This has been the year where many private equity firms started adopting robotic process automation at scale in their investment theses. After the first six months of the year, there are a couple of best practices emerging from our engagements in private equity:
#1 Start with finance to gain experience and bankable value
The finance function is typically the most structured. It also tends to have a lot of manual processes and repetitive tasks that are ripe for automation. It is easy to drive significant cost savings in A/R, A/P and general accounting in weeks and not years.
For most PE investment theses optimizing back-office functions is secondary to driving revenue growth and streamlining the supply chain. Therefore software robots can deliver tangible short term savings while significant transformation initiatives are underway.
#2 Align with investment thesis for highest potential
In a 3-5 year holding period, there is little time for IT experiments and proofs of concepts. Robotic value creation should focus on driving EBITDA growth quickly and support the key pillars of the investment thesis.
With 40-60% typical savings in back-office and 20-30% in the front office, RPA has become a great value driver for PE firms. In fact, firms with very different investment styles have integrated RPA value creation into their overall theses:
- Turnaround firms managed to reduce repetitive back-office processes
- Carveout firms that wanted to keep the spinoff entity costs contained adopted a bot first strategy in building up the workforce
- In growth equity, we’ve seen bots used to contain back-office headcount growth in check as the revenue line expanded
- In rollups, the overlapping functions can be standardized and optimized with functional bots like digital finance clerks and data entry specialist
#3 Speed over perfection
Management teams often want to finish perfecting business processes. Often those projects started years ago with ERP and are still unfinished. Robotic value creation should be all about speed, not perfection
It is easy to change the bots as processes evolve or change compared to the herculean effort of enterprise system changes. Consequently, many firms now look at RPA to help avoid the cost and disruption of major ERP upgrades or IT transformation.
While many IT projects fail to deliver measurable value, most PE firms find RPA a clear example of short term, tangible IT value creation.
#4 Use consulting partners in doing not thinking
PE firms want consultants to focus on getting the results vs thinking about them. Digital transformation consulting projects are plagued with endless proofs of concepts or process evaluations that add little tangible value.
Leading PE firms and portfolio companies focus on delivering several smaller pilots with measurable EBITDA gains. For the price of a process design workshop, dozens of bots can be up and running
#5 Maximize value in holding period
In the 4-5 year holding period robotic value creation projects can be a major value contributor especially in SG&A and contact centers. Automation projects should be rinse and repeat with EBITDA gains delivered quarter after quarter.
The best practice calls for a small core team who knows how to drive value over and over again. PE firms tell me that it is critical not to leave too much money on the table for the next buyer.
Robotic process automation (RPA) also called Digital Workforce (DWF) software takes the mundane repetitive tasks and automates them to free up human workforce for more fulfilling jobs. Increasingly the types of tasks move beyond data entry and reconciliation to more cognitive tasks like process analysis, reporting, and even basic decision making that fit predefine tolerance levels. In the process, some companies reassign part of their human workers and establish a digital workforce. Imagine telling your digital assistant, “Alexa, please close the books, analyze overdue customers and initiate collections, prepare the report package for the board of directors, we’re at capacity so decide which customer order to delay”.
The most advanced automation happens in industries with experience in shared services, outsourcing and offshoring as that required them to displace and replace workforces and document processes well. The digital workforce leaders are in healthcare, telcos, financials and professional services and even manufacturing and the traditionally IT-savvy tech industry is a laggard in comparison.
Many private equity firms have focused on digital initiatives for a while with bankable results in areas like omnichannel commerce, online auctions, predictive analytics in sales and operations. Some of those digital initiatives took years to mature and often the ultimate gains in enterprise value ended up benefiting the next owners. Broad digital transformation is better suited in certain investment styles (growth equity) and does not have the short term benefits needed for others (turnaround, carveouts) or later in the investment cycle.
What is attractive about digital workforce or software based automation, is that projects are done in 3-4 months and EBITDA gains accrue in 9-12 months. This allows more flexibility in various investment styles and theses to incorporate automation as part of the main thesis elements or a side-car to the thesis (more on this below).
Working with firms with various approaches we have seen the following best practices:
- Establish digital workforce targets in due diligence then roll those targets to specific automation projects in the 100-day plan
- Use RPA as value accelerator for broader process optimization projects. While streamlining revenue lifecycle management in a healthcare portfolio company which will take years, you can partially fund the project with RPA-based savings by automating manual processes like claims entry, insurance coding, collections, etc. Some firms call it a side-car to an operating improvement element in the core thesis.
- Use RPA to generate PE-holding specific reporting requirements in the portfolio company like 13-week cash flow or debt reporting. There can be a digital worker (RPA bot) deployed to every new acquisition and start preparing board packages and reports. There is no need to change their ERP system or create massive manual processes in Excel
- Use bots to gather operational data not provided in underlying systems. This is especially true for a portfolio transitioning to Lean/Six Sigma. The cost of upgrading underlying ERP and MES systems could be cost-prohibitive. The bots can do data collection and analytics work.
- In a carveout or rollup, the new entity has to consolidate data from endless systems. Bots can accelerate data migration, data consolidation and even move data between systems. This can delay the need for a major IT consolidation project especially in later years of the holding period. The payback on IT consolidation tends to point outside the holding period.
- Containing SG&A growth in both growth equity and carveout is also an issue. Inefficient processes force back-office cost growth in line or above the top-line growth of the business eroding EBITDA gains. Deploying bots for new backoffice (finance, admin, HR, procurement) jobs can curtail the cost growth.
- Job requisitions – go digital first. Another lean digital workforce practice is to go digital workforce first for new job requirements. Do you need an analyst? It can be a bot. Do you need an accountant? Can probably be a bot. Research shows 40-60% job tasks can be automated. It is a lot easier to automate jobs that have not been filled yet, than to reassign existing staff.
Automation is a rapidly evolving field with PE portfolios demonstrating major impact on EBITDA gains and enterprise value. As the industry matures, benchmarking will evolve to set realistic targets and comparables to better inform PE teams and their investment theses.
Planning is a risk reduction tool, not a value creation one. To create value, you have to have real commercial interactions in the market. Market feedback better informs strategy than brainstorming sessions ever will, yet companies may pay millions for strategy consultants but not the same amount for limited product experiments.
A friend of mine has been a technology investor all his life. He has a particular passion seeing startups take ideas, turn them into minimum viable products (MVP), test them in real markets and pivot as needed for the next iteration of products and services. Getting ideas out of the back-office and into the market quickly, as he would put it. He has guided such agile startups in industries ranging from software through fintech to telco equipment in both the US and China.
Whenever his companies “disrupt” incumbent competitors he admits to always being amazed that bigger rivals with better access to capital, talent, and channels are often incapable of mounting timely defenses in the market.
His observation is that there is a fundamental difference in how startups vs big companies perceive market strategy and more importantly how they spend their time when innovating. Big companies spend 80% of their time planning and strategizing and 20% actually trying products in markets. Startups are the opposite, most of the effort is releases and pivots with real customers and products.
For example, Tencent in China is famous for such iterative approach by funding and incubating mobile startups, encouraging them to launch products early and often and keep pivoting the product, pricing, promotions until the adoption reaches a certain threshold, say 5 or 10 million active users. If it does not, the product is shelved after a certain number of pivots. Early customers are actually funding later experiments and become customers for the future launches of similar products. It becomes a virtuous cycle. He believes this mindset is followed in many larger Chinese companies which is the reason they are moving so much faster in AI, blockchain, automation, fintech and other areas than their European or US counterparts.
No such iterative value creation happens in planning, proofs of concepts, strategy retreats and similar endeavors devoid of real, commercial market feedback. Planning is useful to avoid risk and traps but should be limited to a small portion of innovation time and resources.
Build, ship, get feedback and adjust the approach until the customer’s need is met. That’s value creation and it has always been. There is no ROI on planning alone.
At least that’s what my friend convinced me of.
I had an interesting and unexpected debate on the sidelines of a private equity conference last week in London. It was fascinating because the topic was automation and the protagonist, a hedge fund manager.
For years there have been arguments in various corners of digital advocacy about the future of digital enterprises comprised of mostly automated (doing the work of humans) and even autonomous (working completely without humans) businesses. We have seen this in the blockchain world with decentralized autonomous organizations (DAOs) based on smart contracts but nothing yet mainstream. The DAOs referred to humans as “oracles”, necessary to perform specific tasks in the physical world, like load a pallet, underwrite an insurance policy or raise investment funds. Although some argue, those functions can be eventually automated too.
My friend argued that the core business of hedge funds (trading) is already 80% automated, and the rest can be completely automatic. What’s left for humans is the “strategy bit,” like the investment thesis. On the other hand, traditional businesses, like manufacturing, services, and even technology, are laggards with less than 30-40% of their core business automated.
The most exciting segment of automation is about augmenting human performance. An oil & gas investor at the same event told me that they wanted their high priced engineers freed up from filing documents. A hospital chain wants its physicians ultimately augmented with robots that can take care of patient reports, insurance coding, and even preliminary diagnosis. A strategy firm wants its consultants to be 100% customer facing and file engagement status updates and expense reports with bots.
The brave new world is imminent, and as some of the early adopters of augmented robots have seen, augmented businesses will always outperform traditional ones both in EBITDA and in customer and employee engagement.
In recent years many PE operating groups noticed that acquired companies already had better processes and leaner operations than a decade earlier, especially in larger enterprises. While sourcing programs and improved sales effectiveness still provide benefits, many targets already have good procurement disciplines and most run a form of CRM or various e-commerce tools. More importantly it was getting harder to get step-change improvements with gains beyond 10-20% in most operational areas.
It became clear that the next frontier of operational value creation lied in digital, but consultants and technology vendors rarely addressed the rigorous value creation needs of PE firms. The order of magnitude improvements in effectiveness and efficiency were often anecdotal and some bordered on hyperbole. Yet the results for many are real and bankable with short term dramatic increase in EV.
So where to look for the biggest opportunities for short term, mature, digital value creation technologies?
Efficiency Based Digital Value Creation:
- Robotic Process Automation – HIGH value, LOW disruption, 3-9 months
- I believe robotic process automation and related cognitive AI may become the biggest value creator for PE firms in the next 10 years until labor efficiency gains taper off. The technology can be easily aligned with the investment thesis to focus on one part of the operation or scale across the business. While the focus is on labor efficiencies, RPA reduces errors and compliance risk and can increase customer service. The biggest challenge is the plethora of vendors and as a result some technologies becoming obsolete
- Predictive Analytics for Operations, Predictive Maintenance – MODERATE value, LOW disruption, 6-9 months
- Anyone with manufacturing and complex supply chain will get a boost from such tools. Many operational decisions like monitoring and anomaly detection, root cause analysis can be automated to prevent stoppage and waste. Predictive customer analytics especially consumer focused businesses provide better management of churn, attrition, analysis of customer choices but also related credit risk or anticipated reverse logistics costs. In all these areas value creation is easy to measure and with many vendors can be built in the tools themselves.
- Cloud migration – MODERATE value, HIGH disruption, 12-18 months
- Just get it over with it if you can do it early in the holding period. Cloud technologies are over a decade old, well established and mature. There is absolutely no reason for anyone to hug their own servers. If cloud vendors are secure enough for governments they should work for portfolio companies. There are endless migration and integration tools and providers. Of all digital transformations, this is the most painful to complete and therefore most companies will procrastinate.
Growth and Risk Avoidance Based Digital Value Creation:
- Digital Commerce – MODERATE growth value but sales/marketing is plagued with growth attribution problem
- Growth is clearly a major driver in investment theses and commerce tools absolutely have tangible benefits. They SHOULD be considered for any value creation plan. Many times however they end up falling short due to the business model change required to maximize the value ranging from customer segmentation, through channel conflicts to new compensation plans. Many projects end up with a commerce channel to existing sales and marketing functions and rarely create the multichannel digital interaction this innovation inspires to be and the related value it could create.
- IoT – MODERATE Efficiency value, Risk Avoidance value HIGH at full manufacturing capacity
- While the eventual value of IoT may match the hype, the complexity of broad IoT initiatives are hampered with system interoperability issues and complex projects. There are great use cases in manufacturing in environments running at full capacity like semiconductors or other flow manufacturing. Some portfolio companies also get quick IoT wins in predictive maintenance described above but broader supply chain to manufacturing transformation typically point outside the investment horizon or risk tolerance of PE boards
- Blockchain – Risk Avoidance and Efficiency Results Hard to Measure
- Most firms struggle with enterprise blockchains due to the nature of having to form or join consortia to maximize value. I see few blockchain projects initiated in PE portfolios. I explore blockchain value strategies in my other blog, Blockchain Farm.
The number of public companies have declined 20% in the last 10 years. This trend continues as more companies go private. The number of private equity deals are at an all time high and so are valuations. There is a massive amount of dry powder waiting to be invested as pension funds keep pouring more money into private equity to boost their own lagging returns. The total private equity industry assets in the US are now equal 20% of the S&P valuation.
High valuations and endless capital put pressure on PE firms to invest in more and larger businesses and expand value creation strategies. Firms are adding even more operating partners, technology experts and industry advisors. The PE funds are using their industry and operational expertise to target more and larger carveouts from conglomerates. The complexity of such divestitures require more sophisticated technology tools. Also there is an increasing number of business rollups in industries where the firms have experise. Rollups tend to shape new business models and leverage digital technology even more than traditional buyouts. As a result, PE firms are expanding their use of technology in value creation strategies beyond backoffice, sourcing and reporting tools of the past.
There is a push to digitize all back office and put those processes in the cloud. There is a change in the use of analytics with the focus more on predictive analytics and not on reporting. Most firms have a cybersecurity initiative, sometimes even mandating security practices and tools to the portfolio companies to reduce business risk. Many firms are even experimenting with using machine learning and artificial intelligence especially in supply chain, maintenance and customer interactions. There is a belief that digitizing business models more predictably increase exit valuations than other operating strategies.
Technology partnerships rarely fail due to lack of technical or project management skills. Having spend most of my career in business development (partnership development) roles, I noticed certain patterns beyond the textbook answers on success of strategic partnerships. I hope noone needs to be reminded any more for the need of an executive sponsor or a governance process as advisors still often do. I want to highlight the nuances in leadership and execution that I believe make technology partnerships work. The scope of partnerships range widely from co-marketing and unspecific alliances through co-development or joint ventures to acquisitions and mergers. Regardless of these vehicles certain success elements are common to all.
Business development should create long term value through non-core relationships. Having a partner to deliver the same product or service to the same customer segments you are already engaged with is not really a partnership but outsourcing. True partnerships open avenues to operate outside the current swimlanes, customer influences, segments and product categories of one (or both) of the businesses.
The fundamental truth is that business development cannot be managed or measured the same way as sales is. But it should be managed and measured with similar rigor and business outcome orientation.
So here is the executive summary of what I have learned in my years in business development:
- Make sure business partners have common objectives – Most partnerships have several unrealistic or unstated expectations on either side. Unless both parties are aware of those critical expectations it is impossible to attain success that meets both side’s goals. These discussions have to be explicit and needless to say, both sides have to win given the same business outcomes. Ideally key program executives on both sides should have some of these outcomes in their compensation plans. It cannot be overstated enough that both sides must win and must win often for partnerships to survive.
- Select the right partnership format for any given goal – The financial and resource needs of a longer term co-marketing effort vs a product co-development are very different and clearly produce different business results (mind-share and sales leads vs tangible viable products that may or may not succeed). In my experience and probably rightfully so, most early partnerships are under-resourced for the stated objective. There is nothing wrong with starting small if the expected results are small as well. On the other hand, some partnerships start with lofty goals, due diligence and joint venture, which is fine as long the the expected failure rate is baked in the calculus (be a venture capitalist vs a cash flow oriented investor).
- Balance technical, commercial and program management skills in your partnership team – Most partnership teams are biased towards certain skills, either too technical and end up with technically viable but commercially poor results or too commercial and expect bankable revenues in pilot stages of a partnership. Balancing the roles in different stages of the partnership from concept (technical) through commercial due diligence through execution stacks the odds of success. By the way, never expect the core business to divert resources to step in to augment shortcomings in BD skills.
- Adopt a startup mentality: Fail early and Pivot often – I believe every business development team should think like a Lean Startup. Leaders should remember that noone knows what the ultimate business model will evolve into so experimentation will be key. Once the team understands the goals they should know it is OK to fail and course correct.
- Partnerships should be structured by executives – It is fine to have executive sponsors, but not enough. Most partnerships are structured and operated at a level in the organization that cannot divert the necessary resources, make commercial or technical roadmap changes or tradeoffs. Making all issues an executive escalation is a sure way to slow down or kill the partnership. The radical point here is that partnerships must be formulated by key executive teams and once they are up and running can be handed off to operational and sales managers for proper execution. The criteria for the right executive level is that the person can make the technical or commercial trade-offs necessary and have the backing of the board.
- Partnerships should have clear metrics and accountability from day one – However, leadership style, metrics and accountability should match the stage of the program. Early stage (Pilot Phase) the key metrics are there to encourage fail-early type pivoting or getting sufficient market feedback. Once key pilot projects are completed (Standardization Phase) the KPIs may become adherence to process metrics or adoption of best practices. Once the partnership is operational across the full scope (Scale Phase) the metrics should start looking like core business metrics (pipeline, revenue, NPS, delivery time etc) with the understanding that partnerships are typically outperforming the core in growth metrics and underperforming in efficiency (lower sales productivity or profitability initially).
- Process first, ideas second – It is easy to fall in love with a business idea. Before partnerships are formed, each party should be clear on what success looks like in each of the above stages and when course corrections are needed and yes, when the plug needs to be pulled to redirect resources.
- Contracts should include dedicating the necessary resources – Most contracts deal with IP issues and ownership of the commercial benefits of the partnerships and tend to neglect what each party has to keep doing in the early days of the program. It is very hard to start the project when results are unclear, it is easy to share the spoils once it is up and running, Contracts that specify expectations of people, executive time and even internal and external PR visibility of the effort will increase likelihood that the core business will support the partnership and not detail it.
- The core business will not understand or support the partnership in the early days and that’s OK – BD efforts are a distraction to the core business because they divert attention or resources from daily execution and do not enhance core metrics short term. The great book “The Other Side of Innovation” summarizes a wealth of research on this and I observed them all. Suffice to say, any support from the core should be pre-negotiated and appreciated when it happens.
- Brands do not partner, people do – In my experience it is a very critical realization. Having people in the BD team who like collaborating with others will be critical. It starts with the CEOs but true at all levels. Leadership teams should ensure the team fit and encourage collaboration.
I believe business development is the best growth engine in any company to bring ideas to market. All new innovation will need partners and all partnerships are different. The alternatives to successful business development are very costly whether it is decling revenues, lost marketshare or the excess cost of acquiring innovation we could develop ourselves through effective partnerships.
2017 was another watershed year in Private Equity. Almost all the buyout fund capital growth happened in megafunds ($5B and above) and valuations continued to rise to record heights (15 times EBITDA). There ismore capital available than attractive targets at those valuations. At the same time strategic buyers got more active and also introduced record level buying in corporate venture funds. All of this reinforced the trend that operational value creation continues to be essential not only for growing enterprise value but also winning the deals in the first place. Investment theses get more aggressive in their assumptions for not only operational improvements but also fundamental business model changes especially through digital.
Several of these trends impacted how we at SAP support the firms. Some of the trends impacting technology providers for PE value creation are;
- More sophisticated analytics and modeling especially in sales and spend analytics, even early adoption of AI and machine learning in investment theses
- PE focuses even more on top management talent even more especially in finance and sales. Whatever technology can enable star executives will be critical for growth
- Several firms adopting Digital First to drive business model change first and operational changes later (especially in retail, technology, consumer and manufacturing)
- Carveouts and add-ons continue to grow and require more technology integration and simplification than traditional buyouts
- The number of PE firms doubled since 2010 to above to 8,000 and the pressure to differentiate with technology increases
- Over 50% of companies are held for over 5 years which allows time for technology transformation
- Majority of exits are now to strategic buyers who value technology and process more
- Some firms (Blackstone, Carlyle) launched 10+ year funds and allow more time for transformation
The above trends coupled with the increasing ownership of the economy by private equity may bring in a golden age of digital transformation in PE.
For a good decade now most PE operating teams have gotten very savvy leveraging technology to enhance the investment thesis. Most started by predictive spend analytics to consolidate suppliers, improve contract performance and in general better understand drivers of drain on EBITDA. Depending on the scope of the operating team, many got deeper into the same analytical rigor and predictive models for direct and indirect sales modeling, pricing strategies and even various correlations between capital and operating investments and sales performance. Then the more complex areas of the business came to the fore, designing more agile manufacturing processes and distribution networks.
The true innovation of private equity is not that they solve existing problems better. It is that they solve it much faster and with surgical precision. While large public companies would have hundreds of competing “programs” with five- or 10-year expected returns, PE projects are much fewer and definitely shorter horizon. That’s where technology helped. It is a predictable and repeatable component of change in incremental process improvement. And Private Equity likes to repeat what works.
Something new is emerging at the best PE Operating shops. As private company valuations are at a sustained high level despite public market relief, digital technology is increasingly used as a key component of an updated business model. In the past, in investment theses technology was there to keep track of things or automate the mundane. The realization is there that the most valuable companies have a fundamentally different, digital process based business model from the lower valued peers. These trends have been very strong in China where cash flow based valuations assumptions could not justify exist value expectations, so they focused on public market valuation drivers especially in tech.
As digital disruption as an investment thesis component settles into the PE playbook we will see many more examples of such transformations as APAX Partners taking a traditional publisher and turning their business model into an ecommerce powerhouse (Autotrader UK).
We recently completed some analysis of enterprise software purchasing trends in mid-market and large enterprise businesses. We looked at industry data from the last 7 years and came to a few interesting insights. Private Equity has been known to be conservative in IT investments. As our data shows, as PE becomes a larger part of the economy, they also shift more to cloud solutions in their software choices.
Here are a couple of insights shown in the graph. (Company specific data was removed to show trend)
- Private Equity owns a bigger portion of midmarket and enterprise-size businesses in the US. Industry estimates range from 20-25% in midmarket ($100-500M revenues) and 8-12% in enterprise ($500M and above). We found no analysis on EBITDA basis but assume similar ratios.
- PE operating teams are expanding to focus beyond sales growth and spend management into enterprise IT projects
- As the number of PE portfolio companies grow, we saw the growth rate of PE portfolio driven projects far exceed the growth of non-PE owned company purchases
- This accelerates the trend of more surgical, function-specific cloud projects driven by the investment theses vs enterprise-wide transformations
- The growth of functionally focused projects actually accelerate digital transformation of purchasing, talent management and digital commerce.