Original source: The Paper
Image source: Generated by Unbounded AI
Digital transformation must become a core competency of the organization, which is an important piece of advice for CIOs and IT leaders.
Strategic priorities change significantly every two years or less, from growth in 2018, to COVID-19 and telecommuting in 2020, to hybrid working models and financial constraints in 2022.
The impact of generative AI, including ChatGPT and other large language models, will be a major transformational driver in 2024.
As CIOs begin preparing for the 2024 budget and digital transformation priorities, it is necessary to develop a strategy to identify opportunities to improve the business model, see near-term operational impact, prioritize projects that employees should test the waters, and Develop an AI-related risk mitigation plan.
But with all this excitement and hype, it’s easy for employees to invest time in AI tools that leak confidential data, or for managers to pick shadow AI tools that haven’t been vetted for security, data governance, and other vendor compliance . The bigger challenge is developing a realistic strategy and responding to the Impossible Dreamers. Here, the “impossible dreamer” is a kind of business leader who “goes to the sky in one step”, a kind of hell-level business executive.
Abhijit Mazumder, CIO, Tata Consultancy Services, said: “Transformation priorities should be able to be fundamentally linked to business priorities and what the organization wants to achieve. In most businesses, leadership is also focused on growth and Operational efficiency, but without losing sight of prioritizing resiliency, cybersecurity and technical debt elimination initiatives.”
Here are a few drivers of generative AI that CIOs need to consider when setting their digital transformation priorities.
Develop a game-changing large-scale language model strategy
How will generative AI and large languages affect every industry, for example:
- Utilize the intelligence brought by unstructured data to accelerate drug discovery
- Enable front-line manufacturing assembly workers to solve problems faster and more reliably
- Enable healthcare providers to provide patients with personalized solutions for health issues
- Assist in the development of new insurance, banking and other financial services products based on customer conversations
- Transforming education by providing teachers with new ways to enhance students’ creative thinking, collaboration and problem-solving skills
“CIOs and CTOs now have to not only get creative and do more with less, but also make deliberate investments to outperform their competitors, who may be delaying them,” said Jeremiah Stone, Chief Technology Officer, SnapLogic. Or cut back on your own transformation projects. Prioritize transformational initiatives that create new revenue streams, advance technology adoption, or reduce technical debt, especially considering the opportunities presented by generative AI.”
CIOs may recognize that a transformation program of this scale is a multi-year program that requires evaluating the capabilities of large language models, conducting experiments, and finding a minimum viable and sufficiently secure customer product. But not developing a strategy at all can lead to confusion, and one of the key mistakes IT leaders can make when attending board meetings is to fail to develop a plan for emerging world-changing technologies like generative AI.
Clean and prepare data for private large language model
Generative AI will increase the importance and value of enterprise unstructured data, including documents, videos and content stored in learning management systems. Even if enterprises are not ready to leverage generative AI to transform their industries and businesses, proactive transformation leaders are taking steps to centralize, cleanse and prepare unstructured data for consumption by large-scale language models.
Kjell Carlsson, head of data science strategy and evangelism at Domino’s, said: “With users across the organization clamoring for generative AI capabilities to be part of their daily Secure and scalable access to generative AI models and lets data science teams develop and implement large-scale language models tailored to organizational data and use cases."
There are now 14 large-scale language models outside of ChatGPT. If you have a large dataset, you can use platforms such as Databricks Dolly, Meta Llama, and OpenAI to customize proprietary large-scale language models, or build your own large-scale language models from scratch. Model.
Customizing and developing large language models requires a strong business case, technical expertise, and funding. Peter Pezaris, chief design and strategy officer at New Relic, said: “The cost of training large language models can be extremely high, and the output results are not yet perfect, so leaders should prioritize investing in solutions that help monitor the cost of use and improve the quality of query results. plan.”
Improve efficiency by improving customer support
McKinsey predicted as early as 2020 that artificial intelligence could create a value of US$1 trillion per year, and customer support is an important opportunity. Today, thanks to generative AI, that opportunity is even greater, especially as CIOs funnel unstructured data into large language models and enable service agents to ask and answer customer questions.
“Look for opportunities to leverage GPT-4 and large language models to optimize activities like customer support, especially in automating tasks and analyzing large amounts of unstructured data,” said Justin Rodenbostel, senior vice president at SPR.
Improving customer support is a fast track to delivering short-term ROI through large language models and AI search capabilities. Large language models require centralizing an enterprise’s unstructured data, including data embedded in CRMs, file systems, and other SaaS tools. Once IT departments centralize this data and implement large-scale language models, there is also the potential to improve areas such as lead conversion and HR onboarding processes.
“Enterprises have been stuffing data into SharePoint and other systems for decades, and by cleaning that data and using large language models, it may actually be valuable,” said Gordon Allott, president and CEO of GetK3. "
Reduce risk by communicating around large language models
There are more than 100 tools in the field of generative AI, covering categories such as testing, images, videos, code, speech, and more. So what’s stopping employees from trying a tool and pasting proprietary or otherwise confidential information into their prompts?
Rodenbostel advises: “Leaders must ensure, through research and an acceptable use policy, that their teams use these tools only in approved and appropriate ways.”
There are three departments, and it is the CIO who must collaborate with the CHRO and the CISO to communicate policy and create a governance model that supports intelligent experimentation. First, CIOs should evaluate how ChatGPT and other generative AI will impact coding and software development. IT departments must lead by example, clarifying where and how to experiment and when not to use tools or proprietary data sets.
The marketing sector is a second concern, where marketers can use ChatGPT and other generative AI in content creation, lead generation, email marketing, and more than a dozen common marketing practices. With over 11,000 marketing technology solutions already available, there is plenty of opportunity to experiment and make unintentional mistakes when testing SaaS with new large language model capabilities.
CIOs of leading organizations are creating a registry to onboard new generative AI use cases, define processes for reviewing approaches, and centrally manage the impact of AI experiments.
Re-evaluate decision-making process and delegation
Another important area to consider is how generative AI will impact decision-making processes and the future of work.
Over the past decade, many enterprises have aimed to become data-driven organizations by democratizing data access, educating more business people in data science, and instilling proactive data governance practices. Generative AI unlocks new capabilities, allowing leaders to prompt and get answers quickly, but timeliness, accuracy, and bias are key concerns for many LL.M.s.
“Placing humans at the center of AI and creating a strong framework around data usage and model interpretability will go a long way towards reducing bias in these models and ensuring that all AI outputs All ethical and responsible. The reality is that AI models cannot replace humans when it comes to critical decision-making and should be supplemented rather than allowed to take over entirely.”
CIOs should seek a balanced approach to prioritizing generative AI initiatives, including defining governance, identifying short-term efficiencies, and pursuing opportunities for long-term transformation.
