Revenue
- Account research
- Sales preparation
- CRM intelligence
- Lead and account prioritization
AI Performance
AI Is Becoming a Business Performance Discipline.
AI is changing how companies sell, operate and make decisions. The advantage will come from redesigning work around measurable value, not simply adding tools.
The paradigm shift
From AI Adoption to AI Performance
The first phase of AI adoption centered on access to tools. The next phase is about redesigning workflows, roles and decisions so AI creates productive capacity, commercial advantage and operating leverage.
Phase One
AI Adoption
Phase Two
AI Performance
Focus
Tools and licenses
Redesigned workflows
Ownership
Individual experiments
Prioritized, owned use cases
Scale
Scattered pilots
Governed, repeatable deployment
Measure
Usage and logins
Hours, capacity and cost
The question
“Are people using it?”
“Is it moving margin and EBITDA?”
AI Adoption AI Performance
Focus
Before
Tools and licenses
After
Redesigned workflows
Ownership
Before
Individual experiments
After
Prioritized, owned use cases
Scale
Before
Scattered pilots
After
Governed, repeatable deployment
Measure
Before
Usage and logins
After
Hours, capacity and cost
The question
Before
“Are people using it?”
After
“Is it moving margin and EBITDA?”
Where We Look for AI Value
Finance
- Forecasting
- Variance analysis
- Management reporting
- Scenario modeling
Operations
- Workflow automation
- Resource planning
- Knowledge retrieval
- Process efficiency
Customer Experience
- Customer service
- Knowledge access
- Personalization
- Self-service
Leadership
- Information synthesis
- Decision support
- KPI analysis
- Strategic planning
Our AI Performance Framework
01
Discover
Where can AI create the greatest value?
Understand the organization's processes, technology, data and current use of AI.
02
Prioritize
Which workflows should change?
Identify high-value use cases based on:
Impact × Feasibility × Risk × Investment
03
Build the Economics
How will we measure the return?
Estimate:
Capacity created · Cost reduction · Revenue opportunity · Expected ROI
04
Implement
How will employees and roles be affected?
Establish owners, technology, workflows, training and accountability.
05
Govern
What data and technology are required?
Create appropriate controls around data, security, quality and responsible use.
06
Measure
Track whether AI is actually creating business value.
How We Rank AI Opportunities
Every candidate use case is scored on impact, feasibility, risk and required investment. The matrix turns a long list of ideas into a short, owned set of priorities: quick wins first, strategic bets sequenced, fill-ins as capacity allows, and the rest deliberately deprioritized.
- Quick wins build momentum and fund the program
- Strategic bets get owners, budgets and timelines
- Fill-ins are handled only when capacity allows
- Low-value, hard ideas are deprioritized, not debated
Impact →
Strategic BetsHigh impact, harder to deliver
Quick WinsHigh impact, feasible now
DeprioritizeLow impact, hard to deliver
Fill-InsLow impact, easy to deliver
Feasibility →
The Goal Is Not Fewer People.
The goal is a more productive organization. AI should increase the amount of valuable work each employee can perform.
- Grow without equivalent increases in headcount
- Improve margins
- Respond faster
- Make better decisions
- Redirect people toward higher-value activities
That is operating leverage. And operating leverage creates enterprise value.
What is limiting the next stage of the business?
Let's identify where the greatest opportunity exists.
Whatever the inflection point, the first step is establishing the facts.