AI and decision support – the CIO superpower

AI and decision support – the CIO superpower

Demands on the CIO have never been more intense, or more important. What if there was a way to tame the maelstrom and make better, faster decisions? Natalya Makarochkina, Senior Vice President, Secure Power Division, International Region at Schneider Electric, tells us more…

Natalya Makarochkina, Senior Vice President, Secure Power Division, International Region at Schneider Electric

Over the last decade or so, the trend towards Digital Transformation has rapidly gained pace, riding the accelerating wave of technological development and driven by increasing competitive pressures, emerging growth opportunities and the need to increase efficiency and productivity. Then the pandemic hit, adding to existing volatility and vulnerability, with disruption in global supply chains.

In this environment, it has never been harder for the CIO to be the informed voice of reason and competitiveness when it comes to enterprise technology.

Increasingly, every new effort, whether it is Digital Transformation, environmental, social and governance (ESG), sustainability, or a new enterprise strategy, it falls to the CIO, in one form or another, to implement with digital tools and services, making it even more important for the CIO to be informed, effective and decisive.

What if there was a way to make those decisions in a better-informed, more structured way, and faster? And could such an approach be developed for the whole enterprise?

Technical and organisational challenges

As the pace of technological change becomes more rapid, complexity too, has crept further into enterprise data architectures.

A 2023 industry survey found that 98% of senior IT leaders have been impacted by increasing cloud complexity in some capacity, potentially leading to poor IT performance, loss in revenue and barriers to business growth.

Added to this, data complexity has grown as a major pain point for companies globally, with tech executives feeling the pressure to contain its impact on the business. Technical and organisational challenges may further stunt their enterprise strategies, with 88% citing working across current cloud environments as a barrier, while 32% struggle to align on a clear vision at the leadership level.

Complex decisions

This complexity is impacting the business too, as diversification, changing consumer demand, multiple product, brand and service lines all must be managed and analysed. While this has always been a challenge, omnichannel engagement and the emergence of new consumption trends, has accelerated the situation, further burdening the business. Research has found that nearly two thirds (65%) of business leaders report that decisions they make are more complex now than just two years ago, with more than half (53%) admitting to facing more pressure to explain or justify their decisions.

Tech and consumer brands can find themselves managing multiple products, distribution channels, promotion campaigns and marketing channels at any one time, each more data rich and diverse than ever before.

AI can deal with large amounts of data and reduce complex patterns into manageable loads. This strength of AI means infrastructure and data complexity can be reduced and minimised through optimised design and AI-monitored operation.

When decision-makers have trustworthy AI to cut through this complexity and the deluge of data, they can focus their time on identifying the best option from the recommendations, to develop a competitive advantage in the market.

Trust in AI

As seen with the recent experiences of generative AI tools such as ChatGPT, Artificial Intelligence can serve as a powerful tool to extend human insight and judgment. The growing opinion is that AI has enormous potential to support more and more decision areas in business. However, it must be seen to be working effectively, to develop trust and support from decision-makers and users.

When AI models start guiding strategic decisions, there is a shift in requirements. Users must be able to deeply trust the applications, say researchers. They have to find them indispensable when making major choices. If not, they can end up abandoning them.

The same principles apply to business. Decision support must be applied in a transparent way, allowing the user to keep a key level of control at first, while the system proves itself to be consistently effective and helpful. A follow on from this is the need for strategists to have clear evidence from the system to back up any actions advised. Transparency of process allows people to follow the logical steps made by AI, should the need arise. So-called ‘black box’ AI does not give the reassurance needed if a query arises.

Cultural shift

Going a little further, for AI decision support to be adopted across the enterprise, a cultural shift is required, not just a technological one.

Market dynamics show more decision platforms integrating AI capabilities, reports Forrester, as well as AI-based applications capable of using models for decision-making. The integration of traditional decisioning into AI is starting to hit its stride.

When it comes to AI-based decision-making, the biggest challenge is understanding AI as a major cultural shift instead of an isolated tool in a kit. AI, say the analysts, is very much a custom-fit for the work being done. Therefore, to apply it in as many places as it can to provide value will need that cultural change, as technological tolerance can vary across an organisation. For example, HR might be more willing to deploy AI-assisted decision-making than might a risk management function for cybersecurity. Both functions could benefit from AI assisted decision-making, but only if the underlying culture can see and accept the benefits.

Issues and concerns

However, there are also challenges when making this cultural shift.  An industry survey found that among senior IT leaders, 79% believe Generative AI has the potential to be a security risk, 73% are concerned it could be biased and 59% believe its outputs are inaccurate. This is in addition to legal concerns especially if externally used Generative AI-created content is to be considered factual and accurate, content copyrighted, or comes from a competitor.

AI promise

And yet the promise of AI, combined with the other emerging technologies, in the hands of well-informed business leaders, seems clear. According to one study by MIT Sloane, those businesses that are led by the digitally savvy championing emerging technologies such as AI, outperform other like-sized businesses by 48% on valuation and revenue growth.

Building trust

Trust is key in facilitating the cultural change necessary to employ and implement AI-assisted decision making in enterprise.

CIOs must gradually introduce the features and facilities of AI. Extracting the value of AI requires gaining quick wins, even while developing at enterprise scale.

Research in these areas found that the majority (71%) of organisations state they would trust insights from AI and ML platforms, despite the concerns for security, bias, and transparency. That trust can be developed and built through the reliable and helpful use of AI elsewhere. In our use of AI/ML in the EcoStruxure sphere, we have already shown its utility in managing complex, hybrid environments, but also its vital future role in addressing sustainability challenges. AI embedded in systems such as Data Centre Infrastructure Management (DCIM) and environmental management systems (EMS) can not only manage and optimise, but can also derive insights for wholesale improvement, which will be vital as sustainability targets and deadlines loom.

Embedded AI can also extend to predictive maintenance, where pattern analysis can reveal potential failures before an outage. These wins can help develop that trust for AI and ML within digital infrastructure as they are applied at ever more strategic levels, such as business decision support, strategy validation and implementation.

AI-assisted design and management

AI-powered management and orchestration systems will be critical in allowing organisations to meet their sustainability goals. This capability will be of particular importance in complex cases such as Edge Computing deployments, where architectural complexity will be an issue.

Architectural complexity threatens to be multiplied in Edge deployments, as the relative ease with which such infrastructure can be deployed means that unless great care is taken, their proliferation could be a problem. AI assisted design and modelling, such as through our extensive design tool portfolio, will allow optimal deployments to be made with efficiency and effectiveness uppermost, while also ensuring sustainability concerns are met.

People, amplified

With the increasing integration of AI into more and more applications and services, but specifically decision platforms, the technology has the potential to narrow down myriad options, more easily perform due diligence from data and sources, and significantly reduce complexity.

The CIO can initiate the introduction of technical decision support facilities, learning and optimising as they progress, gradually building trust. With incremental trust-building, the power of AI to amplify human decision-making and discernment can propel businesses towards faster, better and more informed decision-making, leading to a competitive advantage that may have seemed like a superpower just a few years ago.

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