TikTok's NewtonX report examines AI adoption among advertisers — findings on integration, barriers, ROI and how to pilot AI for better ad performance.

Overview: What TikTok's New AI Adoption Data Reveals

TikTok, in partnership with research firm NewtonX, has published a new study that examines how advertisers are adopting generative AI and automation tools across their marketing operations. The report looks at adoption rates, satisfaction levels, implementation hurdles, and how brands measure AI success. In my experience covering ad tech, this kind of industry research helps separate marketing hype from concrete outcomes — and this one has some useful, pragmatic takeaways.

Why AI Automation Is Becoming a Marketing Priority

Advertisers expect AI to drive growth. According to the study, roughly 90% of surveyed advertisers and executives expect AI-driven automation to help drive future business growth, and 93% believe AI will improve job performance. That optimism is grounded in a basic truth: AI excels at processing massive datasets to identify patterns and audience opportunities faster than manual methods ever could.

But optimism and implementation are not the same thing. While many companies want the efficiency and targeting improvements AI promises, fewer have fully integrated the technology into core workflows.

Adoption vs. Integration: The Real Numbers

Only about one fifth (19%) of respondents said they have fully integrated AI into core operations and invested at scale. That gap between expectation and integration suggests an industry in transition: leaders want AI, but practical blockers — skills, compliance and shifting technology — are slowing adoption.

Key barriers to adoption

• Data privacy and compliance: Brands are rightly cautious about using personal data with automated systems.
• Lack of in-house skills: Teams need training or external partners to get the most from AI tools.
• Rapid pace of innovation: Tools change quickly, making long-term strategy and tooling choices harder.

How Companies Are Measuring AI Success

Early adopters tend to measure AI through cost savings, improved efficiency and performance uplift in campaigns. Cost reduction gets the attention of budget owners, but the report — and several practitioners I’ve spoken with — caution that the biggest near-term gains are often:

• Improved efficiency (faster creative testing, automated targeting experiments)
• Expanded opportunity (identifying new audience segments or creative hooks)
• Incremental performance lift (better click-through or conversion rates when AI is used with human oversight)

Experience shows it's rarely a one-step win: you don't just flip a switch and watch costs drop. You combine AI with domain expertise to steer models, validate outputs and continually optimize.

Practical Use Cases: Where AI Works for TikTok Advertisers

TikTok advertisers report meaningful gains when AI is applied to specific tasks rather than entire business functions at once. Notable use cases include:

• Ad creative testing: Automating A/B tests for video thumbnails, copy variations and CTA timing.
• Audience discovery: Using models to identify lookalike segments or underexposed demographics.
• Performance optimization: Real-time bid and budget allocation guided by predicted conversion likelihood.

For example, a mid-size retail advertiser I spoke with used automated creative iteration to reduce time-to-test from weeks to days. The result: a 12% lift in ROAS on their top-performing test cohorts — but only after a creative strategist reviewed and refined the AI-generated variations.

Risks and Oversight: Lessons from Recent Missteps

The report notes — and real-world events confirm — that giving AI unchecked control can produce mistakes. Some organizations that relied too heavily on automation faced quality or compliance issues. One notable example is an AI-driven report error that required public correction and highlighted the need for human oversight [Source: AFR].

Bottom line: AI is powerful, but it must be governed. Clear review processes, role definitions, and audit trails are essential when automated systems influence paid media or external communications.

What Brands Want Next from AI Vendors

Respondents said they'd like AI tools to deliver:

• Stronger privacy and compliance features (built-in data protection)
• Better explainability (insights into why the model recommended an action)
• Integrated onboarding and training (so teams can adopt faster)

These requests show marketers are pragmatic: they want tools that are powerful, safe and usable by real teams — not just flashy demos.

Where to Start: A Practical Framework for Marketers

If your team is evaluating AI for advertising, consider a phased approach:

1. Identify specific use cases (e.g., creative testing or bid optimization).
2. Run small pilots with measurable KPIs and a human reviewer in the loop.
3. Invest in skills — either hire or train staff to steward models and interpret results.
4. Build governance around privacy, compliance and model explainability.
5. Scale where you see consistent gains and capture learning to inform broader rollouts.

In my view, companies that follow this disciplined path are more likely to realize sustainable ROI from AI — without falling into hype-driven traps.

Where to Read the Full Report

TikTok's full 25-page TikTok/NewtonX report is publicly available for marketers who want to dig into the data and charts in detail. The report provides extra insight into how TikTok ad partners are using platform-specific AI tools to improve creative production and campaign outcomes [Source: TikTok for Business].

Key Takeaways

• High expectations, measured integration: Most advertisers expect AI to drive growth, but only a minority have deeply integrated it.
• AI works best with human oversight: The top benefits are efficiency and expanded opportunities, not instant replacement of human roles.
• Governance matters: Privacy, compliance and explainability are critical to safe and effective AI adoption.
• Start small, measure, scale: Pilots with clear KPIs and experienced reviewers accelerate meaningful adoption.

I'm seeing the same patterns across platforms: AI delivers when it's purposefully applied and carefully managed. If you're planning your next campaign, consider a small AI pilot focused on creative or audience discovery — and be ready to invest in the people who will make it work.

Learn more about related AI-in-business coverage in our article How Meta AI Conversations Will Influence Your Ad Feed — What Marketers Must Know. That piece explores how conversational AI can shape ad personalization and privacy trade-offs, which is directly relevant for advertisers weighing automation on platforms like TikTok.