MIT xPRO Certificate in AI Strategy & Google Cloud Certified
We translate enterprise AI discipline into a phased execution model for owner-led and growth-stage teams. Every engagement is scoped around operational outcomes, security controls, and a delivery rhythm your business can sustain.
Executive proof signals
Efficiency Potential
30%+
Targeted support & data entry savings
Security posture
Pass
Control & configuration review
Typical delivery
14d
Strategy to implementation
Engagement model
A+B
Executive advisory with technical execution
A curated operating model that blends advisory depth with implementation speed.
Level 1 · Strategic Core
We establish the operating thesis, identify high-value workflow candidates, and sequence implementation around control requirements and adoption risk.
Primary outcome
Prioritized ROI roadmap
Governance baseline
Security-first rollout controls
Level 2 · Build Track
Deploy workflow automation, internal copilots, and applied integration patterns mapped to your existing systems and team capacity.
Level 3 · Operating Advisory
Guide tool decisions, vendor selection, capability budgeting, and phased implementation governance for sustainable scale.
Transparent Investment
Predictable pricing, zero hidden fees, and clear deliverables designed for growth-stage businesses.
Explore all advisory tiers & full options — more... →
Rapid automated architectural evaluation, readiness score, and prioritized workflow ROI roadmap.
Guided consulting blueprint, vendor selection, risk posture audit, and 60-day action plan.
Comprehensive architectural audit, viability matrix, and prioritized ROI roadmap.
Full production deployment of scoped workflow automation pipelines and system integrations.
Continuous algorithmic validation, model monitoring, security patching, and prompt drift auditing.
Practical Starting Points
Explore common project themes for service businesses. Choose a category to see relevant starting points, then validate fit, data, and safeguards before implementation.
Initial themes are based on recurring small-business AI use cases in public guidance and are not a ranking or a promise of results. Sources:
Enterprise operating discipline translated for growth-stage businesses with practical timelines and measurable outcomes.
Verify Brad Allen on LinkedIn →Leadership pedigree
Experience leading technology programs across high-accountability organizations including Raytheon, Verizon, AT&T, and Hyundai.
Proof of impact
Engagements are scoped around targeting and unlocking operational value first, then translated into delivery milestones and adoption plans.
Verified Credentials
Official Digital BadgesFormally certified foundations combined with practical controls for security, governance, and enterprise AI execution.
MIT xPRO • Emeritus Verified
Google Cloud Certified Badges
Zero-Risk Data Commitment
Concrete technical safeguards designed to ensure your proprietary business data never leaks, never trains public AI models, and remains 100% your intellectual property.
Your customer interactions, internal SOPs, and financial data are isolated. Formal Zero-Data-Retention (ZDR) agreements ensure public AI models never train on your inputs.
Deploy within private Virtual Private Clouds (AWS/Azure VPC) or isolated on-premise LLM runtimes for maximum perimeter boundary protection.
Full AES-256 data encryption at rest and TLS 1.3 in transit. Enforced Role-Based Access Control (RBAC) and immutable audit trail logging.
Continuous prompt drift auditing, hallucination prevention guardrails, and automated vulnerability patching integrated into every deployment.
Thought Leadership
How small-to-midsize businesses can utilize enterprise foundation models without compromising customer PII or proprietary IP.
Moving beyond chatbot hype: A step-by-step framework for calculating net hours saved across 90-day support ticket cycles.
Evaluating latency, cost per 1M tokens, and data privacy trade-offs between open-weights local LLMs and API endpoints.
Illustrative scenarios based on real methodology - not client work (yet). Here's exactly how I'd tackle four common situations.
Illustrative scenario
A mid-size operations team is spending 15+ hours a week answering the same 20 questions - order status, scheduling changes, and basic troubleshooting. Here's the build sequence I'd run:
What this typically targets:
Meaningful reduction in first-response time and support labor hours on repetitive tickets. The exact number depends on ticket mix, which is why step one is a measurement step, not a guess.
Illustrative scenario
A lean office team is losing inbound opportunities because calls roll to voicemail after hours. Here's the build sequence I'd run:
What this typically targets:
Higher answered-call coverage after hours and faster appointment intake without adding overnight staffing overhead.
Illustrative scenario
The product is shipping quickly, but security controls have grown organically. Here's the build sequence I'd run:
What this typically targets:
Lower exploitable risk exposure, clearer ownership of control gaps, and a prioritized path to stronger security posture.
Illustrative scenario
The team wants AI-enabled search and drafting, but policy and contract obligations require tighter data boundaries. Here's the build sequence I'd run:
What this typically targets:
Safe adoption of AI workflows for sensitive documents while keeping control over data handling and compliance boundaries.
Enterprise Leadership Track Record
Endorsements from engineering leaders, directors, and partners across enterprise technology organizations.
"I’ve had the good fortune of working with Brad for a number of years here at Raytheon. We worked together on several enterprise-level projects which included the selection and implementation of applications and services that spanned the entire company. Foremost, his technical expertise, thought leadership, and dedication to the mission were keys to our success. He consistently takes on the tough jobs and does so with unparalleled veracity. Brad is the one person you most definitely want on your team."
Raytheon Enterprise Project Partner
Transparent early-stage offer for teams that want direct founder involvement.
A note from Brad
"I built Allen AI Solutions on 25+ years of enterprise operating experience - most recently at Hyundai Capital America - because I kept seeing the same gap: companies either overpay for generic AI consulting, or underbuild it themselves and create new risk. I'm taking on a small number of founding clients right now, and I'm upfront that you'd be among my first engagements under this name. What you get in exchange is my full attention, founder-level pricing, and a methodology built on real enterprise practice - not a junior team learning on your dime."
Founding Client Program - 3 spots this quarter
I'm opening a limited number of founding-client engagements at founding-client pricing. In exchange, I ask for an honest case study and testimonial once we've delivered results - the kind of proof I can't publish yet because it doesn't exist. If you want to work with someone personally invested in getting your first engagement right, this is the moment to do it.
Book a Free AI Opportunity CallRoutes to the same consultation form.
Clear Answers
Everything you need to know about our security protocols, engagement timeline, expected investment, and operational deliverables.
Let's map your operational gaps, prioritize automation opportunities, and outline a secure, practical roadmap for your team.
30 minutes. High-density, engineering-focused session.
No sales pitch. Zero obligation. You'll speak directly with Brad to review real technical viability, not marketing promises.
We respect your privacy. No spam, ever.
