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Decision Point AI predicts M&A value outcomes pre acquisition as part of corporate due diligence and valuation

Decision Point AI Mergers and Acquisitions tag cloud

Decision Point AI Mergers and Acquisitions tag cloud

Decision Point AI strategic logic M&A workflow in AI

Decision Point AI strategic logic M&A workflow

Decision Point AI human subject matter expert questionnaire in M&A

Decision Point AI human subject matter expert questionnaire in M&A

Decision Point AI predicts risks, issues and answers will the combined firms return good value post acquisition, before acquisition as part of due diligence

Veriluma Limited (ASX:VRI)

NEW YORK, NEW YORK, UNITED STATES, June 14, 2019 /EINPresswire.com/ -- Internationally M&A has a high failure rate

Up to 90% of Mergers and Acquisitions Fail, according to a Harvard Business Review report, the failure rate for mergers and acquisitions (M&A) sits between 70 percent and 90 percent.

Our AI approach is based on comprehensive research into the reasons behind the low success rate utilizing the research of Dr. Thomas Straub, Reasons for Frequent Failure in Mergers and Acquisitions, a comprehensive analysis. Dr. Straub has adopted holistic understanding of M&A as a crucial part of business development.

It is evident that companies’ long-term success is at least partially dependent on their strategic actions. And these are often shaped in practice by merger and acquisition activity. Thus phenomena as varied as globalization, value-chain optimization, or product diversification are often implemented as M&A operations. In no few cases, M&A activity constitutes the company’s strategy. Prof. Dr. Dr. José-Carlos Jarillo 2007.

The focus is therefore on core elements – strategic logic, cultural behaviour and financial benefits. Decision Point AI is able to predict the risks before the acquisition takes place using the following question;

will the combined firms return good value post acquisition?

Then, we list the factors that might provide insight to their combined performance, such as the Strategic Logic, Organizational Behaviour, and Financial Position.

Next, we break down each of the major terms (such as Strategic Logic) into their sub-issues, drivers, and indicators.

Strategic Logic with sub-issues, drivers and indicators

Having listed all of the issues that bear on this problem, (or at least having made a start), we now structure a model that describes the relevant relationships between these issues.

The relationship between the issues is expressed in terms of conditional probability. In the example above, in the real world, we would anticipate it is very likely that IF combined firms have good strategic logic (Combining firms has good strategic logic) THEN the combined firms would deliver good value (Combined firms’ performance delivers good value).

In this M&A example, we use several layers of reasoning from base information that is measurable in the real world, to aggregated ones, such as Production Operation Similarity.

Almost all objects in the system can be annotated with external links to web pages, PDF, images, spreadsheets, etc.

Human expertise is combined with the AI logic in the form of workshop based and individually identified experts in client organisations answering questionnaires to be built into the AI.

Above shows the observations made with respect to all of the issues contained in the questionnaire. Where there is colour there is information. More colour = more certainty. No colour = no data.

Predict early to identify the risks and save costs

A client used a recent example of a takeover on which they were the corporate advisor.  The acquirer was a AUD $350m Enterprise Value ("EV") listed entity, the target a AUD $75m EV listed entity, in the same industry sector. The takeover had completed earlier and earnings and other results were beginning to become apparent.

They found the Decision Point AI methodology thoughtful and comprehensive. In particular, the program asked for a broad range of non-financial factors to be considered, some of which they found were not usually quantified in pre-acquisition analysis of takeovers, but should be.   Acquirers and advisors alike tended to focus on easily quantifiable factors, such as EPS accretion/dilution and return on shareholders' funds. The Decision Point AI methodology could be helpful to assist acquirers and advisors alike in assessing longer term benefits, to each of the employees, clients and shareholders of each entity - acquirer and target.

Next Steps....

Decision Point AI methodology is focused on a part human and part machine integration which requires a major commitment in time and finance to achieve on the first question being answered it although gets a great deal easier over time. The next step is a proof of concept (POC) which costs between USD $65,000 and USD $130,000 as it actually answers a critical business question so practically delivers value. Additionally it requires the involvement and time of senior leadership to create a bespoke structure around their business capability and knowledge. Creating the POC takes 6-8 weeks depending upon complexity and access to subject matter experts within the the client company and involves both workshops and once identified expert questionnaires. The clients intellectual property pertaining to their own knowledge and observations remain their property.

Related Decision Point AI case studies

Author: Karl Smith, Director

Karl Smith
Decision Point
+1 800-650-2487
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