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87. What scope do you want your strategy to cover?
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88. Is the Hardware accelerators for machine learning scope manageable?
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89. What are the record-keeping requirements of Hardware accelerators for machine learning activities?
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90. Is the improvement team aware of the different versions of a process: what they think it is vs. what it actually is vs. what it should be vs. what it could be?
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91. What knowledge or experience is required?
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92. What would be the goal or target for a Hardware accelerators for machine learning’s improvement team?
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93. What is the scope of the Hardware accelerators for machine learning effort?
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94. Has the direction changed at all during the course of Hardware accelerators for machine learning? If so, when did it change and why?
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95. What are the rough order estimates on cost savings/opportunities that Hardware accelerators for machine learning brings?
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96. Will a Hardware accelerators for machine learning production readiness review be required?
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97. What was the context?
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98. Is there a completed, verified, and validated high-level ‘as is’ (not ‘should be’ or ‘could be’) stakeholder process map?
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99. Is there a critical path to deliver Hardware accelerators for machine learning results?
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100. Are there different segments of customers?
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101. How often are the team meetings?
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102. Is Hardware accelerators for machine learning currently on schedule according to the plan?
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103. Has a Hardware accelerators for machine learning requirement not been met?
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104. What is out of scope?
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105. Has anyone else (internal or external to the group) attempted to solve this problem or a similar one before? If so, what knowledge can be leveraged from these previous efforts?
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106. What are the tasks and definitions?
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107. Scope of sensitive information?
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108. Are task requirements clearly defined?
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109. Is the team equipped with available and reliable resources?
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110. What intelligence can you gather?
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111. What are the core elements of the Hardware accelerators for machine learning business case?
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112. What information do you gather?
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113. Are customer(s) identified and segmented according to their different needs and requirements?
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114. Is there a Hardware accelerators for machine learning management charter, including stakeholder case, problem and goal statements, scope, milestones, roles and responsibilities, communication plan?
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115. Has/have the customer(s) been identified?
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116. What are the compelling stakeholder reasons for embarking on Hardware accelerators for machine learning?
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117. What are the requirements for audit information?
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118. Are different versions of process maps needed to account for the different types of inputs?
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119. How do you gather the stories?
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120. Is there any additional Hardware accelerators for machine learning definition of success?
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121. The political context: who holds power?
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122. What are the boundaries of the scope? What is in bounds and what is not? What is the start point? What is the stop point?
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123. What is the definition of success?
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124. What information should you gather?
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125. How did the Hardware accelerators for machine learning manager receive input to the development of a Hardware accelerators for machine learning improvement plan and the estimated completion dates/times of each activity?
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126. Who defines (or who defined) the rules and roles?
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127. What scope to assess?
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128. How do you manage changes in Hardware accelerators for machine learning requirements?
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