Serkan PALA

AI & SAP Solutions Architect | Turning enterprise AI agents into real ERP workflows—permissions, clean data, and production reality over hype.

[Serkan PALA @_serkanpala]

14+ years leading SAP programs (S/4HANA, CRM, BTP) for 20+ enterprises. Since 2025 I've been building AI products that sit on top of existing ERP and finance systems, live in production and used daily.

[Serkan PALA @_serkanpala]

@GoogleDeepMind @FryRsquared @Avsecz @adamgayoso Finding needles in genetic haystacks mirrors enterprise anomaly detection. High-throughput prediction is only the first mile; the bottleneck in clinical and enterprise workflows remains reproducible audit trails, deterministic provenance, and human-in-the-loop verification.

[Serkan PALA @_serkanpala]

@AnthropicAI An IPO marks the shift from frontier lab research to public enterprise accountability. For enterprise adoption, the bottleneck is no longer frontier reasoning—it is enterprise compliance, strict SLAs, data sovereignty, and auditability at core ERP scale.

[Serkan PALA @_serkanpala]

Reasoning models tracing anomalies to physical hardware faults is the holy grail of enterprise observability. In ERP & industrial supply chains, failure rarely lives in code—it lives in sensor drift and state transitions. True agentic systems must debug reality.
https://t.co/MrLUwQiSMk

[Pushmeet Kohli @pushmeet]

Our Gemini team @GoogleDeepMind is making tremendous progress.

Gemini 4 Carbon’s coding and scientific reasoning capabilities are a step change and have accelerated our own science workflows.

Carbon has already helped our team:
- Debug complex experimental flows. In one complex case, it traced anomalous assay results back to a faulty liquid handler and reagent carryover between high-throughput experimental runs.
- Develop strategies to mine massive datasets, making models like AlphaFold 3 even more effective.
- Compress computational workflows like cheminformatics tasks from weeks down to hours.

I am happy to share that Gemini 4 Carbon is now generally available. Together with our updated Antigravity Science plugin, Carbon can greatly assist the work of scientists.

I am really looking forward to seeing scientists across the world use it for their work.

[Serkan PALA @_serkanpala]

@emollick Coding diffused fast because software has instant feedback loops: compilers and test suites provide cheap ground truth. In enterprise ERP and core operations, there is no compiler for business logic. Diffusion stalls where verification requires human audit and real state.

[Serkan PALA @_serkanpala]

@AravSrinivas @dylan522p Calling persistent agents "computers" captures the shift: they are execution environments, not chatbots. But in enterprise systems, an environment without transactional rollback and strict RBAC is just a security vulnerability. Persistence demands state integrity.

[Serkan PALA @_serkanpala]

LLM'leri kurumsal ERP'ye bağlamak bir prompt mühendisliği değil, durum senkronizasyonu problemidir.

İşlemsel sınırlar (transactions), idempotency ve şema düzeyinde RBAC olmadan otonom bir ajan; kontrolsüz ve yüksek varyanslı bir arka plan işinden (batch job) farksızdır.

[Serkan PALA @_serkanpala]

Connecting LLMs to enterprise ERP is rarely a prompt engineering challenge—it is a state synchronization problem.

Without transactional boundaries, idempotent execution, and schema-level RBAC, an autonomous agent is just an unmonitored batch job with high variance.

[Serkan PALA @_serkanpala]

Talking to static docs was a demo toy. Real work lives in relational ERP schemas, transaction logs, and live master data. Naive RAG collapsed the moment semantic similarity met audit trails, row-level security, and schema state. Context without transactional ground truth is noise.

[Serkan PALA @_serkanpala]

@levie Enterprise read/write via MCP is huge, but files don't live in isolation. In ERP & SAP reality, updating an agreement must reconcile with purchasing line items, tax conditions, and role-based SOX controls. The hardest part isn't editing docs—it's deterministic transactional sync.

[Serkan PALA @_serkanpala]

@latentspacepod @aiDotEngineer @CompleteSkeptic Automating everything with System 1 models sounds great until you meet enterprise workflows. Instant execution without deterministic guardrails creates silent failures in ERP transactions. High speed requires even tighter validation loops and idempotent operations.

[Serkan PALA @_serkanpala]

Digital freight brokerage scales only as fast as its ERP integration depth. Connecting automated dispatching directly into shipper SAP TM and carrier execution layers is where the real unit economics survive or fail. Standardizing logistics telemetry across heterogeneous fleets is the hard part.

[Serkan PALA @_serkanpala]

@emollick Industrializing knowledge work always looks viable until you hit legacy ERP integration. The powerloom doesn’t just generate cloth; it requires standardized raw thread. In enterprise, raw thread is messy master data, unwritten business rules, and fragmented audit trails.

[Serkan PALA @_serkanpala]

Connecting LLMs to enterprise ERP is rarely a prompt engineering challenge—it is a state synchronization problem.

Without transactional boundaries, idempotent execution, and schema-level RBAC, an autonomous agent is just an unmonitored batch job with high variance.

[Serkan PALA @_serkanpala]

@GoogleDeepMind Moving from bulk tissue to single-cell resolution mirrors moving from monolithic ERP batch runs to event-driven line-item telemetry. The hardest part is never the pattern synthesis—it is isolating localized variance without drowning downstream state in false-positive noise.

[Serkan PALA @_serkanpala]

Teams deploy AI agents expecting autonomous speed, then hit enterprise latency: real-time ERP schema discovery, multi-tier RBAC checks, and deterministic audit trails.

Speed comes from model synthesis; governance comes from bounded state machines.

Where does your latency spike?

[Serkan PALA @_serkanpala]

@AravSrinivas Browser game prototypes prove model synthesis capability, but enterprise teammate viability starts where self-contained code ends: dirty ERP schemas, role-based authorization, and idempotent write-backs. When an agent touches balance sheets, fun stops and audit begins.

[Serkan PALA @_serkanpala]

@emollick Same inflection point hit enterprise software: building a monolithic ERP with 10k human engineers will soon look like manual stone-carving. But while games optimize for immersion, ERP demands deterministic state convergence. AI will generate code; auditability will govern it.

[Serkan PALA @_serkanpala]

@levie Swarm leverage scales with autonomy, but enterprise bottleneck shifts from generation to state convergence. If 10 agents produce 100 artifacts, reconciliation against ERP master data and compliance policies is where the real margin is won or lost. Determinism beats speed.

[Serkan PALA @_serkanpala]

@GoogleDeepMind @signalapp Formal verification via Lean proofs is massive for cryptographic primitives, but enterprise systems fail at semantic layers: ambiguous business rules, dirty master data, and unverified tenant boundaries. Math-verified protocols need math-verified ERP authorization models.

[Serkan PALA @_serkanpala]

@emollick Multi-agent setups excel at open-ended creative debate, but enterprise workflows collapse without deterministic state machines and strict convergence criteria. If agents negotiate inventory or route planning without transactional ACID locks, debate quickly becomes deadlock.

[Serkan PALA @_serkanpala]

@holgermu @Google Silicon cadence matters, but enterprise ROI is won at the orchestration and inference layer. Dedicated TPUs shine when serving fine-tuned domain models under strict latency SLAs, provided ERP data pipelines don't bottleneck retrieval. Without clean core APIs, compute sits idle.