The strategic approach to AI in the enterprise

When we look back at the cloud, virtualization, or the PC itself, fear of missing out often outweighed careful thought, and many organizations ended up wasting significant time and money reverting implementations that were either unproductive or harmful for their operating models.

We can see the same with artificial intelligence (AI) today. The current narrative is that AI will reshape the entire world, and that any company that is not at the forefront of this revolution will be consigned to the dustbin of history. It doesn’t matter what it’s used for or whether it gets good results. As long as management can report its existence to investors immediately, everything else will take care of itself.

Planning for artificial intelligence

While artificial intelligence is not necessarily doomed to fail, it can lead to complications if not implemented properly. Once AI has taken over a certain process, it is difficult to undo it. Therefore, a little planning is in order if the goal is to use AI as a valuable tool and not just as a technological window dressing.

Currently, the call center is an area where AI is proving extremely useful. Her skills in functions like speech analysis and determining customer intent make her one extremely valuable tool for sales and customer support.

Cobus Greyling, Chief Evangelist at developer of data productivity platform HumanFirst, notes that AI can contribute to all four elements of a modern call center environment: connection, orchestration, resource management, and knowledge and insight. However, some of the specific applications are easier to implement and offer greater business value than others.

Analyzing speech patterns and identifying customer needs are highly doable and profitable, while things like fully conversational self-service support and real-time agent coaching are intermediate and more difficult to develop.

Building knowledge graphs to optimize conversational skills or developing tools to route contacts intelligently are currently challenging projects that offer limited productivity.

Analytics with a purpose

The field of business analytics also encompasses a variety of operations, some more amenable to intelligent automation than others.

Ivy Liu, CEO of e-commerce consultancy Ivy Insights, points out that the Lead scoring can benefit tremendously from faster and more accurate analysis of key performance indicators which in turn allows organizations to revise or abandon underperforming initiatives while duplicating the high-performing initiatives.

In today’s fast-paced digital economy, where profit margins are becoming ever tighter, this is likely to become a key differentiator between successful and unsuccessful businesses.

Essentially, AI provides the tools to support real-time performance monitoring to provide an accurate picture of what is happening now and in the future – and they can be used across a range of processes including sales, marketing, finance and both mid- and long-term strategic development are used.

We can also take a look at the nascent field of DevOps to see how AI can be used to maximum benefit. First off, according to tech writer Binod Anand, AI makes it easier to manage input at every stage of the development process by collecting data from internal and external sources and analyzing it for accuracy, relevance and bias.

It also improves the effectiveness of the testing cycle to weed out bugs and increase overall productivity while speeding up the execution of security reviews.

Despite these benefits, however, overuse of AI can leave unresolved bugs in the DevOps lifecycle that can result in performance delays or outages.

Equally worrisome is the possibility of AI having unethical or disruptive effects on people’s lives, particularly when applied to critical applications such as healthcare, personal finance, and government services.

Too much AI can also become very costly, requiring significant computational and storage resources for training and operation, as well as for verifying the vast amounts of data required to produce appropriate results.

And once an AI model begins to rely on the results of other AI models, the risk of widespread disruption increases exponentially.

The right tool for the job

According to Gartner, AI is most effective when used them for three general use cases becomes:

• process automation
• personalization
• Increased employee productivity

Currently, most of these benefits are achieved by applying AI to one-off point-to-point solutions. Large-scale solutions can create greater value, but these can be difficult to achieve without significant changes to established business processes and how teams interact within the organization.

One thing businesses should keep in mind, especially when it comes to generative AI like ChatGPT, is to keep up to date with new regulatory frameworks, particularly those related to copyright infringement and legal liability.

Also, all forms of AI can run afoul of the law by providing false results due to algorithm instability, poor data, lack of human skills and training, and a host of other factors. The last thing a company wants from its AI investment is a hefty fine or even criminal charges.

Summary

No one has ever claimed that AI is a panacea for all corporate ailments (although some may have suggested this). A sound strategic vision should target the low-hanging fruit first, ie the simplest implementations with the highest yield.

That way, at least, you get the user community’s acceptance that the technology can improve their lives in a meaningful way. As soon as this step is completed, the further implementation will benefit from the internal experience gained up to that point and perhaps also from the AI-supported insights from the first implementation round.

As with any business initiative, AI should start with a goal and a plan to achieve that goal. From there, she should work her way into the business model naturally, not forcefully.

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