Introduction to Negotiation Strategy
At Oak Spring University, we provide corporate level professional Negotiation Strategy and other business case study solution. Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) case study is a Harvard Business School (HBR) case study written by Lauren H. Cohen, Christopher Malloy, William Powley. The Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) (referred as “Cogent Labs” from here on) case study provides evaluation & decision scenario in field of Innovation & Entrepreneurship. It also touches upon business topics such as - negotiation strategy, negotiation framework, Financial management.
Negotiation strategy solution for case study Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) ” provides a comprehensive framework to analyse all issues at hand and reach a unambiguous negotiated agreement. At Oak Spring University, we provide comprehensive negotiation strategies that have proven their worth both in the academic sphere and corporate world.
What’s my BATNA (Best Alternative To a Negotiated Agreement) – my walkaway option if the deal fails?
What are my most important interests, in ranked order?
What is the other side’s BATNA, and what are his interests?
This case examines the intersection of two firms (Cogent Labs-a machine learning software firm in Tokyo; and Google, the technology infrastructure giant) attempting to exploit the benefits of artificial intelligence and machine learning in the financial services sector. The case protagonist, David Malkin, known as the "AI Architect" at Cogent Labs, must decide how best to position his firm for growth. Malkin knew that artificial intelligence had great potential to revolutionize several aspects of the financial services industry, but he also knew that artificial intelligence's greatest achievements to date were in very narrow functions. Malkin further knew that large, sophisticated financial service clients owned a vast array of proprietary datasets that were impossible to replicate. Meanwhile the major "cloud" providers like Google, Amazon, and Microsoft had large-scale computing infrastructures and multi-billion-dollar research and development budgets with which they could (and did) generate innovative artificial intelligence software of their own. Malkin wondered how a small software firm like Cogent Labs without its own proprietary datasets, or a large-scale computing infrastructure, or a multi-billion R&D budget could fit in? Would Cogent Labs' current approach of developing their own proprietary machine learning applications to run on the cloud and sell directly to financial services firms in Tokyo prove to be a sustainable model? Or would Cogent Labs ultimately need to partner/merge with one of the major cloud providers in order to provide the expertise necessary to customize their offerings for financial services clients? Or, was the future even more uncertain; would software firms like Cogent eventually need to create and own new datasets of their own, and build their own infrastructures to host their own new data, in order to avoid being disintermediated in the future if (and when) machine learning expertise became truly commoditized?
By interests, we do not mean the preconceived demands or positions that you or the other party may have, but rather the underlying needs, aims, fears, and concerns that shape what you want. Negotiation is more than getting what you want. It is not winning at all cost. Number of times Win-Win is better option that outright winning or getting what you want.
Options are the solutions you generate that could meet your and your counterpart’s interests . Often people come to negotiations with very fixed ideas and things they want to achieve. This strategy leaves unexplored options which might be even better than the one that one party wanted to achieve. So always try to provide as many options as possible during the negotiation process. The best outcome should be out of many options rather than few options.
When soft bargainers meet hard bargainers there is always the danger of soft bargainers ceding more than what is necessary. To avoid this scenario you should always focus on legitimate standards or expectations. Standards are often external and objective measures to assess the fairness such as rules and regulations, financial values & resources , market prices etc. If the negotiated agreement is going beyond the industry norms or established standards of fairness then it is prudent to get out of the negotiation.
Every negotiators going into the negotiations should always work out the “what if” scenario. The negotiating parties in the “Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP)” has three to four plausible scenarios. The negotiating protagonist needs to have clear idea of – what will happen if the negotiations fail. To put it in the negotiating literature – BATNA - Best Alternative to a Negotiated Agreement. If the negotiated agreement is not better than BATNA then there is no point in accepting the negotiated solution.
One of the biggest problems in implementing the negotiated agreements in corporate world is – the ambiguity in the negotiated agreement. Sometimes the negotiated agreements are not realistic or various parties interpret the outcomes based on their understanding of the situation. It is critical to do negotiations as water tight as possible so that there is less scope for ambiguity.
Many negotiators make the mistake of focusing only on the substance of the negotiation (interests, options, standards, and so on). How you communicate about that substance, however, can make all the difference. The language you use and the way that you build understanding, jointly solve problems, and together determine the process of the negotiation with your counterpart make your negotiation more efficient, yield clear agreements that each party understands, and help you build better relationships.
Another critical factor in the success of your negotiation is how you manage your relationship with your counterpart. According to “Lauren H. Cohen, Christopher Malloy, William Powley”, the protagonist may want to establish a new connection or repair a damaged one; in any case, you want to build a strong working relationship built on mutual respect, well-established trust, and a side-by-side problem- solving approach.
According to
Harvard Business Review
, there are three types of negotiators – Hard Bargainers, Soft Bargainers, and Principled Bargainers.
Hard Bargainers – These people see negotiations as an activity that they need to win. They are less focused less on the real objectives of the negotiations but more on winning. In the “Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) ”, do you think a hard bargaining strategy will deliver desired results? Hard bargainers are easy to negotiate with as they often have a very
predictable strategy
Soft Bargainers – These people are focused on relationship rather than hard outcomes of the negotiations. It doesn’t mean they are pushovers. These negotiators often scribe to long term relationship rather than immediate bargain.
Principled Bargainers – As explained in the seven elemental tools of negotiations above, these negotiators are more concern about the standards and norms of fairness. They often have inclusive approach to negotiations and like to work on numerous solutions that can improve the BATNA of both parties.
Open lines of communication between parties in the case study “Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP)” can make for an effective negotiation strategy and will make it easier to negotiate with this party the next time as well.
Lauren H. Cohen, Christopher Malloy, William Powley (2018), "Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) Harvard Business Review Case Study. Published by HBR Publications.
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