Use the Right Mix of
AI to Create Bigger Business Value
AI to Create Bigger Business Value
The biggest AI wins rarely come from GenAI alone. They come from combining prediction, optimization, generation, and intelligent action to solve bigger business problems and create more value.
Mind the Gap!
GenAI gets the spotlight, but the biggest business gains usually come from combining capabilities. Without the ability to blend prediction, optimization, GenAI, and agents, teams ship impressive demos while higher-value opportunities stay out of reach.
Key Integrated AI Questions
- Are we designing around the problem — or defaulting to GenAI when the answer requires more?
- Where are we leaving value on the table by not combining prediction, GenAI, and agents?
- What do we need to strengthen to design and scale higher-value AI solutions?
The Bottom-Line
When GenAI becomes the default answer, bigger AI value gets left behind.
Build the Readiness to Choose the Right AI for the Job
We help leaders see where integrated AI can create more business value, assess the readiness needed to deliver it, and build a focused plan to strengthen the architecture, modeling, reuse, and delivery enablers behind it.
Launch Pad
Assess Your Readiness
Weeks 1–2
Align the team
- Identify key stakeholders
- Explore what “good” looks like
- Explore Real-World Use Cases
Assess current state
- Review Key Competencies
- Assess Your Readiness
- Add Comments for Context
Define readiness gaps
- Define Group Readiness
- Identify Mis-Alignment
- Capture Group Themes
Mission Control & Lift-Off
Build Your
Plan
Plan
Weeks 3–4
Prioritize the gaps
- Understand High-Impact Gaps
- Explore Gap Closure Options
- Prioritize For Impact & Effort
Build the roadmap
- Define Key Steps
- Align on Ownership
- Define Target Timeline
Define success measures
- Committed Target
- Stretch Goals
- Controls
Accelerate
Accelerate Your Momentum
Weeks 5–12
Execute priority moves
- Execute your plan
- Mitigate Risks
- Validate Your Impact
Drive adoption & change
- Identify Stakeholders
- Communicate Changes
- Action Feedback
Review impact & what's next
- Re-baseline Readiness
- Select Next Gaps
- Update your readiness plan
Outcomes you can expect
Clarity
See which gaps most limit your ability to combine AI capabilities for more value.
Alignment
Align on where integrated AI can create the most business value.
Focus
Prioritize the readiness gaps that matter most for higher-value AI solutions.
Readiness
Strengthen the foundation needed to design and scale integrated AI solutions.
Impact
Improve the odds that bigger AI bets create measurable business value.
The biggest AI wins come from solving the problem with the right
mix of capabilities.
mix of capabilities.
Frequently Asked Questions
1. Overview & Fit
2. Scope & Deliverables
3. Process & Timing
4. Participants & Ways of Working
5. Outcomes & Next Steps
- Who is this Applied AI & ML readiness accelerator for?
Use it when leaders need a clearer view of how classical AI/ML and GenAI should work together. It’s built for AI, data science, product, engineering, architecture, and platform leaders who need to make better decisions about where predictive, optimization, and generative approaches each create the most value. - When should we run an Applied AI & ML readiness accelerator?
Run it before GenAI becomes the default answer to every problem. It’s especially useful when teams need sharper decision criteria for where AI/ML should complement GenAI and where capability investments should go next. - How is this different from a standard AI/ML strategy review?
This is less about a broad AI/ML vision and more about readiness for the current moment. It assesses whether your Applied AI & ML capabilities are positioned to strengthen GenAI outcomes, improve solution fit, and create differentiated value where GenAI alone isn’t enough.
- What exactly gets assessed in Applied AI & ML readiness?
We assess the capabilities that make Applied AI & ML reusable and valuable at scale: models, data science practices, pipelines, architecture choices, decision criteria, team capability, and the ways predictive, optimization, and generative approaches are combined across the enterprise. - What inputs and artifacts should we bring into the accelerator?
Bring the materials that show how AI/ML decisions get made today: roadmaps, model inventories, architecture diagrams, reusable assets, team structures, workflow examples, capability assessments, and use cases where leaders are deciding whether AI/ML should play a larger role. We’ll use what you already have and identify where missing inputs are slowing progress. - What will we receive at the end of the accelerator?
You’ll receive a prioritized view of the readiness gaps that matter most, a clear synthesis of the themes behind them, and a practical plan for strengthening Applied AI & ML over the next several weeks and months.
- How long does the accelerator take?
The work typically begins with a focused assessment over the first few weeks, with the option to extend into a broader 12-week acceleration period if you want structured support as teams close the most important gaps. - How do the three phases work in practice?
First, we assess the current state and identify the gaps. Next, we translate those findings into a prioritized action plan. Then we support teams as they close priority gaps, align decisions, and track what should happen next. - How hands-on is the 12-week period?
It’s hands-on and practical. We work with leaders and working teams to review findings, refine actions, support gap closure, and keep the work tied to real architecture, capability, and solution-design decisions.
- Which teams should participate in the accelerator?
The best results come when product, AI/ML, engineering, architecture, and platform leaders work through the findings together, especially the teams deciding where AI/ML should complement GenAI. - How much time should leaders and working teams expect to commit?
Leaders typically join the kick-off, read-out, prioritization, and key follow-on decisions. Working teams provide the inputs, explain how AI/ML and GenAI are used today, and help shape the actions needed to strengthen readiness. - How will the right teams work together during the accelerator?
The accelerator creates a shared view across model development, architecture, product strategy, and platform teams so decisions about Applied AI & ML and GenAI can move forward with clearer priorities and less duplication.
- What changes when Applied AI & ML readiness improves?
The payoff is clearer visibility into where AI/ML should complement GenAI, teams reduce duplication and poor-fit solution choices, and the organization becomes better equipped to create differentiated value across the enterprise. - How quickly can we act on the findings?
Most organizations can act on the highest-priority gaps quickly because the accelerator is built to produce practical priorities, not just observations. Some architecture and decisioning changes can begin right away, while broader capability-building takes longer. - What should we do after the readiness assessment is complete?
Use the prioritized findings to strengthen Applied AI & ML capabilities, close the most important reuse and integration gaps, align leaders on where AI/ML should complement GenAI, and decide where additional coaching or deeper work will create the most value.
Choose the Right AI for Bigger Wins