Where technology becomes operational
I help bring AI from the demo to the real world.
VP, Solutions Engineering & Partnerships at Kognitos
George Williams works with enterprises adopting AI for complex business operations, with a focus on turning technical capability into production systems that create measurable business value.
He has spent more than two decades in enterprise technology across Cisco, Palo Alto Networks, Zscaler, Veza, and Kognitos. Much of that career has been spent inside high-growth companies during major technology shifts, working directly with customers, shaping go-to-market strategy, and building the teams and operating systems required to scale.
My story
From infrastructure operations to enterprise AI
His career began in infrastructure operations, managing production systems and serving as a senior escalation point when they failed. At Cisco, that foundation expanded into enterprise solution design, complex evaluations, executive customer engagement, and commercial execution.
He joined Palo Alto Networks in 2013, early in its rise from a high-growth challenger into one of the defining companies in enterprise security. Over more than seven years there, he helped scale the major accounts business, win Fortune 1000 customers, and increase the number of seven-figure deals the organization could close. He later joined Zscaler as zero trust became an enterprise priority, then Veza as identity security and authorization emerged as a new category. Today at Kognitos, he is working on the next major shift: AI systems that can interpret information, make decisions, and take action across enterprise operations.
Each transition has reinforced the same lesson for him. New technology creates the opportunity. The companies that turn it into growth are the ones that connect technical differentiation to a business problem customers are willing to act on.
Experience
Building growth around technical differentiation
- Cisco
Learned how enterprises adopt infrastructure
- Palo Alto Networks
Saw how security changes what’s possible
- Veza
Helped define a new approach to identity and access
- Kognitos
Now working on enterprise AI that delivers real value
Solutions Engineering is one of the places where that connection gets built.
At Veza, he led global Solutions Engineering while reporting to the CEO and redesigned the technical sales motion around measurable customer value. Average deal size increased 190% within six months. At Zscaler, he led teams and go-to-market initiatives across large, major, and strategic accounts, contributing to 166% year-over-year ZPA growth and building programs that materially improved technical evaluation performance.
Across those roles, he has built and led teams, developed technical sales motions, worked with partners, and stayed close to the customer environments where the product ultimately has to prove itself.
The common thread is repeatability: understanding why the technology matters, proving that value in a customer’s environment, and building a field organization capable of doing it again.
Why AI matters now
AI creates one of the largest enterprise technology opportunities he has seen because it can change the economics of work itself.
Organizations can reduce manual effort, increase capacity, accelerate decisions, and automate processes that have traditionally scaled by adding people. As AI moves from generating content to taking action across enterprise systems, its potential impact becomes substantially larger.
Realizing that potential requires understanding where AI creates meaningful leverage, how it fits into the customer’s environment, what authority it should have, and how the business will measure the result.
Those are the problems he spends much of his time working on today.
A principle I return to
Technical capability has to become repeatable customer value.
He brings engineering, product, go-to-market, and customer success together to turn technical differentiation into adoption, expansion, and durable growth.
Why I write
Turning AI capability into results
He writes about how companies turn AI capability into operating and commercial results.
His recurring subjects include AI strategy, agents, governance, evaluation, security, and finance transformation. He is particularly interested in the decisions that determine whether a promising technology becomes a durable enterprise capability: where AI creates leverage, how value is proven, how authority is controlled, how the surrounding process changes, and how successful deployments scale.
His perspective comes from working directly with enterprise customers, leading complex evaluations, building field organizations, and helping emerging technologies become repeatable businesses.
The question behind much of what he writes is simple:
How do companies turn what AI makes possible into something customers will buy, deploy, expand, and depend on?
Let’s stay in touch
Read the writing
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