Market forecasts suggest that by 2027, more than 50% of business decisions will be augmented or automated by AI-driven decision intelligence platforms. The shift is not incremental. It marks a transition from dashboards and historical reporting toward predictive, prescriptive, and auditable execution engines embedded directly into operational systems.
In this episode of AppDevANGLE, I spoke with Molham Aref, CEO and Founder of RelationalAI; Joji Philip, Head of Industry GTM, Global Communications Vertical at Snowflake; and Sreedar Rao, Global Telecom CTO at Snowflake, about how decision intelligence (DI) is reshaping telecom infrastructure, programmable networks, and enterprise AI value realization.
The discussion explored why DI is emerging as a distinct category beyond BI and AI, how it enables auditable closed-loop operations, and why architecture decisions made today will determine whether telecom operators can scale toward L4 and L5 autonomous networks.
From Business Intelligence to Decision Intelligence
Molham framed the difference succinctly:
“If running your business was like driving a car, business intelligence is the dashboard. Decision intelligence is the navigation system.”
Business intelligence (BI) tells operators what happened. Decision intelligence determines what should happen next. In telecom environments, that distinction is critical. Network congestion dashboards, churn analytics, and fraud alerts are reactive insights. DI systems, by contrast, actively recommend or automate outcomes: dynamic traffic routing, predictive maintenance, churn prevention, spectrum allocation, and infrastructure planning.
As Molham explained:
“It’s fundamentally more predictive and prescriptive. It’s about what’s going to happen and making decisions on getting a good outcome rather than about what happened.”
For telecom operators managing increasingly programmable networks, the move from visibility to prescriptive action is not optional; it is foundational.
Intelligent Infrastructure Requires a Decision Layer
Joji Philip emphasized that connectivity itself is evolving into a strategic platform. Programmable networks exposed via APIs are no longer just technical constructs; they are outcome-driven services.
“Once you expose network capabilities via APIs, you’re effectively selling decision connectivity.”
In environments such as Open Gateway and CAMARA APIs, enterprises can request specific outcomes, such as latency guarantees, throughput prioritization, and network slicing. But programmable APIs alone are insufficient without a decision engine capable of enforcing policy, orchestrating resources across domains, and proving SLA compliance.
Decision intelligence enables three critical capabilities:
- Policy enforcement and entitlement validation
- Cross-domain orchestration under constraints
- Auditable proof of outcome delivery
Without DI, programmable networking remains aspirational. With DI, it becomes operationally accountable.
Closed-Loop Automation vs. Auditable Decision Intelligence
Sreedar Rao distinguished traditional closed-loop automation from decision intelligence-driven autonomy. Traditional automation relies on predetermined rules and baked-in tradeoffs. But telecom environments are dynamic, multi-variable, and often unpredictable. Static rule sets cannot account for “unknown unknowns.”
“When you build closed-loop automations in the traditional sense, those trade-offs are baked in. A predetermined set of rules aren’t going to be sufficient.”
Decision intelligence introduces real-time reasoning and explicit tradeoff evaluation. Crucially, those tradeoffs must be auditable.
“The decision engine has to know what were the reasons, what were the trade-offs taken at each point in time.” – Sreedhar Rao
Auditability becomes essential as operators pursue L4 and L5 autonomous network operations. Decisions cannot simply be automated; they must be explainable.
Governing DI at Scale: Architecture Matters
Historically, DI required separate technology stacks, duplicated data pipelines, and fragmented governance controls. That complexity limited adoption. Molham highlighted the significance of embedding DI natively within Snowflake’s architecture:
“No data leaves Snowflake. It all runs inside the security perimeter. It’s all governed the same way.”
This architectural convergence reduces:
- Data duplication risk
- Governance complexity
- Latency between insight and action
- Operational cost of scaling DI
Sreedar further outlined the architectural blueprint required for intelligent infrastructure:
- A unified data accelerator layer
- A knowledge plane abstracting telecom domain constructs
- Decision as a Service accessible to agents and applications
“What you need is decision as a service… now you truly have an intelligent infrastructure that can make decisions that are logical, reasonable, and auditable.” – Sreedar Rao
This layered approach separates data ingestion, knowledge abstraction, and decision execution, which allows scale without creating monolithic bottlenecks.
Unlocking GenAI Value Through DI
One of the most important themes in the discussion was the gap between frontier LLM capability and enterprise value realization.
Molham addressed this directly:
“There’s a gap between the potential for value creation and actual value creation in the enterprise.”
GenAI applied to business intelligence is useful, but limited. The real unlock occurs when GenAI is paired with decision intelligence.
“When you bring in GenAI applied to business intelligence and decision intelligence… there’s a real value unlock.” – Molham Aref
DI provides the prescriptive reasoning layer (predictive models, graph reasoning, rule-based reasoning) that LLMs alone lack. At the same time, GenAI reduces the labor required to implement DI by assisting with semantic modeling, feature engineering, and evaluation creation.
The result is a virtuous cycle: GenAI accelerates DI deployment, and DI amplifies GenAI’s business impact.
Telecom-Specific DI Impact Areas
Across the conversation, four high-impact telecom domains emerged:
1. Network Optimization: Dynamic routing, predictive maintenance, digital twins, capacity-energy tradeoffs.
2. Customer Experience & Retention: Churn prediction, hyper-personalization, proactive support.
3. Revenue & Risk Protection: Fraud detection, revenue assurance, strategic marketing optimization.
4. Strategic Infrastructure Planning: Tower placement, spectrum allocation, ESG compliance modeling.
Each of these domains requires cross-functional tradeoffs (cost vs. performance, energy vs. capacity, speed vs. reliability) that static BI tools cannot manage.
Analyst Take
Decision intelligence is emerging as a foundational control layer for AI-native telecom operations. The shift from BI dashboards to prescriptive navigation systems represents more than a tooling upgrade; it is an architectural and operational evolution. Telecom operators moving toward programmable networks, Open Gateway APIs, and autonomous operations require auditable, cross-domain decision engines capable of real-time tradeoff evaluation.
Snowflake and RelationalAI’s approach of embedding DI directly within governed data platforms addresses one of the historical barriers to DI adoption: architectural fragmentation. More broadly, DI may represent the missing link between frontier GenAI capabilities and measurable enterprise value. Without prescriptive reasoning layers, GenAI remains a productivity enhancer. With DI, it becomes a business outcome engine.
For telecom operators preparing for L4 and L5 autonomy, the question now is whether their architecture supports decision intelligence as a first-class service. The next phase of telecom transformation will not be defined by who has the most data, but by who can reason across it, act on it, and prove why.
