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Governing AI for business benefit

Governing AI for business benefit

By Shannon Harris, Managing Director, IBM New Zealand

Credit: IBM

Last month, a group of professionals and industry experts came together to launch an AI Governance website in response to a survey that indicated governance is a key focus although half of businesses are already using some form of AI.  The website aims to educate and advocate the responsible use and governance of AI in New Zealand. It also complements the Privacy Commissioner guidelines on the use of generative AI to ensure compliance and protection of individual rights. 

The industry initiative is a timely move, as here at IBM New Zealand, are seeing so much interest among our clients in applying generative AI to address some of our most challenging issues. Yet at the same time, we have also learnt that many businesses are grappling to govern the development, implementation and management of generative AI. 

The reality is AI governance should be a best practice for any organisation intending to leverage the powerful technology for business benefits. The perennial question remains – how can organisations have a solid grasp over their processes and governance to meet internal and external regulatory requirements?  

The rise of generative AI

Before we dive into the subject of governance, let’s take a step back into the history of AI. Scientists and researchers have been toying around with AI since the 1950s but its impact was not felt until IBM’s Deep Blue beat chess grandmaster Gary Kasparov in May 1997. Since then, the combination of algorithms and neural networks led to formation of deep learning AI systems that quietly penetrated an array of devices ranging from automated production lines to smart phones.  

In 2011, IBM Watson bested two long-running contestants at the Jeopardy! game show, demonstrating its ability to respond to difficult questions. Over the past decade or so, our researchers have honed IBM Watson to cater to our clients’ needs. In March this year, IBM introduced watsonx, a generative AI platform, that would give our clients a toolkit to build their own foundation models to support their business.  

Following the watsonx launch, IBM announced collaborations with industry leaders such as NASA, NatWest, Mitsui Chemicals, Samsung SDS, MMPC, StarHub, FYI, USTA and more; to expand the horizons and capabilities of foundation models. In the process of doing so, we will help them understand, govern, productise and cost-effectively deploy the capabilities of foundation models that are tailored and customised to their needs which is very exciting.  

In September this year, IBM launched watsonx Code Assistant, a generative AI-powered assistant that helps enterprise developers and IT operators code more quickly and more accurately using natural language prompts. The product currently delivers on two specific enterprise use cases.  

First, IT Automation with watsonx Code Assistant for Red Hat Ansible Lightspeed, for tasks such as network configuration and code deployment. Second, mainframe application modernisation with watsonx Code Assistant for Z, for translation of COBOL to Java. These use cases allow our clients to modernise their applications – securely  – while still retaining their current platform of choice.  

Baking governance into generative AI

Issues such as bias, prejudice, inaccuracies and hallucinations in generative AI had put the spotlight on the veracity of the data that is being used to build foundation models.

How do these efforts address the issue of governance? Issues such as bias, prejudice, inaccuracies and hallucinations in generative AI had put the spotlight on the veracity of the data that is being used to build foundation models. Can generative AI be trusted? What sets IBM apart in helping clients train and deploy these models without unnecessary data exposure? How do we commit to maintain data privacy to meet regulatory requirements? Understanding data and its governance is more important than ever as massive amounts of data from multiple sources are being used to train generative AI models.  

Governance and trust is paramount for business sustainability. And this is what sets IBM apart in the marketplace. We established the Trust and Transparency Principles and Pillars in 2018 followed by an AI Ethics Board last year. More recently, we sought to indemnify clients against copyright or other intellectual property claims for using IBM generative AI systems.  

The good news is watsonx.governance which is now generally available, provides organisations with the toolkit to manage risk, transparency and compliance with an eye on future AI-focused regulation. The toolkit is similar to a “nutrition label” to help organisations translate regulations into enforceable policies that would be vital for New Zealand enterprises in developing large language models.  

The CEOs we surveyed are concerned about data lineage and provenance (61%), data security (57%) and data privacy (45%) that are barriers to adopting generative AI. In this environment, AI and data governance isn’t just an IT issue, but a strategy for value creation.  But what we can do with generative AI is defined by how we select, govern, analyse and apply data across the organisation. And trust is built by communicating that process transparently.  

Here is a checklist that can help put governance at the front and center of generative AI deployment:  

  • Governance and guardrails agenda: Make governance a fixture on the executive leadership team’s agenda to balance the power of generative AI with the guardrails required for trustworthy execution.
  • Governance-savvy team: Build and educate your team and the board on generative AI and governance concurrently. Then make AI and data governance a recurring agenda item at board meetings to ensure it gets the attention needed.  Active leadership is important to make it happen and avoid delegation and treating governance as an after-thought.
  • Govern all: Build governance into the entire system from end-to-end and, in each and every stage of the generative AI lifecycle. A piecemeal approach would be a recipe for failure. Break the design and execution of generative AI and data governance out of organisational silos.
  • Appoint a leader: Appoint and empower a senior executive to lead generative AI and data governance across the enterprise. A hands-on, eagle eyed approach would go a long way to mitigate the risks of failure due to fragmented ownership and accountability. And don’t be afraid to pivot when needed.

Businesses in New Zealand can lean into the community experience and expertise to develop a governance framework and baseline when adopting generative AI.

Companies that elevate the AI and data governance conversation to the C-suite have the potential to overcome the obstacles that are hindering their platform ambitions and the opportunity to earn the trust of employees, ecosystem partners and customers.

In light of the announcement by the Privacy Commissioner and collective community approach, businesses in New Zealand have a great opportunity to tap into the power of generative AI to resolve some of their pressing business challenges.  And at the same time, they can lean into the community experience and expertise to develop a governance framework and baseline when adopting generative AI. As the technology continues to evolve, businesses here would be well armed in assessing progress, addressing issues and anticipating risks.

At the core of IBM’s value proposition is that generative AI augments human intelligence. The insights developed belong to the creator and the decisions that are made by AI must be explainable. It goes without saying that a lot of work is needed to adopt and scale generative AI. We have the expertise and experience to help our clients bring the capabilities to life with governance at the heart of it.  

About the author 

Credit: IBM

Shannon has held technology leadership roles in New Zealand and the United Kingdom for over twenty five years. For eleven years she was also a business owner and dealt extensively with suppliers throughout Asia, manufacturing products and retailing in New Zealand. Shannon’s career journey has centred on helping Kiwi businesses accelerate their digital agenda and thrive in a global market.  She’s passionate about cultivating diversity of thought and experience by nurturing the talent pools that exist in our young woman, Māori and Pacifica communities. She believes harnessing untapped talent is the key to building a sustainable and positive future for New Zealand. Shannon is focused on developing and coaching people to reach their full potential; individually, with IBM, and with ecosystem partners.

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