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It\u2019s an increasingly common tale within corporations today: The AI project performs admirably in testing during the pilot phase, gets the green light for a broader rollout\u2026and then stops working properly; Or it fails to deliver the expected business results.\u00a0<\/p>\n
Finger pointing, recriminations, and embarrassment ensue.<\/p>\n
The problem is not always the technology. In fact, the fault is often in the planning, processes, and expectations that companies have established\u2014or not established\u2014around their AI projects, according to business leaders who spoke at a roundtable discussion at Fortune Brainstorm Tech this month.\u00a0<\/p>\n
For starters, not every AI project deserves to be rolled out widely, said Amgen Chief Technology Officer Sean Bruich.\u00a0<\/p>\n
\u201cIt\u2019s so easy with a pilot to let a thousand flowers bloom,\u201d he said. That\u2019s not a bad thing, since it encourages experimentation. But, he said, \u201cthe key to making pilots scale successfully is actually having a wide number of ideas, but a very tight governance on which pilots are actually greenlit.\u201d<\/p>\n
A key criteria before taking the next step, said Salesforce Chief Customer and Commercial Officer Lashonda Anderson-Williams, is understanding the intended outcome of the project. Too many companies are focused on the successful implementation of AI features\u2014the technological bells of whistles\u2014instead of the business outcome, she says.\u00a0<\/p>\n
That mentality is a recipe for disappointment: The AI features work great, but the new technology isn\u2019t driving meaningful business results.<\/p>\n
When it comes to agentic AI,\u00a0 Anderson-Williams noted, a detailed understanding of the workflow\u2014which individuals, groups, or touch points are necessary to complete a task\u2014 is critical. What a lot of companies are finding, she said, is that documentation of the workflow either doesn\u2019t exist or is poorly documented: \u201cWhen you put AI on top of that, the expectation is you\u2019re going to see some magic, and there\u2019s no magic there.\u201d<\/p>\n
Access to data is a particularly common stumbling block that AI projects encounter in the transition from the pilot phase to full deployment.\u00a0With data often scattered in different silos throughout an organization, and with all that data governed by different access privileges and by varying privacy and security considerations, things can get complicate fast. It\u2019s important to map out the contours of the AI project and all the potential data that will be required ahead of time, the panelists stressed. \u201cThe earlier we can uncover that in discovery, the better we\u2019ll be set up for success,\u201d Thomson Reuters Chief Data Officer Caitlin Halferty said.\u00a0<\/p>\n
That also means getting buy-in from the right groups and stakeholders within the organization. \u201cIs there some element of PII (personally identifiable information) or confidential data that\u2019s going to trigger privacy?\u201d Halfery said. If the answer is yes, then the right people need to be part of the project. \u201cIs there a cyber element? Let\u2019s get security on board,\u201d she said.\u00a0<\/p>\n
Amgen\u2019s Bruich echoed the importance of broad buy-in, noting that an AI project that is transformational to the company will by necessity involve leaders in finance, technology, HR, and other groups across the organization.\u00a0A truly impactful AI project, he said, needs to do more than just make work processes more efficient for a small group of employees. It needs to deliver \u201can outcome that matters to the enterprise.\u201d<\/p>\n<\/div>\n
#pilot #test #projects #trouble #scaling<\/p>\n","protected":false},"excerpt":{"rendered":"
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