Drawing on proprietary research involving over 320 C-suite executives and hands-on experience with global clients, BearingPoint identifies the strategic levers necessary to succeed with AI at scale, from process redesign and data integration to organizational governance and culture.
“Many companies still view AI as isolated tools or experiments,” said Frédéric Gigant, Partner and Global Leader Customer & Growth at BearingPoint. “But the real value emerges when AI is embedded across the entire customer journey, from dynamic marketing campaigns to predictive sales and proactive service. This paper offers a roadmap on how companies can get there.”
AI moves to the heart of business operations
Artificial intelligence is no longer an experimental technology, and BearingPoint’s study outlines the structural, cultural, and technological changes required to transition from siloed experimentation to organization-wide transformation.
The report pinpoints ten priorities for sales and marketing leaders, including:
- Reinventing customer journeys with GenAI
- Adopting agile workforce models that blend human creativity with AI efficiency
- Building AI-ready data foundations and governance for hyper-personalization
- Innovating while ensuring AI applications are safe and ethical
In marketing, AI is expected to play a dominant role by 2028, with one-third of organizations planning to use it for campaign personalization, SEO, and brand asset creation. According to the BearingPoint survey, 80% of marketing campaigns will be hyper-personalized using AI insights, while creative AI will generate over two-thirds of content.
From pilots to platforms: Scaling AI transformation
One of the key takeaways from the report is that success with AI depends not only on the technology but also on an organization’s ability to scale it with clear strategic direction and cross-functional coordination. Business and IT must be aligned under a shared transformation vision, and CMOs, sales directors, and heads of customer experience must step into more strategic leadership roles and define AI roadmaps, empowering teams and guiding the ethical adoption of AI.
The study also emphasizes that without strong foundations, particularly in strategy, data, organization, and culture, AI initiatives risk stalling after the initial pilot phases. Scaling AI requires organizations to rethink traditional roles and responsibilities, foster a culture of experimentation, and develop hybrid workforce models that enable collaboration between humans and AI. When integrated into the target operating model, AI enables greater responsiveness, agility, and accountability across every touchpoint of the customer journey.
“AI isn’t just changing how we work, but changing what customers expect,” said Frédéric Gigant. “To remain competitive, organizations must evolve from static workflows to intelligent, responsive systems that deliver value at every stage of the customer journey. That requires new thinking around processes, people, and platforms.”
The report also features benchmarks from industry frontrunners and examples of successful AI use cases, such as agentic AI for sales follow-ups, generative AI for marketing content, and predictive support models that resolve issues before customers raise them.
In B2B sales, AI is maturing fastest in predictive analytics, and over half of B2B sales will occur through conversational interfaces by 2028. According to the BearingPoint survey, technology will augment 90% of buyer-seller interactions across channels like email and web conferencing. AI-enabled tools are also expected to reduce time spent on prospecting and customer engagement by more than 50%.
Strong collaboration between marketing, sales, and IT is a key practice for successfully scaling AI projects
BearingPoint’s approach combines management consulting and implementation expertise, enabling clients to design and execute AI-powered transformations across the entire customer lifecycle. The firm’s cross-functional teams collaborate with sales, marketing, and IT leaders to develop scalable AI strategies, ranging from pilot programs to full-scale redesigns of operating models.
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