Written by In Part 1, we covered the capabilities of Generative AI, and ChatGPT in particular, as well as opportunities for businesses in general.
Now we will zoom into a core business area for Parkwood: Mergers and Acquisitions. To understand the current challenges in executing M&A transactions, and discover the opportunities AI can bring, it can be helpful to first understand a M&A deal flow.
Most M&A transactions follow a common process from idea to post-merger integration as detailed in the following six steps.
Six Steps to Execute an M&A Transaction
Step 1: Define the M&A strategy
- Company mission, vision and values
- M&A strategic objectives and key performance indicators
- M&A team
- M&A guiding principles
- Target screening criteria
Step 2: Identify target companies
- Potential target companies and data collection
- High-level assessment of potential target companies
- Shortlisted potential targets
- Financial statements analysis
- Business valuation: DCF model, comparable company analysis (EBITDA multiple), and precedent transaction analysis
- Targets approved for the business case phase
Step 3: Build a business case and an M&A financial model
- Strategic benefit
- Feasibility
- Financial benefit
- Comprehensive M&A financial model including acquirer model, target model, merger assumptions & analysis, and pro forma model
- Simple Financial model including integration cost, revenue synergy, cost synergy, NPV, ROI, and IRR
- Letter of intent or term sheet
Step 4: Conduct due diligence(CDD) to identify the likely future performance of target
- Work plan including key business case hypotheses and assumptions
- Due diligence to validate key hypotheses and assumptions
- Updated business valuation
- Recommendation to make (or not) a formal offer to acquire the target company
Step 5: Execute the transaction
- Deal structure
- M&A negotiations
- Signing and closing the M&A deal
Step 6: Conduct successful post-merger integration to ensure the company reaches its cost and revenue synergy targets
- Post-merger integration strategy and high-level plan
- Post-merger integration detailed plans
- Implementation and monitoring
Key Challenges in 2023
Challenge 1: How can businesses build enterprise applications which integrate GPT-4?
To start, OpenAI does have, at present, neither the manpower, nor the infrastructure to address the enormous needs and complexities of large companies’ business models. Specifically, the current models significantly restrict the number of requests/minute which can be quoted to potential clients. For example, Microsoft Azure Cloud Services (N.B. Microsoft has invested, so far, in excess of $11 billion in OpenAI) currently limits such quote inputs to 18/minute. Although this may be sufficient for a SME, larger enterprises would have to limit its usage to small workgroups.
A significant second challenge to the use of GPT-4, or CHATGPT+, is the latency of its sequential output. The demand, at a large enterprise level, for these services can, at times, overwhelm the limited computing capacity allocated to a department. The enormous use of memory will have to be addressed, sooner rather than later, in order to improve results.
Yet another issue can be the cost of these services. Microsoft’s Azure,for example, charges between 3-12 cents/750 words. At several thousands transactions per minute, the training and deploying costs can rapidly escalate.
Given these current challenges, which are not alone in the enterprise-wide implementation of GPT-4, you can select more targeted applications which use GPT-4 today, like document processing. This is especially useful to those departments handling large troves of documents at one time, with prolific paper workflow like M&A, Finance, Legal, IP, HR, Marketing, Communications, etc. A structured review will look first at what revisions have been made based on an model template, edited by using GPT-4, followed by additional instructions to analyze text to the GPT-4 model (such as recognize specific patterns, derive pertinent information and do sentiment analysis, spam detection and topic classification, introduce transparent language, use a straightforward workflow, eliminate confused language, state the assumptions made and the basis of estimates, and whether financial records are GAAP-compliant, whether they are audited, etc.), and finally, the clear review and approval process.
All of these processes using GPT-4 save resources, improve customer relations by enhancing the quality and accuracy of communications, while also reducing expenses.
Challenge 2: How can SMEs benefit today from integrating various functions with GPT-4 and CHATGPT+?
Various operational functions of a SME can benefit from integration with GPT-4, such as: a) sales – generate email templates customized to each customer’s needs, magnified by attachment to a sales-lead spreadsheet in a CRM platform; use conversation analytics to transcribe, identify patterns and analyze sales calls while tailoring communications (using LinkedIn backgrounds) to each specific client; b) create marketing strategies and related social media posts (e.g. PPC campaigns, landing pages, etc.) addressing everchanging market demands, such as building outlines around keyword clusters, generating content ideas, strategically updating content, etc., while increasing web traffic, drive engagement and boosting engine rankings by generating quality backlinks from industry influencers and reputable websites; c) respond 24/7 to customer inquiries with natural language proficiency, in multiple languages.
Challenge 3: Can Generative AI help a company’s digital transformation?
The answer is an unequivocal “YES,” and here are some examples:
- Talent Acquisition. A company’s HR Department should develop a data-driven recruiting strategy, creating a model-candidate for each role; define the target audience, then expand talent networks; build the Diversity, Equity & Inclusion initiatives into the recruitment programs; keep updated, for each program, the critical areas’ focus, all while integrating GPT-4.
- Personalized Employee Training. GPT-4 can assist in the creation of unique and creative training programs customized to each employee, or department, specifically in one of the 26 languages GPT-4 can communicate in. Digital tools can also be adjusted on short notice, especially as text to video becomes widely available;
- Marketing. In building a memorable brand based on creating client value, GPT-4 can help you develop tangible, quantified methods to effectively integrate your product/services into the customer’s value creation. The purpose of integrating GPT-4 is to create an “outcome as a service” mentality, both within your organization as well as the client’s, based on relevant data which leads from the customer success metrics. GPT-4 can write a first draft of a blog post, write the website copy based on a detailed description of the product or service, as well as analyze the competition, with their respective strength and weaknesses. Your marketing managers need to focus on becoming product/service marketers, which cannot be automated using GPT-4. What GPT-4 can provide is data, if “mined” from a relevant database, such as: market definition, CAGR (Compound Annual Growth Rate), sales drivers, product/services data trends, historical ROMI (Return On Marketing Investment), historical ABM (Account-Based Marketing), customer-acquisition costs, customer-retention costs, optimized results of “push” vs. “pull” campaigns, and rate of convert leads to MQL (Marketing Qualified Leads), among other. With text-to-video becoming available by year’s end, marketers can use GPT-4 to issue automated, compelling and targeted video press releases, blog and social media posts, and targeted emails, which will evoke customer favorable reactions and generate sales leads as the customer will look at the value-added strategic thinker of your company.
Coming soon: In Part 3, we will examine how AI can be leveraged to overcome these challenges.
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