We can think of AI as software that helps organizations more effectively reach their goals, e.g., by reducing costs and increasing revenues.
Gaining benefits from AI, or any other innovative technology, requires organizational change. New strategies. New job descriptions. New workflows. New org charts. Training.
What makes AI different? Are the challenges faced by organizations adopting AI different from those encountered in adopting other software innovations?
After all, using computers to revolutionize organizations is nothing new. IBM developed the SABRE reservation database for American Airlines back in 1964, replacing manual file cards with a system that could handle 83,000 reservation requests. Pretty disruptive!
So how is AI changing organizations? Let’s take an example – the financial industry. AI’s ability to find patterns in mountains of data can help financial organizations:
- Make more accurate and objective credit decisions by accounting for more complex relationships among a wider variety of factors
- Improve risk management using forecasts based on learning patterns in high volumes of historical and real-time data
- Quickly identify fraud by matching continuously monitored data to learned behavioral patterns
- Improve investment performance by rapidly digesting current events, monitoring and learning market patterns, and making fast investment decisions
- Personalize banking with smart chatbots and customized financial advice
- Automate processes to read documents, verify data, and generate reports.
To make these improvements using AI, a financial organization needs to undertake the sort of activities needed to introduce any new software into their operations and products, such as:
- Establish strategic priorities and budgets
- Clarify and communicate objectives and plans with stakeholders
- Work with software developers/vendors/users to establish and carry out software/system development projects
- Create/modify procedures and organizations to take advantage of the new software
- Hire, train, retrain the workforce as needed
- Monitor results and adapt as required.
What are the special challenges AI brings to these activities?
The first challenge is AI’s high profile. Managers feel compelled to catch the wave of the future, and workers fear they will lose their jobs. As a consequence:
- Managers may undertake AI projects with unrealistic expectations. AI can be extremely effective, however only when there is access to large volumes of data relevant to an operational role that truly benefits the organization
- Employees essential to successful adoption of the new systems may stand in the way or quit if they see AI as a threat.
Clearly due diligence is required in the first case, and effective employee engagement in the second.
A second challenge is an “all or nothing” aspect of AI. To reap the benefits of AI, the core AI technology must be fully integrated with an organization’s IT infrastructure and business operations. Notice in the financial organization example above, how many aspects of the organization could be affected by AI. To successfully integrate AI, an organization must be “all in”. To do this requires particularly high levels of communication, investment, and cross-organizational participation.
A third challenge is that with successful adoption of AI, the requirement for personal growth and change is pervasive, up and down the organization. Leaders, engineers, and operators all need to learn and embrace the changes brought about by AI. For many, this can be an exciting opportunity for career growth and more fulfilling jobs. Others will mourn the lost relevance of hard-won experience. The organization must be prepared to invest in training, re-training, and professional development. The more AI takes over routine data gathering and analysis, the more important ‘soft’ skills will be to every worker.
Finally, a fourth challenge is that even a very capable AI can produce unintended results. For example, although AI-based analysis can lend objectivity to credit decisions, training AIs using historical data can promulgate past biases. Also, when highly-trained AIs encounter situations they have never seen before, the results can be unpredictable. This means AIs need human supervisors, and these supervisors are dealing with a whole new kind of employee!
Next: What kinds of jobs will AI impact?
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