeatedly contact support for similar issues (passwords, access, basic configuration).
High manual workload for low‑complexity tasks.

AHM approach

AI self‑service and guided workflows

Deploys an AI help assistant across web and mobile that can troubleshoot, walk users through step‑by‑step flows, and trigger automated actions (reset credentials, provision access, restart services).
Integrates with ticketing to create, update, or close cases automatically based on conversation outcomes.

Automation of back‑office tasks

Uses RPA plus AI to automate routine follow‑ups: sending confirmations, updating records, scheduling callbacks, and verifying resolution.

Illustrative Results

A meaningful portion of repetitive tickets resolved without human intervention, comparable to other organizations automating 40% or more of inbound requests with virtual agents.
Support staff can focus on complex issues and relationship management, increasing both efficiency and employee satisfaction.