Personal blog of Yzmir Ramirez

What is dual password in MYSQL and how it works was already covered by my colleague Brian Sumpter in Using MySQL 8 Dual Passwords.

However, let me do a brief recap here about it.

Dual password is the MySQL mechanism that allows you to keep two passwords active at the same time. This feature is part of a more extended set of password management features implemented in MySQL 8 to enforce better security and secrets management, like:

• Internal Versus External Credentials Storage
• Password Expiration Policy
• Password Reuse Policy
• Password Verification-Required Policy
• Dual Password Support
• Random Password Generation
• Failed-Login Tracking and Temporary Account Locking

The most important and requested features are the password expiration and verification policy. The problem in implementing them is the complexity of replacing passwords for accounts on very large platforms, like with thousands of applications and hundreds of MySQL servers. 

In fact, while for a single user it is not so complex to change his own password when requested at login, for an application using thousands of sub-services it may require some time. The problem in performing the password change is that while executing the modification some services will have the updated password while others will still use the old one. Without dual password, a segment of nodes will receive error messages in connecting creating service disruption. 

With dual password, it is instead possible to declare a new password keeping the old still active until the whole upgrade has been completed. 

This highlights two very important aspects:

• When automating the password update, it is better to not use a password expiration policy, but base the expiration on the completion of the new password deployment.
• We need to be sure the account we are changing the password to keeps the password active until we need it, and that is correctly removed when done. 

As you see I am focusing on the cases when we have automation and not a single interactive user update. 

How dual password works

Let us assume we have created a user like:

create user dualtest@'192.168.4.%' identified by 'password1';
grant all on test.* to dualtest@'192.168.4.%';

This will generate an entry in MySQL mysql.user table as:

(root@localhost) [(none)]>select user,host, plugin, authentication_string, password_last_changed,User_attributes from mysql.user where user = 'dualtest'\G
*************************** 1. row ***************************
                 user: dualtest
                 host: 192.168.4.%
               plugin: mysql_native_password
authentication_string: *668425423DB5193AF921380129F465A6425216D0
password_last_changed: 2022-11-17 08:31:37
      User_attributes: NULL
1 row in set (0.00 sec)

At this point, our user will be able to connect from any application located in the correct network and act on the test schema. 

After some time, you as the application owner will be notified by your DBA team that the user dualtest is required to change the password in order to respect the security constraints.

Now, there are two options:

1. You have privileges to use dual password (the required dynamic privilege to use dual password is APPLICATION PASSWORD ADMIN).
2. You do not have the right privileges.

In option two, your DBA team must perform the change for you, and then they will let you know the new password.

In option one, you can do the operation yourself. 

In the last case, what you will do is:

ALTER USER 'dualtest'@'192.168.4.%' IDENTIFIED BY 'password2' RETAIN CURRENT PASSWORD;

Then check it is done properly:

select user,host, plugin, authentication_string, password_last_changed,User_attributes from mysql.user where user ='dualtest' order by 1,2\G
*************************** 1. row ***************************
                 user: dualtest
                 host: 192.168.4.%
               plugin: mysql_native_password
authentication_string: *DC52755F3C09F5923046BD42AFA76BD1D80DF2E9
password_last_changed: 2022-11-17 08:46:28
      User_attributes: {"additional_password": "*668425423DB5193AF921380129F465A6425216D0"}
1 row in set (0.00 sec)

As you can see here the OLD password has been moved to the User_attributes JSON field that is used in MYSQL8 to store several values. 

At this point, you can safely roll out the password change and that change can take an hour or a week, with no production impact given the applications will be able to use either of them. 

Once the process is complete, you can ask your DBA team to remove the OLD password, or do:

ALTER USER 'dualtest'@'192.168.4.%' DISCARD OLD PASSWORD;

Then check if the password has been removed properly:

(root@localhost) [(none)]>select user,host, plugin, authentication_string, password_last_changed,User_attributes from mysql.user where user ='dualtest' order by 1,2\G
*************************** 1. row ***************************
                 user: dualtest
                 host: 192.168.4.%
               plugin: mysql_native_password
authentication_string: *DC52755F3C09F5923046BD42AFA76BD1D80DF2E9
password_last_changed: 2022-11-17 08:46:28
      User_attributes: NULL
1 row in set (0.00 sec)

If all is clean the process can be considered complete. 

Of course, all this should be automated and executed by code and not by hand, at the high level it should be more or less like this:

{}Input new password  
- Check for additional_password in User_attributes in mysql.user
<> If no value you can proceed otherwise exit (another change is in place) 
 - Read and store authentication_string for the user you need to change password
 - Change current password with: Alter user ... RETAIN CURRENT PASSWORD
 - Check for additional_password in User_attributes in mysql.user
<> If value is present and match the password stored then you can proceed otherwise exit given there is an error in Dual Password or the passwords are different
 - Run update on all application nodes, and verify new password on each application node 
<> At regular interval check the number of completed changes and check the additional_password in User_attributes in mysql.user to be sure it is still there
[] When all application nodes are up to date 
<> If verification is successful 100%
 - Remove OLD password with: ALTER USER ... DISCARD OLD PASSWORD; 
 - Check for additional_password in User_attributes in mysql.user
<> If no value is present close with OK otherwise report Error for password not removed
() complete

Conclusion

As also Brian mentioned in his post, those are the small things that could make the difference when in large deployments and enterprise environments. Security is a topic that very often is underestimated in small companies or start-ups, but that is wrong, security operations like password rotation are crucial for your safety. 

It is nice to see that MySQL is finally adopting simple but effective steps to help DBAs to implement proper procedures without causing production impact and without the need to become too creative. 

References

Using MySQL 8 Dual Passwords

https://dev.mysql.com/doc/refman/8.0/en/password-management.html#dual-passwords

Work insights platform Fin raised $20 million in Series A funding and brought in Evan Cummack, a former Twilio executive, as its new chief executive officer.

The San Francisco-based company captures employee workflow data from across applications and turns it into productivity insights to improve the way enterprise teams work and remain engaged.

Fin was founded in 2015 by Andrew Kortina, co-founder of Venmo, and Facebook’s former VP of product and Slow Ventures partner Sam Lessin. Initially, the company was doing voice assistant technology — think Alexa but powered by humans and machine learning — and then workplace analytics software in 2020. You can read more about Fin’s origins at the link below.

The new round was led by Coatue, with participation from First Round Capital, Accel and Kleiner Perkins. The original team was talented, but small, so the new funding will build out sales, marketing and engineering teams, Cummack said.

“At that point, the right thing was to raise money, so at the end of last year, the company raised a $20 million Series A, and it was also decided to find a leadership team that knows how to build an enterprise,” Cummack told TechCrunch. “The company had completely pivoted and removed ‘Analytics’ from our name because it was not encompassing what we do.”

Fin’s software measures productivity and provides insights on ways managers can optimize processes, coach their employees and see how teams are actually using technology to get their work done. At the same time, employees are able to manage their workflow and highlight areas where there may be bottlenecks. All combined, it leads to better operations and customer experiences, Cummack said.

Graphic showing how work is really done. Image Credits: Fin

Fin’s view is that as more automation occurs, the company is looking at a “renaissance of human work.” There will be more jobs and more types of jobs, but people will be able to do them more effectively and the work will be more fulfilling, he added.

Particularly with the use of technology, he notes that in the era before cloud computing, there was a small number of software vendors. Now with the average tech company using over 130 SaaS apps, it allows for a lot of entrepreneurs and adoption of best-in-breed apps so that a viable company can start with a handful of people and leverage those apps to gain big customers.

“It’s different for enterprise customers, though, to understand that investment and what they are spending their money on as they use tools to get their jobs done,” Cummack added. “There is massive pressure to improve the customer experience and move quickly. Now with many people working from home, Fin enables you to look at all 130 apps as if they are one and how they are being used.”

As a result, Fin’s customers are seeing metrics like 16% increase in team utilization and engagement, a 25% decrease in support ticket handle time and a 71% increase in policy compliance. Meanwhile, the company itself is doubling and tripling its customers and revenue each year.

Now with leadership and people in place, Cummack said the company is positioned to scale, though it already had a huge head start in terms of a meaningful business.

Arielle Zuckerberg, partner at Coatue, said via email that she was part of a previous firm that invested in Fin’s seed round to build a virtual assistant. She was also a customer of Fin Assistant until it was discontinued.

When she heard the company was pivoting to enterprise, she “was excited because I thought it was a natural outgrowth of the previous business, had a lot of potential and I was already familiar with management and thought highly of them.”

She believed the “brains” of the company always revolved around understanding and measuring what assistants were doing to complete a task as a way to create opportunities for improvement or automation. The pivot to agent-facing tools made sense to Zuckerberg, but it wasn’t until the global pandemic that it clicked.

“Service teams were forced to go remote overnight, and companies had little to no visibility into what people were doing working from home,” she added. “In this remote environment, we thought that Fin’s product was incredibly well-suited to address the challenges of managing a growing remote support team, and that over time, their unique data set of how people use various apps and tools to complete tasks can help business leaders improve the future of work for their team members. We believe that contact center agents going remote was inevitable even before COVID, but COVID was a huge accelerant and created a compelling ‘why now’ moment for Fin’s solution.”

Going forward, Coatue sees Fin as “a process mining company that is focused on service teams.” By initially focusing on customer support and contact center use case — a business large enough to support a scaled, standalone business — rather than joining competitors in going after Fortune 500 companies where implementation cycles are long and there is slow time-to-value, Zuckerberg said Fin is better able to “address the unique challenges of managing a growing remote support team with a near-immediate time-to-value.”

As artificial intelligence continues to weave its way into more enterprise applications, a startup that has built a platform to help businesses, especially non-tech organizations, build more customized AI decision-making tools for themselves has picked up some significant growth funding. Peak AI, a startup out of Manchester, England, that has built a “decision intelligence” platform, has raised $75 million, money that it will be using to continue building out its platform, expand into new markets and hire some 200 new people in the coming quarters.

The Series C is bringing a very big name investor on board. It is being led by SoftBank Vision Fund 2, with previous backers Oxx, MMC Ventures, Praetura Ventures and Arete also participating. That group participated in Peak’s Series B of $21 million, which only closed in February of this year. The company has now raised $119 million; it is not disclosing its valuation.

(This latest funding round was rumored last week, although it was not confirmed at the time and the total amount was not accurate.)

Richard Potter, Peak’s CEO, said the rapid follow-on in funding was based on inbound interest, in part because of how the company has been doing.

Peak’s so-called Decision Intelligence platform is used by retailers, brands, manufacturers and others to help monitor stock levels and build personalized customer experiences, as well as other processes that can stand to have some degree of automation to work more efficiently, but also require sophistication to be able to measure different factors against each other to provide more intelligent insights. Its current customer list includes the likes of Nike, Pepsico, KFC, Molson Coors, Marshalls, Asos and Speedy, and in the last 12 months revenues have more than doubled.

The opportunity that Peak is addressing goes a little like this: AI has become a cornerstone of many of the most advanced IT applications and business processes of our time, but if you are an organization — and specifically one not built around technology — your access to AI and how you might use it will come by way of applications built by others, not necessarily tailored to you, and the costs of building more tailored solutions can often be prohibitively high. Peak claims that those using its tools have seen revenues on average rise 5%, return on ad spend double, supply chain costs reduce by 5% and inventory holdings (a big cost for companies) reduce by 12%.

Peak’s platform, I should point out, is not exactly a “no-code” approach to solving that problem — not yet at least: It’s aimed at data scientists and engineers at those organizations so that they can easily identify different processes in their operations where they might benefit from AI tools, and to build those out with relatively little heavy lifting.

There have also been different market factors that have played a role. COVID-19, for example, and the boost that we have seen both in increasing “digital transformation” in businesses and making e-commerce processes more efficient to cater to rising consumer demand and more strained supply chains have all led to businesses being more open and keen to invest in more tools to improve their automation intelligently.

This, combined with Peak AI’s growing revenues, is part of what interested SoftBank. The investor has been long on AI for a while; but it also has been building out a section of its investment portfolio to provide strategic services to the kinds of businesses in which it invests.

Those include e-commerce and other consumer-facing businesses, which make up one of the main segments of Peak’s customer base.

Notably, one of its recent investments specifically in that space was made earlier this year, also in Manchester, when it took a $730 million stake (with potentially $1.6 billion more down the line) in The Hut Group, which builds software for and runs D2C businesses.

“In Peak we have a partner with a shared vision that the future enterprise will run on a centralized AI software platform capable of optimizing entire value chains,” Max Ohrstrand, senior investor for SoftBank Investment Advisers, said in a statement. “To realize this a new breed of platform is needed and we’re hugely impressed with what Richard and the excellent team have built at Peak. We’re delighted to be supporting them on their way to becoming the category-defining, global leader in Decision Intelligence.”

It’s not clear that SoftBank’s two Manchester interests will be working together, but it’s an interesting synergy if they do, and most of all highlights one of the firm’s areas of interest.

Longer term, it will be interesting to see how and if Peak evolves to extend its platform to a wider set of users at the organizations that are already its customers.

Potter said he believes that “those with technical predispositions” will be the most likely users of its products in the near and medium term. You might assume that would cut out, for example, marketing managers, although the general trend in a lot of software tools has precisely been to build versions of the same tools used by data scientists for these less technical people to engage in the process of building what it is that they want to use.

“I do think it’s important to democratize the ability to stream data pipelines, and to be able to optimize those to work in applications,” Potter added.

UiPath came seemingly out of nowhere in the last several years, going public last year in a successful IPO during which it raised more than $527 million. It raised $2 billion in private money prior to that with its final private valuation coming in at an amazing $35 billion. UiPath CEO Daniel Dines will be joining us on a panel to discuss automation at TC Sessions: SaaS on October 27th.

The company has been able to capture all this investor attention doing something called robotic process automation (RPA), which provides a way to automate a series of highly mundane tasks. It has become quite popular, especially to help bring a level of automation to legacy systems that might not be able to handle more modern approaches to automation involving artificial intelligence and machine learning. In 2019 Gartner found that RPA was the fastest growing category in enterprise software.

In point of fact, UiPath didn’t actually come out of nowhere. It was founded in 2005 as a consulting company and transitioned to software over the years. The company took its first VC funding, a modest $1.5 million seed round, in 2015, according to Crunchbase data.

As RPA found its market, the startup began to take off, raising gobs of money, including a $568 million round in April 2019 and $750 million in its final private raise in February 2021.

Dines will be appearing on a panel discussing the role of automation in the enterprise. Certainly, the pandemic drove home the need for increased automation as masses of office workers moved to work from home, a trend that is likely to continue even after the pandemic slows.

As the RPA market leader, he is uniquely positioned to discuss how this software and other similar types will evolve in the coming years and how it could combine with related trends like no-code and process mapping. Dines will be joined on the panel by investor Laela Sturdy from CapitalG and ServiceNow’s Dave Wright, where they will discuss the state of the automation market, why it’s so hot and where the next opportunities could be.

In addition to our discussion with Dines, the conference will also include Databricks’ Ali Ghodsi, Salesforce’s Kathy Baxter and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.

Buy your pass now to save up to $100. We can’t wait to see you in October!

Is your company interested in sponsoring or exhibiting at TC Sessions: SaaS 2021? Contact our sponsorship sales team by filling out this form.

Over the last couple of years, robotic process automation or RPA has been red hot with tons of investor activity and M&A from companies like SAP, IBM and ServiceNow. UIPath had a major IPO in April and has a market cap over $30 billion. I wondered when Salesforce would get involved and today the company dipped its toe into the RPA pool, announcing its intent to buy German RPA company Servicetrace.

Salesforce intends to make Servicetrace part of Mulesoft, the company it bought in 2018 for $6.5 billion. The companies aren’t divulging the purchase price, suggesting it’s a much smaller deal. When Servicetrace is in the fold, it should fit in well with Mulesoft’s API integration, helping to add an automation layer to Mulesoft’s tool kit.

“With the addition of Servicetrace, MuleSoft will be able to deliver a leading unified integration, API management and RPA platform, which will further enrich the Salesforce Customer 360 — empowering organizations to deliver connected experiences from anywhere. The new RPA capabilities will enhance Salesforce’s Einstein Automate solution, enabling end-to-end workflow automation across any system for service, sales, industries, and more,” Mulesoft CEO Brent Hayward wrote in a blog post announcing the deal.

While Einstein, Salesforce’s artificial intelligence layer, gives companies with more modern tooling the ability to automate certain tasks, RPA is suited to more legacy operations, and this acquisition could be another step in helping Salesforce bridge the gap between older on-prem tools and more modern cloud software.

Brent Leary, founder and principal analyst at CRM Essentials says that it brings another dimension to Salesforce’s digital transformation tools. “It didn’t take Salesforce long to move to the next acquisition after closing their biggest purchase with Slack. But automation of processes and workflows fueled by real-time data coming from a growing variety of sources is becoming a key to finding success with digital transformation. And this adds a critical piece to that puzzle for Salesforce/MuleSoft,” he said.

While it feels like Salesforce is joining the market late, in an investor survey we published in May, Laela Sturdy, general partner at CapitalG, told us that we are just skimming the surface so far when it comes to RPA’s potential.

“We’re a long way from needing to think about the space maturing. In fact, RPA adoption is still in its early infancy when you consider its immense potential. Most companies are only now just beginning to explore the numerous use cases that exist across industries. The more enterprises dip their toes into RPA, the more use cases they envision,” Sturdy responded in the survey.

Servicetrace was founded in 2004, long before the notion of RPA even existed. Neither Crunchbase nor PitchBook shows any money raised, but the website suggests a mature company with a rich product set. Customers include Fujitsu, Siemens, Merck and Deutsche Telekom.

Understanding what you will change is most important to achieve a long-lasting and successful robotic process automation transformation. There are three pillars that will be most impacted by the change: people, process and digital workers (also referred to as robots). The interaction of these three pillars executes workflows and tasks, and if integrated cohesively, determines the success of an enterprisewide digital transformation.

Robots are not coming to replace us, they are coming to take over the repetitive, mundane and monotonous tasks that we’ve never been fond of. They are here to transform the work we do by allowing us to focus on innovation and impactful work. RPA ties decisions and actions together. It is the skeletal structure of a digital process that carries information from point A to point B. However, the decision-making capability to understand and decide what comes next will be fueled by RPA’s integration with AI.

We are seeing software vendors adopt vertical technology capabilities and offer a wide range of capabilities to address the three pillars mentioned above. These include powerhouses like UiPath, which recently went public, Microsoft’s Softomotive acquisition, and Celonis, which recently became a unicorn with a $1 billion Series D round. RPA firms call it “intelligent automation,” whereas Celonis targets the execution management system. Both are aiming to be a one-stop shop for all things related to process.

We have seen investments in various product categories for each stage in the intelligent automation journey. Process and task mining for process discovery, centralized business process repositories for CoEs, executives to manage the pipeline and measure cost versus benefit, and artificial intelligence solutions for intelligent document processing.

For your transformation journey to be successful, you need to develop a deep understanding of your goals, people and the process.

Define goals and measurements of success

From a strategic standpoint, success measures for automating, optimizing and redesigning work should not be solely centered around metrics like decreasing fully loaded costs or FTE reduction, but should put the people at the center. To measure improved customer and employee experiences, give special attention to metrics like decreases in throughput time or rework rate, identify vendors that deliver late, and find missed invoice payments or determine loan requests from individuals that are more likely to be paid back late. These provide more targeted success measures for specific business units.

The returns realized with an automation program are not limited to metrics like time or cost savings. The overall performance of an automation program can be more thoroughly measured with the sum of successes of the improved CX/EX metrics in different business units. For each business process you will be redesigning, optimizing or automating, set a definitive problem statement and try to find the right solution to solve it. Do not try to fit predetermined solutions into the problems. Start with the problem and goal first.

Understand the people first

To accomplish enterprise digital transformation via RPA, executives should put people at the heart of their program. Understanding the skill sets and talents of the workforce within the company can yield better knowledge of how well each employee can contribute to the automation economy within the organization. A workforce that is continuously retrained and upskilled learns how to automate and flexibly complete tasks together with robots and is better equipped to achieve transformation at scale.

Robotic process automation (RPA) is rapidly moving beyond the early adoption phase across verticals. Automating just basic workflow processes has resulted in such tremendous efficiency improvements and cost savings that businesses are adapting automation at scale and across the enterprise.

While there is a technical component to robotic automation, RPA is not a traditional IT-driven solution. It is, however, still important to align the business and IT processes around RPA. Adapting business automation for the enterprise should be approached as a business solution that happens to require some technical support.

A strong working relationship between the CFO and CIO will go a long way in getting IT behind, and in support of, the initiative rather than in front of it.

More important to the success of a large-scale RPA initiative is support from senior business executives across all lines of business and at every step of the project, with clear communications and an advocacy plan all the way down to LOB managers and employees.

As we’ve seen in real-world examples, successful campaigns for deploying automation at scale require a systematic approach to developing a vision, gathering stakeholder and employee buy-in, identifying use cases, building a center of excellence (CoE) and establishing a governance model.

Create an overarching vision

Your strategy should include defining measurable, strategic objectives. Identify strategic areas that benefit most from automation, such as the supply chain, call centers, AP or revenue cycle, and start with obvious areas where business sees delays due to manual workflow processes. Remember, the goal is not to replace employees; you’re aiming to speed up processes, reduce errors, increase efficiencies and let your employees focus on higher value tasks.

Artificial intelligence is becoming an increasingly common part of how customer service works — a trend that was accelerated in this past year as so many other services went virtual and digital — and today a startup that has built a set of low-code tools to help enterprises integrate more AI into their customer service processes is announcing some funding to fuel its growth.

Cognigy, which provides a low-code conversational AI platform that notably can be used flexibly across a range of applications and geographies — it supports 120 languages; it can be used in external or internal service applications; it can support voice services but also chatbots; it provides real-time assistance for human agents and usage analytics or fully automated responses; it can integrate with standard call center software, and also with RPA packages; and it can be run in the cloud or on-premise — has closed a round of $44 million, funding that it will be using to continue scaling its business internationally.

Insight Partners is leading the Series B investment, with previous backers DN Capital, Global Brain, Nordic Makers, Inventures and Digital Innovation and Growth also participating. The Dusseldorf-based company had previously only raised $11 million and spent the first several years of business bootstrapped.

Cognigy is not disclosing its valuation but it has up to now built up a concentration of customers in areas like transportation, e-commerce and insurance and counts a number of big multinational companies among its customer list, including Lufthansa, Mobily, BioNTech, Vueling Airlines, Bosch and Daimler, with “thousands” of virtual assistants now powered by Cognigy live in the market.

With 25% of Cognigy’s business already coming from the U.S., the plan now is to use some funding to invest in building out its service deeper into the U.S., Asia and across more of Europe, CEO and founder Philipp Heltewig said in an interview.

“Conversational AI” these days appears in many guises: it can be a chatbot you come across on a website when you’re searching for something, or it can be prompts provided to agents or salespeople, information and real-time feedback to help them do their jobs better. Conversational AI can also be a personal assistant on your company’s HR application to help you book time off or deal with any number of other administrative jobs, or a personal assistant that helps you use your phone or set your house alarm.

There are a number of companies in the tech world that have built tools to address these various use cases. Specifically in the area of services aimed at enterprises, some of them, like Gong, are raising huge money right now. What is notable about Cognigy is that it has built a platform that is attempting to address a wide swathe of applications: one platform, many uses, in other words.

Cognigy’s other selling point is that it is playing into the new interest in low- and no-code tools, which in Cognigy’s case makes the integration of AI into a customer assistance process a relatively easy task, something that can be built not just by developers, but data scientists, those working directly on conversation design, and nontechnical business users using the tools themselves.

“The low-code platform helps enterprises adopt what is otherwise complex technology in an easy and flexible way, whether it is a customer or employee contact center,” said Heltewig. As you might expect, there are some direct competitors in the low- and no-code conversational AI space, too, including Ada, Talkie, Snaps and more.

Flexibility seems to be the order of the day for enterprises, and also the companies building tools for them: it means that a company can grow into a larger customer, and that in theory Cognigy will also evolve the platform based on what its customers need. As one example, Heltewig pointed out that a number of its customers are — contrary to the beating drum and march you see every day toward cloud services — running a fair number of applications on-premises, since this appears to be a key way to ensure the security of the customer data that they handle.

“Lufthansa could never run its customer services in the cloud because they handle a lot of sensitive data and they want full ownership of it,” he noted. “We can run cloud services and have a full offering for those who want it, but many large enterprises prefer to run their services on premises.”

Teddie Wardi, an MD at Insight, is joining the board with this round. “We are thrilled to be leading Cognigy’s Series B as the company continues on their ScaleUp journey,” he said in a statement. “Evident by their strong customer retention, Cognigy has created an essential product for global businesses to improve their customer experience in an efficient and effortless manner. With the new funding, Cognigy will be able to expand their leadership position to reach new markets and acquire more customers.”

Automation is extending into every aspect of how organizations get work done, and today comes news of a startup that is building tools for one industry in particular: life sciences. Artificial, which has built a software platform for laboratories to assist with, or in some cases fully automate, research and development work, has raised $21.5 million.

It plans to use the funding to continue building out its software and its capabilities, to hire more people, and for business development, according to Artificial’s CEO and co-founder David Fuller. The company already has a number of customers including Thermo Fisher and Beam Therapeutics using its software directly and in partnership for their own customers. Sold as aLab Suite, Artificial’s technology can both orchestrate and manage robotic machines that labs might be using to handle some work; and help assist scientists when they are carrying out the work themselves.

“The basic premise of what we’re trying to do is accelerate the rate of discovery in labs,” Fuller said in an interview. He believes the process of bringing in more AI into labs to improve how they work is long overdue. “We need to have a digital revolution to change the way that labs have been operating for the last 20 years.”

The Series A is being led by Microsoft’s venture fund M12 — a financial and strategic investor — with Playground Global and AME Cloud Ventures also participating. Playground Global, the VC firm co-founded by ex-Google exec and Android co-creator Andy Rubin (who is no longer with the firm), has been focusing on robotics and life sciences and it led Artificial’s first and only other round. Artificial is not disclosing its valuation with this round.

Fuller hails from a background in robotics, specifically industrial robots and automation. Before founding Artificial in 2019, he was at Kuka, the German robotics maker, for a number of years, culminating in the role of CTO; prior to that, Fuller spent 20 years at National Instruments, the instrumentation, test equipment and industrial software giant. Meanwhile, Artificial’s co-founder, Nikhita Singh, has insight into how to bring the advances of robotics into environments that are quite analogue in culture. She previously worked on human-robot interaction research at the MIT Media Lab, and before that spent years at Palantir and working on robotics at Berkeley.

As Fuller describes it, he saw an interesting gap (and opportunity) in the market to apply automation, which he had seen help advance work in industrial settings, to the world of life sciences, both to help scientists track what they are doing better, and help them carry out some of the more repetitive work that they have to do day in, day out.

This gap is perhaps more in the spotlight today than ever before, given the fact that we are in the middle of a global health pandemic. This has hindered a lot of labs from being able to operate full in-person teams, and increased the reliance on systems that can crunch numbers and carry out work without as many people present. And, of course, the need for that work (whether it’s related directly to Covid-19 or not) has perhaps never appeared as urgent as it does right now.

There have been a lot of advances in robotics — specifically around hardware like robotic arms — to manage some of the precision needed to carry out some work, but up to now no real efforts made at building platforms to bring all of the work done by that hardware together (or in the words of automation specialists, “orchestrate” that work and data); nor link up the data from those robot-led efforts, with the work that human scientists still carry out. Artificial estimates that some $10 billion is spent annually on lab informatics and automation software, yet data models to unify that work, and platforms to reach across it all, remain absent. That has, in effect, served as a barrier to labs modernising as much as they could.

A lab, as he describes it, is essentially composed of high-end instrumentation for analytics, alongside then robotic systems for liquid handling. “You can really think of a lab, frankly, as a kitchen,” he said, “and the primary operation in that lab is mixing liquids.”

But it is also not unlike a factory, too. As those liquids are mixed, a robotic system typically moves around pipettes, liquids, in and out of plates and mixes. “There’s a key aspect of material flow through the lab, and the material flow part of it is much more like classic robotics,” he said. In other words, there is, as he says, “a combination of bespoke scientific equipment that includes automation, and then classic material flow, which is much more standard robotics,” and is what makes the lab ripe as an applied environment for automation software.

To note: the idea is not to remove humans altogether, but to provide assistance so that they can do their jobs better. He points out that even the automotive industry, which has been automated for 50 years, still has about 6% of all work done by humans. If that is a watermark, it sounds like there is a lot of movement left in labs: Fuller estimates that some 60% of all work in the lab is done by humans. And part of the reason for that is simply because it’s just too complex to replace scientists — who he described as “artists” — altogether (for now at least).

“Our solution augments the human activity and automates the standard activity,” he said. “We view that as a central thesis that differentiates us from classic automation.”

There have been a number of other startups emerging that are applying some of the learnings of artificial intelligence and big data analytics for enterprises to the world of science. They include the likes of Turing, which is applying this to helping automate lab work for CPG companies; and Paige, which is focusing on AI to help better understand cancer and other pathology.

The Microsoft connection is one that could well play out in how Artificial’s platform develops going forward, not just in how data is perhaps handled in the cloud, but also on the ground, specifically with augmented reality.

“We see massive technical synergy,” Fuller said. “When you are in a lab you already have to wear glasses… and we think this has the earmarks of a long-term use case.”

Fuller mentioned that one area it’s looking at would involve equipping scientists and other technicians with Microsoft’s HoloLens to help direct them around the labs, and to make sure people are carrying out work consistently by comparing what is happening in the physical world to a “digital twin” of a lab containing data about supplies, where they are located, and what needs to happen next.

It’s this and all of the other areas that have yet to be brought into our very AI-led enterprise future that interested Microsoft.

“Biology labs today are light- to semi-automated—the same state they were in when I started my academic research and biopharmaceutical career over 20 years ago. Most labs operate more like test kitchens rather than factories,” said Dr. Kouki Harasaki, an investor at M12, in a statement. “Artificial’s aLab Suite is especially exciting to us because it is uniquely positioned to automate the masses: it’s accessible, low code, easy to use, highly configurable, and interoperable with common lab hardware and software. Most importantly, it enables Biopharma and SynBio labs to achieve the crowning glory of workflow automation: flexibility at scale.”

Harasaki is joining Peter Barratt, a founder and general partner at Playground Global, on Artificial’s board with this round.

“It’s become even more clear as we continue to battle the pandemic that we need to take a scalable, reproducible approach to running our labs, rather than the artisanal, error-prone methods we employ today,” Barrett said in a statement. “The aLab Suite that Artificial has pioneered will allow us to accelerate the breakthrough treatments of tomorrow and ensure our best and brightest scientists are working on challenging problems, not manual labor.”

Robotic process automation (RPA) has certainly been getting a lot of attention in the last year, with startups, acquisitions and IPOs all coming together in a flurry of market activity. It all seemed to culminate with UiPath’s IPO last month. The company that appeared to come out of nowhere in 2017 eventually had a final private valuation of $35 billion. It then had the audacity to match that at its IPO. A few weeks later, it still has a market cap of over $38 billion in spite of the stock price fluctuating at points.

Was this some kind of peak for the technology or a flash in the pan? Probably not. While it all seemed to come together in the last year with a big increase in attention to automation in general during the pandemic, it’s a market category that has been around for some time.

RPA allows companies to automate a group of highly mundane tasks and have a machine do the work instead of a human. Think of finding an invoice amount in an email, placing the figure in a spreadsheet and sending a Slack message to Accounts Payable. You could have humans do that, or you could do it more quickly and efficiently with a machine. We’re talking mind-numbing work that is well suited to automation.

In 2019, Gartner found RPA was the fastest-growing category in enterprise software. In spite of that, the market is still surprisingly small, with IDC estimates finding it will reach just $2 billion in 2021. That’s pretty tiny for the enterprise, but it shows that there’s plenty of room for this space to grow.

We spoke to five investors to find out more about RPA, and the general consensus was that we are just getting started. While we will continue to see the players at the top of the market — like UiPath, Automation Anywhere and Blue Prism — jockeying for position with the big enterprise vendors and startups, the size and scope of the market has a lot of potential and is likely to keep growing for some time to come.

To learn about all of this, we queried the following investors:

• Mallun Yen, founder and partner, Operator Collective
• Jai Das, partner and president, Sapphire Ventures
• Soma Somasegar, managing director, Madrona Venture Group
• Laela Sturdy, general partner, CapitalG
• Ed Sim, founder and managing partner, Boldstart Ventures

We have seen a range of RPA startups emerge in recent years, with companies like UiPath, Blue Prism and Automation Anywhere leading the way. As the space matures, where do the biggest opportunities remain?

Mallun Yen: One of the fastest-growing categories of software, RPA has been growing at over 60% in recent years, versus 13% for enterprise software generally. But we’ve barely scratched the surface. The COVID-19 pandemic forced companies to shift how they run their business, how they hire and allocate staff.

Given that the workforce will remain at least partially permanently remote, companies recognize that this shift is also permanent, and so they need to make fundamental changes to how they run their businesses. It’s simply suboptimal to hire, train and deploy remote employees to run routine processes, which are prone to, among other things, human error and boredom.

Jai Das: All the companies that you have listed are focused on automating simple repetitive tasks that are performed by humans. These are mostly data entry and data validation jobs. Most of these tasks will be automated in the next couple of years. The new opportunity lies in automating business processes that involve multiple humans and machines within complicated workflow using AI/ML.

Sometimes this is also called process mining. There have been BPM companies in the past that have tried to automate these business processes, but they required a lot of services to implement and maintain these automated processes. AI/ML is providing a way for software to replace all these services.

Soma Somasegar: For all the progress that we have seen in RPA, I think it is still early days. The global demand for RPA market size in terms of revenue was more than $2 billion this past year and is expected to cross $20 billion in the coming decade, growing at a CAGR of more than 30% over the next seven to eight years, according to analysts such as Gartner.

That’s an astounding growth rate in the coming years and is a reflection of how early we are in the RPA journey and how much more is ahead of us. A recent study by Deloitte indicates that up to 50% of the tasks in businesses performed by employees are considered mundane, administrative and labor-intensive. That is just a recipe for a ton of process automation.

There are a lot of opportunities that I see here, including process discovery and mining; process analytics; application of AI to drive effective, more complex workflow automation; and using low code/no code as a way to enable a broader set of people to be able to automate tasks, processes and workflows, to name a few.

Laela Sturdy: We’re a long way from needing to think about the space maturing. In fact, RPA adoption is still in its early infancy when you consider its immense potential. Most companies are only now just beginning to explore the numerous use cases that exist across industries. The more enterprises dip their toes into RPA, the more use cases they envision.

I expect to see market leaders like UiPath continue to innovate rapidly while expanding the breadth and depth of their end-to-end automation platforms. As the technology continues to evolve, we should expect RPA to penetrate even more deeply into the enterprise and to automate increasingly more — and more critical — business processes.

Ed Sim: Most large-scale automation projects require a significant amount of professional services to deliver on the promises, and two areas where I still see opportunity include startups that can bring more intelligence and faster time to value. Examples include process discovery, which can help companies quickly and accurately understand how their business processes work and prioritize what to automate versus just rearchitecting an existing workflow.