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7 Ways To Better Sell Artificial Intelligence To The Business

Numerous artificial intelligence efforts are underway — the global AI adoption rate has steadily grown and is now 35%, up four points from the previous year, a recent IBM study show. It is clear, as the study shows, that there are tangible benefits: half of organizations see benefits from using AI to automate IT, business or network processes, including cost savings and efficiency (54%), improvements in IT or network performance (53%), and better customer experiences (48%).

But bringing in AI means changes to the organization and the systems and processes that have shaped the way business is done. Selling AI as a new way requires a degree of finesse and business acumen.

The following are recommendations from industry experts who are committed to greater AI adoption on a daily basis:

Think and understand the problem that needs to be addressed before enabling the technology. “Instead of coming in and asking for a blank check to tackle countless vague goals, identify a single problem and outline a clear strategy on how AI will solve it,” says Sagar Shahcustomer partner at Fractal Analysis. “Emphasise the quick wins that can be achieved in the first 12 weeks of the project. This provides clear parameters for skeptics to compare and assess the results.”

It is a matter of taking more time to understand which problem AI should be applied to. “The biggest drag on AI’s success is relying on unscientific assumptions and broad goals, rather than taking the time to map out the exact problems they’re trying to solve,” Shah says. “Simply put, the more time a company spends on an issue in advance, the better the adoption of AI products will be.”

Set realistic expectations. “Set realistic expectations of what AI can achieve,” says Alex Saricsmart purchasing expert and CMO at Ivalua. “It can be a powerful tool, but algorithms still have a long way to go. It’s important to set realistic short-term goals and work towards improving over time. Leaders need to sell AI in a way that states it helps simplify work and allows employees to focus and become more engaged and productive, rather than crowding them out.” Often people assume that AI means automatic innovation, rather than “really understanding what it means to them and how it will be applied every day,” agrees Shah. “Sometimes a more traditional approach can be more effective than one powered by AI, so buyers need to look seriously at the details.”

Bring talent on board, but don’t try to build everything yourself. While AI is seen as a labor saver, the skills gap remains the biggest barrier to AI adoption, another recent IBM study show. “The talent gap is significant, especially when we look at the most modern algorithm development in AI,” says Flavio Villanustresenior vice president and global chief information security officer for LexisNexis Risk Solutions. “It is usually not cost-effective for a company to develop their own algorithms unless selling services or products using those algorithms is part of their core strategy.” Too many companies “dare to form an in-house team to build AI solutions end-to-end,” said Sivakumar Lakshmanan, co-chief executive officer at antuit.ai, now part of Zebra Technologies.

Think long term. “Another common misconception is that companies must use AI in one go and every project must be an immediate success or else their investment will be wasted,” Shah says. “Adopting AI is a big step for any organization, so to start, focus on applying AI to a handful of strategic problems – only a few of which will immediately work as expected – and then take the lessons you have. collected and apply them to another set of projects, and then another. Companies should also keep in mind that AI is not just a single tool. Instead, it is a culmination of ongoing engineering, design, and behavioral science work. All these elements have to work together.”

Rethink processes. AI simply cannot be superimposed on existing processes to expect success. A common mistake is “putting AI on top of an existing process centered around manual, committee-based decision-making,” Lakshmanan says. “In this case, AI becomes a tick in the box. Instead, adapt the processes to the newer world – for example, you don’t buy an e-book on a device to print and read.”

Ensure transparency and trust in data. “How AI output is generated needs to be made more transparent by the technology itself,” Saric says. “Remember that AI has been used for many years to classify business purchase invoice lines to accurately determine where the budget is being spent. But many approaches are a black box, and when users inevitably discover errors among millions of classified rules, they lose trust and no longer rely on the data.” The quality of AI output “depends heavily on data quality and volumes, but significant amounts of organizational data are still scattered across many systems and of questionable quality,” he adds. “Organizations need to take control of enterprise-wide data to unleash the potential of AI. Building a solid data foundation, leveraging master data management solutions and platforms with unified data for specific functions – vendors, customers – can address this, but is not widely available today.”

Involve employees at all levels. “Companies, once they have identified the problem, fall into the trap of taking a tool into production without significant collaboration and end-user input,” Shah says. “To drive the adoption of AI tools, companies need to co-create them with end users so that they not only solve a problem in theory, but also in practice. Even with robust co-creation and problem framing, sustainable adoption relies on companies and their AI partners constantly working on improvements. New needs and wrinkles appear all the time. And the only way to ensure that your business is set up for long-term success is to accept that AI is a living breathing tool that needs to be continuously adapted, not a one-time plug-and-play solution.”

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