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Ashok has a decades long career as a software engineering innovator. As the founder of private equity owned Digital.ai and special advisor, CEO and board member at TPG, he shares his unique insights about digital transformation initiatives.
Q: What are some of the key challenges Digital.ai’s customers encounter on their journey towards digital transformation? Where do you see them reaching out to you for support?
Most companies desire to improve process efficiency and use DevOps software to accelerate product development, etc. but are more focused on business outcomes like revenue growth or winning new customers. Thus, the challenge lies in aligning the business outcomes with the effectiveness of the engineering and delivery teams. For them, Digital.ai brings an end-to-end consolidated DevOps toolchain, using AI and data-driven decisions to remove the silos and drive transformation. Digital.ai, with its developing tools, is also helping build intelligent applications.
Q: Can you provide some examples of how companies have transformed themselves with Digital.ai?
Nike wanted to generate more sales through Nike Digital (via a direct-to-consumer model) over traditional retail channels. For that they needed to create a host of mobile apps and use them to acquire, engage and activate customers. Digital.ai helped Nike with its business model transformation by reverse engineering, using the outcomes they were trying to achieve (i.e. increasing digital sales share). This was achieved by moving SAP S/4Hana to the cloud, automating processes using Digital.ai tools, DevOps value stream delivery and analytics solutions. The outcome was accelerated revenue through Nike Digital, exceeding targets.
In another example, Digital.ai, partnered with Levi Strauss & Co. to drive digital transformation by shifting to digital channels, launching offerings like virtual try-outs, etc. along with simultaneously focusing on ESG metrics. All these initiatives led to increased sales from a different revenue stream.
Q: Is the industry moving towards an outcome-based approach for automation/transformation projects, with metrics like benefit realisation (cost or time) being a key point of discussion?
The move towards an outcome-based approach is increasing, as Outcome-as-a-Service is directly related to business goals and you can pay per outcome to reduce customer risk. The key challenge here lies in who takes the risk to measure and own the outcome. It is better to have business-centric outcomes, for example, in the case of a mortgage application the onboarding time being reduced from 45 days to 45 minutes, or an application where we can measure the lifetime value of a customer, etc.
Q: What are your views on AIOps, software and data pipelines being adopted by enterprises?
In adopting software and data pipelines, one can start with algorithm, fix outcomes that you want to achieve and then use data to optimize the algorithm. Digital.ai is looking at a much bigger problem of trying to reduce the ambiguity associated with AI, to justify its usability, as strict regulations from Europe are expected to come into place for AI-based systems.
Change risk prediction is a key task in AIOps. It helps enterprises move faster in an environment where development teams focus on changes, while governance practices are in place to reduce risk associated with the change. Using Digital.ai you can predict the next change by learning from the changes made. Companies like Morgan Stanley, for example, is looking positively at this kind of a use case as change risk management helps them predict which processes to automate.
Q: In the PE world, there is a lot of importance on feature expansions and faster time to market – which in turn has a positive impact on valuation of the company. How does Digital.ai manage the expectation on feature expansions and valuation?
Digital.ai is brought to focus on both – combined product-market fit as well as individual products. But, from a PE perspective, the challenge is to grow the combined platform aggressively and show a total accretive effect. That’s why Digital.ai has started focusing on solution-market fit.
Let’s take the use case where customers face complexities in testing their mobile phone application, because it might be protected by facial recognition, biometrics, etc. The procedure detects a hacking scenario and stops the testing process. In such a scenario, Digital.ai offers a combined solution of mobile application development, testing, and security together. This kind of approach helps Digital.ai to prove a solution-market fit and allows it to go to new areas that can be pitched to customers. This way, Digital.ai expands into its customer’s value chain and integrate the whole platform to create value.
Due to long sales cycles in the enterprise software space, it is difficult to present an accurate picture of growth. Generally, the leading indicators of growth are the number of POC’s completed, demos performed, etc. which provide a broader understanding of future growth and thus its valuation.
Q: How does Digital.ai prioritize changes to its platform roadmap and building new features for its customers?
Changes to product roadmap are prioritized based on long- and short-term customer benefits – the highest priority is being given to retaining customers. Digital.ai believes in the Office365 paradigm where customers get access to the whole suite of products even if their use case is limited to a few products as we speak, Digital.ai is undergoing a shift from vertical solutions to horizontal offerings, focusing on customer persona and their journey based on outcome-based solutions which are important to CXOs.
Q: What is the GTM strategy for Digital.ai and how do you leverage your partner ecosystem?
The company looks at propensity to buy based on the problem-market fit in order to determine which customers to go after. Apart from direct sales cases, Digital.ai partners with system integrators who are used in a consultative role.
Digital.ai has products which are not strategic to the portfolio, and this is an area where the company looks at partners and contractors. Managing cloud-based applications is an area where Digital.ai is looking for partnerships for shared revenue business models to hedge risks.
Q: Talent has become a crucial factor in the past year. How is Digital.ai looking at talent – building or tapping into various talent pools across the globe?
Digital.ai has acquired five companies through which it now has centres in Israel, India (Bangalore, Chennai, Gurgaon), Atlanta and Boston. The company has Centres of Excellence (CoEs) such as CoEs for analytics, security, testing, etc. in specific regions which are based purely on the talent pool availability.
An innovative strategy adopted by Digital.AI has been to ensure people can constantly upskill and shift left in career progression to higher value roles. This helps a person who starts off in the QA team to get into software development. The idea is to empower people to enable internal movements and grow into other roles.
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About Ashok Reddy
Ashok comes from an engineering background having worked across different roles in Automation, Design Engineering and DevOps. He has an master’s degree in AI and Machine learning from Georgia Institute of Technology. Served in leadership roles in companies like IBM (VP, DevOps) and BroadCom (SVP, Enterprise Software Division) before coming in as the CEO for Digital.AI. Prior to the IBM role, Ashok has worked as an engineer in Honeywell, Novartis and Fluor Corporation.