Here's how you can drive innovation by leveraging artificial intelligence in business.

We all get stuck sometimes. But AI can be your secret weapon for fresh ideas. Imagine a super-powered research team that analyzes mountains of data, unearthing hidden patterns and trends. That's AI! It can spark crazy product ideas you might have never thought of, or predict what customers will want next. Plus, AI can handle all the boring repetitive tasks, freeing up your team to brainstorm like wild geniuses. Think of it as your own personal innovation factory! Of course, AI is a tool, not a magic trick. You still need your human brain for the big picture and that creative spark. But with AI on your side, there's nothing you can't achieve

Conceptually, it seems best to consider Ai as “enhanced intelligence” (ei). It is a technology tool, like any other, however extremely massive in scope and potential impact. It acts as the “lifeblood” of our data systems, tools, and repositories and as such is both critical, and also a potentiate of error (sometimes undetected). Human curation is essential.

Artificial intelligence with automatic, self-building upgrades and databases from existing information resources on the internet and 24/7 control of ESG application and a digital bank can easily achieve the following: - The innovation/idea/improvement can be instantly implemented in a completely different environment capable of creating any kind of potential in terms of raw materials, finance, human resources, etc. and building it into a universal functionality as a future law/regulation/guideline by transferring it from environment to environment.

Start small with a rules based analysis (and yes Excel or Google sheets is fine) that focuses on what-if scenarios, then automate these what-if scenarios to create meaningful values / outcomes that you can look at in the context of your business. As you mature in your use of automation and can provide additional data to train your use cases. Then start analyzing past / seasonal data and compare that with current results from your business (say inventory levels for the summer post COVID with this year's values). Finally look at why these patters either repeat or under what conditions they do not repeat. At its heart, this is the power of data analysis.

Além de analisar dados brutos, a IA pode apoiar na análise de scripts, fazendo corre??es ou propondo otimiza??es. Um excelente exemplo é o GitHub Copilot, que tem apoiado justamente nesse ponto. Além disso, existe outra solu??o da Microsoft apoiada por IA e que traz ganhos de eficiência e produtividade: Power Automate, que também é apoiada pelo Copilot.

In today's market, a company is no longer only selling to its customers. You have to manage the backstage actors (neighbors that provide recommendations), social media (those 5-star ratings on websites) and influencers (family and friends). I use the design principle of Intelligent Process Automation (IPA) to shift left predictions of next best actions for potential customers using design thinking sessions, while using the brilliance of automation to streamline process for all experiences (purchase, customer support, enquiry, returns etc.) for end customers.

Usando IA para rotinas básicas, junto com automa??o de tarefas de organiza??o pessoal, tive excelentes ganhos de produtividade. Automatizar fluxos de mensagens via e-mail, triagem de informa??es através de ferramentas que tem IA como base pode ser um excelente caminho para come?ar a usar essa tecnologia de uma formas mais simples e aplicável ao cotidiano.

By using predictive analytics you might get new perspectives on segmenting your customers (such as micro segments) going beyond traditional personas and other criteria. This combined with predictions on optimal selection of channels (e.g., next best action recommendations) opens new dimensions to derive innovative ways how to engage with customers! For example refined SEA strategies combined with digital channels tailored to (micro) customer segments

Predictive analytics on its own needs to be contextualized to the market and business that it serves. Traditional analytics looks back in time to identify patterns that can provide insights. Correlating this with current parameters (economic or consumer led) is required to ascertain the probability of success. In my experience, I have seen customers effectively use CRM tools and campaigns when the sliver of the addressable market is understood based on past purchase behavior, is targeted and campaigns planned ahead of time.

Leveraging artificial intelligence, specifically predictive analytics, can drive innovation in business by enabling organizations to anticipate future trends, customer behaviors, and market dynamics. AI-powered predictive analytics can identify patterns and correlations that humans might overlook, providing valuable insights for strategic decision-making. This enables businesses to proactively respond to changing market conditions, optimize processes, and personalize customer experiences. Moreover, predictive analytics can facilitate the development of innovative products and services by identifying emerging opportunities and anticipating customer needs.

Personalization is for more than sales, and extends to the level of practical utility and successful outcomes. In a field such as law, for instance, user must consider their own fact pattern, jurisdiction, relevant legal subject matter, and stage of events. So many factors must be appropriately considered and weighted in order to provide the most helpful results.

It is possible, and probably ideal, to have an individualized personal build orientation. One which would favor individual local and personal circumstances, compared to a one-size-fits-all omnibus db. The trend toward SLMs (small language models) versus LLMs seems positive, offering to deliver more and better exacting outputs for diverse users.

Ethische Aspekte betreffen nicht nur die Funktionsweise eines KI-Systems in Bezug auf externen Impact (z.B. in Bezug auf Robustheit des Modells, Fairness, Erkl?rbarkeit, Transparenz, Schutz der Nutzer), sondern auch eine menschenzentrierte Umsetzung im Unternehmen. Hierzu z?hlt dann eine klare Positionierung des nutzenden Unternehmens in Bezug auf menschlicher Verantwortung für das Arbeitsergebnis, sowie Autonomie der Mitarbeiter (Das Unternehmen erkennt den Mitarbeiter als Experten an der die finale Entscheidungskompetenz hat) und Mitbestimmungsrechte von Betriebsr?ten bei der Einführung.

Ich würde dem Artikel noch Aspekte der Regulierung von KI Systemen hinzufügen. HIerzu z?hlen dann die DSGVO, Urheberrechtsgesetze, EU KI Act, DORA, EU Haftungsrichtlinien, etc.