Uniphore

Today, the Uniphore team gathered at our Palo Alto office for an End of Summer Luau, bringing our people together over great food, good conversation and time to connect with colleagues. Moments like these remind us that strong teams grow through collaboration and meaningful connections. Here’s to team bonding, shared moments and celebrating the people who make Uniphore a great place to be!

For technology leaders scaling enterprise AI, the biggest constraint often sits beneath the model: data foundations that cannot keep pace with changing business processes and systems. Very pleased to see MIT Technology Review Insights explore this challenge produced in partnership with Uniphore   The report points to three areas that shape AI at scale: data readiness, governance and architecture.   For enterprise teams, that means: → Automating data discovery and preparation across complex estates → Maintaining control over where data lives and models run → Replacing brittle centralized pipelines with composable infrastructure → Querying intelligence where data already resides → Building an architecture that can evolve as data, models and business processes change   Scaling AI requires an architecture built for change from the start. Without a strong data foundation and the right governance, moving from AI pilots to production remains difficult. I share in the report that as data and business processes change, enterprise AI needs an architecture that can evolve with them. With a sovereign architecture, enterprises can build and adapt intelligence on their own terms.   Read more here: https://lnkd.in/g8DEW9xb

CIOs deploying AI platforms face a production problem we see across enterprise deployments. In partnership with MIT Technology Review Insights report, produced with our team at Uniphore with expert insights from leaders from KPMG, IDC, Databricks, we identify the real blocker behind enterprise AI scale. Data quality and readiness account for 43% of enterprise AI failures, tied with gaps in technical maturity. Model quality often takes the blame, but the foundation usually causes the failure. Our view for CIOs starts below the model layer – AI agents need governed, accessible, AI-ready data, shared context, model flexibility and a control layer that can orchestrate providers without forcing every data asset into a new format or location. Composable architecture gives enterprises a way to swap models as the ecosystem changes. AI sovereignty keeps intelligence under enterprise control. Together, they reduce vendor lock-in, lift data residency and formatting barriers. Plus, it provides AI systems the context to act across functions instead of inside isolated workflows. The key takeaway for CIOs: the AI scale mandate starts with infrastructure, not model selection. Read more in the latest report.

CMOs and marketing teams can measure campaign spend results after launch, but often cannot prove which spend caused the outcome. Yet, with campaign simulation they’re able to forecast customer-level outcomes before budget gets committed. We recently hosted a webinar with IDC Research Director Tapan P. and Uniphore Director of Product Management Joe Pulickal, on how Marketing AI changes campaign forecasting and decisioning.     The key shift discussed: move from segment averages to individual customer context.     Digital twins represent individual customers and let marketing teams simulate different audiences, messages, channels, timing and other campaign variables before launch. Teams can compare predicted conversions, revenue, cost and drop-off. Then refine the plan before spending.     The model also improves with each cycle:  Simulation → campaign → measurement → model retraining → sharper prediction.     The business case comes down to decision quality. Forecast the cost-to-outcome relationship for individual customers and campaign scenarios before money is spent.

Marketing teams have more customer data than ever, yet many still struggle to turn fragmented signals into useful customer intelligence. Broad segmentation can support planning and reporting, but modern marketing teams face a harder question: What might an individual customer need next and what evidence supports that prediction? Join us for our upcoming livestream featuring Stephen Howlett, Atlassian Platform Lead and Uniphore’s Rama S Saripalle, VP, Customer Success.   Hear how organizations are exploring AI-powered marketing, from the limits of traditional customer segmentation and the role of predictive models to the importance of connected customer data, governance and organizational alignment. We’ll also discuss how digital twins and continuous learning systems could shape customer intelligence, improve personalization and give marketing teams better insights for decision-making.   The Future of Customer Intelligence: Moving Beyond Segments to AI-Powered Marketing Thursday, October 1 | 10:00 am PT | 1:00 pm ET Save your spot 👇

CIOs evaluating enterprise AI platforms face a scale problem: predicting individual customer behavior across millions of customers can make frontier-model economics prohibitive. Purpose-built SLMs give Uniphore Marketing AI a different path. Marketing teams already have plenty of customer data, but the more challenging problem sits in turning those signals into predictions, simulations and autonomous decisions at the speed customers expect. Uniphore Marketing AI puts a digital twin behind every customer – a continuously updated predictive model trained on individual interaction history, campaign responses, purchase patterns and service signals. Each digital twin uses a customizable small language model fine-tuned on the individual customer. Learned weights carry customer context without repeatedly consuming large-model context-window tokens, lowering cost and latency. The Marketing Flywheel: ↳ Know: Enterprise-wide signals unified into a digital twin of every customer, updated continuously. ↳ Plan: Describe your campaign goal. Get a full strategy: audience, journey, messaging and budget allocation in minutes. ↳ Simulate: The campaign is simulated node by node against every customer’s digital twin before a dollar is committed. See predicted conversions, drop-off points and cost at every stage before launch. ↳ Create and Activate: Marketing agents activate personalized experiences across email, SMS, paid media and events in real time. ↳ Measure: Actual results are compared against simulation predictions at every campaign node. A live feedback loop shows not only what happened, but what to change next. ↳ Self-Learn: Every outcome automatically makes the system smarter. Each customer’s digital twin sharpens. The simulation models retrain. The loop closes and opens again, sharper than before. CMOs can test audience changes, channel shifts, messaging alternatives, timing, and investment scenarios before the budget gets allocated. Simulation returns predicted conversions, drop-off rates, revenue and cost at each stage. For CIOs, the architecture also addresses control. Models train on enterprise data within governed infrastructure, while the model layer can route different use cases. Marketing moves from segment averages and post-campaign analysis toward individual-level prediction, pre-campaign simulation, autonomous execution and continuous learning.

CMOs deploying AI for customer intelligence face a model-fit problem: marketing needs systems that can learn from individual customer behavior, not only generate content from a general-purpose model. A digital twin combines identity, customer history, predictive scores and behavioral embeddings, giving a purpose-built small language model (SLM) the context to simulate individual customer responses. The strategic shift is significant: ↳ Move from static segments and historical benchmarks toward individualized prediction and simulation. Uniphore Marketing AI brings these capabilities together through digital twins, purpose-built SLMs, simulation and continuous learning, giving marketing and technology leaders a path toward more predictive, individualized customer decision-making. Every campaign makes the next one smarter. Automatically, without human intervention. Hear the full discussion with Claire Darling, Uniphore Director of Product Management Joe Pulickal and Scott Brinker, Analyst and Advisor at chiefmartec – link in the comments.

Over the last 48 hours, I’ve been asked the same question several times: What does it actually mean to “pace” AI? I don’t read this as slowing AI development, investment in data centers, GPUs or innovation. The pacing is about what happens before increasingly capable models are released broadly. That could mean peer review between leading AI labs, independent testing or some form of government evaluation. There are also lessons to study from China, where new AI models already go through a thorough review process before release. In software, we used to talk about technology becoming “enterprise grade.” That meant it had been tested, hardened and scrutinized enough that large organizations could deploy it at scale with confidence. AI is entering that same phase. This is AI’s moment to be ready, with the testing, safety and security that should come with broader adoption. Development can keep moving quickly. The release process can become more disciplined. And enterprises can adopt AI with greater confidence.

Most CIOs underestimate the largest AI cost because it never appears on the invoice. API spend attracts attention, but the larger expense comes from enterprise knowledge that never compounds. Every prompt sent to a rented frontier model starts from the same baseline, while years of proprietary compliance decisions, customer interactions and operational expertise remain separate from the model your teams use every day.    A frontier API gives competitors access to the same foundation model and cannot reproduce a model trained on your organization's domain expertise. Additionally, token consumption increases as agentic workloads scale. Data movement introduces governance and security risk. Models that never learn from enterprise knowledge force employees to solve the same problems repeatedly instead of building institutional intelligence.    Here’s what we’ve learned from our customers using Uniphore’s domain-tuned small language models:   → 565% reduction in total cost of ownership  → 12.8x lower latency  → 90% fewer hallucinations on the same workloads compared with frontier model deployments    Sovereign deployment also keeps enterprise data inside the security perimeter. Prompts remain under enterprise control, and outputs never contribute to external model training, regardless of vendor policy changes or regulatory requirements.