diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 02305479bef67..228e630f95863 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1192,6 +1192,26 @@ def map(self, mapper):
     __le__ = _cat_compare_op(operator.le)
     __ge__ = _cat_compare_op(operator.ge)
 
+    def _validate_insert_value(self, value) -> int:
+        code = self.categories.get_indexer([value])
+        if (code == -1) and not (is_scalar(value) and isna(value)):
+            raise TypeError(
+                "cannot insert an item into a CategoricalIndex "
+                "that is not already an existing category"
+            )
+        return code[0]
+
+    def _validate_searchsorted_value(self, value):
+        # searchsorted is very performance sensitive. By converting codes
+        # to same dtype as self.codes, we get much faster performance.
+        if is_scalar(value):
+            codes = self.categories.get_loc(value)
+            codes = self.codes.dtype.type(codes)
+        else:
+            locs = [self.categories.get_loc(x) for x in value]
+            codes = np.array(locs, dtype=self.codes.dtype)
+        return codes
+
     def _validate_fill_value(self, fill_value):
         """
         Convert a user-facing fill_value to a representation to use with our
@@ -1299,15 +1319,8 @@ def memory_usage(self, deep=False):
 
     @doc(_shared_docs["searchsorted"], klass="Categorical")
     def searchsorted(self, value, side="left", sorter=None):
-        # searchsorted is very performance sensitive. By converting codes
-        # to same dtype as self.codes, we get much faster performance.
-        if is_scalar(value):
-            codes = self.categories.get_loc(value)
-            codes = self.codes.dtype.type(codes)
-        else:
-            locs = [self.categories.get_loc(x) for x in value]
-            codes = np.array(locs, dtype=self.codes.dtype)
-        return self.codes.searchsorted(codes, side=side, sorter=sorter)
+        value = self._validate_searchsorted_value(value)
+        return self.codes.searchsorted(value, side=side, sorter=sorter)
 
     def isna(self):
         """
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index cbb30763797d1..d38f77aaceb01 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -20,7 +20,7 @@
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import CategoricalDtype
-from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna, notna
+from pandas.core.dtypes.missing import is_valid_nat_for_dtype, notna
 
 from pandas.core import accessor
 from pandas.core.algorithms import take_1d
@@ -734,15 +734,10 @@ def insert(self, loc: int, item):
         ValueError if the item is not in the categories
 
         """
-        code = self.categories.get_indexer([item])
-        if (code == -1) and not (is_scalar(item) and isna(item)):
-            raise TypeError(
-                "cannot insert an item into a CategoricalIndex "
-                "that is not already an existing category"
-            )
+        code = self._data._validate_insert_value(item)
 
         codes = self.codes
-        codes = np.concatenate((codes[:loc], code, codes[loc:]))
+        codes = np.concatenate((codes[:loc], [code], codes[loc:]))
         return self._create_from_codes(codes)
 
     def _concat(self, to_concat, name):