Executive leaders from UneeQ and Zeta Global discuss the revolutionary impact of AI technologies that enable enhanced customer experiences and improved sales performances.

Topics Include:

• Dave Cristini introduces panel on AI in advertising and marketing.
• Panel explores personalized experiences at scale with privacy focus.
• UneeQ creates AI-powered digital humans for brand interactions.
• Zeta Global uses AI to optimize customer messaging.
• LLMs combined with traditional ML empowers marketers to create models.
• Marketers can now build models without needing data scientists.
• AI agents integrated into systems can take action, not just respond.
• Agent chaining orchestrates sophisticated marketing actions automatically.
• AWS Bedrock provides tools to shape AI marketing future.
• Hyper-personalization becoming more achievable through AI automation.
• Ethics requires authenticity in brand AI representation.
• Transparency about data usage builds customer trust.
• Win-win approach: AI should augment teams, not just reduce costs.
• Integration difficulties remain a major challenge for AI implementation.
• AI agents have limited context windows and memory.
• Solution: Create specialized agents with persistent viewpoints.
• Companies need strong integration capabilities before implementing AI.
• Privacy regulations impact AI use in global marketing.
• Highly regulated industries require careful AI implementation strategies.
• Generative AI creates compliance challenges with unpredictable outputs.
• Digital humans eliminate judgment, revealing new customer insights.
• Banking clients discovered customers didn't understand financial terminology.
• Zeta improved onboarding with AI agents for data mapping.
• AI data mapping increased NPS scores and accelerated monetization.
• CMOs and CIOs increasingly collaborating on AI initiatives.
• Tension exists between marketing (quick wins) and IT (security).
• Strategic alignment with approved infrastructure enables scaling AI solutions.
• CEOs have critical role in aligning AI goals across departments.
• Internal AI use case: practicing sales with digital humans.
• Sales teams achieved 500% higher sales through AI role-playing.

Participants:

• Danny Tomsett – Chief Executive Officer, UneeQ
• Roman Gun – Vice President, Product, Zeta Global
• David Cristini – Director, ISV Sales, North America – Business Applications, AWS

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Ash Pembroke, Portfolio CTO of Caylent, discusses the critical balance of data accuracy in the era of Gen AI for the benefit of boosting innovation.

Topics Include:

• Ash Pembroke, Portfolio CTO of Caylent, self-identifies as a "recovering data scientist."
• Caylent is an AWS native services company.
• Data quality remains an issue despite Gen AI.
• Contrasts legalism versus mysticism in data quality.
• Legalism: accurate data when applications need it.
• Mysticism: insights that help decision-making.
• Traditional data foundations approach is being challenged weekly.
• Gen AI developments force rethinking of solution architectures.
• Teams share solutions through giant Slack threads.
• Example: Vector databases questioned after model context protocol.
• Still do traditional data assessments, but stay flexible.
• Integration and data processing constantly get abstracted.
• Data strategy equals architecture strategy equals business strategy.
• Traditional approach: standardize data across engineering teams.
• New approach: allow business users to innovate.
• Bring valuable techniques back to the organization.
• Case study: North Sea wind turbine alerts.
• Initially seen as data quality issue, revealed new predictive failure signal.
• Gen AI enables local experimentation by business users.
• Blurring lines between enterprise enablement and software building.
• BrainBox AI case study: energy optimization across buildings.
• Architecture decisions impact ability to scale products.
• Work with business edges rather than looking for patterns.
• Gen AI can process information from these working groups.
• Think about data as a product, not asset.
• Redimensionalize dependencies across your organization.
• Now's a good time to attack data quality.
• New tools help visualize complexity across organizations.

Participants:

· Ash Pembroke – Portfolio CTO, Caylent

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New Relic's Head of AI and ML Innovation, Camden Swita discusses their four-cornered AI strategy and envisions a future of "agentic orchestration" with specialized agents.

Topics Include:

• Introduction of Camden Swita, Head of AI at New Relic.
• New Relic invented the observability space for monitoring applications.
• Started with Java workloads monitoring and APM.
• Evolved into full-stack observability with infrastructure and browser monitoring.
• Uses advanced query language (NRQL) with time series database.
• AI strategy focuses on AI ops for automation.
• First cornerstone: Intelligent detection capabilities with machine learning.
• Second cornerstone: Incident response with generative AI assistance.
• Third cornerstone: Problem management with root cause analysis.
• Fourth cornerstone: Knowledge management to improve future detection.
• Initially overwhelmed by "ocean of possibilities" with LLMs.
• Needed narrow scope and guardrails for measurable progress.
• Natural language to NRQL translation proved immensely complex.
• Selecting from thousands of possible events caused accuracy issues.
• Shifted from "one tool" approach to many specialized tools.
• Created routing layer to select right tool for each job.
• Evaluation of NRQL is challenging even when syntactically correct.
• Implemented multi-stage validation with user confirmation step.
• AWS partnership involves fine-tuning models for NRQL translation.
• Using Bedrock to select appropriate models for different tasks.
• Initially advised prototyping on biggest, best available models.
• Now recommends considering specialized, targeted models from start.
• Agent development platforms have improved significantly since beginning.
• Future focus: "Agentic orchestration" with specialized agents.
• Envisions agents communicating through APIs without human prompts.
• Integration with AWS tools like Amazon Q.
• Industry possibly plateauing in large language model improvements.
• Increasing focus on inference-time compute in newer models.
• Context and quality prompts remain crucial despite model advances.
• Potential pros and cons to inference-time compute approach.

Participants:

• Camden Swita – Head of AI & ML Innovation, Product Management, New Relic

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/