A rapidly growing number of businesses are adopting artificial intelligence (AI) to reduce their operational cost, improve customer experience and/or generate new sources of revenue.
This significant adoption growth is due to the high potential of AI to create significant business value. It is estimated that AI augmentation alone “will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally by 2021.”
However, successfully adopting AI brings about a host of business and technical challenges including but not limited to the following:
Defining an AI strategy that can deliver demonstrable positive business outcomes (which use cases will yield the highest level of return on the investment?).
Ensuring explainable AI — the ability to provide transparency on AI-driven decision making.
Addressing ethical issues related to AI, including how critical decisions are made based on insights provided by complex algorithms.
Adopting a company culture that sees AI not as a threat, but as a tool to augment human thinking and make better and faster decisions.
Ensuring legal and compliance risks are being properly addressed.
Driving human-machine collaboration — reimagine decision making and work through advanced cognition models that simulate human thinking with greater precision and speed.
Coupling AI-driven solutions to core decision support and transactional systems and connecting advanced AI systems to traditional applications and technical infrastructure.
Ingesting structured and unstructured internally and externally generated data that is spread across multiple silos.
Ensuring the validity of data that will be exposed to AI-driven solutions.
Have The Right Business Focus
AI projects fail or succeed to the extent that specific use cases are identified that have the potential to demonstrate meaningful business value. Defining the business value that AI and ML solutions can deliver around a specific use case is paramount. While AI promises considerable economic benefits, gaining broad traction with business stakeholders is only possible if specific business outcomes can be identified upfront.
For example, in financial services, more sophisticated risk analysis, anti-money laundering, advanced claims management, creditworthiness evaluation and intelligent customer onboarding have become prime focus areas. In manufacturing, predictive supply chain management, predictive maintenance and smart demand forecasting are where most of the investment is going. And in retail, predictive inventory planning, recommendation engines and hyperpersonalized customer engagement have become key competitive opportunities. All industries have specific use cases where AI is transforming the business.
Aside from having the right business focus, upskilling the workforce to work with AI is equally critical to implementing modern AI-based solutions. The ability to democratize AI transformation across the enterprise by adopting tools and capabilities to enable business users to quickly test algorithms will also be crucial to gaining traction.
The Right Foundation For AI
Aside from identifying the right business use case to leverage AI, companies are increasingly faced with having the right technical infrastructure to support modern AI applications. The integration of traditional software and data environments with modern ML and deep learning applications is proving to be a formidable challenge.
A good place to start is the pursuit of a modern enterprisewide technical architecture. Just like a blueprint for the architecture of a building specifies how electrical, plumbing, staircases, passages, telecommunications and other facets are to be built, a layered technical architecture provides the foundation that defines how data can be ingested and leveraged across traditional and AI-based solutions.
The information harvesting layer of a modern technology architecture is where data is efficiently scanned and cataloged. Market-leading services and solutions can be leveraged to connect with any data source inside or outside the enterprise. The services within this architecture layer automatically extract, unify and organize information, leveraging semantic technologies that enable the ingestion of this data into the knowledge fabric.
The knowledge fabric layer is where enterprise data is converted into knowledge. The most common and efficient way of representing an enterprise’s knowledge domain and artifacts — that is understood by both humans and machines — is enterprise knowledge graphs (EKGs). An EKG is a perfect way of relating your structured and unstructured information and discovering facts about your organization.
The previous layers are all about preparing data to support AI algorithms without being concerned about data collection. This layer is where AI models and algorithms can be embedded into the very core of the architecture to create valuable insights with the potential to augment human thinking across disciplines.
Human And Machine Consumption
Ultimately, derived knowledge has to be consumed by humans or machines in an intuitive manner. The human and machine consumption layer provides easy-to-use interfaces across web, mobile and API services to enable data access to the knowledge fabric layer.
Workflow Orchestration And Security
All previously identified processes need to be managed (i.e., scheduling and monitoring, using a workflow orchestration tool, etc.). A workflow orchestration tool allows the enterprise to define the entire data pipeline. The data pipeline may include data harvesting in batch or real-time streaming, training and evaluation tasks, monitoring model performances, applying AI models in batch and feeding the result back to the knowledge fabric.
AI solutions require high-quality data that is standardized and aggregated across the enterprise. The power of AI systems to work on complex problem solving on a 24-7 basis means that an enterprise's technical architecture must deliver a continuous flow of data upon which smart decisions can be made. This means a continuous harvesting of data in multiple formats both within and outside of an organization’s traditional boundaries. Not having access to the right data creates the risk that complex AI algorithms will use outdated or incorrect data.
Companies that successfully leverage the capabilities of AI — aside from ensuring the right focus around specific use cases that can show positive results — must also spend time defining the underlying technical architecture to enable this new generation of solutions.