Headline

STEM | Industry Partner | AI Workspace Enhancement

About

Many leaders I talk to in DFW are considering one or two alternatives right now with company-wide AI adoption: 1) waiting until the time is right for staff or 2) signing a license agreement for a platform that can't touch their real data needs. Until IP leakage becomes a problem. The knowledge that runs your organization is at risk every time a team member ctrl + v pastes into an LLM from Microsoft, Open AI, Anthropic, or Alphabet. Exposing Your proprietary research, clinical protocols, SOPs, regulatory submissions, manufacturing runbooks. Leaving behind digital breadcrumbs and forensic evidence in chat bots for legal adversaries to subpoena in court. What choice is there? Either stack GPUs on premises, use AI on non-sensitive data only, quietly work around compliance policy (real risk), or avoid AI entirely and fall behind. None of those are good options. Federal guidelines on AI in regulatory submissions and the rise of CAIO roles make this matter even more demanding. What you do is implement a better way. Deploy the same public models ( ChatGPT, Claude, Gemini ) inside your own Azure or AWS environment, with governed access to your internal knowledge. Your cloud. Your data. Role-based controls on who can question what. The economics are worth a look. For an average organization, the three year investment range is exceedingly large for AI infrastructure and related services. If you're leading AI decisions at a company in North Texas — especially if knowledge is scattered across SharePoint, Outlook, and siloed databases — then I'm curious what friction you can attest to with security and how you manage IP risk in the process? Let’s compare notes. DM me or find me outside.

Location

Southlake

Work experience

Solution Advisor at Allata, Sep 2024 - Present · 1 yr 10 mos, Dallas, TX, In the middle of advanced technology and Fortune 500 business objectives. Background in biology, economics, and enterprise architecture. Delivering turnkey solutions that improve business outcomes and best practices. Partnering with executive leadership on digital transformation and AI initiatives, anchored in market realities and honest trade-offs. ▸ Manage bookings across enterprise accounts spanning manufacturing, healthcare, and life sciences ▸ Lead MBR/QBR governance and strategic relationships ▸ Architect "The Allata Way" start small, prove value fast, scale what works ▸ Collaborate cross-functionally with delivery teams and alliance partners to engineer business value

Sr. Alliance Manager at Amazon Web Services (AWS), 2021 - 2024 · 3 yrs, Dallas-Fort Worth Metroplex, Strong track record of growing businesses through strategic partnerships. Builds trusted relationships with senior executives to drive revenue growth and meet customer demands. Leads teams effectively, manages complex projects, and works across departments to ensure success. Fosters collaboration and promotes the value of partnerships. https://www.youtube.com/watch?v=uW7SULcsOpI

Sr. Alliance Manager at Perficient, 2018 - 2021 · 3 yrs, Dallas-Fort Worth Metroplex, Red Hat alliance leader.

AI Ops and ARM at IBM Turbonomic, 2015 - 2018 · 3 yrs, Dallas-Fort Worth Metroplex, Enterprise projects in TOLA region. https://youtu.be/7xgydFbRv18?si=yxVXISwycweWBHpw

AI / ML Ops at Red Hat, 2008 - 2015 · 7 yrs, Dallas-Fort Worth Metroplex, Fortune 500 projects in Central U.S. https://youtu.be/D9-MPTjn1W0?si=TX0wCaWk87Rwlhhv

Education

Bachelor's degree, Economics, Biology, Hampden-Sydney College

Master of Business Administration (MBA), The University of Dallas