up to 95% of AI initiatives<\/a> fail to produce meaningful ROI, leaving executives wondering if agentic AI is worth the risk.\u00a0<\/p>\nSpark, one of the country\u2019s biggest digital service providers, sees a pathway to real results if firms take the right approach. \u00a0<\/p>\n
Why AI projects fail\u00a0<\/p>\n
There\u2019s a phrase that\u2019s common in the tech industry: \u201cthe graveyard of AI pilots\u201d.\u00a0<\/p>\n
\u201cIf you start with technology as the answer and work backwards, that\u2019s really a recipe for missing the brief,\u201d says Liz Urquhart, Spark\u2019s General Manager of Business Products and Services.\u00a0<\/p>\n
\u201cGarbage in, garbage out.\u201d\u00a0<\/p>\n
Urquhart has witnessed the landscape shift from isolated pilots to agentic AI deployments impacting everything from customer service to cybersecurity. Based on Spark\u2019s experience internally and with enterprise clients deploying AI agents, she says successful AI projects are the product of rigorous foundations, clear strategy, and smart change management.\u00a0<\/p>\n
Spark\u2019s AI Accelerator is designed to incubate its clients\u2019 AI projects to give them the best chance of success. \u00a0<\/p>\n
\u201cThat\u2019s where we get them to really distil the problem they\u2019re trying to solve, or identify the opportunity they want to pursue,\u201d Urquhart explains. \u201cWhat might success look like? It\u2019s not just technological success. Are you trying to improve customers\u2019 experience? Productivity output? Employee experience?\u201d\u00a0<\/p>\n
Practical AI in action\u00a0<\/p>\n
For Urquhart, the key is to \u201cstart small, iterate and have the checks and balances in place, so that the transformation feels more manageable\u201d. \u00a0<\/p>\n
Spark\u2019s journey with agentic AI began in its contact centres, freeing staff from repetitive tasks to focus on helping customers.\u00a0<\/p>\n
\u201cIn our contact centres we\u2019ve seen what I think of as a triumvirate of outcomes,\u201d Urquhart says. \u201cOne is productivity gains as our contact centres resolve issues faster. But equally, customer satisfaction scores have improved because calls are resolved faster.\u201d\u00a0<\/p>\n
A third, often overlooked outcome, is the increased job satisfaction employees experience as they are freed from mundane and repetitive tasks.\u00a0<\/p>\n
Spark also harnesses AI agents in back office, technical, and security domains. It is early days for the technology, which requires a careful approach to governance and human oversight. But with AI agent technology evolving rapidly, it\u2019s likely to serve as a significant productivity booster in most industries \u2013 if businesses pursue the right approach to its development and use.\u00a0<\/p>\n
The five pillars of Spark\u2019s approach for AI success\u00a0<\/p>\n
1. Data excellence\u00a0<\/p>\n
\u201cAI will learn based on what it\u2019s fed, so getting those data foundations right, both the construct of the foundations as well as the quality of the data, is all-important,\u201d says Urquhart.\u00a0<\/p>\n
That doesn\u2019t mean you need to wait until you have the perfect data.\u00a0<\/p>\n
\u201cIf you\u2019ve got a specific opportunity or problem in mind, work through what are the critical data that you need to have cleaned up. Start with that and make sure you\u2019ve got that right.\u201d\u00a0<\/p>\n
2. Technical infrastructure\u00a0<\/p>\n
Modern platforms like Salesforce, SAP and ServiceNow have agentic AI built in. But don\u2019t expect to just press a button and put the AI agents to work straight away. Most organisations have run on a mix of legacy systems and applications and cloud platforms. How will AI agents navigate this landscape to get the information required to do the task accurately and securely?<\/p>\n
\u201cYou need to choose a cloud model that can scale and is cost-effective to address those massive data sets that are going to grow over time as you deploy these solutions,\u201d says Urquhart.\u00a0<\/p>\n
3. Governance and oversight\u00a0<\/p>\n
Nor are AI agents \u2018set and forget\u2019 solutions. Nothing stands still, says Urquhart. \u00a0<\/p>\n
\u201cYou will build an agent, and even over six to twelve months, there\u2019ll be a proliferation of other sources of data.\u201d\u00a0<\/p>\n
It must be governed and measured. There must be proper guardrails, human oversight, and benefit tracking, she says.\u00a0<\/p>\n
\u201cIt doesn\u2019t need to be hundreds of people looking after the stuff all the time, but it\u2019s not nothing. It\u2019s a continued investment.\u201d\u00a0<\/p>\n
4. Change management\u00a0<\/p>\n
If you don\u2019t get buy-in from the people who are going to be working with AI agents, the chances of the project succeeding are greatly reduced. This has long been the death knell for tech projects, with staff sticking to what they know and are comfortable with.\u00a0<\/p>\n
The potential for AI agents to enable employees to do more meaningful work is great. But that requires the technology being seen as an opportunity rather than a threat. \u00a0<\/p>\n
5. Skills and capability development\u00a0<\/p>\n
Crucial to building that trust and acceptance as AI agents change how aspects of an organisation operate is upskilling the workforce.\u00a0<\/p>\n
\u201cWe actually have to teach people how to be AI engineers, to oversee the AI agents and do testing and quality control. It\u2019s a different sort of approach now,\u201d Urquhart says.\u00a0<\/p>\n
Spark has invested in AI skills programmes across the organisation, giving every employee the ability to learn to use AI tools and platforms that can augment or transform how they work.\u00a0<\/p>\n
Avoid the pitfalls\u00a0<\/p>\n
Spark has developed a roadmap, based on those five pillars, for navigating the risks and reaping the rewards of agentic AI.<\/p>\n
By paying due attention to these key priorities, companies can avoid the graveyard of pilots and turn potential into performance.<\/p>\n
Learn more about Sparks Data and AI solutions.<\/a> <\/p>\n","protected":false},"excerpt":{"rendered":"Artificial intelligence agents \u2013 autonomous digital entities that analyse data, automate decisions, and act independently \u2013 are set…\n","protected":false},"author":2,"featured_media":53759,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[365,363,364,111,139,69,145],"class_list":["post-53758","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-new-zealand","tag-newzealand","tag-nz","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/53758","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/comments?post=53758"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/53758\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media\/53759"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media?parent=53758"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/categories?post=53758"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/tags?post=53758"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}