The Future of Business AI Is Human
L.E.K. Consulting is a Business Reporter client.
This fall, game designer Jason Allen entered a picture, “Theatre d’Opera Spatial,” into the Colorado State Fair fine arts competition. It won first prize in the digital arts category.
Nothing unusual there. Except that Allen didn’t really create the artwork. He used Midjourney, an artificial intelligence (AI) text-to-image generator. Did he cheat, or was he simply demonstrating the power of AI to perform human tasks—even those involving substantial creativity?
AI is increasingly transforming organizations across all sectors of the economy. It’s being used to improve and automate processes, strengthen customer relationships and generate predictive analytics, and even to support human resource management.
Three-quarters of business executives anticipate that AI will make business processes more efficient, as well as create new business models. It is therefore no surprise that, despite economic uncertainty, funding is still flowing into AI startups, with annual investment continuously increasing since 2014, and total investment reaching $77.5 billion in 2021.
Getting AI right
However, it is not simple for businesses to use AI effectively. According to Stuart Robertson, a Partner at L.E.K. Consulting, a successful AI implementation requires four things: the right problem, the right data, the right expertise and the right organizational structure. If any of these isn’t right, the AI project is doomed to failure.
Having appropriate goals—the right problem—means applying AI to problems of high business value, where AI techniques can extract maximum value from data. This is an increasingly rare issue for mature organizations, many of which have been exposed to AI-powered services for some years.
There is also a need to ensure that appropriate resources—the right expertise and the right data—are in place. AI skills and experience may be in short supply, but they are readily available to organizations that can pay. And most organizations “run on data,” meaning that the issue isn’t usually having the right data, but rather deciding on how to best use AI to analyze it.
But even by addressing the right problem using the right expertise and data, many organizations fail to deliver real value from AI. That’s because putting AI technology and data scientists together in a room is not going to deliver magic solutions to business problems unless an organization changes the way it is working.
The right organizational structure
Along with investing in AI technology and skills, organizations also need to invest in restructuring the way that they work. “In our experience, the most common point of failure is at the organizational configuration level,” warns Robertson.
Reconfiguring the organization so that it works effectively with AI does not have to be complicated. AI should not be regarded as a separate discipline in a specialist knowledge silo. Instead, organizational roles and processes need to be adjusted so that people can work alongside AI algorithms, using AI to support their decisions on a day-to-day basis. AI should be integrated into workflows, rather than being treated as an external support tool that people use from time to time.
Treated in this way, AI can enhance the way that people work, reducing risk, increasing efficiency and the quality of decisions and enriching job satisfaction.
Managing the outcomes of AI models
To be successful, any investment in AI needs to demonstrate improvements to business outcomes, effectively (and cost-effectively) solving the problem it was set up to address. This requires constant monitoring of the quality of the decisions that the AI system delivers, and their effects on the wider organization.
For example, while the AI algorithm may have been developed using highly structured processes, and trained using highly representative data, models can drift over time as small errors get repeated and amplified, or circumstances and data change since training. A “human in the loop” is needed to retrain, test, tune and retune the model as it is used.
It is at this interface between AI and human where some of the most difficult AI management problems are found, and their solutions can only be developed by humans.
The future of AI technology
Ever-more powerful computers are improving the effectiveness of AI systems and creating new ways of delivering support to decision makers. Quantum computing, which allows calculations to be undertaken simultaneously rather than sequentially, may become commercially available by the end of the decade, exponentially accelerating processes and enabling complex problems, predictions and simulations to be calculated in seconds.
AI is only going to get more useful and universally applicable for enterprises. More and more businesses have gone beyond experimenting with it and are now using it in earnest to improve business outcomes. And they may be doing better than they think they are; it’s easy to undervalue progress with AI because of the media hype around AI.
Any business that has made a start with AI—to automate a process, build a product recommendation engine or analyze media content around their brand—is on their way to success. New and better opportunities will arise as organizations learn from their experiences and technology continues to evolve.
Today’s AI is very much a controllable management tool. It certainly needs the right techniques and conditions to be effective. But more than technology and processes, it needs people. Working closely together, people and AI systems represent a powerful future for business.
L.E.K. can help you define how to reengineer your organization to take full advantage of the potential of AI. For more information, please visit lek.com.
This article originally appeared in Business Reporter.
Image: iStock id1329751785
