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AI: ‘Market buzz’ morphs into a gold rush for firms 

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Many industries are now prioritising budget allocations for generative AI in their board meetings, recognising its transformative potential.  PHOTO/Michael Kakumirizi

The pursuit of profit in this generation is a strategic confronted battle, a reality well recognised by today’s business leaders. 
The post-COVID era has amplified these challenges, requiring exceptional resilience and strategic acumen. Contributing factors include rising input costs due to macroeconomic conditions, cash-strapped consumers, and the shift towards superficial advancing technologies driving a race for business growth. Additionally, management, regulation, and competition play significant roles.

In the hunt for a way through, a section of firms have tried Artificial Intelligence (AI) for more business value by cutting costs and increasing returns with a strong asset base.
And ChatGPT is among the top sought generative AIs. It is an acronym for “Chat Generative Pre-trained Transformer.” For those of us not in the genius club, this means the brilliant minds at OpenAI, a tech firm, crafted a technology that can chat with you just like a human would.
This generative AI (gen AI) instantly became a market buzz when the world discovered it in 2023.

This year, several companies are all in to test its capabilities of deriving business value. And many surveys have been made in that regard.
One that Prosper Magazine tracked was conducted by QuantumBlack AI of the global technology research firm McKinsey.
The survey, which included 1,363 respondents from the fields of business, legal and professional services, healthcare and pharmaceuticals, media and telecommunications, consumer goods and retail, energy and materials, technology, advanced industries, and media, was released on May 30.

Its results indicate that, after witnessing tangible benefits from its use, 65 percent of respondents are regularly using gen AI, nearly twice as many as in the research firm’s previous survey, which was conducted just ten months ago.
This year only, AI adoption has jumped to 72 percent, up from the previous six years’ findings, which showed AI adoption hovering around 50 percent.
Some of the notable benefits that companies are noticing are cost decreases and revenue jumps, and the most appetite has been realised from professional services, energy and materials.

Why? AI has potential to examine previous performance of companies, behaviour and other metrics that can be used to improve and optimise processes and results. 
This is very important when looking at customer buying behaviour, predicting trends or detecting fraud.
The average firm uses it in marketing and sales, in product and service development and IT.

Others in design development, scientific literature and research review, data management and risk departments.

Many industries are now prioritising budget allocations for generative AI in their board meetings, recognising its transformative potential.
According to McKinsey’s data, some are considering investing more than 5 percent of their digital budgets in generative AI solutions, as opposed to nongenerative, analytical AI solutions.

Others want to spend more than 20 percent on analytical AI than on gen AI. 
“Looking ahead, most respondents—67 percent—expect their organisations to invest more in AI over the next three years,” the survey notes show.
But are those investments paying off? 
For the first time, QuantumBlack investigated the value created by gen AI use by business function and discovered that human resources, supply chain, and inventory management have the highest proportion of respondents reporting cost savings of more than 5 percent.

Alex Singla, the senior partner and global co-leader of QuantumBlack, AI by McKinsey says generative AI (gen AI) is no longer a novelty in 2024. 
“The technology’s potential is no longer in question. While most organisations are still in the early stages of their journeys with gen AI, we are beginning to get a picture of what works and what doesn’t in implementing—and generating actual value with— the technology,” he states, emphasizing that the business goal must be paramount. 

“Leaders must avoid the trap of pursuing tech for tech’s sake. The greatest rewards will go to those who are not afraid to think big. As we’ve observed, the leading companies are the ones that are focusing on re-imagining entire workflows with gen AI and analytical AI rather than simply seeking to embed these tools into their current ways of working,” he explains.

“Enterprises will need to design a gen AI stack that is robust, cost-efficient, and scalable for years to come. They’ll also need to draw on leaders from throughout the organisation. Realising profit-and-loss impact from gen AI requires close partnership with human resource, finance, legal, and risk to constantly readjust the resourcing strategies and productivity expectations,” Mr Singla stated.

But as businesses begin to see the benefits of gen AI, they are also recognising the diverse risks associated with this technology. 
These include risks related to data management, like data privacy, bias, or Intellectual Property (IP) infringement, as well as risks related to model management, like erroneous output or a lack of explainability.
But firms consider inaccuracy, cyber security and IP infringement to be more threatening.

By focusing on these risks, they have made less effort to consider workforce and labour displacement, and they are not increasing efforts to mitigate them. 
“In fact, inaccuracy—which can affect use cases across the gen AI value chain, ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organisations are actively working to mitigate,” QuantumBlack noted.

This is because a section of the companies—44 percent—have already encountered unfavorable outcomes from the application of generative AI, such as explainability, accuracy, and cyber security.
Lareina Yee, senior partner at McKinsey and chair of the McKinsey Technology Council, says to prevent this responsibly, AI needs to be introduced gradually, and putting company values first in education can help in this regard. 

“Organisations must establish clear principles for how they apply generative AI (gen AI) and set up guardrails to ensure its safe implementation,” she writes, adding that recognising the importance of data security means that company-level data and prompts remain within the enterprise walls. 
“For that to happen, the enterprise must have secure contracts with large language model and application providers, as well as robust training, to make sure employees understand the difference between enterprise tools and public tools so that code or proprietary data are not inadvertently shared in public models,” she says.

She does, however, stress that businesses must remember that generative AI deployment necessitates technical teams’ familiarity with risk and mitigation techniques. 
“Gen AI solutions are probabilistic models that can make mistakes or inadvertently amplify biases in training data, so testing models before they are deployed is essential. Without a robust testing approach, it is hard to deliver on responsible AI,” she says.