AI-based decision-making automation

The holy grail of solving enterprise decision latency

Current information systems & processes create significant data and information latency that cause expensive delays and additional risk in business decision making.

In a number of large companies, both mission-critical, as well as strategic business intelligence for decision making, is delivered from data Warehouses and data-lakes through analytics and business intelligence tools. In a number of cases, business users and analysts use BI tools (e.g. Congos/SAS ), Visualisation tools (e.g. Tableau, Power BI). Despite several advancements in the BI layer and visual analytics, the process of analysis to arrive at actionable insight and optimal decisions is long drawn out, expansive, sub-optimal quality and does not scale.

A recent McKinsey survey gave strong signs of growing levels of frustration with broken decision-making processes, with the slow pace of decision-making deliberations, and with the uneven quality of decision-making outcomes. Fewer than half of the survey respondents say that decisions are timely, and 61 percent say that at least half the time spent making them is ineffective.The opportunity costs of this are staggering: about 530,000 days of managers’ time potentially squandered each year for a typical Fortune 500 company, equivalent to some $250 million in wages annually.- Three Keys to faster, better decisions)

The problem of decision latency has its roots in several stages of information processing:

1. Inability to have all diverse data — contextual and historical, multi-structured, at the point of decision making

2. Inability to process all causal factors to learn market/performance drivers

3. Inability to get an idea of all possible alternative future scenarios in time

4. The inability of teams to collaborate, build and evaluate the future scenarios that align best with the growth strategy

The solution, therefore, needs to learn from all data, automate modeling & analysis and augment business decision making with appropriate integration and interaction within the process:

The new paradigm of Augmented Automated Decisions in a Quantified Market and Industry 4.0

AI enabled systems such as Singular Intelligence automate sourcing, harmonisation, modeling and analysis of multi-structured data for defined business user journeys to significantly improve timeliness and quality of decision making. Such competences when scaled have significant benefits for organisations.

The process and paradigm have the power to really transform the integration of decision makers with data: imagine a dialogue between a Commercial director and an AI bot, dynamically interacting with him supporting agile data-based decisions :

The compelling business case of automated, always on decision system: Your data is your unique asset and the most efficient way to build competitive advantage with it is to use all of them and build a scalable in-house competence using AI tools rather than agencies, external consultants and manual data analysis.

The diverse, disparate internal and external data is unique and an asset for competitive advantage. This data when combined creates new and more valuable insight than the human mind is able to infer when looking at them in isolation. There are serious limitations on teams using manual processes:

The ability of human data scientists to provide analysis and insight at scale and granularity is limited to backward one-time analysis rather than always on agile forward-looking agile systems. Further, the ability to collate and derive insight from this data using traditional business intelligence, analytics tools and techniques is limited. The emerging software capabilities have huge business advantages for enterprise-wide use moving to an automated always-on process. The current manual process and agency based approach which will fade away as the pressure of agile decisions meets improved functionalities of AI-based decision products with reducing the unit cost of adoption and growing realisation of benefits.

Interested in knowing more? Contact :

• Sarvesh Kumar, CEO, Sarvesh@singularintelligence.com
• Steve Gladwell, Industry Director, Steve@singularintelligence.com
• Nick Marr, Commercial Director, Nick@singularintelligence.com
• Jean Littolff, Marketing Director, Jean@singularintelligence.com