How to scale AI without losing digital authenticity

Marketers have good reasons to scale AI. It can help teams create faster, make experiences more relevant, and take repetitive work off people's plates. Customers can see the upsides, too - in Sitecore's Digital Authenticity Index, 80% say AI can be useful or beneficial when used responsibly to improve accuracy, relevance, or credibility.

Consumers are less certain about what happens as AI use grows. Half of consumers expect increasing AI use to reduce digital authenticity, compared with 39% who think it will improve it. As brands scale AI, customers encounter the results in the content they read, the experiences tailored to them, and the service they receive.

Digital authenticity is the extent to which a brand’s digital experiences feel credible, relevant, and responsibly delivered. As AI takes on more work across marketing, those standards give you a useful way to decide what deserves to scale.

AI-generated content can weaken digital authenticity when it is inaccurate, generic, inconsistent with the brand, or published without enough care. Editorial standards matter more as AI increases the amount of content a team can produce.

Content is a good place to see this dynamic in action. Marketers have spent several years experimenting with AI across the creative process, and consumers are getting familiar with the results. The Digital Authenticity Index found that 60% say AI-generated content is easy to spot, rising to 74% among Gen Z. That doesn't tell us whether people identify AI correctly every time. It does tell us they're paying attention to the content brands put in front of them.

For content teams, the finished work still has to earn the customer's attention and trust. AI can explore ideas, adapt existing material, create variations, or get a first draft moving. But feed it an outdated product claim and you can reproduce the mistake in seconds. Generic source material tends to produce generic content. A vague brief can get halfway around the world before anyone realizes something’s wrong.

These habits become more important as AI increases output. For content strategists, digital authenticity means thinking about the source material AI can use, who keeps it current, and where the finished information will appear. The same information may eventually reach a customer through your website, search, email, or a personalized experience.

Once AI starts shaping more than content, the same questions move into the wider customer experience.

Some of the biggest AI-related threats to digital authenticity are inaccurate content, poor use of customer context, unclear AI involvement, and automation that makes human help harder to reach.

The Digital Authenticity Index describes AI as an authenticity multiplier. Weak content practices can spread inconsistency faster, and unclear data practices give customers more reason to question what they're seeing. Automation can create more distance when human help is already difficult to reach. Good source information, useful customer context, and clear ownership give AI a stronger foundation to work from.

Look at what already exists before expanding an AI experience. Problems that are manageable at small scale become harder to contain once AI starts repeating them.

AI personalization can support digital authenticity when it makes an experience more useful and respects the customer's context and boundaries. AI can respond to far more customer signals, so the quality of that context matters. 80% of consumers say AI can be useful or beneficial when used responsibly to improve accuracy, relevance, or credibility.

Give personalization better context

• Use current intent to help the customer move forward.
• Carry useful context across the journey so customers don't have to repeat themselves.
• Let people correct preferences or assumptions that no longer fit.

Use content personalization to improve relevance
Content personalization supports digital authenticity when it helps customers find information that fits their current needs and respects their preferences.

Use customer context to shorten the path to a useful answer and carry that context through the journey so people don't have to keep reminding you who they are or what they're trying to do.

Transparency supports digital authenticity by helping customers understand when and how AI is shaping the content or experience they receive.

The Digital Authenticity Index found that 65% of consumers say undisclosed AI use decreases trust. Applying that finding gets complicated once you look at how AI is actually used in a marketing team. Most content no longer fits neatly into “human” or “AI-generated.” AI might fix punctuation or create a first draft that a writer later rebuilds. A typo fix and a 2,000-word draft clearly aren't the same level of contribution.

Regulators and technology companies are starting to wrestle with the same distinction. Anthropic has announced text watermarking for future Claude models in response to new EU transparency requirements. The company acknowledges the limits of the signal: its watermark can indicate that Claude was likely involved, though it cannot distinguish between Claude writing a piece and heavily editing one.

Whatever happens with watermarking, marketers still need to decide what customers should understand about AI’s role in the experience. For published content, an AI disclosure can show that the technology played a part. Editorial responsibility tells the reader who checked the facts, shaped the argument, and stood behind the finished work.

In other experiences, customers may need different information. Someone receiving a personalized recommendation may care about why they're seeing it and what information shaped it. A customer using automated service needs clear expectations about the interaction.

For content teams, aim for clarity. Disclose AI involvement when it helps the reader understand something important about the content and keep editorial responsibility with the people publishing it.

Human accountability supports digital authenticity by keeping people responsible and accessible as more customer interactions become automated. It has the widest gap in the Digital Authenticity Index - 95% of consumers expect easy access to human support and visible accountability behind digital experiences, while only 56% believe brands deliver it well.

Keep people within reach

Automation will eventually encounter something the workflow didn't anticipate. Plan for that before the customer finds it.

• Give customers an obvious route to human help where the stakes or complexity call for it.
• Give someone inside the business clear responsibility for the AI experience.
• Review recurring failures so the same problem doesn't keep reaching customers.

Digital authenticity can give marketing teams a consistent way to evaluate AI as its role grows. Use the framework when you're deciding where to expand AI across content and customer experiences. Look at the quality of the information behind it, how customer context is being used, and where people need greater clarity or access to support.

What you find can inform the next decision. You may need to improve source content before generating more from it, adjust how personalization uses customer data, or make ownership clearer around an automated experience.

Keep the framework in the conversation as those experiences change. It gives your team a shared way to consider customer trust alongside the speed and scale AI makes possible.