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Six in 10 Australian Workers Have Caught AI Getting It Wrong at Work

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New Appian research finds the most significant AI errors encountered had financial, compliance, or reputational impact consequences

SYDNEY, Oct. 8, 2026 /PRNewswire/ — Sixty percent of Australian workers have personally identified an error made by an artificial intelligence (AI) tool at work, according to new research from Appian (Nasdaq: APPN).

The Australian workplace study found that while workers widely report AI mistakes, more than one in five said employees in their organisation are discouraged from challenging its recommendations or decisions. Against that backdrop, only 14% fully trust the AI-generated outputs used within their organisation.

Charlie Hutchinson, SVP Asia-Pacific and Japan at Appian, said organisations cannot expect workers to embrace AI if they do not trust its outputs or feel able to question them. “It is hardly surprising that complete trust in AI is so low when workers are catching it making mistakes and are discouraged from questioning its outputs,” said Hutchinson.

“For businesses, mistrust creates a lose-lose situation. Employees may avoid AI or spend time checking everything it produces, wiping out the productivity gains it was meant to deliver. Alternatively, they may continue using an output they suspect is wrong because they do not feel able to challenge it. AI needs to operate within the same governance processes and controls as the rest of the business, with people able to step in before a significant error occurs.”

AI mistakes are undermining worker trust

AI errors are already affecting customers and business operations. One in five workers said the most significant AI error they encountered caused a customer service issue, while 13% reported operational disruption.

Almost half said the most significant AI error they experienced resulted in consequences beyond minor inconvenience, including financial, compliance, or reputational impact.

The potential for AI to make incorrect decisions was the greatest concern for 25% of respondents, followed by data privacy and security risks at 22%, and a lack of transparency around how AI reaches decisions at 20%.

“Every visible error gives workers another reason to question whether AI should be trusted with more consequential work,” said Hutchinson. “Employees are often the last line of defence before an incorrect output reaches a customer or becomes part of a business decision. If they are discouraged from speaking up, organisations remove one of their most important safeguards.”

As a result of these challenges, almost all workers (94%) said AI should not be allowed to make decisions affecting employees or customers without limits or human oversight. This is reinforced by findings that senior management remains more trusted than AI, with 52% placing greater trust in senior leaders compared with 29% who trust AI more.

Businesses recognise the risk, but controls are lagging

Global research from Harvard Business Review Analytic Services, sponsored by Appian, found a significant gap between recognising the risks of AI and acting on them. While 92% agreed AI agents need rules-based guardrails to operate safely and effectively, less than half (48%) said their organisation has defined them.

As businesses move AI from standalone productivity tools into core processes that directly affect employees and customers, the consequences of that gap will become more significant.

“Businesses know AI needs rules, but many are deploying it before those rules are in place. Doing so without the right controls can quickly lead to unexpected token costs, operational disruption and accountability gaps,” said Hutchinson. “Governance cannot be bolted on after AI is already influencing decisions. AI needs to be embedded within governed business processes that control what it can access, define the rules it must follow, and determine when a decision must be handed to a person. That gives employees the visibility and confidence to use AI while allowing businesses to capture its productivity benefits without giving up accountability.”

Workers reject AI without accountability

Two-thirds (66%) of Australian workers believe decisions affecting employees or customers should either require human review and approval or always be made by people. A further 28% would restrict AI to low-risk decisions.

“Australian workers are rejecting unchecked AI. Businesses will only realise the full value of AI if employees build trust, are willing to use it within critical processes, and confident enough to act on its outputs. That requires governed processes where AI recommendations and autonomous agent outputs can be systematically reviewed, validated, or overridden and human judgement remains part of important decisions,” concluded Hutchinson.

If you are ready to leverage AI automation for critical processes with structure, reliability and control, start here.

Appian Australian Research Methodology:
Appian commissioned Zoho Research to survey 500 Australian workers in Q3 2026.

About Appian:
Appian provides AI automation for mission-critical work.. We automate complex processes in large enterprises and governments. Our platform is known for its unique reliability and scale. We’ve been automating processes for more than 25 years and understand enterprise operations like no one else. For more information, visit appian.com. (Nasdaq: APPN) 

Follow Appian: LinkedIn, YouTube, Instagram, Facebook, and X.

Forward-Looking Statements

This press release includes forward-looking statements. All statements contained in this press release other than statements of historical facts are forward-looking statements. The words “anticipate,” “believe,” “continue,” “estimate,” “expect,” “intend,” “may,” “will,” and similar expressions are intended to identify forward-looking statements. These forward-looking statements are subject to a number of risks and uncertainties, including the risks and uncertainties set forth in the “Risk Factors” section of Appian’s most recent Annual Report on Form 10-K, quarterly reports on Form 10-Q, and other filings with the Securities and Exchange Commission. Appian is under no duty to update any of these forward-looking statements after the date of this press release to conform these statements to actual results or revised expectations, except as required by law.

 

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SOURCE Appian

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‘Kung fu tea’: US journalist Douglas’ journey into the art of Wuyi rock tea

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BEIJING, Oct. 8, 2026 /PRNewswire/ — A news report from chinadaily.com.cn:

Three hundred years ago, a British man stole the seeds of Wuyi rock tea to plant them elsewhere. Three centuries later, US journalist Douglas travels to Xiamei village at the foot of Wuyi Mountain in Fujian province to learn the age-old craft.

After the entire experience, he understands why people call it “kung fu tea”. For him, picking tea requires “eagle claw skills” and shaking the leaves demands “tai chi hands”. Every step is filled with a sense of mastery and dedication.

Why did Douglas travel all the way to Xiamei village to learn the art of tea-making? What are his insights on the 13,000 km Tea Road? Watch the video to find out.

View original content to download multimedia:https://www.prnewswire.com/news-releases/kung-fu-tea-us-journalist-douglas-journey-into-the-art-of-wuyi-rock-tea-302903196.html

SOURCE chinadaily.com.cn

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IQuest-Q1 Draws Early Developer Attention Across Coding and Agentic Workflows

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BEIJING, Oct. 8, 2026 /PRNewswire/ — IQuest-Q1 has drawn early praise from developers and industry watchers since launch, particularly for its performance on coding, software engineering, interactive application generation, and long-horizon agentic workloads.

The model weights and technical materials are publicly available:

GitHub — GitHub – IQuestLab/IQuest-Q1Hugging Face — https://huggingface.co/IQuestLab/IQuest-Q1Blog — https://iquestlab.github.io/

IQuest-Q1 is trained for the work developers actually do: navigating a repository, driving a terminal, calling tools, holding a long context in mind, and finishing multi-step tasks without losing the thread. Reasoning, tool use, long-context understanding, and multi-step execution were part of the training objective from the start — not adapted afterward.

Early external discussion has begun to explore IQuest-Q1’s capabilities. One highlighted its evaluation across application building, 3D spatial generation, code diagnosis and repair, and complex tool-assisted workflows, while another publicly shared use case described turning a written product brief into an interactive SaaS analytics dashboard. A third-party technical overview examined the sparse Mixture-of-Experts architecture, integration with Claude Code and Codex CLI, and the infrastructure requirements of self-hosting a 320B-parameter model.

These early observations align with IQuest Research’s official demonstrations, which show IQuest-Q1 working across interactive application generation, code debugging, and multi-step tool use.

One-shot interactive applications

From a natural-language prompt, IQuest-Q1 emits runnable interactive apps in a single pass.

An FPS game. In one generation, the model produces the 3D scene, character movement, health, scoring, mode switching, and an in-game shop for resources and gear — code, file layout, interaction logic, and visuals in the same pass.

A racing game. Continuous scene extension — track geometry, foreground/background transitions — is where one-shot generations usually fall apart. IQuest-Q1 handles this class of spatially continuous, interaction-heavy app without special prompting.

Debugging a real RL run

IQuest-Q1 will also drop into an existing codebase and training stack and fix what’s actually wrong.

An RL run went off the rails. Starting from the training curves, the model pulled logs and execution traces, reasoned back through likely causes, and localized the bug in code. After the patch and a restart, it read the new metrics and confirmed recovery.

The root cause: a stray space had been inserted into the training trajectory. Removed, metrics came back up.

Multi-step work in a real environment

Given a workspace with heterogeneous information and tools, IQuest-Q1 runs multi-step tasks — reading, calling tools, and correcting itself as new information lands. In office settings it works across chat, cloud docs, spreadsheets, and comment threads, pulling context together into analysis, drafts, and revisions that are ready to hand off.

Architecture

The architecture and training approach behind these capabilities are outlined below.

Decoder-only Transformer with a sparse Mixture-of-Experts feed-forward. ~320B total parameters, ~15B active per token.

Training runs in three stages — pre-training, mid-training, post-training — bringing up code fluency first, then extending into longer, harder tasks.

Post-training focuses on software engineering, long-horizon agentic tasks, and general reasoning, using supervised fine-tuning and reinforcement learning. On top of that, Multi-Teacher On-Policy Distillation (MOPD) consolidates strengths from several teachers on the student’s own on-policy rollouts, so the student picks up capability without inheriting any one teacher’s bias profile.

Validated updates, datasets, and workflows feed into the next iteration. Pipelines, training configs, eval harnesses, and tooling are versioned and reused — capability work and infrastructure work compound instead of getting rebuilt each cycle.

Evaluations

IQuest-Q1 posts balanced results across benchmarks covering code, software engineering, terminal use, tool use, and agentic tasks:

NL2Repo — repository-level code generationCyberGym — cybersecurityTerminal-Bench 2.1 — terminal operationDeepSWE v1.1 — long-horizon codingJobBench — professional office workflows

Full numbers, baselines, and evaluation setup are in the technical report.

IQuest-Q1 was developed by IQuest Research. Developers and research teams interested in participating in upcoming early-access testing programs can apply for trial access via email: research@iquestlab.com 

CONTACT: 
IQuest Research
research@iquestlab.com 

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SOURCE IQuest Research

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INFiLED Launches NX Series for Stage Productions

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SHENZHEN, China, Oct. 9, 2026 /PRNewswire/ — INFiLED, launched the NX Series in early October, a heavy-duty rental LED solution for stage productions. Developed for live events, touring productions, outdoor festivals and temporary indoor or outdoor installations, NX Series is designed for projects where LED systems must hold large structures, shift between stage forms and keep onsite work moving under tight schedules.

Recent INFiLED rental projects place the release in a real production context. Shenzhen Happy Valley‘s outdoor summer festival stage showed the scale of a layered five-screen rental layout, while UMK2026‘s live stage production reflected the precision required for large-format screens and overhead LED elements. Together, they point to stage environments where larger visual surfaces, dimensional design and tighter onsite schedules now converge.

NX is introduced for this kind of stage work, where creative direction and technical execution need to stay aligned from load-in to show time. The platform brings structural capacity, multi-form configuration, rental-ready protection, faster service and stable visual performance into the same system.

Larger Stages Ask More of the System

Large stage displays now do more than fill a background. They shape the scene, define depth and often become part of the performance architecture. Before content appears on screen, the LED structure has already passed through transport, lifting, alignment, rehearsal and operation.

NX is specified for stacking and hanging up to 20 m, with optional wind-resistant braces and locks for demanding sites. That capacity matters for large suspended or stacked LED structures, especially in outdoor environments where structural planning carries the whole production.

The cabinet design follows the same logic. A dual-layer magnesium cabinet keeps panels lighter in handling while retaining the rigidity needed for alignment, with cabinet weights of 6.8 kg and 10.9 kg across its two cabinet options. Top quick locks and bottom alignment pins support faster connection and more consistent geometry as the screen grows.

Creative Forms Need Repeatable Mechanics

Stage design rarely stays within a flat rectangle. Productions move between curves, staggered layers, stepped contours, wraparound corners and compact scenic structures. In rental work, those forms need to return accurately across different venues, crews and schedules.

NX supports flat displays, curved layouts, staggered and stepped surfaces, and heart-shaped creative forms. With NX Edge, the system forms 90° curved transitions for wraparound content. In selected configurations, NX also works with MC Series for right-angle and cube structures, and with Xmk3 Series for larger curved forms.

For precision curves, NX offers a –10° to +15° range in 2.5° steps. The degree-based adjustment gives designers room to shape the stage while giving technicians a clear mechanical reference during installation and repeat use.

Onsite Workflow Shapes the Final Image

A strong stage image depends on work the audience never sees. Load-in, locking, alignment, cabling, service and rehearsal all influence whether the screen stays on schedule and performs cleanly when the show begins.

NX addresses those pressure points through several rental-focused details:

Top quick locks and bottom alignment pins support faster assembly and consistent screen geometry.The unified integrated PDU centralizes power and signal distribution, reducing cabling complexity across the system.Optional clip-on masks for outdoor configurations reduce disassembly steps during onsite service.Outdoor versions feature full encapsulation and IP65-rated protection for exposed environments.

Visual performance remains part of the same production path. Outdoor stages need brightness that stays clear in stronger ambient light, so NX reaches up to 5,000 nits outdoors. A 7,680 Hz refresh rate supports smoother motion and camera capture, while 16-bit grayscale and a 10,000:1 contrast ratio preserve tonal control, depth and clarity for stage content.

NX Series expands INFiLED’s rental LED portfolio with a heavier-duty option for stage production applications where scale, form and timing carry equal weight. For INFiLED, whose work spans rental, fixed-installation and commercial display environments, the release adds another layer to its engineering focus – LED systems shaped around the conditions of real projects, not only around the appearance of the screen.

For stage teams, that focus matters most when the technology fades into the work. The structure holds, the workflow keeps moving and the image reaches the audience as intended.

Website: www.infiled.com
Contact: info@infiled.com

SOURCE INFiLED

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