Study of 300+ executives identifies misalignment between marketing and IT as key barrier to AI transformation

CHICAGO, Sept. 10, 2025 /PRNewswire/ — Bounteous, a leading global digital transformation consultancy, released a new white paper titled “The AI Whitespace: Addressing Challenges to Unlock Potential.” The report helps enterprises accelerate artificial intelligence (AI) adoption by identifying and closing organizational gaps between marketing and technology functions.

Based on a survey of more than 300 senior executives across North America and Europe, the report highlights key misalignments slowing AI adoption and provides a framework to help enterprises align, invest, and lead with AI. The full report is available here for enterprises looking to lead with AI.

With generative AI rapidly moving from experimentation to business-critical operations, Bounteous emphasizes that successful AI integration requires more than technical implementation; it demands a coordinated, company-wide transformation.

“Integrating AI across a business isn’t just a technology play; it’s an organizational shift,” said Martin Young, EVP, Data & AI at Bounteous. “To bridge the gap from early experimental wins to more impactful value across core business functions, organizations transform skillsets across their workforce.”

Young was recently appointed to lead the company’s global AI practice, reinforcing its commitment to helping clients scale AI initiatives responsibly and effectively. With more than 20 years of experience driving digital transformation, Young brings deep expertise in AI strategy, data governance, and enterprise change management.

“The AI Whitespace” provides C-level executives with practical strategies to assess AI maturity, identify organizational bottlenecks, and chart a path toward scalable, business-driven AI adoption.

Additionally, for the third time in a row, Bounteous was recognized as a Representative Vendor in the 2025 Gartner® Market Guide for Global Digital Marketing Agencies. The report noted, “Agencies are making significant investments in AI training and technology,” citing the Bounteous merger with Accolite Digital as an example.

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About Bounteous
Bounteous is a premier end-to-end digital transformation consultancy dedicated to partnering with ambitious brands to create digital solutions for today’s complex challenges and tomorrow’s opportunities. With uncompromising standards for technical and domain expertise, we deliver innovative and strategic solutions in Strategy, Analytics, Digital Engineering, Cloud, Data & AI, Experience Design, Digital Experience Platforms, and Marketing. Our Co-Innovation methodology is a unique engagement model designed to align interests and accelerate value creation. Our clients worldwide benefit from the skills and expertise of over 4,500+ expert team members across the Americas, APAC, and EMEA. By partnering with leading technology providers, we craft transformative digital experiences that enhance customer engagement and drive business success. Discover more about our impactful work and expertise by visiting www.bounteous.com and following us on X, LinkedIn, Facebook, and Instagram.

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SAN JOSE, Calif.–(BUSINESS WIRE)–Sep 10, 2025–

Today, Adobe (Nasdaq:ADBE) announced the general availability of AI agents that will reshape how businesses build, deliver and optimize customer experiences and marketing campaigns. Powered by the Adobe Experience Platform (AEP) Agent Orchestrator, Adobe is also creating an AI platform for businesses to manage and customize agents from Adobe and across third-party ecosystems—ensuring agents can understand context, plan multi-step actions, refine responses and more. AEP, used by many of the world’s leading businesses to connect real-time data across their organization, anchors Adobe’s offerings in a deep understanding of enterprise data, content and workflows. This provides the foundation for agents to take contextually relevant actions and deliver ROI.

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“Adobe has long helped businesses deliver engaging experiences to their customers, by turning digital data into actionable insights. We are now leveraging agentic AI to build specialized agents and embedding them into data, content and experience creation workflows,” said Anjul Bhambhri, senior vice president of engineering, Adobe Experience Cloud. “Our agentic AI innovations are elevating customer experience orchestration by reimagining processes, unlocking productivity for marketing teams and delivering personalized experiences at scale to drive growth.”

Over 70% of AEP customers are already using Adobe’s AI Assistant, which is the conversational interface that will enable teams to interact with agents across Adobe and third parties. Brands including The Hershey Company, Lenovo, Merkle, Wegmans Food Markets, Wilson Company and others have been working with Adobe’s agentic AI offerings to enhance capabilities within their organizations—and deliver impactful customer experiences.

Redefining Customer Experience Orchestration with AI agents

Adobe also announced the general availability of AEP Agent Orchestrator, including a reasoning engine where decision science and language models will enable dynamic and adaptive reasoning. This will ensure user intent can be interpreted from natural language prompts, to dynamically determine which agents are activated as part of an orchestrated plan. With AEP as the foundation, Agent Orchestrator will drive contextually relevant and goal-oriented automated actions, with support for refinement using a “human-in-the-loop” approach.

Out-of-the-box AI agents —which will be surfaced directly within category-leading Adobe enterprise applications such as Adobe Real-Time Customer Data Platform, Adobe Experience Manager, Adobe Journey Optimizer and Adobe Customer Journey Analytics—can be leveraged by businesses to enhance the skills of marketers and accelerate Customer Experience Orchestration (CXO). These AI agents include:

• Audience Agent will enable teams to quickly create, scale and optimize audiences that can be activated for personalization initiatives. High value audiences can be leveraged quickly through actionable recommendations and monitored closely to meet organizational goals and KPIs.

• Journey Agent in Journey Optimizer simplifies the creation and orchestration of customer journeys and campaigns across channels such as web, mobile, app, email and more. The agent will create journeys based on defined goals, optimize touchpoints based on aspects such as customer drop-off and uncover insights to fine-tune interactions.

• Experimentation Agent analyzes experimentation performance data, enabling teams to hypothesize new ideas for optimization efforts, analyze causal impact and identify predicted conversion or lift. It will be available in Journey Optimizer Experimentation Accelerator, for teams to easily turn experiments into AI-powered insights that drive growth.

• Data Insights Agent in Customer Journey Analytics (CJA) streamlines the process of deriving insights from signals across an organization, enabling any team to visualize, forecast and remediate customer experience initiatives. This agent is now available in CJA as well as across other Adobe enterprise applications.

• Site Optimization Agent delivers always-on support for teams to manage brand websites for high performance. The agent automatically detects and raises issues impacting customer engagement—such as broken back links or low performing pages—which can then be easily addressed via Adobe Sites Optimizer.

• Product Support Agent enhances the process of resolving issues for Adobe customers, leveraging a vast array of knowledge sources and organizational data to help users get the most value out of their enterprise applications. This now includes troubleshooting support—including case creation and tracking—directly in user workflows.

Unified experience for customizing and extending AI agents

Coming soon, Experience Platform Agent Composer provides a single interface for businesses to customize and configure AI agents based on brand guidelines, organizational policy controls and more. This will enable teams to fine-tune AI agent actions and shorten time-to-value. Additionally, new developer tools including an Agent SDK and Agent Registry will enable developers to build, extend and orchestrate agentic apps—expanding use cases into new industries and user personas.

As teams embrace agentic AI to augment daily work and drive better results, interoperability amongst AI agents in different ecosystems is critical. Agent Composer equips businesses with tools to drive multi-agent collaboration using the Agent2Agent protocol. This extends the value of agentic AI across more workflows, with customization capabilities that address specific needs. Adobe also announced new agentic AI partnerships with Cognizant, Google Cloud, Havas, Medallia, Omnicom, PwC and VML, enabling seamless execution of workflows across agents, as well customization across industries and use cases.

About Adobe

Adobe is changing the world through digital experiences. For more information, visit www.adobe.com.

© 2025 Adobe. All rights reserved. Adobe and the Adobe logo are either registered trademarks or trademarks of Adobe in the United States and/or other countries. All other trademarks are the property of their respective owners.

View source version on businesswire.com:https://www.businesswire.com/news/home/20250910079322/en/

CONTACT: Public relations contacts

Kevin Fu

Adobe

kfu@adobe.com

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SOURCE: Adobe

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Copyright Business Wire 2025.

Dr. Foteini Agrafioti is the senior vice-president for data and artificial intelligence and chief science officer at Royal Bank of Canada.

As organizations consider adopting artificial intelligence, they are faced with three options: build the AI technology in-house, buy it from a third-party vendor or develop a hybrid solution with third-party support.

These options have pros and cons but one constant should exist across all models: organizational support. Stakeholders, from business leaders, technology teams, functional partners and end users must have a strong understanding and commitment to the AI approach if it is to be successful.

The main benefit of building a proprietary AI solution is customization. An organization has full control over a product, its capabilities, data security, design and ensuring it’s tailored to business needs.

A solution that is custom built for a specific problem is typically also more accurate because it is truly adapted to the business specifications. As statisticians always argue, an off-the-shelf tool may be able to achieve a good enough predictive accuracy, but a tool that is designed for the specific data problem will always surpass that.

In-house development allows teams to focus precisely on the business problem while ensuring safety and security requirements are met. This approach also provides flexibility, so the technology can adapt to evolving business needs.

But building everything in-house can be time consuming and expensive. Drawbacks of in-house development include costly upfront and ongoing investment in talent, technology and infrastructure. Commoditized technology can be easily leveraged and deployed to save time, scale and achieve business results faster.

Commercial platforms and products have unique value propositions, including the advantage of leveraging ongoing vendor investment and speed to market – they are limited in customization which can be restrictive when it comes to maintaining a competitive market advantage.

A hybrid ecosystem balances customization, investment and speed to market. RBC approaches the architecture of our AI ecosystem by first focusing on the needs of our business, inferring a set of necessary capabilities and then selecting suppliers for those capabilities, including building in-house where we see an advantage. We engage with products from different vendor partners but retain the option to switch to competitive products or build alternatives internally.

With 19 million global clients across five businesses, we have access to data comprising billions of client transactions, which is why industry-leading products in combination with our proprietary AI models such as ATOM deliver the most value for our business.

To accomplish this, we’ve developed a standardized environment for the development and deployment of services, which embeds all necessary security and resiliency controls but also makes it easy for development teams to adopt and use every day.

While this infrastructure is necessary because of the sensitive data we host, such as personal and financial information, it may not be a priority for businesses operating in a less risk-averse sector. For these businesses or even for segments of RBC where we have less need for strict governance, adopting commonly available vendor AI products can provide a good return on investment.

We bring all our data together onto a centralized data platform, Lumina, that systematically collects and curates data across business lines and enables us to build cutting-edge AI in a safe and scalable way.

Delivering innovative AI models and solutions requires diverse and deep collections of data and Lumina provides this while ensuring that all data is used only according to our strict privacy and data protection policies. It’s true that executing on a hybrid strategy requires a deep investment in talent and culture, but it also requires organizational support and collaboration across the business to solve real complex problems.

So, the question remains: how should an organization build its AI ecosystem? The key lesson we have learned is that no solution is ever fully build-or-buy – a healthy environment is a blend of both.

An organization may consider buying a product when there’s no competitive advantage to building a customized version of the same capability, given the product meets organizational requirements such as security, reliability and ethics. Examples of this are using a customer support chat bot, engaging in personalized email marketing or providing customers with recommendations for their next purchase based on their transaction history.

Building a product may be an option when there is an opportunity to integrate valuable intellectual property that likely won’t be available in an off-the-shelf product such as proprietary algorithms or data sets.

While each strategy has its distinct advantages and drawbacks, focusing on the needs of the business will help establish an optimal operating model that supports the necessary capabilities required to successfully deploy an AI ecosystem effectively and securely.

This column is part of Globe Careers’ Leadership Lab series, where executives and experts share their views and advice about the world of work. Find all Leadership Lab stories at tgam.ca/leadershiplab and guidelines for how to contribute to the column here.