The relevance of this is around the terminology that Peak AI has introduced. They call what they offer “Decision Intelligence” and are crafting a market space around it. Peak’s basic premise was to build AI not as a business goal for itself but as a business service aided by a solution and limited to particular types of added value. The goal of Peak AI is to identify where Decision Intelligence can add value, and help the company build a business case that is both achievable and commercially viable.
For example, UK hard landscaping manufacturer Marshalls worked with Peak AI to streamline their bid process with contractors. This allows customers to get the answers they need in terms of bid decisions and quotes quickly and efficiently, significantly speeding up the sales cycle.
AI-as-a-Service is not a new concept. Canadian start-up Element AI tried to create an AI services business for non-tech companies to use as they might these days use consulting services. It never quite got there, though, and was acquired by ServiceNow last year. Peak AI is looking at specific elements such as sales, planning and supply chain for physical products in how decisions are made and where adding some level of automation in the decision is beneficial. The Peak AI solution, CODI (Connected Decision Intelligence) sits as a layer of intelligence that between the other systems, ingesting the data and aiding in its utilisation.
The added tool to create a data-ingestion layer for business decision-making is quite a trend right now. For example, IBM’s Causal Inference 360 Toolkit offers access to multiple tools that can move the decision-making processes from “best guess” to concrete answers based on data, aiding data scientists to apply and understand causal inference in their models.
Implications on Business Processes
The bigger problem is not the volume of data, but the interpretation of it.
Data warehouses and other ways of gathering data to a central or cloud-based location to digest is also not new. The real challenge lies with the interpretation of what the data means and what decisions can be fine-tuned with this data. This implies that data modelling and process engineers need to be involved. Not every company has thought through the possible options for their processes, nor are they necessarily ready to implement these new processes both in terms of resources and priorities. This also requires data harmonisation rules, consistent data quality and managed data operations.
Given the increasing flow of data in most organisations, external service providers for AI solution layers embedded in the infrastructure as data filters could be helpful in making sense of what exists. And they can perhaps suggest how the processes themselves can be readjusted to match the growth possibilities of the business itself. This is likely a great footprint for the likes of Accenture, KPMG and others as process wranglers.
Intellibot is the latest in a string of purchases by ServiceNow that reveals their intention to embed AI and machine learning into offerings. In 2020, they acquired Loom Systems, Passage AI (both January), Sweagle (June), and Element AI (November) in addition to Attivio in 2019. These acquisitions were integrated into the latest version of their Now Platform, code-named Quebec, which was launched earlier this month. As a result, Predictive AIOps and AI Search were newly added to the platform while the low-code tools were expanded upon and became Creator Workflows. This means ServiceNow now offers four primary solutions – IT Workflows, Employee Workflows, Customer Workflows, and Creator Workflows – demonstrating the importance they are placing on low-code and RPA.
ServiceNow was quick to remind the market that although they will be able to offer RPA functionality natively once Intellibot is integrated into their platform, they are still willing to work with competitors. They specifically highlighted that they would continue partnering with UiPath, Automation Anywhere, and Blue Prism, suggesting they plan to use RPA as a complementary technology to their current offerings rather than going head-to-head with the Big Three. Only a month ago, UiPath announced deeper integration with ServiceNow, by expanding automation capabilities for Test Management 2.0 and Agile Development projects.
Expansion in India
The acquisition of Intellibot, based in Hyderabad, is part of ServiceNow’s expansion strategy in India – one of their fastest growing markets. The country is already home to their largest R&D centre outside of the US and they intend to launch a couple of data centres there by March 2022. The company plans to double their local staff levels by 2024, having already tripled the number of employees there in the last two years. The expansion in India means they can increasingly offer services from there to global customers.
“Buyers will find that many of the automation capabilities that they currently purchase separately will increasingly be integrated in their enterprise applications. This will resolve integration challenges and will be more cost-effective.”
The cloud hyperscalers are also likely to play a growing role in the RPA market over the next year. Microsoft and IBM have already entered the market, coming from the angle of office productivity and business process management (BPM), respectively. Google announced just last week that they will work closely with Automation Anywhere to integrate RPA into their cloud offerings, such as Apigee, AppSheet, and AI Platform. More interestingly, they plan to co-develop new solutions, which might for now satisfy Google’s appetite for RPA rather than requiring an acquisition.
Here are some of the trends to watch for RPA, AI and Automation in 2021. Signup for Free to download Ecosystm’s Top 5 AI & Automation Trends Report.