软件/数字原生公司AI/ML能力的真实状态

Research shows executives believe it's an imperative to competeand grow, but initiatives are falling short of expectations The Real State of AI/ML Capabilities in Software and Digital Native CompaniesIntroduction3Business Readiness vs. Executive Eagerness: The AI Conundrum5The 'Big Short' on Expectations6Competing on Urgency8Get Going or Get Left Behind9Future Proof9AI Skills Gap106 Actions You Can Take Now12The AI/ML Imperative13Business Outcome Alignment14Execution Ownership15Conclusion17Research Methodology18SoftServe Inc.19 INTRODUCTION HOW MUCH OFA PRIORITY IS ITWITHIN COMPANIES? Since the 1950s, artificial intelligence(AI) has gone through seasons ofinterest: summers of investmentsand winters of disinterest. If you payattention to headlines today, you’dsay it’s a bright, sunny day for AI, withwaves lapping and a cold drink withinreach. But if you’re a software ordigital native company, you might notwant to put away that parka just yet.Everyone is talking about AI. But noteveryone is benefiting from it. Counterintuitively, new data showsmany software and digital nativecompanies are missing the genuinebusiness value AI and its mostbeneficial incarnation, machinelearning (ML), have to offer. HOW AND WHERE ISIT BEING APPLIED? WHO WITHIN THEORGANIZATIONOWNS AI STRATEGY? This global study, conducted duringfall 2022, was designed to giveinsights into the ground-level stateof AI/ML within software and digitalnative companies. AND WHAT ARE THEIMPLICATIONS OFTHAT OWNERSHIP? Respondents were asked to give their take on the futureof AI/ML use, and were boldly asked: If software anddigital native companies don’t successfully invest now inAI/ML, will they be around in five years? READ ONTO FIND OUT THE RESULTS. Meanwhile, let’s whet your appetite. One finding showed that insteadof leveraging AI/ML technologiesfor business value and impact —forecasting business conditionsand revenue and predicting whena customer is ready to buy, orembedding the technologies intonew product features, or prescribingpractical actions to quicken time tomarket, or using it to heighten deeperlevels of customer personalizationand engagement — software anddigital native companies have oftenplaced too much emphasis onexperimentation, testing, and proofof concept — or what we call “science projects” — trying to figure outwhat’s possible, without determiningfirst what’s needed by the market ortheir customers. These findings and many othersare captured in the following pages.If you see yourself among thesepages, in the closing of this report,recommendations are offered foryou to consider. Of course, a trusted AI/ML partnerlike SoftServe can help your companytake advantage of AI/ML capabilitiesto better compete and grow. The Real State of AI/ML Capabilities in Software and Digital Native CompaniesWE’RE CONFIDENT YOU WILL FINDTHE DATA INSIGHTFUL, AS YOUVENTURE TO USE AI/ML TO DRIVEYOUR BUSINESS OUTCOMES. BUSINESSREADINESS VS.EXECUTIVEEAGERNESS:THE AI CONUNDRUM WHERE IS THE PRESSURE FOR AI/ML PROJECTS COMING FROM? AI/ML AT YOUR ORGANIZATION MUST BE MORE THANJUST A GIMMICK TO APPEASE CURIOUS PROGRAMMERS. IT executives and leaders point to business leadership as one reason theirAI/ML strategy is ill-conceived. Nearly a third of IT executives (32%) say theircompany leadership treats AI/ML as a marketing tool to enhance brandperception, rather than using the technology to drive business outcomes orfuel product strategy. When grading the C-suite’s AI/ML IQ, more than half (53%) say pressure toimplement AI/ML is coming from their leadership. Meanwhile, 47% say theircompany board is also applying pressure. The Real State of AI/ML Capabilities in Software and Digital Native CompaniesYet, a worrisome 72% of IT executives say their leadership doesn’t fullyunderstand the technical capabilities of AI/ML further highlighting the tech-first, business-later strategy primarily behind most AI/ML capabilities today. There is pressure from across the board to implement AI/MLtechnology. While employees and customers account for thesame amount of pressure — nearly as much as competitors —leadership, the board, and competitors universally account formore pressure to implement AI/ML than customers. THE 'BIG SHORT'ON EXPECTATIONS WHAT ABOUT USING AI/ML TOINCREASE PROFITABILITY? With AI/ML investments consistently underperforming, companies are fallingbehind. Nearly two-thirds (63%) say AI/ML capabilities at their organizationare not fully realized. Leadership wants to turn AI/ML into a moneymaker — even if they’renot sure how. An overwhelming 86% say leadership at their organizationdoes not fully understand the path to monetizing AI/ML. The bottom line is the top metrics for measuring AI/ML success are increasedprofitability (38%), improved customer experience scores (33%), or increasedproductivity (33%). Even though leadership has a general understanding of how to use AI/ML,there are clearly disconnects between leadership, IT, and product s