Annual tech stack review season. Time to assess what’s working, what isn’t, and where to invest next.

This year, AI adds new dimensions to the review. Here’s how to evaluate your stack with AI in mind.

The Review Framework

Part 1: Current State Assessment

Before thinking about what’s next, understand what you have.

Inventory everything:

• All software subscriptions
• AI features within existing tools
• Standalone AI tools
• Integration between systems
• Data flows

Don’t skip anything. Shadow IT counts. Free tools count. That thing someone signed up for and forgot about counts.

Document actual usage:

• Who uses each tool?
• How often?
• For what purposes?
• Which features are actually used?

There’s often a gap between what you’re paying for and what you’re using.

Part 2: Performance Review

For each significant tool:

Is it delivering value?

• What problem does it solve?
• How well is it solving it?
• What would happen if you removed it?

Is it right-sized?

• Are you using the features you’re paying for?
• Do you need features you don’t have?
• Is the tier appropriate?

Is it maintained?

• Who owns this tool?
• When was configuration last reviewed?
• Is it up to date?

Is it integrated?

• How does it connect to other tools?
• Are integrations working well?
• Are there integration gaps?

Part 3: AI-Specific Questions

Now layer in AI considerations.

For tools with AI features:

Are you using the AI features?

Many tools now include AI. Often those features go unused.

If you’re paying premium for AI features you don’t use, consider downgrading.

Are the AI features working?

If you are using them:

• How accurate are they?
• How much time do they save?
• What’s the quality of output?

AI features that produce low-quality output may be doing more harm than good.

Could AI features replace other tools?

Sometimes AI features in one tool can replace functionality you’re paying for elsewhere.

CRM AI might replace standalone lead scoring tool. Document AI might replace separate extraction software.

Look for consolidation opportunities.

For standalone AI tools:

What’s the ROI?

You should be able to answer:

• What did we invest?
• What did we get back?
• Is this worth continuing?

If you can’t answer these, you need better measurement.

Is it integrated properly?

Standalone AI tools often exist in silos. Are they connected to the systems they should enhance?

Is it being maintained?

AI tools need ongoing attention. Are prompts being refined? Is performance monitored? Are updates applied?

Part 4: Gap Analysis

What AI capabilities are you missing?

Review each major business function:

• Sales: AI assistance available?
• Marketing: AI capabilities in use?
• Operations: Automation opportunities?
• Customer service: AI support deployed?
• Finance: AI analysis available?

Not every function needs AI. But gaps should be conscious choices, not oversights.

What integration gaps exist?

Data should flow between systems. Where are manual bridges? Where is data duplicated?

AI often helps with integration gaps—extracting, transforming, routing data.

What data isn’t being used?

You probably have data that could be valuable:

• Customer interactions
• Transaction histories
• Operational metrics
• Support inquiries

AI can analyze data at scale. What data are you sitting on?

Part 5: Opportunity Identification

Based on gaps and performance issues, identify opportunities:

Quick wins:

• Better use of existing AI features
• Simple automation additions
• Tool consolidation
• Right-sizing existing subscriptions

Strategic initiatives:

• New AI capabilities that address real problems
• Integration improvements
• Data analytics projects
• Process automation

Rank by:

• Business impact
• Implementation effort
• Risk level
• Resource availability

The AI-Specific Review Questions

Ask these about your overall AI posture:

Strategy Questions

Do we have an AI strategy?

Not a formal document necessarily. But clear direction on where AI fits and doesn’t fit.

Is AI investment aligned with business priorities?

AI investments should connect to what matters most.

Who owns AI decision-making?

Someone should be responsible for AI coherence across the organization.

Capability Questions

Do we have the skills to use AI effectively?

• Can staff prompt effectively?
• Do they know AI strengths and limitations?
• Is there training in place?

Do we have data readiness?

• Is data quality sufficient for AI?
• Is data accessible to AI tools?
• Are governance issues addressed?

Risk Questions

Are AI risks managed?

• Data privacy addressed?
• Output quality monitored?
• Errors being caught?

Is AI lock-in understood?

• What vendor dependencies exist?
• What are switching costs?
• Are there exit paths?

The Review Meeting

Block 2-4 hours with key stakeholders. Have them prepare by reviewing tools they own.

Agenda:

1. Inventory review (30 min): Walk through current tools and usage
2. Performance discussion (45 min): What’s working, what isn’t
3. AI assessment (30 min): AI-specific evaluation
4. Gap identification (30 min): What’s missing
5. Opportunity prioritization (30 min): What to do about it
6. Action planning (15 min): Who does what by when

Document outcomes. Assign owners. Set follow-up dates.

Common Findings

Reviews typically surface:

Unused subscriptions: Tools people forgot about. Features nobody uses.

Duplicate functionality: Multiple tools doing the same thing.

Integration gaps: Manual data movement that could be automated.

Right-sizing opportunities: Premium tiers when basic would suffice.

AI feature underuse: Paying for AI features that go unused.

Maintenance neglect: Tools that need attention but aren’t getting it.

Each finding is an opportunity.

Post-Review Action

Don’t let review insights die. Take action:

Immediate (next 30 days):

• Cancel unused subscriptions
• Downgrade where appropriate
• Assign neglected tool owners

Near-term (next 90 days):

• Address top integration gaps
• Implement quick wins
• Start planning strategic initiatives

Ongoing:

• Monthly check-ins on action items
• Quarterly mini-reviews
• Annual comprehensive review

Getting Outside Perspective

Annual reviews benefit from fresh eyes. Internal teams have blind spots.

AI consultants Brisbane and similar specialists can facilitate reviews or provide independent assessment. They see patterns across many organizations.

Their perspective catches things internal teams miss.

Building Review Capability

Over time, develop internal review capability:

• Templates for consistent evaluation
• Dashboards for ongoing visibility
• Regular check-in rhythms
• Clear ownership structure

Annual review shouldn’t be a scramble. It should be culmination of ongoing attention.

The Review Deliverables

At the end of review, you should have:

1. Current state documentation: What you have, what’s used, how it performs
2. Issue list: Problems identified during review
3. Opportunity list: Improvements identified, ranked
4. Action plan: What happens next, who owns it, when
5. AI assessment: Specific evaluation of AI capabilities and gaps

This becomes your roadmap for the coming year.

Making It Stick

The review is only valuable if actions follow.

What helps:

• Clear ownership of each action
• Regular progress check-ins
• Leadership visibility
• Tied to budget cycle

What hurts:

• Actions without owners
• No follow-up rhythm
• Review as checkbox exercise
• Disconnected from decisions

Team400 and similar advisors can help establish ongoing review practices that create lasting value.

The Bottom Line

Annual tech stack review is essential. With AI now woven through business software, the review needs new dimensions.

Ask the AI-specific questions:

• What AI features exist?
• Are they being used?
• Are they working?
• What’s missing?
• What’s the AI strategy?

Document findings. Prioritize opportunities. Take action.

That’s how you keep your tech stack healthy in the AI era.