What is it?

 

Automation, machine learning (ML) and artificial intelligence (AI) are input-based technologies with varying degrees of autonomous capabilities. Automation is solely fixed on repetitive, instructive tasks, whereas ML and AI behave with an element of independence and freedom from data input. Now becoming increasingly available as business applications, these so-called intelligent technologies are primarily being used to generate real-time insights, predictive analysis and data-driven forecasts, helping to make business nimbler and more responsive.

In turn, businesses are empowered to make smart, competitive and effective business decisions based on razor-sharp information. Prior to intelligent technologies – which are continually evolving in capability and scope every day – finance executives relied on a significant amount of manual planning, analysis and reporting to generate the insights that would inform financial planning and business strategy. Although ML has been with us for some time in native forms – such as speech recognition, predictive text, image recognition and spending pattern identification – it’s nothing compared to the new and emerging intelligent technologies available to businesses today.

 

When AI goes wrong

 

AI has the power to be truly transformative – but equally ruinous if not specified, implemented and managed expertly. Here we outline key risks and opportunities for financial executives and leaders. As well as streamlining manual processes, intelligent technologies allow finance executives to focus on truly human thought – creative initiatives, new business, strategic growth planning and training, for example. These applications are basically workhorses that empower finance executives to allocate their time more productively to the benefit of business growth.

 

When AI goes wrong

 

Potential risks to consider

 

 1. Diminished competitive advantage

Not effectively harnessing AI business tools or applications will undoubtedly impact competitiveness. High on the agenda of your business rivals, intelligent technologies are now considered fundamental to establishing a strong and sustained competitive advantage. Automation, ML and AI are being deployed to drive efficiency, productivity, cost savings and ultimately, growth. It’s therefore in every finance executive’s best interest to lead transformation. Failing to jump on the fast-moving train of intelligent technologies will mean your business is left behind, and the longer you wait, the harder it will become to catch up.

And, as AI becomes more accessible, even smaller enterprises should seriously consider earmarking budget for AI technology. The extent to which, of course, will be relative to your strategic goals and the additional business value which can be expected.

2. Underutilised investments

As a finance executive, you need every technology cost to deliver a favourable, fast return on investment and correlate with tangible improvements in business processes. With automation, ML and AI being new to many companies, an inherent lack of understanding about how they can work and be used to best effect can lead to chronic under-utilisation. Whether companies purchase a product that isn’t fit for purpose, integrate poorly or fail to adequately train colleagues, the result is the same – intelligent technologies that are superficial, expensive and add no demonstrable value. In the worst-case scenario, the tools may actually slow business down by being badly configured.

Wasted costs and unplanned remedial spend run the risk of negatively impacting financial and business planning, cash in bank and profit. The only way to avoid this is with considered planning, specialist support and training.

3. Inflated cyberattack risk

 Today’s acutely competitive, digitally evolving business landscape can encourage rushed technology implementations, with finance executives sometimes feeling pressured to sign off spend. As such, cybersecurity can get overlooked in the haste. Many decision-makers also don’t realise that a specialist cybersecurity product often needs to accompany automation, ML and AI tech, which invites avoidable but severe risk to a business.

Automation, ML and AI applications serve as additional channels for exploitation, with cybercriminals devising ever more cunning ways to access funds and data via new technology vectors.  And, it goes without saying that a security breach isn’t good news for financial or business stability. The nature of AI also means that it’s integrated with several business systems, so that it can harvest and learn from how your business and its customers behave. Therefore, cyber attackers could use your AI systems as a trojan horse and cut off the IT you need to trade. Needless to say, the risks to your bottom line, revenue, stability and financial planning are high.

 

 

How to minimise risk

 

1. Undertake a business value mapping exercise

 Bring together key team with members from IT, finance, operations and senior management to complete a company business value mapping exercise. Identifying where business processes are lacking, underperforming or underutilised will enable you to make more effective investments through clear visibility of where automation, ML and AI can deliver most benefit.

As mentioned earlier in this chapter, business AI implementation has become something of a race. Aspire not to adopt ASAP, but to adopt valuable intelligent technologies as quickly, safely and Responsibly (ASAR) as is feasible to do so. This often marks a step into the unknown, so be sure to work with a specialist IT and technology partner to reap performance and growth rewards without delay.

 2. Book a cybersecurity audit

The only way to truly know your cybersecurity vulnerabilities is to audit your entire IT estate – newly-implemented intelligent technologies being high on the list. An audit will reveal weaknesses and vulnerabilities and accurately pinpoint business needs to fortify defenses to avoid costly and disruptive cyberattack via ML or AI channels.

 

 

3. Arrange regular specialist training

To ensure that ML and AI investments continue to deliver and contribute to business growth and financial efficiency, arrange for any colleagues that will directly use intelligent technologies to be fully and regularly trained. This will ensure proper use but also help to encourage users to identify other uses for the technology.

4. Assign an intelligent technologies owner

This individual will be responsible for performance monitoring and training. Their role will flag up where automation, ML and AI technologies can be optimally utilised and identify upgrade or reconfiguration requirements.

 

For information about how K3 can help you to safely benefit from AI, click here. For more great free cybersecurity resources, visit our Security Surgery. Or to chat with one of our advisors, contact us via email or call us on 0844 579 0800

 

by Craig Bradshaw

Head of Account Management

A technology enthusiast working in the Technology industry for almost 20 years, looking to deliver exceptional customer experience.

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