What is the smart way forward with AI?

What is the smart way forward with AI?

AI, especially generative AI, has the potential to be transformative. Following on from the widespread adoption of the cloud over the past 15 years, this is the next evolution in how we use technology. How can organizations activate it to achieve business benefits?

It’s a question on the minds of technology leaders. The Nash Squared 2023 Digital Leadership Report, which takes the views of more than 2,100 technology leaders around the world, found that seven in ten technology leaders believe the benefits of AI outweigh the risks – but only 15% of them feel prepared for the demands of the technology. Generative artificial intelligence.

Only two in ten have an AI policy and more than a third (36%) have no plans to even try one at this time. As our report reflects, there appears to be “excitement, confusion and anxiety in equal measure.” For perhaps the first time in my career, people are having real conversations about “just because we can, should we?” Artificial intelligence raises a whole new set of questions and discussions.

The report also notes that although widespread AI applications have been limited so far (to only 10% of organizations), we are now reaching a tipping point due to the growing popularity of generative AI.

It’s something I’m seeing everywhere: almost all companies are wondering what adoption of AI and generative AI specifically could look like. What are the potential productivity benefits, what are the risks, and what are the costs?

Parallels Cloud

I’ve mentioned the cloud already – and in many ways, the point we’re at is reminiscent of the early days of the cloud and SaaS. Subsequently, many IT managers and digital leaders were nervous about the move, fearing the emergence of “shadow IT” and the loss of control within the organization. But the most successful leaders realized that this was unstoppable, and they needed to embrace and manage it by leading the way rather than trying to micro-manage it.
The same applies here. In fact, it applies more than that. Because while there was a degree of optionality across the cloud and SaaS – it was essentially up to the IT manager if and when the organization would move in – with generative AI there isn’t the same element of choice: the employees in the company will Start using it (and already have). Realistically, there is nothing an IT manager can do about it.

Education and guidance for employees

This is why supportive guidance and policies for employees are essential because there are some clear risks to generative AI. At a basic level, these include:

  • Copy and paste commercially sensitive information
  • Using AI in research without knowledge of the results may be flawed
  • Using artificial intelligence to create content, but without knowing about potential copyright issues
  • Overestimating the usefulness of AI – Currently, most people can often detect whether an article or other content has been written by AI

Data privacy and confidentiality is a particular issue – it is the second biggest concern in our Digital Leadership Report (36% of technology leaders), trailing only the need for effective regulation (42%). Although hesitation about creating AI policies is understandable in such a new field, it must be overcome as soon as possible. It is better to have an incomplete policy and commit to updating it than to have no policy at all. Basic protocols should be clear and understandable. Employees need support to make good decisions. Besides, companies should support their employees by enhancing AI knowledge – by holding training and awareness sessions, discussion forums, online training resources etc.

Building approach

Our research finds that nearly half of organizations have some form of AI implementation or pilot. When it comes to generative AI, that number is about a third. My advice to them for success – and for other businesses that haven’t started yet – is to remember a few simple basic principles.
First, remember that AI is not the sole property of the IT function – so create a multidisciplinary team to look at it with involvement of other key stakeholders such as HR, Finance, Legal and Marketing. Also consider giving overall responsibility for AI to one person on the business leadership team as part of their role. This will provide greater clarity on accountability. In many companies, responsibility for AI is completely amorphous at present. Having an AI leader will also help take this out of the theoretical discussion of the board or executive committee and move it into a more practical, action-oriented realm.
Don’t try to use AI to solve everything at once – be clear about the specific use cases you want to use it for. This could be any of a myriad of things including:

  • To automate a particular process to make it faster and more efficient
  • To help employees produce content more easily, such as reports, articles, reviews, presentations, meeting summaries, or document templates
  • To research different models and designs for some products and services
  • To find specific facts or pieces of information from a very large data field
  • To improve the experience of external customers, such as help functions or predictive capabilities, and anticipate their needs

Identify the areas that have the highest potential to add value and focus on them. We did this in our business by assembling a multi-skilled team to create an intelligent chatbot called “BonBon”. Using OpenAI technology, this chatbot now allows our customers to automate tasks with human-like interaction, such as onboarding new employees or answering customer inquiries.
It is also highly recommended that you consider working with an independent third party technology consulting firm that can provide you with objective advice and guidance. In such a new field, this is the right time to intensify consultations.

Be clear about your ambition

Finally, be clear about your company’s ambition. Do you want to be an early adopter, lead the way and create a competitive advantage? Or a fast follower, with less risk and potentially lower cost? Or are you content to move much more slowly – what some might describe as “late” – to reduce risk and wait until the technology and use cases become more widely available and its power is proven?
For some, early adoption makes a lot of sense — such as companies with large numbers of people who use technology to do the same things, like a service center or customer service process. For these companies, cost savings may be the primary driver. Fast followers are likely to be companies that see the opportunity to boost value creation by harnessing AI to free people to focus on more value-added tasks.
Wherever you are on the spectrum, AI will have a huge impact. There is some noise of course, but this will change over time. We usually tend to overestimate the impact of new technology in the short term and underestimate its impact in the medium to long term. The goal must be to harness AI, with humans controlling the decision-making process, to achieve real improvements in efficiency, performance and outcomes – we want it to be ubiquitous but not omnipotent. This is the balance we need to strive for collectively.
gGeorge Lynch is the Head of Technology Consulting at NashTech, part of Nash Squared

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