My observation says that — current AV industry = mostly software AI (cloud / PC / server-based).
But the shift to hardware-based AI in AV has already started, just not fully visible yet.

Let’s break this properly from an AV-engineering perspective.

1. Reality Today (2024–2026)

AV is NOT purely software AI anymore

We already have early hardware AI inside AV components, but it's:

• Hidden inside DSPs / chipsets

• Limited to specific functions

• Not marketed as “AI hardware AV” yet

Examples:

• AI noise reduction in DSPs (Shure, Biamp, QSC)

• Auto-framing cameras (Crestron 1Beyond, Poly, Cisco)

• Beamforming mics with tracking

These are running on embedded DSP + AI accelerators, not cloud
Modern DSP chips already include neural engines + ML accelerators

2. Why AV still feels “Software AI”

Because of 3 main limitations:

1. Compute Requirement

• AI (vision, speech) needs heavy processing (GPU/NPU)

• Traditional AV hardware = low-power DSP focused

2. Cost vs Market

• AV hardware must be:

  • Reliable

  • Long lifecycle (7–10 years)

  • Cost-sensitive

    AI chips increase BOM significantly

3. Flexibility

• Software AI (cloud) = easy updates

• Hardware AI = fixed capability (unless reprogrammable)

3. What is Changing NOW

We are entering the Edge AI era

Meaning: AI runs inside the device (hardware) instead of cloud

Key technologies enabling this:

• Edge SoCs (System-on-Chip)

• NPUs (Neural Processing Units)

• AI DSPs

• FPGA-based AI (like adaptive compute platforms)

These chips are designed for real-time, low-latency AI at device level

4. Where Hardware AI is Already Coming in AV

A) Audio

• AI DSP chips (echo cancellation + voice isolation)

• Context-aware audio (speaker tracking, noise classification)

Already happening (automotive & pro AV crossover)

B) Video 

• AI cameras with:

  • Auto framing

  • Speaker tracking

  • Gesture recognition

Runs on edge AI chips which is not included in the designs.

 C) Control Systems

• Predictive AV systems

• Self-healing rooms

• Usage-based automation

Will require embedded AI processors

D) AV-over-IP + AI

• AI embedded in endpoints:

  • Encoders/Decoders

  • Switches (yes!)

  • Streaming nodes

5. Timeline — When Full Hardware AI AV Will Happen

Now (2024–2026)

• Hybrid systems (DSP + partial AI hardware)

• AI mostly “feature-level”

6. What Future AV Hardware Will Look Like

Think of this:

A DSP won’t just process audio

It will:  Understand speech context, identify speakers, Optimize acoustics dynamically

A camera won’t just capture

It will: Understand meeting intent, Track engagement, Adjust framing intelligently

A control system won’t just trigger
It will: Predict user behavior, Auto-configure rooms

7. Key Insight

AV is moving from: Signal Processing → Intelligence Processing

Traditional DSP: EQ, compression, routing

Future AI Hardware: Understanding + decision-making + adaptation

8. Why Should we Care about this?

This shift will redefine roles:

Today:  AV Programmer / DSP Designer

Tomorrow:

• AV + AI System Architect

• Edge AI integrator

• Data-driven AV designer

Final Conclusion for this discussion I can say that...

"Hardware-based AI in AV is already starting, but Mainstream adoption = next 3–5 years (2026–2030)"

And after that: AV systems won’t just “work” — they will think