Moving from hype to value

Integrating rapidly evolving AI technologies into your organization so they deliver tangible value is challenging. Generative AI is already showing potential within enterprises, and the rapid growth in data platforms such as Microsoft Fabric enables faster and more efficient data analysis. These advances create opportunity but can add complexity.

[55% of global IT experts believe generative AI is leading disruption in AI. ]

In fact, the HCLTech Trends 2024 report highlights that CIOs feel there is a lack of actionable strategies to help them implement AI technologies effectively and responsibly to ensure high-quality AI outcomes.

More data, more functions, managed more responsibly

Ensuring that AI is only used for its intended purposes becomes more complex as its adoption extends beyond the tech team. As it becomes truly cross-functional, the challenge of establishing principles and guardrails around the use of AI — and generative AI — in turn becomes the responsibility of the wider business. With data’s susceptibility to bias, generative AI’s unstructured data and the ever-expanding loop of technologies harnessing more data across numerous business functions and multiple human touchpoints, taking a holistic and structured approach to responsible AI use is a business imperative.

Our five-point framework offers a structured approach to help you begin to navigate these opportunities and complexities while unlocking business value.

Our five-point framework offers a structured approach to help you begin to navigate these opportunities and complexities, while unlocking business value.

Five-Point Framework

Through our relationship with Microsoft, we have the technologies available to deploy AI solutions at scale and for business benefit. We take a grounded approach to developing and implementing AI and generative AI solutions, focusing on identifying and delivering what is practically possible. This framework highlights practical ways to move your organization beyond AI hype to realize its value.

1. Establish an AI council

   It is important for leadership to help set your organization’s AI strategy, as well as oversee that the policies and guidelines created align with your values on transparency and your appetite for risk. However, responsibility needs to go beyond leadership. Establishing a cross-functional AI Council comprised of representatives from multiple business units — including senior-level accountability — embeds AI across the enterprise and ensures diverse viewpoints are considered. It also ensures AI policies are clearly and widely communicated.

   In addition to defining strategy, the council should also oversee investment decisions and measure outcomes. This bird's-eye view will lead to more robust governance and a more integrated, cost-effective route to implementation.

    

   Key considerations:

   1. Do your AI principles reflect your culture and values?
   2. Are AI policies aligned with business outcomes?
   3. Do you understand the limitations of AI?
   4. Is there robust governance and accountability?
   5. Do you have the agility to evolve as technology changes?

2. Leverage best practices

   Look outside your organization and harness best practices from a variety of sources to create policies that are practical, proven and compliant. Regulations may differ across jurisdictions, but they are evolving fast. The EU’s AI Act became law in March 2024, and in the US, the blueprint for an AI Bill of Rights has been established. Incorporate good practices from across our industry, such as Microsoft's Responsible AI Framework, which is based on six core principles, and Well-Architected Framework, which supports compliance. The considerable investments Microsoft has made to develop and publish responsible AI frameworks and associated toolkits underpin the importance of ensuring best practices are made widely available.

   Critical partners and external advisors can provide a rounded view. At the industry level, some sectors are early adopters of AI technology and can offer learning insights. HCLTech is partner to four of the world’s five largest health service providers, with AI and personalized healthcare projects well advanced. Consider academic institutions, too, and take advantage of research to connect with and reassure all stakeholders to ensure “fairness to everybody” is embedded.

   Key considerations:

   1. Are you leveraging best practices widely from outside your organization?
   2. Do you have a process to capture and implement these learnings continuously?
   3. Are you fully compliant with emerging guidelines and regulations, especially regarding data protection?
   4. Do your guidelines connect your AI values to your technology?
   5. Are policies practical to implement? Is there follow-up?
   6. Have you established toolkits that are accessible and available across all business use cases?

3. Champion organization-wide adaptation

   With the accelerating adoption of generative AI, success in AI now goes beyond success in IT. AI is a business opportunity, and this premise needs to be at the heart of your approach. Creating AI champions within wider lines of business will be key to driving the responsible adoption of Microsoft AI solutions. Extending usable data across your organization can potentially create value exponentially, but there are caveats.

   AI champions can foster a positive culture of experimentation and collaboration while communicating that technologies must be limited to their intended purposes. It’s important to give champions a safe sandbox environment to experiment in to encourage innovation. Forward-thinking companies might dismantle traditional barriers to encourage cross-functional collaboration — for example between the AI development team and UX designers — as well as encourage wider collaboration with partners and their multi-disciplinary approach.

   Key considerations:

   1. Are you devolving AI across your workforce?
   2. Have you appointed AI champions in each of your business units?
   3. Are employees clear on the intended purpose of AI technologies?
   4. Do you have guardrails to limit functions and check usage?
   5. Do you encourage cross-functional collaboration?

4. Focus on training, both upskilling and preskilling

   Microsoft generative AI’s rapid transformational capabilities underscore the need to upskill and even preskill your workforce to confidently foster experimentation and product development. Prioritize investment in training to comprehensively educate employees, especially developers and data scientists, about responsible AI and the consequences of conscious and unintentional biases.

   Now is the time to build responsible practices, such as the importance of model explainability, to help eliminate prejudice from future data sets and ensure the trustworthiness of AI outputs. Make the most of your partners and external advisors to confidently access the latest capabilities and overcome any existing skills shortages internally.

   Key considerations:

   1. Are you proactively training employees on responsible AI?
   2. Do you operate a policy of model explainability to help eliminate bias?
   3. Do you design, train and test templates for inclusion and fairness?
   4. Is the transparency and traceability of structures and processes openly shared?
   5. Do employees have access to wider coaching and guiding expertise as new tech emerges — including from external partners?

5. Create a continuous feedback loop 

   The exponential pace of change in AI technologies underscores the need for a wide-reaching and effective process to capture and act on your experiences quickly. A solid feedback mechanism means you can continually reevaluate your strategy and adjust your investment to stay up to date with the technology and maximize returns. It also fosters a culture of experimentation that allows you to quickly take advantage of innovation to drive product quality and improve features and benefits.

   The more feedback you can continuously capture, the better. From customers engaging with your frontline services to your behind-the-scenes developers, encourage wide-ranging and active participation in your process — and then act on it by looping it back to your AI Council. Critically, ensure that progress and updates are regularly shared with the entire business, and consider producing an annual AI transparency report – and making it public.

   Key considerations:

   1. Have you created a 360° feedback loop across all stakeholders?
   2. Does it capture real-world evidence of how AI is playing out?
   3. How frequently is this happening?
   4. How do you implement and monitor change?
   5. Are you adjusting your overarching AI principles to reflect feedback?

Taking control

Microsoft AI and generative AI technologies are exciting and evolutionary. Balancing the opportunities and complexities they bring is about taking control and adopting a structured and responsible approach. Building trust within the business and with clients and regulators is a critical part of this. At HCLTech, we take a responsible approach, fusing our proven engineering expertise with Microsoft technologies to enable you to identify and implement AI-based opportunities that maximize value.