Empowering Enterprise AI: Teknikos' Hybrid Approach to AI Adoption at the Snapdragon Customer Xperience Summit
Teknikos had the privilege of leading the session “Mobilizing on-device and hybrid AI tools for employees” at the Snapdragon Customer Xperience Summit, where we shared our vision for the future of enterprise AI adoption. The session, led by our CTO Jon Khoo and CDO Elissa Kotler, centered on The Frontier Firm Journey, and described a clear, practical path to adopting AI that puts users first and blends on‑device and cloud intelligence to deliver measurable value.
Why This Topic, and Why Now
At Teknikos, our north star hasn’t changed: the best technology disappears so that only the experience - and the value - remains. It guided our earliest touch applications two decades ago and continues to drive our AI work today. The industry’s rapid AI uptake has outpaced ROI for many, largely because adoption stalls when tools depend on extensive training, user acumen, and constant cloud consumption. Our answer is a hybrid‑first, user‑centered approach that meets people where they work.
The Frontier Firm Journey
At the summit, we framed AI adoption through the lens of Microsoft’s concept of the Frontier Firm – defined as an organization that fundamentally rearchitects its operations around artificial intelligence to unlock rapid scaling, operational agility, and accelerated value creation. This transformation unfolds in three distinct phases:
Human + AI Assistant: Everyone gets faster and better with assistants.
Human + Agent Teams: Employees delegate focused tasks to agents.
Human‑Led, Agent‑Operated: People direct end‑to‑end processes run by agents.
This Frontier Firm roadmap gives leaders a shared vocabulary and benchmarks to gauge progress and plan for scale. It also reiterates the value of human capital in the advancing role of AI.
Our AI Adoption Playbook
By recognizing the challenges to scale - like energy, performance, cost, and security – Teknikos developed an adoption playbook to help organizations strategically architect and build future-ready, scalable AI systems.
NPU Activation & Hybrid AI: Use the processor that’s right for the job. Run models on‑device when privacy, latency, cost, or reliability matter, and burst to the cloud for scale or complexity. Hybrid architectures will become increasingly critical as AI scales.
Dynamic Runtime Selection: Let the application decide – automatically - between local and cloud execution based on policy, context, and connectivity. Users shouldn’t have to think about runtimes; they should just receive the results.
Zero‑Prompt & Generative UI: Shift from chat‑only interactions to experiences that apply the value of AI to anticipate intent and deliver familiar interface components. Users navigate standard UI; the system does the prompting in the background.
What we demonstrated
We showed how the principles of our playbook come together in two demos that feel intuitive because they’re built around real work - not AI for AI’s sake:
PDF Explorer: An on‑device experience that parses, organizes, and reasons over documents with NPU acceleration, no internet required. This demo highlights the workload capacity of the NPU while delivering critical privacy and connectivity benefits.
HelpChat: The same user experience can run locally or in the cloud, with an evaluator choosing the best path in real time based on policy and connectivity. HelpChat proves that using the right processor for the job is the practical answer to scaling AI.
The Outcomes Matrix leaders keep asking for
To make trade‑offs explicit, we shared an Outcomes Matrix that compares cloud, local, and hybrid approaches across privacy, latency, cost, reliability, and compliance. The takeaway is simple: hybrid architectures win because balancing power and practicality uses each environment for what it does best.
A familiar philosophy, evolved for AI
Our perspective on adoption comes from decades of designing solutions that blend hardware, software, and human experience. We know that adoption happens when apps are so intuitive that nobody needed a tutorial or training, and the technology balances cost and performance considerations. Today, the same is true for AI: when the interface feels familiar and the system inherently handles the complexity - policies, prompts, runtime decisions - adoption follows.
What this means for customers
Faster value realization: Assistants and agent teams improve existing work now, while you build toward human‑led, agent‑operated processes.
Lower risk and spend: On‑device execution trims cloud costs and reduces data movement, helping with compliance and reliability.
Users, not operators: Solutions that anticipate intent and reduce dependency on prompt engineering make it possible for every employee to generate quantifiable value from AI.