diff --git a/test/TritonIntelGPU/materialize-block-pointer.mlir b/test/TritonIntelGPU/materialize-block-pointer.mlir
index f2fc81d71e..71596c3ccc 100644
--- a/test/TritonIntelGPU/materialize-block-pointer.mlir
+++ b/test/TritonIntelGPU/materialize-block-pointer.mlir
@@ -17,7 +17,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, triton_gpu.target = "xpu", t
     %5 = tt.load %3 {boundaryCheck = array<i32: 1>, cache = 1 : i32, evict = 1 : i32, isVolatile = false, padding = 1 : i32} : !tt.ptr<tensor<64x32xf16, #dot_a>>
     %6 = tt.load %4 {boundaryCheck = array<i32: 0>, cache = 1 : i32, evict = 1 : i32, isVolatile = false, padding = 1 : i32} : !tt.ptr<tensor<32x64xf16, #dot_b>>
 
-    // CHECK: tt.load {{.*}} {boundaryCheck = array<i32: 1>, padding = 1 : i32, triton_intel_gpu.block_io = "column_major"}
+    // CHECK: tt.load {{.*}} {boundaryCheck = array<i32: 1>, padding = 1 : i32}
     // CHECK: tt.load {{.*}} {boundaryCheck = array<i32: 0>, padding = 1 : i32, triton_intel_gpu.block_io = "column_major"}
     %7 = tt.make_tensor_ptr %arg0, [%c0_i64, %c0_i64], [%c1_i64, %pitch], [%c0_i32, %c0_i32] {order = array<i32: 0, 1>} : <tensor<64x32xf16, #dot_a>>
     %8 = tt.make_tensor_ptr %arg0, [%c0_i64, %c0_i64], [%c1_i64, %pitch], [%c0_i32, %c0_i32] {order = array<i32: 0, 1>} : <tensor<32x64xf16, #dot_b>>
diff --git a/third_party/intel/lib/TritonIntelGPUTransforms/MaterializeBlockPointer.cpp b/third_party/intel/lib/TritonIntelGPUTransforms/MaterializeBlockPointer.cpp
index 9a0b5e4f9d..601e3694e9 100644
--- a/third_party/intel/lib/TritonIntelGPUTransforms/MaterializeBlockPointer.cpp
+++ b/third_party/intel/lib/TritonIntelGPUTransforms/MaterializeBlockPointer.cpp
@@ -51,6 +51,8 @@ struct TritonIntelGPUMaterializeBlockPointerPass
       LDBG("Found make tensor ptr op: " << makeTensorPtrOp);
       auto ptrType = cast<tt::PointerType>(makeTensorPtrOp.getType());
       auto tensorType = cast<RankedTensorType>(ptrType.getPointeeType());
+      auto dotLayout = ttgi::getDotEncoding(tensorType);
+
       Operation::operand_range shape = makeTensorPtrOp.getShape();
       unsigned rank = shape.size();
       LDBG("Rank: " << rank);
@@ -97,10 +99,26 @@ struct TritonIntelGPUMaterializeBlockPointerPass
                                128 / tensorType.getElementTypeBitWidth()))
           return;
 
+        const bool isRowMajor = fastChangeDim == rank - 1;
+        if (dotLayout) {
+          // Check if the load is being used in a dot layout, and if so is this
+          // the first op and is it a transposed row major matrix. If so, skip
+          // the block ptr attribute as performance is worse than if we remove
+          // the tensor pointer
+          LDBG("dotLayout: " << *dotLayout);
+          const unsigned opIdx = dotLayout->getOpIdx();
+          auto dotOrder = dotLayout->getThreadOrder();
+          const bool valueRowMajor = (dotOrder[0] == 1 && dotOrder[1] == 0);
+          if (opIdx == 0 && valueRowMajor ^ isRowMajor) {
+            LDBG("Skipping block pointer attribute for transposed A matrix in "
+                 "dot operation");
+            return;
+          }
+        }
+
         loadOp->setAttr(ttgi::TritonIntelGPUDialect::getBlockIOAttrName(),
-                        StringAttr::get(context, fastChangeDim == rank - 1
-                                                     ? "row_major"
-                                                     : "column_major"));
+                        StringAttr::get(context, isRowMajor ? "row_major"
+                                                            : "column_major"));
       }
     });
   }