The Outcome-Based Autonomous Enterprise Journey

Innovation The Outcome-Based Autonomous Enterprise Journey Shailesh Manjrekar Forbes Councils Member Forbes Technology Council COUNCIL POST Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based) Jun 16, 2022, 06:00am EDT | Share to Facebook Share to Twitter Share to Linkedin Shailesh Manjrekar is the Vice President of AI and Marketing at CloudFabrix , the inventor of Robotic Data Automation Fabric™ (RDAF™).

getty Every enterprise, small, medium or large, aspires to be an autonomous enterprise, particularly in this data economy. What it means at a CXO level is to be able to predict and prevent outages and security breaches, improve time to market and time to insights, deepen customer insights and improve customer experience. However, these goals are difficult to achieve with multicloud and hybrid distributed, cloud-native applications and platforms that are complex and abstract.

Fundamentally observing these platforms becomes a challenge due to data quality issues, the data and AI skills gap and data silos. An enterprise that aspires to be an autonomous enterprise needs to implement a data-first, AI-first and automate-everywhere strategy, where business and IT drive each. To better visualize this, think about mobility, which is often beholden to the gas station grid.

Increasingly, mobility is becoming more and more beholden to the electric grid. Similarly, an autonomous enterprise is beholden to its AI and data capabilities. With all the rage surrounding observability, open telemetry and the MELT-based data management, customers are getting overwhelmed by how and where to start and demonstrate tangible outcomes.

It becomes very easy to get lost in the trees instead of looking at the forest as a whole. The following is a blueprint that your organization can follow when progressing through levels of autonomy. Each level provides a progressive path, is interdependent and can be initiated serially or in parallel.

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A successful implementation of the AI data grid includes: • Data Integration: This involves full-stack data sources integrated with other distributed data sources at the edge and in the multicloud. • Data Automation: This involves metadata extraction, the creation of data dictionaries, cross-domain enrichment, contextualizing data with real-time topology discovery, creating data models and deriving application dependency mapping (ADM). • No-Code/Low-Code: This self-service platform empowers even citizen developers and leverages data and AI/ML bots.

• Data Fabric: This data fabric should eliminate silos, enable “in-place analytics” and data collection. 2. Predictive This level of autonomy prevents and predicts issues with root-cause and regression analysis using explainable AI/ML pipelines that identify anomalies and trends.

A good AI data grid should be able to perform cross-domain contextual learning and leverage explainable AI for classification, clustering and regression analysis. It should also be able to use natural language processing to better understand events, alerts, metrics and logs based on various attributes. Explainable AI should also support supervised, unsupervised and federated learning and should be reproduceable, secure and traceable.

3. Prescriptive Once you identify a root cause or anomaly, what action can you take to remediate it? This is done using service ops for incident creation, management and resolution. A good AI data grid should leverage the ecosystem at hand for bidirectional IT service automation, e-bonding, NLP insights, DevOps automation, CI/CD pipelines, security automation and analytics, network automation, robotic process automation, DataOps automation and more.

It should provide real-time alerts and integrate with existing chat and collaboration platforms. 4. Cognitive In this final phase, data intelligence and automation should become part of your information systems.

A good AI data grid should be able to provide radar views with a business dashboard that showcases capacity and resource utilization reports. It should include customizable dashboards and templates that you can adjust for your individual business needs. Holistic, data-driven insights based on multiple personas is also essential at this stage.

By walking through these four phases, your business will be well on its way to embracing an outcome-based autonomous journey. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? Follow me on Twitter or LinkedIn .

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