Confronting the Future: Challenges and Opportunities in AI Adoption | Richard Crampton

Hi, everyone. For those who don't know me, my name is Richard Crampton, I work for Atlassian as a senior IT service management solution engineer. And I have the great privilege of half an hour of your time today to discuss some of the challenges and opportunities in adopting artificial intelligence. So, let's get started. In terms of agenda, I'd like to start, if that's all right, with a bit of background and history around AI. From here, we'll move on to some of the challenges facing everyone adopting AI, before moving forward with the opportunities that AI can present. Lastly, I'd like to summarize some thoughts about AI, and then we'll leave time, around five minutes or so, for any Q&A at the end. So, hopefully, that all works for everyone as an agenda. So, let's start with the background. AI has been around in various forms for a number of years now, but the capability and adoption has really grown exponentially in the last two to three years. But let's take a step back. So, as you noted, AI is not new. This is a headline from the New York Times in 2018, noting that AI tiptoes into the workplace. Now, we're now much further on with AI adoption than 2018, and it's making fundamental changes to the way that we work and collaborate together. Indeed, at Atlassian, we truly believe that AI is changing the fabric of teamwork itself. Whether you're working in a Dev team, building the next amazing product, working in a marketing team, creating a very unique campaign, or as a service agent supporting customers, you will be partnering with AI in order to succeed. So, we strongly believe that the winning teams of the future will consist of human teammates supplemented by AI teammates. And through this major shift in teamwork, Atlassian will be here to help welcome AI into your business. So, I've got an interesting statistic. We did a survey called the State of AI in Service Management, facilitated by CITE Research, and it took in responses from around 500 professionals in August of this year. Now, this survey found a remarkable 88% of organisations are already leveraging AI in some capacity or other. Furthermore, the top motivators for this investment and adoption were, one, using AI to fuel data-driven decisions at 80%. This isn't actually all that surprising if we look at other studies, such as an IDC study, that found that 75% of execs, VPs and directors are investing in decision intelligence initiatives. Second place was boosting workplace efficiency. 78% of responders said they were really interested in AI for this. In third, it was AI being the champion for customer satisfaction at 64%. Now, these findings align with broader trends observed in recent studies. For instance, a global benchmark study on generative AI found that, while enthusiasm remains really, really high, many organisations are now taking a more measured approach to implementation in 2024. Only 63% of companies are planning to increase AI spending this year and next year, compared to 93% in 2023. That's as they grapple with the rising implementation costs, data security concerns, and challenges in moving projects beyond the initial pilot stages. And organisations are also leveraging a diverse set of KPIs, key performance indicators, to measure the success of AI implementations. And customer satisfaction, or CSAT, leads the pack, 42% of organisations using it as their primary metric. For me, that underscores the critical role that AI is playing in enhancing the customer experience. There's also a strong focus on operational improvements, such as cost, again with 42% using operational cost savings as a measurement of success of their initiatives. One percent less to 41%, organisations have a KPI of how accurate their AI data model is and its overall performance. And lastly, time savings with 36% focused on the time being saved by AI-driven efficiencies. Now, these figures highlight a balanced approach to AI adoption. So, the first is, focus on value. Focus on what the desired outcomes of the AI initiative are, what it will enable, what will be the overall value. And please note, value is subjective. It will not be the same for a CIO versus CFO. Second, start where you are. It can be really difficult to start using AI, and we've seen on the maturity, it takes time, especially so if you may be concerned of the quality of your current data. But you can't analyse it without testing. So, this could be spilling up a new test instance, a sandbox, doing some experimentation, starting with that data feedback to understand where you are today. Thirdly, progress iteratively with feedback. Very much an agile approach, right? We've seen almost all organisations are measuring the success of AI initiatives. And our measurements are absolutely essential, but also, an agile mindset and the ability to make small changes, small iterations and test quickly is key. It is no coincidence that the most mature organisations are those most able to embrace this agile mindset. It's already part of their culture. Collaborate and promote visibility. Democratize AI and promote and evangelize the successes to excite and motivate others. Think and work holistically. Think about all aspects. It's not just about the technology, but the people, the mindset, and the skills required for success. Keep it simple and practical. Don't overcomplicate it, especially at the start. Avoid bloating your AI initiatives with all the cool things it could do, and focus on what means most in getting that out the door. And last, and this could have been written specifically for AI, optimise and automate, right? Think about how AI can fully or partially automate processes, but also optimise others. There are so many different use cases, and AI is absolutely key in driving this one forwards. So, I'll finish with some thoughts. While AI is exciting, it hasn't been significantly intuitive for...