diff --git a/python/ray/air/util/tensor_extensions/arrow.py b/python/ray/air/util/tensor_extensions/arrow.py
index 2ee8179a626ab..91b5daecbefe9 100644
--- a/python/ray/air/util/tensor_extensions/arrow.py
+++ b/python/ray/air/util/tensor_extensions/arrow.py
@@ -5,7 +5,10 @@
 import numpy as np
 import pyarrow as pa
 
-from ray.air.util.tensor_extensions.utils import _is_ndarray_variable_shaped_tensor
+from ray.air.util.tensor_extensions.utils import (
+    _is_ndarray_variable_shaped_tensor,
+    _create_strict_ragged_ndarray,
+)
 from ray._private.utils import _get_pyarrow_version
 from ray.util.annotations import PublicAPI
 
@@ -721,9 +724,7 @@ def _to_numpy(self, index: Optional[int] = None, zero_copy_only: bool = False):
             arrs = [self._to_numpy(i, zero_copy_only) for i in range(len(self))]
             # Return ragged NumPy ndarray in the ndarray of ndarray pointers
             # representation.
-            arr = np.empty(len(self), dtype=object)
-            arr[:] = arrs
-            return arr
+            return _create_strict_ragged_ndarray(arrs)
         data = self.storage.field("data")
         shapes = self.storage.field("shape")
         value_type = data.type.value_type
diff --git a/python/ray/air/util/tensor_extensions/pandas.py b/python/ray/air/util/tensor_extensions/pandas.py
index e23cce7b91987..4201be97201f9 100644
--- a/python/ray/air/util/tensor_extensions/pandas.py
+++ b/python/ray/air/util/tensor_extensions/pandas.py
@@ -44,7 +44,10 @@
 from pandas.core.indexers import check_array_indexer, validate_indices
 from pandas.io.formats.format import ExtensionArrayFormatter
 
-from ray.air.util.tensor_extensions.utils import _is_ndarray_variable_shaped_tensor
+from ray.air.util.tensor_extensions.utils import (
+    _is_ndarray_variable_shaped_tensor,
+    _create_strict_ragged_ndarray,
+)
 from ray.util.annotations import PublicAPI
 
 try:
@@ -1422,9 +1425,7 @@ def _is_boolean(self):
 
 
 def _create_possibly_ragged_ndarray(
-    values: Union[
-        np.ndarray, ABCSeries, Sequence[Union[np.ndarray, TensorArrayElement]]
-    ]
+    values: Union[np.ndarray, ABCSeries, Sequence[np.ndarray]]
 ) -> np.ndarray:
     """
     Create a possibly ragged ndarray.
@@ -1438,11 +1439,8 @@ def _create_possibly_ragged_ndarray(
         return np.array(values, copy=False)
     except ValueError as e:
         if "could not broadcast input array from shape" in str(e):
-            # Create an empty object-dtyped 1D array.
-            arr = np.empty(len(values), dtype=object)
-            # Try to fill the 1D array of pointers with the (ragged) tensors.
-            arr[:] = list(values)
-            return arr
+            # Fall back to strictly creating a ragged ndarray.
+            return _create_strict_ragged_ndarray(values)
         else:
             # Re-raise original error if the failure wasn't a broadcast error.
             raise e from None
diff --git a/python/ray/air/util/tensor_extensions/utils.py b/python/ray/air/util/tensor_extensions/utils.py
index 3b7e60a579fbb..f28928b54c2ce 100644
--- a/python/ray/air/util/tensor_extensions/utils.py
+++ b/python/ray/air/util/tensor_extensions/utils.py
@@ -1,3 +1,5 @@
+from typing import Any
+
 import numpy as np
 
 
@@ -20,3 +22,23 @@ def _is_ndarray_variable_shaped_tensor(arr: np.ndarray) -> bool:
         if a.shape != shape:
             return True
     return True
+
+
+def _create_strict_ragged_ndarray(values: Any) -> np.ndarray:
+    """Create a ragged ndarray; the representation will be ragged (1D array of
+    subndarray pointers) even if it's possible to represent it as a non-ragged ndarray.
+    """
+    # Use the create-empty-and-fill method. This avoids the following pitfalls of the
+    # np.array constructor - np.array(values, dtype=object):
+    #  1. It will fail to construct an ndarray if the first element dimension is
+    #  uniform, e.g. for imagery whose first element dimension is the channel.
+    #  2. It will construct the wrong representation for a single-row column (i.e. unit
+    #  outer dimension). Namely, it will consolidate it into a single multi-dimensional
+    #  ndarray rather than a 1D array of subndarray pointers, resulting in the single
+    #  row not being well-typed (having object dtype).
+
+    # Create an empty object-dtyped 1D array.
+    arr = np.empty(len(values), dtype=object)
+    # Try to fill the 1D array of pointers with the (ragged) tensors.
+    arr[:] = list(values)
+    return arr