Agentic AI and the new playbook for growth

Agentic AI for enterprise growth is moving from pilot projects to platform-scale bets. Over the last weeks, major platforms and analysts showed how AI agents are changing ad buying, B2B sourcing, customer experience, and the kind of organizational change leaders must make. This matters because these shifts affect revenue, customer loyalty, and operational speed. Therefore, business leaders must understand what these advances do, and what they do not do. Additionally, the headlines point to both opportunity and a clear warning: technology alone will not deliver growth without governance and new ways of working.

## Amazon’s ad strategy: Agentic AI for enterprise growth

Amazon used its UnBoxed conference to present an integrated AI ad stack that promises new flexibility in buying and measurement. The pitch is straightforward. Therefore, advertisers can buy across formats and use AI-driven insights to make faster decisions. Additionally, measurement promises to be more tightly connected to outcomes. This could reshape how growth teams allocate budgets and measure return.

For marketers, the practical change is twofold. First, ad buying becomes more consolidated and real-time. As a result, teams may shift from channel-by-channel planning to platform-led campaigns. Second, measurement becomes embedded. Therefore, reporting should move from static dashboards to continuous optimization loops.

However, this is not just a technology shift. It affects go-to-market (GTM) models. Growth leaders will need new skills in data stewardship, vendor management, and cross-functional coordination. Additionally, procurement and legal teams will face questions about data sharing and attribution. Looking ahead, Amazon’s stack may accelerate consolidation in ad tech. Therefore, brands that move quickly can gain efficiency. Yet, those that ignore organizational change risk missing the benefits.

Source: Marketing Dive

Alibaba’s B2B push: Agentic AI for enterprise growth in sourcing

Alibaba.com introduced an "AI Mode" that brings agentic automation to global sourcing. At its CoCreate Europe event, the company pointed to rising European orders and supplier participation. Therefore, the move signals that B2B commerce platforms are now applying the same agentic ideas found in consumer-facing tools to procurement and supplier matching.

For buying teams, this matters because agentic AI can automate routine tasks. For example, it can shortlist suppliers, negotiate terms, or recommend shipping options. Additionally, suppliers benefit from faster discovery and more consistent demand signals. As a result, platform-led commerce could scale sourcing activity while reducing manual work.

However, automation brings trade-offs. Buyers must maintain oversight. Therefore, governance rules, audit trails, and clear escalation paths are essential. Also, supplier relationships still need human management for complex deals. Looking forward, agentic features will likely become a differentiator for marketplaces. Consequently, procurement teams should experiment now, but design guardrails that keep strategy and risk management tightly connected.

Source: Digital Commerce 360

Why CMOs still need organizational change

Gartner’s research warns that AI agents alone will not fix marketers’ problems. Nearly two-thirds of marketers expect AI to dramatically change their role. However, many are not making the organizational shifts needed to see results. Therefore, adopting agentic tools without rethinking processes, skills, and governance will likely produce disappointment.

What does this mean for marketing leaders? First, they must align structure to new workflows. Additionally, performance metrics should reward continuous optimization and experimentation. Second, AI governance must be institutionalized. For example, teams should define acceptable uses, data controls, and escalation paths. Third, talent strategies must evolve. Therefore, hiring should mix domain expertise with platform and data literacy.

Furthermore, CMOs should treat agentic AI as an operational and strategic challenge. Tools can automate tasks, but strategy still hinges on brand judgment and customer context. As a result, organizations that pair agentic capabilities with new operating models will gain the most. Conversely, teams that only layer agents onto old processes will see limited impact.

Source: Marketing Dive

Conversational CX and Agentic AI for enterprise growth

Across EMEA, companies are moving conversational AI from experiments to core service. A Twilio study highlighted that businesses are accelerating investment to boost customer loyalty. Therefore, conversational agents are now central to meeting customers at scale with personalized and fast service.

The practical benefits are clear. Conversational AI reduces response times. Additionally, it can route complex issues to the right human agents. As a result, firms can deliver consistent experiences across channels and time zones. Moreover, when combined with predictive insights, conversational systems can anticipate needs and prevent churn.

However, deploying conversational AI requires attention to integration. For instance, systems must connect to CRM, order history, and fulfillment data to be truly useful. Therefore, teams should prioritize data pipelines and API maturity. Also, language and cultural nuances in EMEA mean that localization matters. Looking ahead, conversational AI will be a retention tool as much as a cost lever. Consequently, customer experience leaders should treat investments as strategic, not merely tactical.

Source: CX Today

Picking predictive CX platforms: trade-offs and priorities

As organizations chase real-time impact, the market for predictive CX platforms is changing. Decision-makers now look beyond dashboards to platforms that deliver measurable outcomes. Therefore, platform selection hinges on three practical criteria: data architecture, model performance, and integration maturity.

First, data architecture determines how quickly a platform can turn signals into action. Platforms with clean ingestion and fast pipelines enable real-time personalization. Second, model performance matters, but so does explainability. Therefore, teams should test both accuracy and interpretability. Third, integration maturity dictates how smoothly a platform will fit into existing stacks. Consequently, the best vendors provide robust APIs and pre-built connectors.

Additionally, cost and vendor lock-in are real concerns. Some platforms offer strong predictive power but require heavy migration. Therefore, pilot projects and phased rollouts help manage risk. Also, leaders should measure early wins in retention or conversion, not vanity metrics. Finally, vendor comparisons must include governance capabilities, such as audit logs and human-in-the-loop controls.

In short, the right predictive CX platform will accelerate experienced-based decisions. Therefore, invest in vendors that balance speed, transparency, and integration.

Source: CX Today

Final Reflection: Pulling the threads together

Across advertising, B2B commerce, customer experience, and vendor selection, the theme is consistent: agentic AI for enterprise growth is real, but not automatic. Major platforms like Amazon and Alibaba are embedding agentic features to streamline buying and sourcing. Additionally, conversational and predictive CX tools are maturing and delivering measurable outcomes. However, Gartner’s warning is crucial. Technology does not replace the need for organizational redesign, governance, and human judgment.

Therefore, leaders should take a balanced approach. First, pilot agentic tools in tightly scoped use cases with clear KPIs. Second, build governance and human oversight into deployments. Third, upgrade data and integration capabilities so agents deliver real business outcomes. Finally, treat vendor selection as strategic. Choose platforms that enable speed, explainability, and safe automation.

Looking forward, the companies that win will be those that combine agentic capabilities with new operating models. Consequently, AI will be a growth accelerator for teams that are ready to change how they work.