{"id":73603,"date":"2025-09-04T09:54:03","date_gmt":"2025-09-04T08:54:03","guid":{"rendered":"https:\/\/www.cxtoday.com\/?p=73603"},"modified":"2025-09-04T09:54:03","modified_gmt":"2025-09-04T08:54:03","slug":"why-ai-buying-takes-too-long-and-how-to-speed-it-up-miratech","status":"publish","type":"post","link":"https:\/\/www.cxtoday.com\/contact-center\/why-ai-buying-takes-too-long-and-how-to-speed-it-up-miratech\/","title":{"rendered":"Why AI Buying Takes Too Long \u2013 and How to Speed It Up"},"content":{"rendered":"

Artificial intelligence is no longer a novelty in customer experience.<\/p>\n

From smarter routing in the contact center to predictive analytics that spot churn risk, AI has shifted from hype to hard value.<\/p>\n

Yet while the technology itself moves fast, the way enterprises go about buying it often feels stuck in a bygone era.<\/p>\n

Too many businesses still treat AI acquisitions like they\u2019re buying servers in the 1990s.<\/p>\n

The result? Bloated procurement cycles, endless rounds of approvals, and criteria that don\u2019t quite fit the problems at hand.<\/p>\n

Decisions can take months, even years, and by the time a deal is signed, the chosen solution may already be behind the curve.<\/p>\n

\u201cHistorically, large organizations procure investments by aggregating input from many different stakeholders into an RFP process,\u201d explained Erik Delorey, Director of Innovation at Miratech.<\/p>\n

\u201cWhat ends up happening is that people start focusing on the features and capabilities of an investment and not the business outcomes.\u201d<\/p><\/blockquote>\n

That\u2019s where things start to go wrong. For example, instead of asking, \u2018How do we reduce churn?\u2019, buyers focus too heavily on features like sentiment analysis accuracy.<\/p>\n

\u201cThey obsess over features,\u201d Delorey continued, \u201crather than focusing on business outcomes.\u201d<\/p>\n

Old Playbooks, New Problems<\/strong><\/p>\n

Another major contributor to inadequate AI buying processes is legacy procurement frameworks.<\/p>\n

Many enterprise teams still bring experience from decades of running on-premise data centers. Those instincts show up in their RFPs.<\/p>\n

\u201cWhat\u2019s critically important to AI platforms today is transparency in decision-making,\u201d said Delorey.<\/p>\n

\u201cBut the RFP process often hasn\u2019t been updated. So, you\u2019ll hear stakeholders asking, \u2018What kind of monitoring events can be streamed into my log analysis tool?\u2019 because that\u2019s what they\u2019ve done for 30 years.\u201d<\/p>\n

For example, that means buyers request features designed for infrastructure monitoring, rather than the decision records that actually matter for compliance and governance.<\/p>\n

Such an outdated requirement is then used as a make-or-break factor for the purchase, as Delorey explained:<\/p>\n

\u201cThey end up choosing the vendor that ticks the event-streaming box, even if that vendor misses ten other features the business actually needs.\u201d<\/p><\/blockquote>\n

It\u2019s a classic case of buying the wrong thing well, rather than the right thing quickly.<\/p>\n

Shifting from Checklists to Outcomes<\/strong><\/p>\n

While it is helpful to point out shortcomings and poor strategies, many of you will undoubtedly be asking, \u2018What\u2019s the alternative?\u2019<\/p>\n

For starters, fewer people in the room.<\/p>\n

Rather than pulling in dozens of siloed stakeholders, Delorey suggests setting up a cross-functional task force with clear roles.<\/p>\n

\u201cA lot of times, procurement becomes like an AI engine itself,\u201d he said.<\/p>\n

\u201cThere are a billion parameters being weighed, but the goal is just to get to a purchase, not to restructure the argument for how to achieve the right business outcome.\u201d<\/p>\n

Agile pilots are another way forward. Instead of exhaustive RFPs that take six months to write, enterprises can run small, contained tests that deliver insight in weeks.<\/p>\n

This mirrors the iterative way AI systems themselves are deployed. It also reduces the risk of overcommitting to a vendor that looks good on paper but has underdelivered in practice.<\/p>\n

What to Really Look for in an AI Partner<\/strong><\/p>\n

So, if feature checklists are a no-go, what should buyers be focusing on?<\/p>\n

Delorey points to a few key essentials that are often overlooked:<\/p>\n