AI Agents can be true allies to co-pilot business applications for growth & efficiencies

What are the top 3 business applications where an AI Agent can add value in your organisation or for a specific function?

Highlights from AgentForce conference:

❌ Disconnected relationships with your prospects and customers hurt growth across the different phases of customer relationships*: Awareness, Conversion, Engaged, Loyalty, Advocacy with direct impact on Total CustomerLifeTimeValue (CLTV).

🔎 Build end-to-end customer relationship journeys and map the different touchpoints: Having worked with complex on and offline B2B and B2C businesses, it can be highly valuable to design end-to-end customer experience mapping. A great addition is to align teams, review customer care KPIs, and ask customers to identify & quantify frictions that can be removed or avoided.

🥇 Actionable Data at scale: Merge Customer 360 + Actionable Data + Agents.

🌵 Challenges:

Get data out of the platform, make sense of the data, assess what is actionable.

This is often a challenge for organisations to embrace- especially in the context of silos across teams/functions, data, and technology -which could disrupt customer experience & disconnect data points from actions to implement: Can AI agents be the efficient enablers to deploy actionable data with scale across touchpoints?

🙋‍♂️ Customer-driven approach powers the entire lifecycle (powered by DataCloud at SF) with:

• Intelligence for commerce

• Communication capping (keep it natural for customers to engage)

• Enable real-time personalisation across channels (this is true power of a CDP)

• Expand & optimise paid media capabilities with the like of Einstein Marketing Intelligence

⚠ Differences between chatbot and Agent:

• Chatbot uses simple flow: repetitive, reactive, scripted, provides information

• Agent: Understands situation & helps fix the problem

Focus on Agent 👍

• Applies Natural Language instruction (even easier with voice instructions)

• Embeds AI reasoning

• Minimum training required

• Exponential capabilities combining GenAI, LLMs, and company data

• Build trust from the outset (Please embed in your data governance at all time)

🧠 Building block for ASA (AgentForce Service Agent - a bit of a mouthful):

• Define jobs with a TOPIC

• Confirm the specific ACTIONS

• Provide KNOWLEDGE for context: multiple sources & categories

In short: Topics >> Input <> ASA: reading + knowledge >> Output

I attended a workshop demo to help build an AI agent with AgentForce. I can see the myriad of opportunities ahead across functions for any organisations!

Want to learn more about how AI agents work and how they are transforming business & application development alike: read this Interesting article.

I am Joris, A Digital Business Leader working with leadership teams to drive profitable growth and Business transformation with a customer-centric expertise.

Follow me Joris Peucheret for practical content about Digital, Ecommerce, Marketing, Growth & Transformation.

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*Customer Lifecycle visualisation