Adaptive, self-optimizing, fully autonomous, and carbon neutral.

This brief list may well describe next-era manufacturing. It certainly contains some of the delineating features many industry leaders strive to attain for their production lines.

Add socially beneficial and scalable—across product lines and factory fleets—and we have the rough pixels of a familiar holistic vision for the enterprise of tomorrow.

Down to the business of bringing this vision (either wholly or in part) into practical focus, future-oriented industrials increasingly explore both IIoT data platforms and artificial intelligence. However, the real catalyst resides in a third consideration, which completes the smart manufacturing triad—merging OT and IT.


It is essential to remember that synthesizing operational and informational technology in plants depends heavily on new technological capabilities and adoption across teams. Personnel cross-functionality and the centralization & security of industrial data intelligence are essential preconditions. Fulfilling them necessitates that plant teams realize, collectively, the promise of the fully connected, secure, and increasingly autonomous factory. 

For these reasons, OT/IT  alignment lies at the heart of the change management dimension for smart factory initiatives. 

And perhaps this is why its enabling mechanisms are easily glossed over. In any event, advice on this third consideration—to industrials who are digitally transforming—usually takes the form of best practices around building a “framework” or “roadmap” for “bridging the OT/IT divide”.

But the degree to which the ideal mechanisms for OT/IT synthesis are vaguely articulated is significant—especially when you consider that without securely and practically synthesizing OT and IT, data-connectivity platforms and AI-driven solutions for production have no basis. 

But what if the requisite OT/IT skills recalibration across departments was inherently embedded in the creation process of a digital-first, AI-driven factory?

This is the case with AI-as-a-Service.


AI-as-a-Service is a three-pronged technological process proven to expedite the Digital Maturity journey and harmonize interaction between OT and IT teams.

We can extend the optical metaphor to visualize how factory personnel experience AI-as-a-Service in three dimensions. In taking a picture, photographers (from a technological point of view) factor in three critical variables: shutter speed, aperture, and ISO—known colloquially as the ‘three kings,’ or the exposure triangle. 

For clarification, we can consider the IIoT platform as a factory’s ‘shutter speed’—set to fast during the ingestion of a mass of historical plant data but reduced by degree during its pre-processing and orchestration. 

Next, the success of a connected, data-driven plant depends on the extent to which OT and IT personnel are kept out of the dark. In other words, how does AI-as-a-Service get the ‘ISO level’ right—so that factory stakeholders are clear about the vision for moving the needle on production and the means to work towards this vision in a daily workflow.

Finally, the AI modeling needed to generate prescriptions from this data (to optimize machinery or processes) can be seen as aperture refinement—in the sense that data scientists interpret the depth of field (or relative points of focus) of the AI discovery process. For example, with unsupervised deep learning, a best-of-best (BOB) region amongst other operating regions is highlighted in two-dimensional space. The BOB serves as a target superior to other operational clusters.

However, before exploring the AI-as-a-Service exposure triangle, the question of data security that arises with OT/IT convergence needs to be addressed.


Traditionally, OT departments have focused almost purely on projects. Data security questions, meanwhile, have been the remit of IT departments. 

However, in the IIoT-enabled plant, the convergence of manufacturing process control and data connectivity renders cybersecurity cross-departmental.

During the Digital Maturity journey, we work with our manufacturer clients to ensure the proper governance of their industrial data security—by making use of the modern software security practices that are inherent to our AI-as-a-Service.

We appreciate that the IIoT-enabled factory is a distributed system with standardized communication and remote accessibility. By prioritizing the secure intersection of the operational technology that is associated with critical plant infrastructure and networked tech, DataProphet’s AI-as-a-Service ensures against connectivity risks. 

For example, we provide support across many on-premise and cloud environments and isolate client data to tenant-specific blob—or bucket—storage and databases. We also deliver world-class data, password, and network encryption—thus rigorously limiting data, network, and system access. Finally, all user passwords are managed through encryption in the context of a world-class management framework.


Security guaranteed, manufacturers are mindful of the potential value of their industrial data. But they are also wary of the fuzzying effect of discerning its utility when highly correlated. The right  IIoT connectivity solution ensures teams make the most of their innate asset.

To this end, four connectivity building blocks must be laid down for an adaptive IIoT platform to enable value via AI.

First, at the layer of data perception, plants can ensure flexible integration of sensors, machines, edge devices (with real-time AI and compute capabilities), and databases. 

The perception layer establishes a foundation from which an IIoT platform can successfully network, collect, and store edge data. 

Data is collected in various ways: via Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA), and Manufacturing Execution Systems /Quality Management Systems (MES/QMS). As for data storage, edge-to-cloud (or edge-to-prem) integration provides safety, security, and remote access for an evolving production environment. At the networking layer, experts can ensure seamless communication via numerous protocols.

However, a connectivity solution is not yet complete with perception, networking, and storage in place. Full connectivity happens at the upper layer—the application. Application is where an IIoT platform executes its intelligence remotely. It does so at the human-machine interface (HMI). At the HMI, a good connectivity platform enables centralized, real-time reporting and intuitively hierarchical data visualization. It also provides operators with analytics capabilities, alarms system integration, and KPI monitoring.


As stressed earlier, an IIoT/AI combination must have a basis in daily operations. And for this to happen, informational technology (IT) and operational technology (OT) need to merge in a way that is meaningful for all plant personnel.

During our deployments, we work with customers to integrate and utilize the deep learning capabilities of our AI—from the edge of production to the human-machine interface (HMI). In this way, DataProphet’s AI-as-a-Service embeds the uplift of digital skills and harmonizes the interaction between the IT and OT teams. 

It begins with an AI-readiness assessment (a deep dive into a plant’s process and quality data systems). Once Digital Maturity is established, the AI system is installed and pipelined from edge to cloud to network. A commissioning test then demonstrates the impact of the AI system and quantifies its value. 

Crucially, during the entire process—network engineers, IT systems owners, plant operations personnel, production operators, and control engineers engage with our data scientists and software engineers. We collaborate to deploy a data-derived AI solution against agreed KPI benchmarks. The process usually takes between three and four months. 

After successful deployment, DataProphet ensures continued access, reports, training, support, full model maintenance, and adaptive remodeling for our AI system—cognisant of a dynamic production environment. 

In this way, successful collaboration between OT and IT is intrinsic to each phase of the smart factory initiative, and it endures post-installation. Going forward, production, quality, maintenance, plant operators, and process engineers interact daily with a centralized, self-consistent IT system. At the same time, prioritized AI prescriptions are delivered to an intuitive HMI with role-based access.


In essence, an IIoT platform aims to automate the upleveling of a manufacturing process in perpetuity—by drawing actionable intelligence from plant equipment and the associated manufacturing process. In other words, industrials need the Data Maturity layering of an IIoT platform to unleash next-paradigm production guidance. This navigation typically comes with state-of-the-art machine learning technologies.

Deep learning applied as prescriptive analytics parses the live and historical data orchestrated by the IIoT platform from a learned manifold of production. It discovers optimal operation regimes. These are delivered as prioritized prescriptions for plant operators to enact proactive setpoint adjustments—for the best possible production outcomes ahead of production loss. Ideally, the system and the people behind it achieve everything without forestalling production or putting it at risk. New regimes should operate within critical tolerances, only relaxing specific non-critical tolerances based on pre-discovery of the best holistic optimal.

Manufacturers who harness deep learning prescriptions via AI-as-a-Service can continuously drive immediate and measurable process and machine health improvement. They achieve this by creating a production feedback loop whereby the system learns with each iteration of the process—automating root cause analysis and embedding expert execution, which become part of the system’s dynamic, growing intelligence.

Moreover, with AI-as-a-Service, the diverse stakeholders (including those across the previous IT/OT divide) participate in a self-consistent IIoT system—each with relevant and coherent access via the HMI to the automating intelligence of the AI-enabled factory. 

DataProphet’s AI-as-a-Service offers a robust production system that places the power of deep learning in the collective hands of plant personnel. We ensure that an inclusive working vision for the future of production is framed in the reality of the present.