# How Executives and Individual Contributors Differ When It Comes to AI

> How to identify and bridge three gaps between executives and individuals when it comes to AI: the transparency gap, resource gap, and optimism gap.

Source: https://asana.com/resources/executives-differ-on-ai

## How executives and individual contributors differ when it comes to AI

_This article was originally published on_[Reworked](https://www.reworked.co/digital-workplace/how-do-executives-and-individual-contributors-differ-when-it-comes-to-ai/)_._

We live in an era where technology zips and zags through our lives. AI is seemingly everywhere—yet it’s also entangled in a mess of buzzwords and grand promises.

In many ways, AI is a transformative technology. But when we look back on history, its promises and challenges are not all that unique. History teaches us that technology implementation is always complex and rarely goes as planned.

As my teammate [Mark Anthony Hoffman](https://www.linkedin.com/in/mark-anthony-hoffman/) recently shared at Gartner’s 2024 Digital Workplace Summit, technology resistance is an age-old phenomenon. Plato and Socrates warned against the pen, arguing it induced forgetfulness in the learners’ souls and minds and that knowledge should be spoken and remembered. Almost 2,000 years later, in 1440, Gutenberg invented the printing press. The new technology made the written word widely available, but it also eliminated the jobs of scribes, who lived and profited from Plato’s detested pen. Scribes protested the arrival of the printing press in European cities, claiming that it made words and writing cheap and meaningless.

Another reality of technology is that it affects everyone differently—in particular those at different levels of the organizational totem pole. We’re seeing this with AI too. Our research at Asana’s [Work Innovation Lab](https://asana.com/work-innovation-lab) shows three main areas of divergence between executives and individual contributors in today’s organizations:

## 1. The Transparency Gap

Our [research](https://asana.com/work-innovation-lab/the-state-of-ai-at-work/) reveals a striking discrepancy: while 44% of executives believe they have effectively communicated their AI plans, just 25% of individual contributors feel informed about these strategies.

This gap suggests a breakdown in communication, eroding trust and dampening enthusiasm toward AI adoption. Imagine playing a game of telephone where the message is your company’s AI strategy. A clear directive issued by the C-suite becomes increasingly distorted as it trickles down through the managerial layers, until it reaches individual contributors as muddled whispers.

Addressing this requires a more deliberate communication approach:
- Regular, clear updates on AI projects, objectives, and impacts—such as through internal newsletters that feature project highlights, challenges and next steps.
- Interactive town hall meetings or brown bags, where employees at all levels can ask questions directly to executives in a two-way dialogue.
- Using digital forums and work management platforms to encourage open discussions about AI initiatives.

## 2. The Resource Gap

When it comes to AI training and resources, we also see a gap. About 25% of executives say they’re providing AI training for their teams, but only 11% of individual contributors say they have access to the necessary tools and knowledge.

This gap suggests that despite good intentions, the execution of AI training programs is falling short. It's not enough for executives to allocate resources for training; these resources must be accessible and relevant to all levels of the organization.

Organizations need to launch targeted AI literacy programs tailored to different roles, ensuring all employees have the opportunity to learn about and comfortably interact with AI. These programs should cover the basics of AI, ethical considerations, and practical applications relevant to the participants' daily tasks.

## 3. The Optimism Gap

Optimism about the potential of AI to drive organizational success also varies up and down the hierarchy. Sixty-one percent of leaders are optimistic about AI's role in achieving company objectives, but only 46% of individual contributors feel the same. Bridging this gap requires clear communication about how AI can augment human capabilities and relieve employees from mundane tasks to allow for more strategic work.

## A Human-Centric Strategy

Our recent [State of the IT Leader](https://asana.com/work-innovation-lab/state-of-the-it-leader/) report found that 63% of IT executives regret not choosing technologies more carefully. Investing in cutting-edge AI technology is futile without a concerted effort to bridge these gaps—transparency, resource and optimism.

The success of your AI adoption hinges not on the technology alone but on a full strategy that addresses these human-centric aspects. AI technology needs to be chosen carefully, prioritizing vendors that adopt a human-centric approach. Without a deliberate effort to navigate these human dimensions, the most advanced AI technology may as well be a pen without ink or a press without paper—full of potential, yet unrealized.

AI の現状

- [AI 導入で変わる意思決定プロセス: 経営戦略と現場を同期させる次世代のガバナンス](/ja/resources/ai-decision-making-process)

AI の現状

#### コンテンツライター

AI による意思決定とはAI による意思決定とは、人工知能が膨大なデータから最適解を導き出し、人間の判断力を高度に拡張するプロセスです。その根幹にあるのは、経験や主観ではなく客観的な数値を拠り所とするデータ駆動型 (データドリブン) 経営です。変化の激しいビジネス環境において、経営層は市場動向や財務データを可視化することでリスク管理に基づいた迅速な判断を行 ...

- [自治体 AI 導入完全ガイド 2026: 官公庁の最新活用事例と行政 DX を成功させる 5 つのステップ](/ja/resources/public-sector-ai-adoption)

AI

#### コンテンツライター

自治体、行政における AI 導入の現状と背景現在、日本の多くの自治体の現場では、多様化する住民ニーズへの対応や深刻な労働力不足という大きな課題に直面しています。この課題解決の切り札として、人工知能、とりわけ生成 AI の活用が急速に広がっています。総務省の調査や各地の報告によれば、自治体における生成 AI の利用は、もはや一部の先進的な取り組みにとどまらず ...

- [中小企業のための AI 活用例 18 選｜補助金や事例、アイディア](/ja/resources/smes-ai-applications)

AI の現状

#### コンテンツライター

中小企業こそ AI を戦略的に導入することで、競争力の強化と持続的な成長を実現できます。本記事では、中小企業がどのように AI の活用を進め、DX (デジタルトランスフォーメーション) を実現できるのか、具体的な活用事例も交えながら解説します。中小企業にとって AI が不可欠な理由2030 年問題や 2050 年問題で代表されるように、深刻な人手不足の時代 ...

- [自治体における AI データ分析実践ガイド: 業務効率化へのステップと成功事例](/ja/resources/local-gov-ai-data-analysis-guide)

AI の現状

現代の地方自治体が直面する人手不足と財政の壁。これらの課題を乗り越え、持続可能な行政サービスを実現するためには、データ駆動型行政への転換が不可欠です。膨大な行政データから真の価値を引き出し、具体的な業務効率化と住民サービス向上へとつなげるための「AI データ分析の実践ガイド」をご覧ください。はじめに: データ駆動型行政への転換現在、日本の地方自治体は「AI ...

- [How executives and individual contributors differ when it comes to AI](/ja/resources/executives-differ-on-ai)

AI の現状

AI の現状

- [The Work Innovation Lab 所長](/author/rebecca-hinds)

This article was originally published on Reworked.We live in an era where technology zips and zags through our lives. AI is seemingly everywhere—yet it’s also entangled in a mess ...
