Remaking Search Without Losing the Margin: An Implementation Plan, a Balanced Scorecard and a Defended Recommendation for Alphabet, 2026 to 2028
[Student Name]
University of Phoenix
MGT/498: Strategic Management
Week 5 Learning Team Assignment
[Instructor Name]
[Date]
This paper analyzes a real public company using its published annual report and public statements; the plan, targets and timeline are proposals written for a model paper.
Across this course, the analysis of Alphabet found a highly profitable search business threatened by generative AI assistants and legal limits on its distribution, supported by resources few rivals can match: billions of users, custom AI chips, leading research and its own data centers. Week 4 selected a strategy: remake search with AI while growing Cloud, which sells those same capabilities to businesses. A strategy chosen in a report is only a hypothesis until it has owners, dates and numbers that can prove it wrong. This paper sets out how Alphabet should implement the strategy from 2026 to 2028, how it should measure progress and why the recommendation holds.
Implementation Plan
Structure and leadership
Implementation needs clear ownership. The plan assigns the transformation of search to the head of Google's search and advertising organization, with a single cross-functional team combining search product, advertising and Google DeepMind staff, reporting jointly to the chief executive. Cloud's AI offerings remain with the Cloud chief executive, but a shared model roadmap ensures that search and Cloud use the same underlying models and chips. This structure follows the principle of integrating new efforts at the senior level while giving the team room to move quickly.
Phase 1, 2026: Prove the model
Expand AI-generated answers and conversational search to all major markets and languages where regulation allows. Test at least three advertising formats inside AI answers with a representative sample of advertisers and measure revenue per query against standard results. Publish a clear policy on how AI answers link to and credit publishers, to reduce conflict with content creators and regulators.
Phase 2, 2027: Scale what works
Roll out the advertising formats that match or approach standard search revenue per query. Integrate AI search across Android, Chrome and the Google app. Offer the same models and chips to enterprise customers through Cloud with dedicated capacity.
Phase 3, 2028: Optimize
Reduce the cost per AI answer through more efficient models and next-generation chips. Adjust capital spending to demand. Report progress against the scorecard publicly in investor materials.
Resources
Capital spending will remain very high; Alphabet's 2024 investment in property and equipment, near $52.5 billion, is set to rise further (Alphabet Inc., 2025). The plan does not add to that total but directs it toward capacity that serves both search and Cloud, so each dollar of infrastructure supports two revenue streams.
Balanced Scorecard
Kaplan and Norton (1992) introduced the balanced scorecard to track strategy through financial measures and the operational measures that drive future financial results. Kaplan and Norton (2008) later emphasized linking the scorecard to a management system in which leaders review the measures regularly and adjust the strategy. The scorecard below uses 2024 as the baseline.
Financial perspective
Maintain an operating margin of at least 30%, compared with about 32% in 2024. Grow search and other advertising revenue at least in line with the overall digital advertising market. Raise Cloud operating margin from about 14% in 2024 to at least 20% by 2028.
Customer perspective
Hold or grow the number of daily search users as AI answers roll out, measured internally. Achieve advertiser return on ad spend inside AI answers within 10% of standard search ads by the end of 2027. Grow Cloud's backlog of contracted revenue each year.
Internal process perspective
Reduce the computing cost per AI-generated answer by half by 2028. Launch AI search features in all major regulated markets within six months of the U.S. launch. Hold what Alphabet pays for distribution, relative to what search earns, steady despite changes to default agreements.
Learning and growth perspective
Retain key AI research staff, with annual voluntary turnover among senior researchers below 10%. Train all search and advertising product managers on AI product design by 2027. Meet published goals for carbon-free energy use at data centers each year.
Reviewing the Scorecard
The executive team should review the scorecard quarterly, with a deeper strategy review each year in which the team asks whether the measures still reflect the strategy or whether the strategy itself needs to change. Each measure has a single owner who explains any miss and the planned response.
Risks and Contingencies
Three risks could derail the plan. First, AI answers may earn less advertising revenue than traditional results; if revenue per query in AI answers stays more than 20% below standard results by mid-2027, the team will slow the rollout of AI answers for commercial queries while continuing to test formats. Second, regulators may impose remedies that restrict how AI answers use Alphabet's own services or data; designing for the strictest regime and engaging regulators early reduces this risk. Third, infrastructure costs may rise faster than revenue; the operating margin floor of 30% is the trigger for a review of capital spending.
Defending the Recommendation
The strongest objection is that remaking search risks cannibalizing the most profitable business in the company's history, and that Alphabet could simply wait for the market to settle. The answer is that waiting does not protect the business. Substitutes are already available, users are already trying them and legal remedies have already weakened the default agreements that kept users in Google's search box. Porter (2008) notes that substitutes limit an industry's profitability by placing a ceiling on what can be charged; the question is whether Alphabet owns the substitute or cedes it to rivals. With its integrated AI stack and scale, Alphabet is better placed than any rival to lead the shift, and the plan's staged tests and margin floor limit the damage if the transition proves harder than expected.
Conclusion
Alphabet should remake search with AI while growing Cloud on the same models and infrastructure, implemented in three phases from 2026 to 2028 with clear owners. A balanced scorecard with targets in four perspectives, from operating margin to researcher retention and carbon-free energy, will show whether the strategy is working, and defined triggers will prompt adjustment if it is not. The recommendation holds because the alternative, defending an unchanged search page, cannot stop users from moving to AI answers; it can only decide who serves them.
References
Alphabet Inc. (2025). Form 10-K: Annual report for the fiscal year ended December 31, 2024. U.S. Securities and Exchange Commission.
Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71-79.
Kaplan, R. S., & Norton, D. P. (2008). Mastering the management system. Harvard Business Review, 86(1), 62-77.
Porter, M. E. (2008). The five competitive forces that shape strategy. Harvard Business Review, 86(1), 78-93.
How this MGT 498 Week 5 example is structured
The University of Phoenix library guide for MGT/498 organizes the course around an Alphabet Inc. case, and many sections end with implementation, metrics and a defended recommendation, often as a learning team paper. The paper moves from the choice made in Week 4 to how it would be carried out, then to how success would be measured and finally to why the choice still holds against its best counterargument. Every target is tied to a figure from the company's own reporting so it can be checked. Students search this week as MGT 498 Week 5, MGT498 Wk 5 or MGT/498 Wk 5; all three are the same assignment.
MGT/498 Week 5 questions, answered
What does MGT/498 Week 5 usually ask for?
MGT/498 closes with strategy implementation and evaluation for the case company, often as a learning team paper. Many sections ask for an implementation plan, a balanced scorecard or similar measurement system, risk analysis and a final recommendation. Your own instructions set the format.
What is a balanced scorecard?
A performance measurement framework, introduced by Kaplan and Norton, that tracks an organization's strategy through four perspectives: financial, customer, internal business process and learning and growth. It balances financial results with the drivers of future performance.
How do you defend a strategic recommendation?
State the strongest objection to it fairly, then show why the recommendation still holds or how the plan manages the risk. A recommendation that ignores its best counterargument is less convincing.
Write yours, or have the desk draft it
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