The benefits and challenges of adopting AI in Digital Experience Platforms (DXPs)

As we get deeply entrenched into the Digital economy, both as consumers and

businesses, it is becoming increasingly clear that all the leading Digital Experience

Platform (DXP) vendors – Adobe, Sitecore, Acquia etc. are embracing AI technologies to

deliver memorable experiences. Besides, the ability to anticipate customers’ needs and

preferences and deliver fulfilling experiences has become a strategic imperative for all

enterprises, across industries and size.

I believe this trend is irreversible and for the right reasons.

As the DXP supports consumers’ engagement with a brand, the AI tools are

continuously harnessing the information, including our interactions, preferences,

behaviors etc. to:

Deliver personalized content, offers, recommendations etc. to increase conversion and enhance customer loyalty.
Create seamless and interactive experiences, with Chatbots and Virtual Assistants.
Enable real-time decision making, with contextually relevant engagement.
Enhance user convenience and engagement with Image and Voice recognition tools.

All these benefits endear us as consumers to the brand, while enhancing a company’s

revenues. But I believe that the adoption of AI at scale, done without enough

precautions, is fraught with lots of risks including:

Bias – AI Models learns from data, and biased data leads to biased outcomes from the model. DXP vendors must ensure that the AI Models are transparent and the right kind and quality of data is used to train and optimize the models.
Reasoning limitations – Some AI Models, such as the ones developing using sophisticated algorithms like DNNs (Deep Neural Networks), are difficult to interpret. The inability to explain the outcomes/recommendations from these Models can dilute consumers’ trust in the brand and in regulated industries, lead to serious concerns.
Data Security – Any breach of sensitive and confidential information will lead to legal implications, besides loss of reputation for the company.
Performance & Cost – Most of the platforms are cloud hosted and sustain performance as scale increases, but it comes with a huge, hidden cost. Running the AI models to optimize them leads to a lot of computational and storage resource consumption that in turn translates into higher costs.
Skill Gaps – Finding the right resources, skilled to continuously re-tool the models to adapt them to market realities has become an on-going challenge for all companies, especially the SMBs who can’t easily afford them.
Justifying the Return on Investment (ROI): The ROI of AI in DXPs in not immediate and in fact take several years before companies see significant results. Hence, companies should internally set clear expectations with all stakeholders and develop capabilities that are aligned to the company’s business strategy and operating model.

To reduce these risks, we recommend DXPs implement a careful and ethical strategy for

incorporating AI. This includes conducting comprehensive risk assessments, ensuring

transparency in AI decision-making, and complying with data privacy regulations.

Moreover, consistent monitoring and frequent audits enable early identification and

resolution of potential concerns.