When we discuss AI in IT delivery, the main point isn’t that AI will take over everyone’s jobs. The bigger shift is that AI will cut down on the number of handovers between people. Right now, a feature might go from a Business Analyst to a Product Owner, then to a developer, tester, DevOps, and finally support. Each step adds meetings, tickets, explanations, and sometimes delays.
With AI, many of these tasks won’t go away completely, but
they’ll be combined into broader roles. Teams might get smaller in some areas,
but more importantly, the way teams are structured will change.
Before going into more details, you can find below a
possible mapping of new roles, covering the full SDLC.
|
Before |
With AI |
|
BA + PO + Process Analyst + Data Analyst |
Product Discovery Lead |
|
Scrum Master + PMO + Project Coordinator |
Delivery Manager |
|
Developer + QA Automation + Basic Tester |
AI Product Engineer |
|
Manual QA + QA Analyst + Test Case Writer |
Quality Engineer |
|
DevOps + Build + Release + Environment |
Platform OR Release Reliability Engineer |
|
Cloud + Infra + Sysadmin |
Platform Engineer |
|
NOC + Monitoring + Incident Support |
AIOps |
|
L1 Support + Desktop + Knowledge Support |
Digital Workplace Automation Engineer |
In the next sections, we will cover each main phase of SDLC
and explore what the future roles might look like.
1. Requirements and discovery
Before AI, discovery often needed several people: a Business
Analyst, a Product Owner, a Process Analyst, and sometimes a Data Analyst. Each
person handled a small part of the work. The BA wrote requirements, the Product
Owner managed the backlog, the process analyst mapped flows, and the data
analyst reviewed numbers.
With AI, one stronger role, the Product Discovery Lead, can
handle many of these tasks. AI can summarise meetings, create user stories,
draft acceptance criteria, and analyse feedback. The focus shifts from writing
documents to really understanding the business problem.
|
Before |
With AI |
What |
|
Business Analyst |
Product Discovery Lead |
Requirements-only Business Analyst |
|
Product Owner |
Product Discovery Lead OR Product Manager |
Junior Product Owner focused only on backlog admin |
|
Process Analyst |
Product Discovery Lead |
Process Documentation Analyst |
|
Data Analyst |
Product Discovery Lead |
Basic reporting Data Analyst |
2. Planning and delivery management
Right now, planning and delivery might include a Scrum
Master, Project Manager, PMO Analyst, and Project Coordinator. Some of these
roles are still needed, especially for large programs. But many of the tasks
are repetitive, such as status reports, meeting notes, RAID logs, sprint
summaries, and dependency tracking.
AI can automate much of this admin work. Because of this,
these roles might combine into a Delivery Manager role. This person does more
than just organise meetings; they focus on delivery flow, blockers, risks, and
dependencies.
|
Before |
With AI |
What |
|
Scrum Master |
Delivery Manager |
Ceremony-only Scrum Master |
|
Project Manager |
Delivery Manager OR Program Manager |
Project Manager focused only on tracking |
|
PMO Analyst |
Delivery Manager |
PMO Reporting Analyst |
|
Project Coordinator |
Delivery Manager |
Status Reporting Analyst |
|
Delivery Manager |
Delivery Manager |
Delivery Coordinator |
3. Technical design and architecture
In architecture, AI will be very helpful, but it won’t
replace senior judgment. AI can create diagrams, compare architecture options,
and write decision records. However, people still need to handle trade-offs,
risks, integration, scalability, and costs.
In the future, roles will look more like AI-native Solution
Architects or Technical Leads. They’ll use AI as a design assistant, but people
will still make the final decisions.
|
Before |
With AI |
What |
|
Solution Architect |
AI-native Solution Architect |
Junior Solution Designer |
|
Tech Lead |
AI-native Technical Lead |
Technical Diagram Designer |
|
Security Architect |
Security Architect OR AI Security Architect |
Security checklist-only reviewer |
|
Data Architect |
Data OR AI Architect |
Architecture documentation analyst |
4. Development and testing
This might be the biggest change. Right now, developers and
testers are usually separate. Developers write code, QA writes test cases,
automation engineers create scripts, and manual testers run regression tests.
With AI, one engineer can generate code, tests, test data,
mocks, and documentation. This means future teams will have more AI-enabled
Product Engineers who focus on quality. Developers will need to take more
responsibility for quality. Testers will shift from manual work to quality
strategy, exploratory testing, and risk-based testing.
|
Before |
With AI |
What |
|
Frontend Developer |
AI-enabled Product Engineer |
Junior Frontend Developer doing basic UI only |
|
Backend Developer |
AI-enabled Product Engineer |
Junior Backend Developer doing boilerplate only |
|
Integration Developer |
AI-enabled Product Engineer |
Simple API Developer |
|
QA Automation Engineer |
Quality Engineer |
QA Automation as isolated separate role |
|
Manual QA Tester |
Quality Engineer |
Manual QA Tester, Regression Tester |
|
QA Analyst |
Quality Engineer |
Test Case Writer |
|
UAT Coordinator |
Product Discovery Lead OR Quality Engineer |
UAT Coordinator as standalone role |
5. DevOps, release and infrastructure
Before AI, there were often separate people for DevOps,
build, release, environments, cloud, and infrastructure. Much of this work
relied on scripts, pipelines, checklists, and repeatable processes.
AI, along with platform engineering, will combine many of
these roles into a Platform or Reliability Engineer role. The focus will shift
from manual deployment to self-service platforms, reusable pipelines, automated
checks, and reliable cloud foundations.
|
Before |
With AI |
What |
|
DevOps Engineer |
Platform Engineer |
DevOps focused only on pipeline tickets |
|
Build Engineer |
Platform Engineer |
Build Engineer |
|
Release Manager |
Platform OR Reliability Engineer |
Manual Release Coordinator |
|
Environment Manager |
Platform Engineer |
Environment Booking Coordinator |
|
Cloud Engineer |
Platform OR Reliability Engineer |
Cloud Provisioning Engineer |
|
System Administrator |
Platform Engineer |
Traditional Sysadmin |
|
Infrastructure Engineer |
Platform Engineer |
Infrastructure Administrator |
6. Operations and support
Operations and support will also change a lot. Today, L1
support, NOC analysts, and monitoring teams spend a lot of time on standard
tickets, dashboards and alerts. AI agents can already help with password
resets, known issues, ticket routing, alert correlation and incident summaries.
In the future, roles like AIOps, Reliability Engineer and
Digital Workplace Automation will become common. People will still be needed
for complex incidents and important decisions, but not for every simple ticket
or alert.
|
Before |
With AI |
What |
|
L1 Service Desk Agent |
Digital Workplace Automation Engineer |
L1 Service Desk Agent |
|
Helpdesk Analyst |
Digital Workplace Automation Engineer |
Helpdesk Analyst |
|
Desktop Support |
Digital Workplace Automation Engineer |
Basic Desktop Support Technician |
|
Password Reset Operator |
Digital Workplace Automation |
Password Reset Operator |
|
Access Request Operator |
Digital Workplace Automation |
Access Request Operator |
|
NOC Analyst |
AIOps OR Reliability Engineer |
NOC Analyst |
|
Monitoring Specialist |
AIOpsOR Reliability Engineer |
Dashboard Monitoring Specialist |
|
Incident Coordinator |
AIOps OR Reliability Engineer |
Incident Scribe, RCA Documentation Analyst |
Final thought
The future IT team will not only be smaller; it will also be
more efficient. It will be more integrated. Roles will merge where the work is
repetitive, document-heavy, checklist-driven or based on handovers.
People will be the gates where judgment, architecture,
business ownership, security, quality and human coordination are important.
Comments
Post a Comment