In today’s rapidly evolving business landscape, conducting thorough due diligence has become paramount for successful investments and acquisitions. To navigate this complex process effectively, a comprehensive technology due diligence checklist is indispensable.
By following the checklist below, investors and organizations can make informed decisions, mitigate risks, and ensure the alignment of technology assets with business objectives.
What is technical due diligence?
Technical due diligence is a comprehensive assessment of a company’s technical aspects. It’s typically conducted during mergers and acquisitions (M&A), investments, partnerships, and other business transactions.
The purpose of technical diligence is to assess the technological capabilities, risks, and opportunities associated with the target entity.
Why is tech due diligence important?
Here are the key reasons to conduct technology due diligence:
- Risk mitigation. It identifies potential risks and vulnerabilities, enabling decision-makers to take proactive measures.
- Value assessment. It evaluates technology assets and growth potential, aiding informed investment decisions.
- Integration planning. It assesses compatibility and integration requirements for seamless technology integration.
- Compliance. It ensures adherence to regulations and identifies any legal or compliance gaps.
- Strategic planning. It guides long-term strategies by identifying strengths, weaknesses, and growth opportunities.
Who conducts tech due diligence?
Technical due diligence is conducted by a team of experts with specialized knowledge in various technical domains, including engineers, developers, data specialists, security experts, legal experts, and project managers.
Technical due diligence checklist
Here’s a due diligence technical checklist outlining key areas to consider during the transaction:
- Business strategy and roadmap
- Organizational structure and management
- Software and technology
- IT infrastructure and systems
- Product quality
- Software development lifecycle and business tools
- Customer care
- Portfolio investment balance
- Spin-off scenarios.
1. Business strategy and roadmap
The goal is to determine if the organization has a clear and cohesive strategy and roadmap in place. This part of the technology due diligence checklist typically includes:
- A healthy SWOT competitive awareness
- Consistency between the business strategy and overall business goals
- Alignment with the business roadmap and strategy
- Product management planning mechanics and abilities
- Execution discipline
- Revenue models and the scalability of monetization strategies
- Thorough competitive landscape understanding
2. Organizational structure and management
The objective is to assess the technology team setup and ensure that the team has the appropriate skills to execute the roadmap efficiently. Here are the steps to follow:
- Study the organizational structure and reporting structure
- Evaluate the balance of interdisciplinary teams and functional areas
- Evaluate communication channels within the organization
- Identify any gaps in skills that may hinder the successful execution of the roadmap
- Assess the effectiveness of recruitment processes and talent development initiatives
- Review employee satisfaction and retention rates
3. Software and technology
The M&A technology due diligence checklist should also include the evaluation of software architecture. This helps determine if it aligns with industry best practices, scalability requirements, and business objectives. Here’s what to check for:
- Quality and maintainability of the software applications and underlying codebase
- Scalability strategy
- Technical debt and management approach
- Security design and secure programming principles
- Data architecture and management lifecycle (collection, storage, usage, distribution, disposal)
- Data security (encryption, access controls, compliance with data privacy regulations)
- Intellectual property ownership
- Error handling
4. IT infrastructure and systems
The aim is to understand the current state, capabilities, and potential risks associated with the IT infrastructure. This section of the due diligence technology checklist includes:
- Evaluation of infrastructure deployment model (on-premises, cloud-based, or hybrid) to determine its suitability for the intended goals
- Assessment of cloud and data centers approach for deploying physical infrastructure in cloud environments and data centers
- Infrastructure’s ability to scale to accommodate the intended thesis
- Infrastructure resilience and reliability to recover from disruptions
- Deployment processes and their frequency
- Roles and responsibilities for the internal IT team
- Business continuity and disaster recovery approach
- Business applications management (performance, maintenance, and alignment with business needs)
- Tools and processes for monitoring and managing the performance of the IT infrastructure
5. Product quality
This aims to gain insight into the product’s features, design, user experience, and production issues. More specifically, it examines:
- Methodologies and processes for testing and ensuring product quality
- Code coverage
- Ability to detect and address bugs early in the development process
- Test case management process and tools
- Bug backlog management
- Design and user experience of the product (usability, accessibility, and adherence to design best practices)
- Customer feedback loop (how customer feedback is collected, analyzed, and incorporated into product improvements)
6. Software development lifecycle and business tools
The goal is to conduct a business tools overview and assess their effectiveness and suitability. Here’s what this part of the diligence process includes:
- Agile methodologies and commitment to continuous improvement
- Release planning and management process
- Sprint planning and management process
- Delivery trends and performance metrics across multiple release cycles to identify areas for improvement and bottlenecks
- KPIs employed throughout the software development lifecycle, including metrics for productivity, quality, and timeliness of deliverables
- Effectiveness of collaboration and communication tools used within the SDLC
- Quality of document management practices to ensure proper transfer and retention of project-related knowledge
7. Customer care
This part of the technical due diligence process aims to explore the effectiveness and capabilities of customer support within the organization. This includes:
- Customer-focus mindset and end-to-end customer support process
- Defect rates and management process
- Escalation rates and management process
- Delineating between support and engineering teams and ensuring clear roles and responsibilities
- Evaluation of tools and technologies used in customer support
- SLAs for customer care, including response times and resolution times
- Availability and effectiveness of support across various channels (phone, email, chat, social media, etc.)
The aim of cybersecurity due diligence in IT is to identify potential vulnerabilities, risks, and gaps in the organization’s cybersecurity defenses. More precisely:
- Physical security strategy
- History of breaches and management
- Compliance requirements and relevant cybersecurity regulations and industry standards
- Effectiveness of network security measures, such as firewalls, intrusion detection, and prevention systems
- Effectiveness of the organization’s identity and access management practices
- The organization’s security awareness and training programs
9. Portfolio investment balance
The tech due diligence process should also include exploring the portfolio investment balance to understand synergy, strategy, and efficiency across the portfolio. It includes:
- Evaluation of architectural uniformity across products, considering the use of standardized frameworks, technologies, and design principles
- Level of efficiency for code leverage and reusable components strategy
- Team structure and resource allocation strategy across different products
- A healthy strategy, including product roadmaps, market positioning, and differentiation
10. Spin-off scenarios
The goal is to assess various aspects of a specific business unit or division being separated from its parent company. Here’s what to prepare:
- Organizational chart after the split, including reporting lines, management roles, and overall governance structure
- Roles and responsibilities of key personnel within the spin-off entity
- Security gaps or vulnerabilities that may arise as a result of the separation
- Hosting and deployment independence
- Contractual agreements, licensing arrangements, and IP concerns
- Financial systems, reporting capabilities, and compliance with regulatory requirements
- Branding and marketing transition plan
5 tips to prepare for the tech due diligence process
Following a comprehensive due diligence M&A checklist is just one of the elements in ensuring a successful business transaction. Here are other recommendations that you might find helpful:
- Prepare key personnel. Identify key individuals who will be involved in the due diligence process and ensure they are available and prepared to answer questions or provide clarifications during the due diligence process.
- Prepare the data room. Set up a secure and well-organized data room to store and share due diligence documents, ensuring easy access and confidentiality for the due diligence team.
- Seek professional assistance. Engage experienced professionals, such as legal advisors, accountants, and industry experts, who can provide guidance, review documentation, and assist in preparing a comprehensive technical due diligence report.
- Be transparent and open. Approach the due diligence process with transparency and openness. Be prepared to provide accurate and detailed information, address any concerns, and maintain clear lines of communication throughout the process.
- Utilize management and assessment tools. Consider implementing relevant management and assessment tools to improve internal processes, streamline workflows, track KPIs, and identify areas for improvement.
- Technology due diligence is crucial in evaluating the technical aspects of a company during M&A, investments, and other business transactions
- The tech due diligence checklist covers areas such as business strategy, organizational structure, software and technology, IT infrastructure, product quality, software development lifecycle, customer care, cybersecurity, portfolio investment balance, and spin-off scenarios
- The top recommendations for successful due diligence are to seek professional expertise, leverage data rooms, utilize management and assessment tools, and maintain transparency throughout the entire process.
The artificial intelligence (AI) industry survived its ‘winter’, in terms of limited hardware capacities, some time ago. Now, this sector seems to be a new investment hotspot. Nevertheless, many businesses and investors are still struggling to navigate this technologically complex field.
In his article for the M&A Community, Bruce Molloy, CEO of Springboard AI, and Artificial Intelligence / Machine Learning visionary, shares his insights about deals involving AI companies, the future of this market, and the use of Big Data during mergers and acquisitions.
What to consider during an AI-related M&A deal
There are several aspects to consider in the acquisition or sale of an AI company. Here are some ways to think through an M&A process.
“Buying/selling” for personnel (or “acquire to hire”)
Because of the scarcity of talent in AI, companies may be acquired solely for their personnel. The needed talent profile may vary over time and from company to company. Still, generally, there is a need for data scientists, AI model builders/strategists/leaders with domain expertise, and engineers who can turn AI models and the accompanying data into scalable production systems. A colleague of mine was offered $10M for a new company consisting of five PhDs, and this was before they had even decided on a product. There are numerous other examples with higher per-person numbers. Deal structure should consider both retention and integration issues.
Buying/selling proven Al models and accompanying data
A company I am familiar with was three times more successful at closing help desk tickets than others, according to industry averages. Their AI models were clearly proven, which reduced acquirer risk. With these kinds of performance metrics, pro formas can be created and value projected. Valuation approaches include financial estimates of increased profitability in addition to the strategic advantages this technology would bring; for example, more effective problem resolution would increase customer satisfaction. Again talent retention is essential, and it’s important that initial training data is of good quality. Equally important is the ability to capture and generate new data while enhancing the model.
The data, just the data!
They say that “the company with the most data wins,” and if you look at the success of Google or Amazon, it’s clear that their massive datasets are of significant importance. That being said, the nature and quality of the data must be examined in greater detail.
For instance, is the data riddled with junk or bias? Is the data in conflict with a regulation, whether GDPR or HIPPA? Does the data have limited shelf life such as customer and marketing data, given that consumers are in constant flux?
Data in the physical sciences and life sciences will typically have a longer shelf life, but it must be evaluated. Importantly does the company to be acquired have the ability to generate new data on an ongoing, “evergreen” basis? Lacking a future-looking data strategy and evergreen data creation capabilities, acquired data alone will likely lose its value.
On a different note, certain data rich companies such as Google or Amazon are able to generate more data as they acquire more data. Data begets data.
Another approach to data is seen in Reinforcement Learning (RL). This AI approach, which is gaining popularity, uses two AIs to compete against one another. RL was demonstrated in AlphaGo Zero’s performance in the game of Go, where the system learned how to play and improve itself through competition. The system ended up beating the best player in the world without existing data or training by humans. Reinforcement Learning is being used for simulation, modeling, prediction, and also the rapid mastery of games.
Strategic/new business model
I’ve been surprised by the number of mid-tier CEOs who have first asked for help in streamlining their operations using AI, and then asked for help in creating new data products or even new business models from this work. For example, the CEO of a commercial real estate company was interested in enhancing his operational and sales processes. Following that project, he wanted to monetize his data, algorithms, and insights to create new revenue streams, essentially creating new data products and a new business model. There are several ways M&A might help in this process, such as the acquisition of a mobile tech startup or a small data analytics firm to help in the monetization of these new capabilities.
Amazon is a great example of how new AI products and data-driven business models can spring from a core business.
Giants or diversity: How the future of the AI-market may look like
There are many niche AI startups in every industry. Andrew Ng’s quote about AI being “the new electricity” is absolutely right. AI is being used more and more in every aspect of business and life.
I’ve seen the consolidation of AI companies of all sizes, from very small startups to mega platforms. Many times consolidations will involve adding smaller AI capabilities to a larger business architecture. The larger platforms will seek to create highly generalized, large-market capabilities, while mid-tier and smaller consolidators will have a tighter industry and application focus.
AI start-ups are like modules
You can think of each of these AI startups as modules which fit into a larger whole. And it’s not just the AI algorithms but also the AI expertise, domain knowledge, and essential data that comprise these modules. Software engineering and business skill are also important. There are large numbers of these 3-10 person startups (modules) coming out of academia and entrepreneurs’ basements.
AI modules can work as ensembles
With consolidation you can imagine collections of these modules working together as an ensemble. For example, one module might be good at understanding customer requests, another at dealing with difficult customers, and a third at streamlining payments and deliveries. All three would be working together to enhance the customer experience. A fourth, analytics AI, might sit on top of these three, discovering trends and giving direction.
As AI capabilities are acquired by, and absorbed into a larger parent company, it will be important for the parent company to have not only the technical and data frameworks, but a strategic, top-down view of direction and success – it’s easy to get lost in smaller scope processes.
Interestingly, as we start to create hierarchies of AI specialization we move in the direction of biological and cognitive architectures. Such specialized architectures can be found in the brain. AI has constantly drawn inspiration from nature and the processes we find in ourselves – and this will no doubt continue.
AI, data, and M&A deals
Data science and AI are being used by Venture Capital, Private Equity and M&A in several areas such as deal sourcing, deal evaluation, and due diligence. There are nearly 100 venture capital groups that base their investment strategy on data and proprietary algorithms. In some cases these firms have standardized their data collection and use it to predict the success of contemplated investments. One can imagine the same approach used by bankers to predict the success of a contemplated merger or acquisition.
But We Don’t Have The Data. You may not have enough data initially to use machine learning or predictive analytics to provide confidence in a contemplated merger; however, there are other areas of AI and data science that can be helpful immediately such as Natural Language Understanding and expert systems.
Natural Language Understanding (NLU) capabilities can be applied to the analysis of contracts, reports, policies, regulations etc. for purposes of general understanding, and due diligence. Imagine being able to apply this capability to a collection of hundreds of contracts to determine dollar amount, end date, whether they are auto-renewing, etc. – and having these values placed in a standard spreadsheet for quantitative analysis. If not ready to use immediately, many of the NLU systems can be easily configured.
NLU can also be used to topically tag and organize large amounts of text, helping evaluators quickly get to what they need.
Expert Systems. A form of AI which preceded machine learning, expert systems have been used for decades. They are essentially rule-based systems built upon the heuristics of experts. A relatively small number of well thought out rules can be very powerful, for example, in determining which acquisition targets from a large target set should make the first cut. Expert systems apply these rules and decision-making automatically. As an added bonus, the definitional work that goes into creating an expert system can provide a good starting point for larger machine learning systems.
Automation = Greater Throughput. M&A like so many other endeavors is a numbers game. There are automation techniques that can simply remove drudge work and make a firm more productive. If through automation you can reduce your deal sourcing efforts by 50%, you can increase your deal flow by 2x. Building on this example, deal evaluation, and due diligence can similarly be increased 2x.
Will the Merger Be Successful? There may not be a historical instance that exactly matches the merger you’re contemplating. Still, one of the powerful features of machine learning and deep learning is that it can generalize and make predictions based on related instances, sometimes drawing unexpected conclusions as to whether something will or will not work. Although this approach requires more historical data, it opens valuable possibilities.
On another tack, cultural mismatch is one of the major failure points in M&A. This has been widely studied, as has the assessment and scoring of organizational culture. Scoring measures can span any number of areas such as innovation, values, and introversion-extroversion, etc. These measures, represented as vectors, provide a good method for matching and determining the degree of fit across large numbers of pairings.
AI will never completely replace human judgment, and the expert decisions that are the result of years of experience, but it can increase productivity and value through insights and the amplification of expert decision making. So, yes, it will change the way M&A is done, and it can help set deal value.
If this is an area you’re interested in pursuing, I would recommend becoming familiar with what your peer companies are doing in the areas of data and AI. Of greatest importance, put together a strategic approach to data so that with each new deal, you are collecting more and more data, and, importantly, the right data in a standard and consistent way.
“Although AI is already in use in thousands of companies around the world, most big opportunities have not yet been tapped.” This 2017 quote by Erik Brynjolfsson and Andrew McAfee, Director and Co-director of the MIT Initiative on the Digital Economy, is no less relevant in 2021. For almost three centuries, technological innovations have been the main drivers of economic growth.
It seems that currently, artificial intelligence, AI, is poised to have this kind of transformational impact. This technology is already widely known. Nevertheless, businesses are only just beginning to uncover the potential behind AI and its adjacent spheres. To better understand the economic, political, and organizational disruptions that developments in the AI sector are bringing, the M&A Community talked to Bruce Molloy, CEO of Springboard AI, a visionary, inventor, and a strategic consultant in the Artificial Intelligence and Machine Learning space.
The AI industry is booming now. Can we expect an ‘AI arms race’ among the top players?
Well for starters let’s talk about what AI is – and it’s very much a moving target. Years ago a system that could win at checkers or suggest spelling corrections was considered artificially intelligent. We’re now well beyond that. Capabilities once called AI are absorbed into regular software and are no longer called AI. A lot of recent success in AI has been the result of plentiful data in digital formats coupled with machine learning and its powerful, predictive capabilities. That being said, reinforcement learning, new classes of visual and audio recognition, adversarial networks and self-improving models, to name a few, will continue to advance AI.
Who are these top players?
There are a number of leaders in AI Software such as Facebook, Amazon, Microsoft, Google, and Apple (FAMGA), and yes, they are in an “arms race.” I believe Google has the best AI and will continue to be the leader, given its enormous global collection of content, its theoretical, scientific mindset (which is quite future-looking), and its stellar collection of talent.
Amazon, a more pragmatic contender, is a formidable number two. With its dominance in e-Commerce, it can mine all customer interactions while gaining powerful insights into every product and product class it carries. The Amazon “data hose” is constantly nourishing every aspect of their AI models. And Alexa is not insignificant. Alexa is able to mine not only the conversational exchanges with customers but also the nuances and emotional content in each customer’s voice. This collected data will help give rise to emotional intelligence and also provide initial building blocks for general artificial intelligence (GA).
The Microsoft strategy is quite different. Given that it has a foothold in nearly every major Corporation in the form of word Excel, SQL, PowerPoint, etc., Microsoft can incrementally add whole new sets of AI features and enjoy immediate adoption. Like Google and Amazon, Microsoft has access to tremendous amounts of data; however, in Microsoft’s case it is corporate data, not internet, consumer data. Microsoft’s strategy could be thought of as DIY AI which will help front-run the democratization of AI. They are also a strong contender.
Facebook is good at what they do, and Apple is playing catchup. There are many other challengers – and any number of new disruptors who were “born AI.” In AI hardware, chip manufacturer Nvidia is a clear leader with challengers like Intel and Graphcore.
What countries have the potential to enter the major league?
To determine who might be leading this race, one could simply count the number of patent applications filed by country. This past fall, China led in the number of applications filed, followed by the US, South Korea, India, and Europe (as a group). China’s 4,600 applications represented twice as many applications as the other four combined. By this measure, China is clearly ahead.
Are there any other parts to this story?
Surely. Innovation, culture, national support, and financial resources (think VCs) are also key determinants. Essential is the risk-taking, creativity, and entrepreneurial cross-fertilization coming from tech hubs like the Silicon Valley and other similar environments: Tel Aviv, Berlin, Lisbon, Austin, etc. The venture capital community is also critical as it provides coddling and enormous financial support to these startups. Breakthroughs require the freedom to experiment and fail – and to succeed.
An example of experimentation and disruption is found in Geoffrey Hinton. Hinton, a luminary now at Google, was one of the seminal pioneers in neural nets and deep learning. When he first came on the scene, AI was shaped by the austerity of logic and math in the form of expert systems and symbolic computing. Hinton thumbed his nose at this and fashioned a richer and messier approach inspired by biology and human neurons. It worked, and it is still working today quite well.
Time will tell who will win the AI arms race, a race which should more properly be thought of as multiple races given the many facets of AI (cybersecurity, military, healthcare, marketing, finance, autonomous driving, robotics, and the list goes on.) The lead may change multiple times in each race. While the US has a remarkable entrepreneurial spirit coupled with immense VC backing, China is a nation obsessively focused on AI and is providing outsized resources through government investment.
I think the US can win many of these races if it puts together a national “moon shot” initiative as it has done in the past. But if the US approach continues to be entirely market-based and piecemeal, without the resources and coordination of the US government, it’s a coin toss who wins.
Endnote: Just as we’re getting a handle on the odds of these races, quantum computing will begin commercial deployment in 2023-2026. Then a whole new set of races begin – stay tuned for “Quantum Machine Learning.”
AI is a highly complex, technical topic. From your point of view, what should any business leader know about AI?
AI typically combines computer engineering, data science, logic, multi-variate calculus, and statistics, along with domain knowledge and a good amount of data. Also, implementing AI requires industrial-strength scaling and deployment analogous to the factory that scales up models created in the laboratory.
This being said, business leaders should not be intimidated at all; they can understand what AI can do in straightforward ways, including the opportunities it creates, the problems it solves, the risks it poses, and where it is going. Each of these four areas can be understood conceptually, intuitively, and with accessible examples. Business leaders need to have a confident grasp of the subject, not feel intimidated, and importantly, not get drawn into the weeds.
Here’s what Bruce Molloy recommends for any business leader.
Rapid AI immersion
You should get up to speed on AI quickly. The C-Suite and board should consider developing a conceptual framework for thinking about AI, and good examples to support their understanding. Workshops and training should be given at all levels. DIY tools such as Microsoft’s Azure or a myriad of others can be made available to all who want them, like the way all employees were given Word, PowerPoint, and Excel when they first came out.
One effective approach for senior managers and/or the board to get down the learning curve would be a 3-hour workshop, split into two sections. The first section would be an overview with case studies and examples. The second section would be a group exercise to evaluate top priority use cases for the business. These use cases can then be stack ranked according to:
- Data availability
- Resources required
The top use case becomes the basis for a 90-day “AI Sprint” with “proof of concept” and “proof of value” being the goals. Ideally one should strive for 10x-ers, although getting part way there should be a success. This is a way to get senior management and the board involved, track results, and create a starting point for larger implementations.
Dive in deep and take a good look around
Find out what your competitors are doing, find out what your industry is doing, find out what other industries are doing; then bring these learnings back into your own company.
Get AI connected throughout the organization, not in silos
I had one CTO proudly tell me he had cloistered 11 PhDs in a lab up in Rhode Island. Do not take this approach. Make sure AI practitioners are embedded within business units and vice versa. Get AI into the fabric of the organization where it can be informed by business domain expertise.
Realize there are no experts who know it all
There are too many dimensions, too many industries, too many applications. Find meta-experts who can create “quick assemble” teams for each application.
Give serious thought to your AI data strategy
Are you collecting the right data? Simply having enormous data warehouses and data lakes does not make this data valuable. This data could be like the old boxes of stuff we all have accumulating in our attics.
Should you “instrument” or create new data collection capabilities in the areas of operations, sales and the marketplace in order to get more of the right data? Are you monitoring your data for quality, bias, regulatory compliance, etc.? Do you have data governance in place? Perhaps most importantly, are you collecting data with a strategic mindset to the future, such that even if you do not use the data right now, it could be highly valuable in the future for various AI models including prediction?
Always keep an eye to the future
I promise you in two years the world of AI will look much different than it does now. Certain projects that currently require teams of top scientists, will be realized simply through plug-and play-platforms and “no-code AI.” Some essential data will be made available on data exchanges, and there will be a much larger team of engineers for hire who can pull all this together. Processing power using quantum computers will increase at a “doubly-exponential” rate according to Neven’s law, way beyond the speed of change predicted by Moore’s doubling law. Value creation will continue to shift from engineering to innovation and strategy as whole new types of products and business models emerge. Be forever thinking new products and new business models enabled by AI.
- Investment in AI and its adoption has increased in 2020, even during the pandemic.
- AI strategies are built on network effects so that additional data or investment can create additional value in a cycle.
- Optimal value in AI transactions is to be found via the synergy of data, analytical models, and the people working on them.
‘In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. Find something that can be made smarter by adding online smartness to it,’ writes Kevin Kelly in his book The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future. As the pace of technological development grows, artificial intelligence (AI) is changing all industries.
In the uncertainties of 2020, AI is being used by many businesses as the main tool to offer stability and to gain a competitive edge. Machine learning and AI technologies can contribute to various processes via the assistance, augmentation, and automation they provide.
It’s not only the obvious industries like finance, automotive, and healthcare that can greatly benefit from leveraging AI’s potential. During an M&A Community webinar, we discussed what stands behind the ‘AI hype’, how these technologies can create value for different industries, and what to consider during any AI-related M&A transaction.
The new spring of AI
Even though the sphere of AI went through a ‘winter’ some time ago, in terms of limited hardware capacities, this branch is now blooming again, says Bruce Molloy, CEO at Springboard.ai, diving deep into the tech industry, namely the present, and the future of the Artificial Intelligence (AI) technologies. ‘I don’t think there will be any more winters, it’s carrying on at a terrific pace because of the explosions of data, because of the chips and their GP use.’
He points out that according to IDC predictions, 25 percent of the Fortune Global 500 will gain a competitive edge from quantum computing by 2023. Since capitalizing on the laws of quantum mechanics, quantum computing is set to potentially bring degrees of transformation to certain industries. ‘This will certainly change AI, and much else,’ Molloy stresses.
A lot of progress is already being made, he adds, which has resulted in a lot of hype. However, as the Gartner research shows, the industrialization of AI platforms paves the way for the reusability, scalability, and safety of AI, which gives speed to its adoption and growth. According to a recent Gartner survey, the C-suite is steering AI projects, as nearly 30% of them are directed by CEOs. ‘Having the C-suite in the driver’s seat accelerates AI adoption, and investment in AI solutions,’ Gartner concludes.
What creates value in AI companies?
AI strategies are built around fundamental flywheels, as Dr. Anand Rao, Global AI Lead at PwC, explains. He recommends startups and corporations exploit the following network effects to create more value.
- Data Network
Effect: more data → smarter algorithms → more value → more users → more data, and so on.
- Cognitive Network
Effect: more investment → better expertise → smarter algorithms → more value → more users → lower costs, and so on.
Dr. Rao believes that the industry can boom even under the current economic conditions. ‘VC funding of AI is hitting new records despite the pandemic, the latter might even be accelerating investments in AI,’ he says. For instance, according to the PwC CB Insights MoneyTree report, total global funding in this sphere in Q3’20 has surpassed its previous milestone of Q4’18. China and India, meanwhile, are emerging as strong players in this market.
Extracting value when acquiring AI companies may be a difficult question for a potential buyer. Molloy suggests at least three ways to look at potential AI benefits for a company:
- Make processes smarter and more efficient (from targeted ads on Facebook and Google, up to making predictions in financial apps)
- Enhance current, and create new products (from personal assistants such as Alexa, up to real estate recommendation apps)
- Develop business models (from ridesharing with Uber, up to predictive retail shipping)
As AI deals include several details and nuances that make such transactions special, dealmakers should evaluate the peculiarities of the technology, as well as data of the target, Dr. Rao adds. Additionally, he says, integration plans should take into account responsible AI principles and practices in order to extract a deal’s value well after it is closed.
How businesses can look at AI transactions
Molloy suggests several options.
- Aqua-hire (Acquire-to-hire)
A solution for a company looking for talent. For instance, 5 PhD holders were offered $10M before they even had a product. The expert says that there are many examples on the market (up to $5M per employee.) Molloy recommends though to keep in mind that top skill sets are shifting and could be completely different in 5 years.
- Data & Algorithms
Here, it is all about the data and the know-how. One of the recent examples is the world-class chatbot developers who came from the Watson Jeopardy team that got snapped up by Google Hello Vera.
- Revenue and Possible Profits
Here it’s all about operating companies benefitting from artificial intelligence. This option may be especially useful for SaaS businesses to gain added value to their products with AI.
- Rapid Data and/or Customer Creation
In this case, the acquisition of an AI company may provide a ‘network effect’ to gain additional clients or datasets that can boost a business.
Optimal value in AI transactions is to be found via synergies, Molloy adds. Three components, namely the data, the models, and the people, and their interplay are what should be important for a potential buyer. ‘It is not deterministic but rather probabilistic,’ he concludes.
Market expectations concerning M&A dynamics were made null and void in 2020. However, the pessimistic outlook at the beginning of the Covid-situation seems to have been disproved. This is especially true in the tech market, where mergers and acquisitions surged in Q3. ‘Transactions in tech and telecommunications totaled $205 billion in the July through September period, nearly six times as much as in the second quarter and the second-highest quarterly level in nearly two decades’, quotes the WSJ a report from 451 Research.
There are expectations that as the global economy moves toward recovery, investors will likely be targeting technology providers. During a topical M&A Community webinar, lots of questions were asked about the outlook for the current situation as well as trends to keep in mind in the industry. Here are some of them, answered by our panelists, from the valuation of AI assets to IoT, blockchain, and ESG (Environmental and Social Governance) in business.
How to value AI assets?
Clearly, it depends heavily on the business you’re looking at. If you’ve got an early-stage company which is making money quickly, it would typically be valued by its revenue. A reasonably successful company might be valued at a six to ten times the revenue number.
If it is a really hot B2B SaaS business, it could be valued at thirty times its revenue number or even more. However, these examples are extremely rare and are actually driven by their true success.
Here, nevertheless, some cautionary words are necessary. Now, AI is often stuck on logos to make them more ‘exciting’, just as people did with ‘blockchain’ a couple of years ago. What you really need to understand clearly is how the business you’re investing in is going to make a profit over time.
The learnings over the years are that all of these businesses eventually end up being valued with reference to generating real cash profits, as companies like Google and Facebook do today.
The other question on the valuation of AI assets is data, i.e. what data a company has and how proprietary that data is. If it has data that investors are interested in, and that is not easily obtainable, it can make a company a good target.
One more factor that can impact the value of an AI company is people. It is not the data alone which is essential as you would definitely want to keep the people who can work with this data, who understand it and can build upon it with their expertise.
The third one, the ‘secret sauce’ in this recipe, would be the data science models that are able to extract valuable insights from this data.
Where is the United States on the AI curve?
There is an arms race going on, especially with China. A tremendous amount of effort and resources is going into developing AI technologies in China. In the US, some amazing technologies and innovations are still emerging in Silicon Valley. The Chinese, meanwhile, are not only copying those but also furthering the technology.
What can make a difference to the American position in this ‘competition’? One leading example is the US investment in STEM, getting kids into science and technology, into newer kinds of disciplines like data science.
Definitely, on a global scale, it is a US vs China situation, and then other countries, namely the EU, coming into play. Nevertheless, it is still a bipolar competition. Currently, most of the innovations are coming from the American side.
Now, however, there are discussions that AI has reached the stage when it’s more about engineering than science. The latter is still the US prerogative. Meanwhile, Beijing has been investing heavily in engineering, so nowadays you can see various combinations of existing technologies as well as the widespread use of them in China.
It is no wonder, given the size of the Chinese population. If you want to hit a million, ten million, a hundred million users in the Western world, it may take years. But in China, you can do it in a couple of months! That’s how fast things are moving there. So, in terms of engineering, the Chinese are probably going to be pulling ahead soon. Still, on the entrepreneurial and scientific sides, the USA has the upper hand.
At the same time, quantum computing is an area to look at. We can expect its development to ramp up in the next 2-5 years. Of course, quantum computing will not take over everything that AI is doing; there are only certain types of applications where quantum computing will be good.
What are the lifecycle issues and risks for IoT and other data-centric technologies during M&As?
Technology has broadly been an area with some spectacular disasters in M&A. One of the most ‘prominent’ of them was, perhaps, HP’s acquisition of Autonomy Corp. for $11 bn that is still rumbling through the courts.
The fundamental issues concerning M&A in IoT, or in tech in general are; how will it help you to create something that your customers value, how will it generate revenue, how will it allow you to be more cost-efficient or how can it help make your business less volatile. In other words, with all these factors to consider, it’s easy to get lost in the excitement of buying new technology.
One of the main challenges is connected to security issues. If you are a large corporation, acquiring an IoT business, it would be relatively easy for you to ensure that a system is working, but really difficult to lock it down in terms of security.
Another question, related to the IoT sphere: Are these smart sensors really so smart? There could be billions of sensors gathering and sending data at every moment, but are they really necessary to make decisions at a local level? It’s clear that you’ll need to clearly understand where and how you make decisions, and how the IoT sensors can in fact help you to do that.
How important is it for a CFO to be ESG-competent?
Indeed, ESG is becoming a really big thing now. Every CFO I know got hit with these questions through this year. In many cases, they have been coming from investment managers who don’t have their heads around this question very well yet.
Let’s put aside all of the ESG excitement and focus on the fact that there are a lot of serious underlying issues that relate to the efficiency of business operations. How does a business use the resources available to it? What kind of risks does it take? What do its relations with stakeholders look like? Which factors may impact its growth over time?
So, firstly, CFOs have to fully understand where the underlying investors are coming from. Secondly, one needs to step back from the noise and say, ‘Can we look at this in a way that will allow us to drive our business better?’ That is, instead of constantly worrying about ESG-compliance, you can make it into something you can be excited about and use it to fuel the growth of your business.
The third challenge is to realize that there are much bigger things going on. I mean, we are going through the ‘robot revolution’, in which we are automating a big slice of the tertiary economy. It is causing huge shifts in the workforce, and if you don’t manage that, it will create substantial risks for individual businesses, as well as entire economies.
What can we expect from blockchain technology?
By their definition, blockchain technologies require multiple parties to come together. It is essentially a consortium play. So, to come to a joint decision, you’ll need to unite dozens of businesses, lawyers, organizations, etc. Their agreement on some common standards is quite difficult, thus far it has been a substantial barrier to the implementation of blockchain.
Fundamentally, I believe, this technology has potential. After a surge in popularity a couple of years ago, though, it has met several challenges and slowed down.