The CIO’s Guide to Artificial Intelligence

The basics of artificial intelligence

Gartner defines AI as applying advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action.

Common definitions of AI focus on automation and, as a result, often fail to make clear the opportunities available to IT and business leaders. AI is technology that emulates human performance, typically by learning from it.

“ Look for critical business points where human interaction or human expertise adds value”

The most common mistake with AI is to focus on automation rather than augmentation of human decision making and interactions. If CIOs focus only on further automation via AI, they miss the hidden opportunities for greater personalization and differentiation. AI can augment humans, as it can classify information and make predictions faster and at higher volumes than humans can accomplish on their own.

CIOs should look for critical business points where human interaction or human expertise adds value. They should find examples where such value is manifested in very large amounts of data, especially where the data includes the outcomes that they desire to affect — where customer interactions record whether the customer’s experience was positive, whether a purchaser added an item to a cart, or whether a brake disc was revealed to be worn the predicted amount. They then should consider how AI might augment those efforts to create even more value.

Potential use cases

AI technology covers a wide range of potential use cases across industries. Typically, common AI applications analyze contextual interaction data combined with historical data in real time.

Here are next-generation examples of use cases:

• Retail: Use an on-premises robot to bring requested items (e.g., different size, color) to a consumer waiting in a dressing room.  
• Sales: Transcribe and analyze online sales meetings and calls and condense sales calls into actionable summaries.
• Finance: Augment tax preparers’ expertise with AI techniques to optimize tax returns for each taxpayer. Algorithms process client answers to questions and a text analyst looks at legal and regulatory changes.
• Security: Monitor video feeds to detect, prioritize and alert about potential or actual security incidents, based on intelligent image analysis.
• Emergency services: First responders can identify victims more quickly, allowing for accelerated retrieval of medical information and first aid.

Read more: How to Build a Business Case for AI

Assess AI maturity

Typically, AI is used to enhance existing applications and processes. For example, it might automate decisions or classify complex data. Both of these examples would traditionally require human intervention and, consequently, increased costs. But AI enables the enterprise to accelerate the process.

To establish a strategy, measure your organization against the AI maturity model. This model can be used as a framework to identify where your organization is on the potential growth curve, communicate with management and decide what steps need to be taken. No matter where your organization is on the map and how far it has to go, ensure that strategies are highly adaptive, with ample room for experimentation.

Pick a spot on the AI model

AI is complicated, and many enterprises are still figuring out how to implement and gain value from the technology. Organizations can fall anywhere on the maturity model, with most currently in the awareness phase and a handful in the transformational phase.

1. Awareness: Conversations about AI are happening, but not in a strategic way, and no pilot projects or experiments are taking place.
2. Active: AI is appearing in proofs of concept and possibly pilot projects. Meetings about AI focus on knowledge sharing and the beginnings of standardization conversations.
3. Operational: At least one AI project has moved to production and best practices, and experts and technology are accessible to the enterprise. AI has an executive sponsor and a dedicated budget.
4. Systematic: All new digital projects at least consider AI, and new products and services have embedded AI. Employees in process and application design understand the technology. AI-powered applications interact productively within the organization and across the business ecosystem.

Overcome the AI obstacles

When asked about top barriers to AI, enterprises cited finding use cases and defining strategy, security/privacy, risks and integration complexity. Nearly two of three organizations cited finding a starting point as a concern.

This plays out further when considering expected AI project timelines versus actual project timelines. Most organizations start an AI project with a plan to launch the project within two years. However, organizations past the initial planning process estimate it will take four years.

Organizations need to set realistic timelines for AI projects and ensure the desire to push forward with a popular technology doesn’t overrule realistic drawbacks and planning. The hype itself can be a problem, alongside other logistical and strategic challenges.

“ AI projects face unique obstacles due to their scope and popularity, misperceptions about their value, the nature of the data they touch”

Further, it is difficult to determine an AI project’s ROI because most organizations are too early in the process to see any return. Most ROI will be seen in cost reduction and efficiency, as that’s how AI is currently used. However, as enterprises evolve their AI expectations and projects, the technology will mature to have more transformative and strategic impacts.

“AI projects face unique obstacles due to their scope and popularity, misperceptions about their value, the nature of the data they touch and cultural concerns,” says Andrews. “To surmount these hurdles, CIOs should set realistic expectations, identify suitable use cases and create new organizational structures.”