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Heres the one thing you should never outsource to an AI model
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreIn a world where efficiency is king and disruption creates billion-dollar markets overnight, its inevitable that businesses are eyeing generative AI as a powerful ally. From OpenAIs ChatGPT generating human-like text, to DALL-E producing art when prompted, weve seen glimpses of a future where machines create alongside us or even lead the charge. Why not extend this into research and development (R&D)? After all, AI could turbocharge idea generation, iterate faster than human researchers and potentially discover the next big thing with breathtaking ease, right?Hold on. This all sounds great in theory, but lets get real: Betting on gen AI to take over your R&D will likely backfire in significant, maybe even catastrophic, ways. Whether youre an early-stage startup chasing growth or an established player defending your turf, outsourcing generative tasks in your innovation pipeline is a dangerous game. In the rush to embrace new technologies, theres a looming risk of losing the very essence of what makes truly breakthrough innovations and, worse yet, sending your entire industry into a death spiral of homogenized, uninspired products.Let me break down why over-reliance on gen AI in R&D could be innovations Achilles heel.1. The unoriginal genius of AI: Prediction imaginationGen AI is essentially a supercharged prediction machine. It creates by predicting what words, images, designs or code snippets fit best based on a vast history of precedents. As sleek and sophisticated as this may seem, lets be clear: AI is only as good as its dataset. Its not genuinely creative in the human sense of the word; it doesnt think in radical, disruptive ways. Its backward-looking always relying on whats already been created.In R&D, this becomes a fundamental flaw, not a feature. To truly break new ground, you need more than just incremental improvements extrapolated from historical data. Great innovations often arise from leaps, pivots, and re-imaginings, not from a slight variation on an existing theme. Consider how companies like Apple with the iPhone or Tesla in the electric vehicle space didnt just improve on existing products they flipped paradigms on their heads.Gen AI might iterate design sketches of the next smartphone, but it wont conceptually liberate us from the smartphone itself. The bold, world-changing moments the ones that redefine markets, behaviors, even industries come from human imagination, not from probabilities calculated by an algorithm. When AI is driving your R&D, you end up with better iterations of existing ideas, not the next category-defining breakthrough.2. Gen AI is a homogenizing force by natureOne of the biggest dangers in letting AI take the reins of your product ideation process is that AI processes content be it designs, solutions or technical configurations in ways that lead to convergence rather than divergence. Given the overlapping bases of training data, AI-driven R&D will result in homogenized products across the market. Yes, different flavors of the same concept, but still the same concept.Imagine this: Four of your competitors implement gen AI systems to design their phones user interfaces (UIs). Each system is trained on more or less the same corpus of information data scraped from the web about consumer preferences, existing designs, bestseller products and so on. What do all those AI systems produce? Variations of a similar result.What youll see develop over time is a disturbing visual and conceptual cohesion where rival products start mirroring one another. Sure, the icons might be slightly different, or the product features will differ at the margins, but substance, identity and uniqueness? Pretty soon, they evaporate.Weve already seen early signs of this phenomenon in AI-generated art. In platforms like ArtStation, many artists have raised concerns regarding the influx of AI-produced content that, instead of showing unique human creativity, feels like recycled aesthetics remixing popular cultural references, broad visual tropes and styles. This is not the cutting-edge innovation you want powering your R&D engine.If every company runs gen AI as its de facto innovation strategy, then your industry wont get five or ten disruptive new products each year itll get five or ten dressed-up clones.3. The magic of human mischief: How accidents and ambiguity propel innovationWeve all read the history books: Penicillin was discovered by accident after Alexander Fleming left some bacteria cultures uncovered. The microwave oven was born when engineer Percy Spencer accidentally melted a chocolate bar by standing too close to a radar device. Oh, and the Post-it note? Another happy accident a failed attempt at creating a super-strong adhesive.In fact, failure and accidental discoveries are intrinsic components of R&D. Human researchers, uniquely attuned to the value hidden in failure, are often able to see the unexpected as opportunity. Serendipity, intuition, gut feeling these are as pivotal to successful innovation as any carefully laid-out roadmap.But heres the crux of the problem with gen AI: It has no concept of ambiguity, let alone the flexibility to interpret failure as an asset. The AIs programming teaches it to avoid mistakes, optimize for accuracy and resolve data ambiguities. Thats great if youre streamlining logistics or increasing factory throughput, but its terrible for breakthrough exploration.By eliminating the possibility of productive ambiguity interpreting accidents, pushing against flawed designs AI flattens potential pathways toward innovation. Humans embrace complexity and know how to let things breathe when an unexpected output presents itself. AI, meanwhile, will double down on certainty, mainstreaming the middle-of-road ideas and sidelining anything that looks irregular or untested.4. AI lacks empathy and vision two intangibles that make products revolutionaryHeres the thing: Innovation is not just a product of logic; its a product of empathy, intuition, desire, and vision. Humans innovate because they care, not just about logical efficiency or bottom lines, but about responding to nuanced human needs and emotions. We dream of making things faster, safer, more delightful, because at a fundamental level, we understand the human experience.Think about the genius behind the first iPod or the minimalist interface design of Google Search. It wasnt purely technical merit that made these game-changers successful it was the empathy to understand user frustration with complex MP3 players or cluttered search engines. Gen AI cannot replicate this. It doesnt know what it feels like to wrestle with a buggy app, to marvel at a sleek design, or to experience frustration from an unmet need. When AI innovates, it does so without emotional context. This lack of vision reduces its ability to craft points of view that resonate with actual human beings. Even worse, without empathy, AI may generate products that are technically impressive but feel soulless, sterile and transactional devoid of humanity. In R&D, thats an innovation killer.5. Too much dependence on AI risks de-skilling human talentHeres a final, chilling thought for our shiny AI-future fanatics. What happens when you let AI do too much? In any field where automation erodes human engagement, skills degrade over time. Just look at industries where early automation was introduced: Employees lose touch with the why of things because they arent flexing their problem-solving muscles regularly.In an R&D-heavy environment, this creates a genuine threat to the human capital that shapes long-term innovation culture. If research teams become mere overseers to AI-generated work, they may lose the capability to challenge, out-think or transcend the AIs output. The less you practice innovation, the less you become capable of innovation on your own. By the time you realize youve overshot the balance, it may be too late.This erosion of human skill is dangerous when markets shift dramatically, and no amount of AI can lead you through the fog of uncertainty. Disruptive times require humans to break outside conventional frames something AI will never be good at.The way forward: AI as a supplement, not a substituteTo be clear, Im not saying gen AI has no place in R&D it absolutely does. As a complementary tool, AI can empower researchers and designers to test hypotheses quickly, iterate through creative ideas, and refine details faster than ever before. Used properly, it can enhance productivity without squashing creativity.The trick is this: We must ensure that AI acts as a supplement, not a substitute, to human creativity. Human researchers need to stay at the center of the innovation process, using AI tools to enrich their efforts but never abdicating control of creativity, vision or strategic direction to an algorithm.Gen AI has arrived, but so too has the continued need for that rare, powerful spark of human curiosity and audacity the kind that can never be reduced to a machine-learning model. Lets not lose sight of that.Ashish Pawar is a software engineer. DataDecisionMakersWelcome to the VentureBeat community!DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.You might even considercontributing an articleof your own!Read More From DataDecisionMakers
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