AI and the Future of GRC Consulting

If you've been following the consulting industry, you've probably noticed the shift. McKinsey, Deloitte, EY - they're all racing to integrate AI into their practices. EY committed $1.4 billion to AI strategy. KPMG: $2 billion. The Big Four see what's coming.

But here's what's less discussed in the GRC space: a significant portion of traditional consulting value wasn't about strategic insight. It was about knowing how to operate complex tools.

The Complexity Problem

Think about a typical SoD remediation engagement. A consultant comes in, spends a week getting oriented with your GRC tool, runs some reports, exports data to Excel, massages it into usable format, analyzes the results, and presents findings. Four to six weeks later, you have a remediation plan.

Break down where that time actually goes:

Activity	Time Spent	Value Added
Learning/navigating tool interface	20-30%	Low
Exporting and reformatting data	15-25%	None
Building custom visualizations	10-20%	Medium
Actual analysis and recommendations	25-35%	High
Documentation and presentation	15-20%	Medium

The bulk of billable hours goes to activities that don't require human expertise - they require familiarity with a specific tool's quirks and export formats.

This wasn't an accident. Complex interfaces created dependency. The harder a tool was to learn, the more valuable the consultant who had mastered it.

What AI Changes

AI agents don't need guided workflows or elaborate dashboards. They need structured data they can reason about.

Give Claude or GPT a well-formatted JSON file containing your SoD violations, user assignments, role structures, and usage statistics. Ask it to identify quick wins. Within seconds, you get analysis that would have taken a consultant days:

Based on the usage data provided, I've identified 47 users (32% of
violations) who have never executed the conflicting transactions.
These represent low-risk remediation candidates.

Of these, 23 users have the conflicting access through a single role
(ZS:FI:AP-PROCESS) that provides no other actively-used transactions.
Removing this role assignment would resolve their violations with zero
business impact.

The remaining 24 users have mixed usage patterns...

The AI doesn't care that your GRC tool has a 47-step process to generate this report. It doesn't need training on which menu contains which function. It just needs the data.

The Consulting Model Shift

According to Harvard Business Review, consulting firms are moving from a "pyramid" model to an "obelisk" - fewer junior staff doing research and data manipulation, more senior advisors focused on judgment and client relationships.

This makes sense. When AI can handle data analysis, the value shifts to:

Understanding business context that isn't in the data
Making judgment calls about acceptable risk
Navigating organizational politics around access changes
Designing sustainable governance processes

These require human expertise. Clicking through complex interfaces doesn't.

What This Means for GRC Tools

Most GRC platforms were designed when complexity served a purpose:

Vendor perspective:

Complex tools require implementation partners (services revenue)
Steep learning curves reduce churn (customers don't want to re-train)
Proprietary visualizations lock in data (harder to switch vendors)

Consultant perspective:

Tool expertise becomes a billable skill
Complex exports justify data transformation work
Custom reporting justifies ongoing engagements

Neither of these benefits the customer. And in an AI-enabled world, they become active liabilities.

The Data Format Question

Here's a practical example. You need to analyze 500 users with SoD violations and identify remediation candidates.

Traditional GRC tool approach:

Navigate to the right report (hope you remember where it is)
Set 15 filter parameters (hope you get them right)
Run the report (wait 10 minutes)
Export to Excel (lose some formatting)
Clean up the export (30 minutes of manual work)
Build pivot tables to make sense of it
Finally start your actual analysis

AI-ready tool approach:

Generate JSON export with all relevant data
Feed to AI agent with your question
Get analysis

The difference isn't just speed. It's that the second approach lets you iterate. Ask follow-up questions. Explore different angles. The AI can process the same data dozens of ways in the time it takes to run one traditional report.

The Visualization Question

Here's something worth considering: why does your GRC tool need built-in dashboards at all?

Your organization probably already has:

Power BI, Tableau, or Looker for business intelligence
Established visualization standards and practices
Users trained on these tools
AI assistants that can generate charts on demand

When you lock security data into proprietary visualization layers, you're forcing users to learn yet another interface. You're preventing them from integrating GRC data with other business metrics. You're making it harder to get the views they actually need.

Data visualization is a solved problem. Every BI tool does it. Every AI can generate charts. What's not solved is getting clean, structured access risk data out of GRC tools in the first place.

How We Approached This at MTC Skopos

We made some deliberate choices based on where we see the industry heading.

AI-Native Data Exports

Every analysis in MTC Skopos exports to structured JSON. Not as an afterthought - as a primary output format.

{
  "risk": {
    "id": "F071",
    "level": "High",
    "description": "Post accounting entries AND process treasury payments"
  },
  "affected_users": [
    {
      "username": "JSMITH",
      "conflicting_functions": {
        "FI08": {
          "execution_count": 0,
          "last_execution": null,
          "source_roles": ["ZS:FI:AP-PERIODEND:C"]
        },
        "TR02": {
          "execution_count": 147,
          "last_execution": "2025-10-15",
          "source_roles": ["ZS:TR:PAYMENTS:C"]
        }
      },
      "remediation_complexity": "medium"
    }
  ]
}

This works immediately with any AI agent. No consultant needed to "translate" the output.

No Visualization Lock-In

We deliberately don't embed complex dashboarding. MTC Skopos provides the data. You visualize it however makes sense for your organization.

Want to build Power BI dashboards that match your other security reporting? The data's available. Want your AI assistant to generate ad-hoc charts during a meeting? Feed it the JSON. Want to integrate with your existing GRC reporting framework? No proprietary formats to wrestle with.

Your data. Your tools. Your choice.

Simple Interface

Traditional GRC tools hide functionality behind layers of menus, tabs, and configuration screens. We took the opposite approach: a simple interface that produces comprehensive outputs.

The goal isn't to impress with elaborate UI. It's to get you the data you need with minimal friction.

Practical Implications

If you're evaluating GRC tools or planning your access risk management strategy, consider:

Data portability:

Can you export complete analysis results in structured formats (JSON, CSV)?
Or are you locked into proprietary reports and dashboards?

AI compatibility:

Can an AI agent consume your tool's output directly?
Or does every analysis require manual data transformation?

Visualization flexibility:

Can you use your existing BI tools?
Or are you forced into the vendor's visualization layer?

Interface complexity:

How long does it take a new user to run their first analysis?
Does the tool require certification or extensive training?

The Bottom Line

The consulting model built on "knowing where to click" is losing relevance. AI doesn't need guided tours through complex interfaces - it needs clean data it can reason about.

This isn't necessarily bad news for consultants. The valuable work - understanding business context, making judgment calls, designing governance processes - still requires human expertise. What's changing is that tool operation and data manipulation are no longer billable skills.

For organizations, this means the tools you choose matter more than ever. Platforms designed around consultant dependency become obstacles in an AI-enabled world. Tools that provide clean, structured, portable data become force multipliers.

The question isn't whether AI will change GRC consulting. It's whether your tools will help or hinder that change.

Interested in seeing how AI-ready GRC tooling works in practice? [Learn more about MTC Skopos] or [contact our team] for a demonstration.