Supercharge Your Software Engineers with AI Power
As a fractional consultant, I bring 20+ years of VP Eng/CTO/Architect
expertise to supercharge your engineering team with AI
Questions or to Schedule a Session: fszale@gmail.com
Software Engineering with AI at the Core
Phase 1
Learn and Analyze (30-60 Days)
Objective: Establish a baseline, align with stakeholders, and set the stage for AI and PMF gains.
Duration: 30-60 days, depending on client complexity.
Parallel Workstreams:
Client-Facing Discovery
Engineering Deep Dive
Deliverable by Day 60: A unified "Kickoff Deck" combining both reports, presented to leadership with initial AI tooling and PMF recommendations.
Phase 2
Implementation (6-12 Months)
Objective: Deploy AI tools, refine processes, and accelerate Product Market Fit with iterative wins.
Duration: 6-12 months, with weekly and monthly progress reviews.
Activity Sequence:
AI Tooling Adoption (Months 1-3)
Architecture Refinement (Months 2-4)
Engineering Experience Overhaul (Months 3-6)
PMF Acceleration via Idea Evaluator (Months 4-9)
Cultural Shift: Owner’s Mindset (Months 6-12)
Continuous Delivery (Months 6-12)
Reporting: Dashboards to management with KPIs, qualitative wins and next steps.
Outcomes
Continous KPIs
Rate of Innovation: Double Your Innovation: 2 to 4-6 Features per Quarter
Cycle Time: Cut Cycle Time 30-50%: 10 Days to 5-7 Days
Turnover Rate: Slash Turnover Below 10%
Phase 1
Objective: Establish a baseline, align with stakeholders, and set the stage for AI and PMF gains.
Step 1: Assess current engineering processes, including cycle time, turnover, and tech stack, to establish a baseline (e.g., cycle time = 10 days, turnover = 20%).
Step 2: Evaluate client needs, such as PMF status, revenue goals, and retention challenges, by interviewing client success, sales, and marketing teams.
Step 3: Identify AI opportunities, such as replacing manual QA with AI agents or using Grok for code reviews, aligning with your "AI first before human hire" approach.
Step 4: Define metrics, core values, and OKRs, ensuring a single source of truth for tracking progress.
Outcome: This phase establishes baselines for all KPIs (e.g., Cycle Time, Turnover Rate), enabling measurable progress in Phase 2.
Phase 2
Objective: Transform your engineering team into an AI-powered, PMF-focused powerhouse, delivering measurable wins—faster cycles, stable talent, and soaring revenue.
- AI Tooling Adoption (Months 1-3)
What: Roll out AI tools like Grok for code reviews, ChatGPT for scripting, and AI agents for QA automation. Train 20% of your team as AI champions through hands-on workshops.
Value: Cuts manual tasks (e.g., 50% of QA automated), speeding delivery.
Outcome: Cycle Time drops 20-25% (e.g., 10 → 7-8 days) by Month 3; Rate of Innovation ticks up with faster ideation.
Why: Specifies tools and training, links to early KPI wins—sets the stage for efficiency.
- Architecture Refinement (Months 2-4)
What: Redesign your system for PMF—map outcomes (e.g., 99.9% uptime, 2x speed) to budget and skills. Embed AI for ingestion (data pipelines), compute (processing), and distribution (CICD), with self-healing features like auto-scaling.
Value: Creates a lean, scalable backbone—less downtime, more focus on features.
Outcome: Cycle Time improves further (10% more, e.g., 7-8 → 6-7 days); Revenue per FTE grows as output rises.
Why: Clarifies AI’s role in architecture, ties to scalability—grounds PMF prep.
- Engineering Experience Overhaul (Months 3-6)
What: Streamline workflows—pre-configured dev environments (e.g., Docker), 20% tech debt refactor, 1-day CICD cycles via GitHub Actions, and AI-driven docs/tests (50%+ automated). Define "Done" for quality.
Value: Engineers ship faster, stress less—focus shifts to innovation.
Outcome: Cycle Time hits 30-50% reduction (5-7 days); Rate of Innovation reaches 3 features/quarter; Turnover Rate dips (e.g., 20% → 15%).
Why: Breaks into specifics (e.g., 1-day CICD), shows multi-KPI impact—core efficiency driver.
4. PMF Acceleration via Idea Evaluator (Months 4-9)
What: Use the Idea Evaluator (People/Process/Tech, 80/20, Revenue/Risk) to score and test product ideas with AI insights (e.g., Grok ranks concepts). Run 5-10 sales calls with client success to build a "hell yes" case study, refining via 30-60-90 sprints.
Value: Nails demand, not guesswork—products customers love.
Outcome: Client Retention Rate rises (70% → 75-80%); ARR grows 10-20% (e.g., $2M → $2.2M-$2.4M); Rate of Innovation sustains at 3-4 features.
Why: Integrates "Path to PMF" tactics, links to retention and revenue—heart of PMF push.
5. Cultural Shift: Owner’s Mindset (Months 6-12)
What: Embed EOS (metrics, Level 10 meetings, OKRs) and consider Topgrading (hire/coach 1-2 A-players). I will share my YouTube training content (e.g., "Systems Over Goals") and gamify AI use (e.g., "Prompt Leaderboard") to boost engagement.
Value: Aligns team to a unified vision—retains talent, drives ownership.
Outcome: Turnover Rate falls below 10%; Revenue per FTE nears $200k-$250k; Rate of Innovation hits 4-6 features/quarter.
Why: Ownership mindset ensures lasting change.
6. Continuous Delivery & Revenue Focus (Months 6-12)
What: Implement PMF-driven CICD—weekly releases tied to client feedback. Prioritize top 20% ideas (Idea Evaluator) for revenue impact. Integrate engineering with client success for real-time iteration.
Value: Delivers what customers want, fueling growth and loyalty.
Outcome: ARR surges 20-40% ($2M → $2.4M-$2.8M); Client Retention Rate reaches 80-85%; Revenue per FTE achieves $250k+.
Why: Emphasizes revenue sprint cycles and connects to all business KPIs.
What You Get
Retain Top Talent with Stable, Innovative Teams
Drive Product Innovation for Peak Valuation
Cut Risk and Optimize Operations Fast
Accelerate Product Market Fit
About Me
20+ Years Leading Software Teams
VP of Engineering | CTO | Principal Engineer
Scaled Startups with EOS, Topgrading