Intelligent automation is transforming organizations in every sector by enhancing efficiency, quality, speed, and insight. However, as automation usage matures, many businesses are recognizing shortcomings in conventional tooling around lack of customization, high licensing costs, and vendor dependencies. This drives growing interest in open automation as a modern, flexible alternative.
What is Open Automation?
Open automation refers to leveraging open-source software, open standards, and modular architecture to automate processes and build intelligent systems. It provides full transparency into system design and operation – a sharp contrast to "black box" proprietary solutions.
Key benefits driving open automation adoption include:
- Lower TCO: Avoid per-user licensing fees and vendor lock-in
- Customizability: Tailor systems to unique needs without restrictions
- Flexibility: Integrate seamlessly across diverse technology stacks
- Innovation: Accelerate enhancement through community collaboration
- Trust: Audit and validate system security/compliance
Market Growth Trajectory
Open source software is now ubiquitous across enterprises, with an estimated 65% of companies using it in some form. The open automation market specifically is projected to grow at a 16.2% CAGR through 2027 as per Markets and Markets analysis.
Driving this trajectory are several factors:
Mainstreaming of Open Philosophies: Concepts like inner sourcing and democratization of automation are entering common business vocabulary. Platform flexibility and skills leverage is taking priority over simple out-of-box functionality.
Proliferation of Open Standards: Cross-industry open standards like OPC UA for interoperability reduce friction in integrating diverse automation components.
Maturing Open Ecosystems: Repository networks like GitHub now host millions of mature open modules – abundant building blocks to assemble custom solutions.
Clearly open automation possesses powerful tailwinds for expanded usage.
Real-World Open Automation Success
While momentum clearly exists, proof points demonstrating open automation‘s capabilities better justify adoption:
Global Financial Institution
This worldwide bank faced highly manual, spreadsheet-based provisioning processes that took over three months per branch. By implementing Red Hat‘s low-code automation solution, it reduced provisioning to under one hour while saving significantly on licensing. Customization for specific financial use cases was straightforward, and will only expand through internal skills development.
Manufacturing Facility Network
A leading electric vehicle component supplier automated production across 6 plants with Litmus Automation‘s LoopEdge hardware and software stack. Edge-based localized automation increased yield over 10% while lowering costs. Interoperability with AWS IoT Cloud and on-premise servers was frictionless, providing hybrid infrastructure. Ad-hoc support from Litmus delivered rapid issue resolution not possible through standard vendors.
County Hospital System
Over 50 distinct information systems across this regional hospital group created immense reporting overhead. Pentaho open analytics brought together data from all systems for a unified view – identifying optimization opportunities that reduced patient discharge timing 8%. Flexibility to augment standard visualizations with healthcare-specific ones was critical for clinical staff adoption.
The above examples provide tangible evidence of open automation optimizing metrics from profitability to patient care through flexibility and customization simply not feasible through conventional approaches.
Open Automation Innovation
While offering proven value today, open automation‘s ultimate advantage may be future-proofing. The vast expertise behind popular projects like Ansible, Jenkins, and OpenNebula virtually assures they will evolve to support next-generation technological advances.
Most compelling are integrations bridging automation and artificial intelligence:
- ML-Enhanced Automation: AI agents can enhance automation by processing unstructured data as inputs, self-tuning based on outcomes, and recommending responses. This creates an autonomous feedback loop raising efficiency over time.
- MLOps Pipeline Automation: Complex machine learning model development pipelines involve myriad orchestrated stages. Jenkins and Kubernetes open automation helps data scientists reliably build, test, deploy and monitor these assemblies faster.
- Observability for AI Governance: Transparency into data flows and transformations powering AI is mandatory for ethics and compliance. Open source data lineage tools provide this visibility where proprietary versions often cannot.
The open ecosystem offers a fertile ground for these and other combinations of automation and analytics – the assembly lines of the AIOps factory.
The Myths Holding Back Adoption
If open automation indicates such promise, why haven‘t more organizations embraced it? Concerns and misconceptions remain common obstacles:
| Myth | Reality |
|---|---|
| Immature Tools | Most flagship open automation platforms have 10+ years of hardening and community support behind them. |
| Security Risks | Transparency and rapid patching often makes open source more secure. |
| Lack of Support | Developer forums provide ample peer-level assistance. Paid help from vendors and managed service providers is abundantly available. |
| Inferior Interface | Open source tools focus engineering effort on technical capabilities over glossy UIs. But many now offer excellent graphical options. |
The perceptions above should not deter open automation investments given careful platform evaluation and execution.
Open Automation Traction by Industry
A natural question is whether open automation garners more traction in certain verticals. The horizontal nature of many tools brings broad applicability – but some patterns emerge:
| Vertical | Key Drivers |
|---|---|
| Software/Technology | Aligns with DevOps culture. Accelerates CI/CD. |
| Manufacturing | Need for shop floor system interoperability. |
| Financial Services | Customization for unique banking needs. |
| Healthcare | Integration across disparate medical systems. |
Compliance demands also make open automation‘s code transparency especially valued in regulated sectors like aerospace, pharmaceuticals, and energy.
Evaluating Solutions
With growing options, professionally evaluating tools against requirements avoids misalignment. Some leading open platforms and distinguishing capabilities:
| Platform | Key Strengths |
|---|---|
| Ansible | Agentless IT automation and config management |
| Jenkins | Scalable, resilient CI/CD pipelines |
| OpenNebula | Data center and multi-cloud orchestration |
| Puppet | Broad infrastructure provisioning and management |
| Argos Labs | Low-code process automation |
The above presents a sampling of diverse capabilities catering from systems-level IT automation up through cloud orchestration and process-centric RPA.
Building the Business Case
As with any strategic IT investment, constructing a compelling business case and total cost of ownership model for open automation requires multiple considerations:
- Licensing Comparison: Evaluate perpetual license plus maintenance costs of proprietary software against low or no licensing fees for open automation alternatives over a 5-year horizon.
- IT Infrastructure Costs: Compare needs for additional servers, storage, and networking equipment between solutions – open source options generally have lower hardware requirements.
- Training and Ramp-Up: Consider existing staff skills in open tools like Python and Ansible compared to ramping on entirely new proprietary platforms. Favorable resource allocation differences often exist.
- Support: Most open automation platforms have abundant self-service help resources and community assistance available. Evaluate need and costs for vendor professional services.
- Customization: Determine prospective expenses associated with highly customized proprietary software over time against having full environment control with open automation.
Running through the above variables offers an accurate TCO comparison and compelling basis for investment approval.
The Future of Open
Most experts foresee open automation playing an integral role in coming waves of digital transformation:
- A 2022 Deloitte survey of business and technology leaders found 77% believe open source will be important over the next couple years – up from 65% in 2020.
- Gartner predicts 45% of new enterprise IT investments will shift from proprietary to open source by 2025 compared to 25% currently.
- A Red Hat commissioned study conducted by Harvard Business Review Analytic Services reveals 64% of organizations have an open hybrid cloud strategy underpinning flexible automation.
The writing appears clearly on the wall – open approaches represent the future for delivering technology capabilities efficiently, reliably, and securely. Now is the time for leaders to gain open automation literacy and evaluate pilots to stay ahead of the curve.
Conclusion
Intelligent automation is rightfully achieving broad horizontal diffusion. But first-generation tools have unmasked challenges around customization, interconnectedness, and total cost that open automation purports to solve. With extensive real-world precedents demonstrating open‘s efficacy – from manufacturing shop floors to hospital reporting – progressive organizations are embracing it as a sustainable automation backbone. Combining accessibility, transparency, and community-driven innovation, open automation delivers the cornerstone for constructing truly smart, responsive business processes.
