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Presentation Skills 101: A Guide to Presentation Success

Getting the perfect presentation design is just a step toward a successful presentation. For the experienced user, building presentation skills is the answer to elevating the power of your message and showing expertise on any subject. Still, one can ask: is it the same set of skills, or are they dependable on the type of presentation?

In this article, we will introduce the different types of presentations accompanied by the skillset required to master them. The purpose, as always, is to retain the audience’s interest for a long-lasting and convincing message.

Table of Contents

The Importance of Presentation Skills

Persuasive presentations, instructional presentations, informative presentations, inspirational presentations, basic presentation skills, what are the main difficulties when giving a presentation, recommendations to improve your presentation skills, closing statement.

Effective communication is the answer to reaching business and academic goals. The scenarios in which we can be required to deliver a presentation are as diverse as one can imagine. Still, some core concepts apply to all presentations.

 We define presentation skills as a compendium of soft skills that directly affect your presentation performance and contribute to creating a great presentation. These are not qualities acquired by birth but skills you ought to train and master to delve into professional environments.

You may ask: is it really that evident when a presenter is not prepared? Here are some common signs people can experience during presentations:

• Evasive body language: Not making eye contact with the audience, arms closed tightly to the body, hands in pockets all the time.
• Lack of interest in the presenter’s voice: dull tone, not putting an effort to articulate the topics.
• Doubting when asked to answer a question
• Irksome mood

The list can go on about common presenter mistakes , and most certainly, it will affect the performance of any presented data if the lack of interest by the presenter is blatantly obvious.  Another element to consider is anxiety, and according to research by the National Institute of Mental Health, 73% of the population in the USA is affected by glossophobia , which is the fear of public speaking, judgment, or negative evaluation by other people.

Therefore, presentation skills training is essential for any business professional who wants to achieve effective communication . It will remove the anxiety from presentation performance and help users effectively deliver their message and connect with the audience.

Archetypes of presentations

Persuasive presentations aim to convince the audience – often in short periods – to acquire a product or service, adhere to a cause, or invest in a company. For business entrepreneurs or politicians, persuasive presentations are their tool for the trade.

Unless you aim to be perceived as an imposter, a proper persuasive presentation has the elements of facts, empathy, and logic, balanced under a well-crafted narrative. The central pillar of these presentations is to identify the single factor that gathered your audience: it could be a market need, a social cause, or a revolutionary concept for today’s society. It has to be something with enough power to gather critiques – both good and bad.

That single factor has to be backed up by facts. Research that builds your hypothesis on how to solve that problem. A deep understanding of the target audience’s needs , concerns, and social position regarding the solution your means can offer. When those elements are in place, building a pitch becomes an easy task. 

Graphics can help you introduce information in a compelling format, lowering the need for lengthy presentations. Good presentation skills for persuasive presentations go by the hand of filtering relevant data and creating the visual cues that resonate with what your audience demands.

One powerful example of a persuasive presentation is the technique known as the elevator pitch . You must introduce your idea or product convincingly to the audience in a timeframe between 30 seconds and less than 2 minutes. You have to expose:

• What do you do 
• What’s the problem to solve
• Why is your solution different from others 
• Why should the audience care about your expertise

For that very purpose, using engaging graphics with contrasting colors elevates the potential power of your message. It speaks professionalism, care for details, and out-of-the-box thinking. Knowing how to end a presentation is also critical, as your CTAs should be placed with care.

Therefore, let’s resume the requirements of persuasive presentations in terms of good presentation skills:

• Identifying problems and needs
• Elaborating “the hook” (the element that grabs the audience’s attention)
• Knowing how to “tie” your audience (introducing a piece of information related to the hook that causes an emotional impact)
• Broad knowledge of body language and hand gestures to quickly convey your message
• Being prepared to argue a defense of your point of view
• Handling rejection
• Having a proactive attitude to convert opportunities into new projects
• Using humor, surprise, or personal anecdotes as elements to sympathize with the audience
• Having confidence
• Be able to summarize facts and information in visually appealing ways

You can learn more about persuasive presentation techniques by clicking here .

In the case of instructional presentations, we ought to differentiate two distinctive types:

• Lecture Presentations : Presentations being held at universities or any other educative institution. Those presentations cover, topic by topic, and the contents of a syllabus and are created by the team of teachers in charge of the course.
• Training Presentations : These presentations take place during in-company training sessions and usually comprise a good amount of content that is resumed into easy-to-take solutions. They are aimed to coach employees over certain topics relevant to their work performance. The 70-20-10 Model is frequently used to address these training situations.

Lecture presentations appeal to the gradual introduction of complex concepts, following a structure set in the course’s syllabus. These presentations often have a similar aesthetic as a group of professors or researchers created to share their knowledge about a topic. Personal experience does tell that course presentations often rely on factual data, adequately documented, and on the theoretical side.

An example of a presentation that lies under this concept is a Syllabus Presentation, used by the teaching team to introduce the subject to new students, evaluation methods, concepts to be learned, and expectations to pass the course.

On the other hand, training presentations are slide decks designed to meet an organization’s specific needs in the formal education of their personnel. Commonly known as “continuous education,” plenty of companies invest resources in coaching their employees to achieve higher performance results. These presentations have the trademark of being concise since their idea is to introduce the concepts that shall be applied in practice sessions. 

Ideally, the training presentations are introduced with little text and easy-to-recognize visual cues. Since the idea is to summarize as much as possible, these are visually appealing for the audience. They must be dynamic enough to allow the presenter to convey the message.

Those key takeaways remind employees when they revisit their learning resources and allow them to ruminate on questions that fellow workers raise. 

To sum up this point, building presentation skills for instructional presentations requires:

• Ability to put complex concepts into simpler words
• Patience and a constant learning mindset
• Voice training to deliver lengthy speeches without being too dense
• Ability to summarize points and note the key takeaways
• Empathizing with the audience to understand their challenges in the learning process

The informative presentations take place in business situations, such as when to present project reports from different departments to the management. Another potential usage of these presentations is in SCRUM or other Agile methodologies, when a sprint is completed, to discuss the advance of the project with the Product Owner.

As they are presentations heavily dependent on data insights, it’s common to see the usage of infographics and charts to express usually dense data in simpler terms and easy to remember. 

Informative presentations don’t just fall into the business category. Ph.D. Dissertation and Thesis presentations are topics that belong to the informative presentations category as they condense countless research hours into manageable reports for the academic jury. 

Since these informational presentations can be perceived as lengthy and data-filled, it is important to learn the following professional presentation skills:

• Attention to detail
• Be able to explain complex information in simpler terms
• Creative thinking
• Powerful diction
• Working on pauses and transitions
• Pacing the presentation, so not too much information is divulged per slide

The leading inspirational platform, TEDx, comes to mind when talking about inspirational presentations. This presentation format has the peculiarity of maximizing the engagement with the audience to divulge a message, and due to that, it has specific requirements any presenter must meet.

This presentation format usually involves a speaker on a stage, either sitting or better standing, in which the presenter engages with the audience with a storytelling format about a life experience, a job done that provided a remarkable improvement for society, etc.

Empathizing with the audience is the key ingredient for these inspirational presentations. Still, creativity is what shapes the outcome of your performance as people are constantly looking for different experiences – not the same recipe rephrased with personal touches. The human factor is what matters here, way above data and research. What has your experience to offer to others? How can it motivate another human being to pursue a similar path or discover their true calling?

To achieve success in terms of communication skills presentation, these inspirational presentations have the following requirements:

• Focus on the audience (engage, consider their interests, and make them a part of your story)
• Putting ego aside
• Creative communication skills
• Storytelling skills
• Body language knowledge to apply the correct gestures to accompany your story
• Voice training
• Using powerful words

After discussing the different kinds of presentations we can come across at any stage of our lives, a group of presentation skills is standard in any type of presentation. See below what makes a good presentation and which skills you must count on to succeed as a presenter.

Punctuality

Punctuality is a crucial aspect of giving an effective presentation. Nothing says more about respect for your audience and the organization you represent than delivering the presentation on time . Arriving last minute puts pressure on the tech team behind audiovisuals, as they don’t have enough preparation to test microphones, stage lights, and projector settings, which can lead to a less powerful presentation Even when discussing presentations hosted in small rooms for a reduced audience, testing the equipment becomes essential for an effective presentation.

A solution for this is to arrive at least 30 minutes early. Ideally, one hour is a sweet spot since the AV crew has time to check the gear and requirements for your presentation. Another benefit of this, for example, in inspirational presentations, is measuring the previous presenter’s impact on the audience. This gives insights about how to resonate with the public, and their interest, and how to accommodate your presentation for maximum impact.

Body Language

Our bodies can make emotions transparent for others, even when we are unaware of such a fact. Proper training for body language skills reduces performance anxiety, giving the audience a sense of expertise about the presented topic. 

Give your presentation and the audience the respect they deserve by watching over these potential mistakes:

• Turning your back to the audience for extended periods : It’s okay to do so when introducing an important piece of information or explaining a graph, but it is considered rude to give your back to the audience constantly.
• Fidgeting : We are all nervous in the presence of strangers, even more, if we are the center of attention for that moment. Instead of playing with your hair or making weird hand gestures, take a deep breath to center yourself before the presentation and remember that everything you could do to prepare is already done. Trust your instincts and give your best.
• Intense eye contact : Have you watched a video where the presenter stared at the camera the entire time? That’s the feeling you transmit to spectators through intense eye contact. It’s a practice often used by politicians to persuade.
• Swearing : This is a no-brainer. Even when you see influencers swearing on camera or in podcasts or live presentations, it is considered an informal and lousy practice for business and academic situations. If you have a habit to break when it comes to this point, find the humor in these situations and replace your swear words with funny alternatives (if the presentation allows for it). 

Voice Tone plays a crucial role in delivering effective presentations and knowing how to give a good presentation. Your voice is a powerful tool for exposing your ideas and feelings . Your voice can articulate the message you are telling, briefing the audience if you feel excited about what you are sharing or, in contrast, if you feel the presentation is a burden you ought to complete.

Remember, passion is a primary ingredient in convincing people. Therefore, transmitting such passion with a vibrant voice may help gather potential business partners’ interest.  

But what if you feel sick prior to the presentation? If, by chance, your throat is sore minutes before setting foot on the stage, try this: when introducing yourself, mention that you are feeling a bit under the weather. This resonates with the audience to pay more attention to your efforts. In case you don’t feel comfortable about that, ask the organizers for a cup of tea, as it will settle your throat and relax your nerves.

Tech Skills

Believe it or not, people still feel challenged by technology these days. Maybe that’s the reason why presentation giants like Tony Robbins opt not to use PowerPoint presentations . The reality is that there are plenty of elements involved in a presentation that can go wrong from the tech side:

• A PDF not opening
• Saving your presentation in a too-recent PowerPoint version
• A computer not booting up
• Mac laptops and their never-ending compatibility nightmare
• Not knowing how to change between slides
• Not knowing how to use a laser pointer
• Internet not working
• Audio not working

We can come up with a pretty long list of potential tech pitfalls, and yet more than half of them fall in presenters not being knowledgeable about technology.

If computers aren’t your thing, let the organization know about this beforehand. There is always a crew member available to help presenters switch between slides or configure the presentation for streaming. This takes the pressure off your shoulders, allowing you to concentrate on the content to present. Remember, even Bill Gates can get a BSOD during a presentation .

Presentations, while valuable for conveying information and ideas, can be daunting for many individuals. Here are some common difficulties people encounter when giving presentations:

Public Speaking Anxiety

Glossophobia, the fear of public speaking, affects a significant portion of the population. This anxiety can lead to nervousness, trembling, and forgetfulness during a presentation.

Lack of Confidence

Many presenters struggle with self-doubt, fearing that they may not be knowledgeable or skilled enough to engage their audience effectively.

Content Organization

Organizing information in a coherent and engaging manner can be challenging. Presenters often grapple with how to structure their content to make it easily digestible for the audience. Artificial Intelligence can help us significantly reduce the content arrangement time when you work with tools like our AI Presentation Maker (made for presenters by experts in presentation design). 

Audience Engagement

Keeping the audience’s attention and interest throughout the presentation can be difficult. Distractions, disengaged attendees, or lack of interaction can pose challenges.

Technical Issues

Technology glitches, such as malfunctioning equipment, incompatible file formats, or poor internet connectivity, can disrupt presentations and increase stress.

Time Management

Striking the right balance between providing enough information and staying within time limits is a common challenge. Going over or under the allotted time can affect the effectiveness of the presentation.

Handling Questions and Challenges

Responding to unexpected questions, criticism, or challenges from the audience can be difficult, especially when presenters are unprepared or lack confidence in their subject matter.

Visual Aids and Technology

Creating and effectively using visual aids like slides or multimedia can be a struggle for some presenters. Technical competence is essential in this aspect.

Language and Articulation

Poor language skills or unclear articulation can hinder effective communication. Presenters may worry about stumbling over words or failing to convey their message clearly.

Maintaining appropriate and confident body language can be challenging. Avoiding nervous habits, maintaining eye contact, and using gestures effectively requires practice.

Overcoming Impersonal Delivery

In virtual presentations, maintaining a personal connection with the audience can be difficult. The absence of face-to-face interaction can make it challenging to engage and read the audience.

Cultural and Diversity Awareness

Presenting to diverse audiences requires sensitivity to cultural differences and varying levels of familiarity with the topic.

In this section, we gathered some tips on how to improve presentation skills that can certainly make an impact if applied to your presentation skills. We believe these skills can be cultivated to transform into habits for your work routine.

Tip #1: Build a narrative

One memorable way to guarantee presentation success is by writing a story of all the points you desire to cover. This statement is based on the logic behind storytelling and its power to connect with people .

Don’t waste time memorizing slides or reading your presentation to the audience. It feels unnatural, and any question that diverts from the topic in discussion certainly puts you in jeopardy or, worse, exposes you as a fraud in the eyes of the audience. And before you ask, it is really evident when a presenter has a memorized speech. 

Build and rehearse the presentation as if telling a story to a group of interested people. Lower the language barrier by avoiding complex terms that maybe even you aren’t fully aware of their meaning. Consider the ramifications of that story, what it could lead to, and which are the opportunities to explore. Then, visualize yourself giving the presentation in a natural way.

Applying this technique makes the presentation feel like second nature to you. It broadens the spectrum in which you can show expertise over a topic or even build the basis for new interesting points of view about the project.

Tip #2: Don’t talk for more than 3 minutes per slide

It is a common practice of presenters to bombard the audience with facts and information whilst retaining the same slide on the screen. Why can this happen? It could be because the presenter condensed the talk into very few slides and preferred to talk. The reality is that your spectators won’t retain the information you are giving unless you give visual cues to help that process. 

Opt to prepare more slides and pace your speech to match the topics shown on each slide. Don’t spend more than 3 minutes per slide unless you have to introduce a complex piece of data. Use visual cues to direct the spectators about what you talk about, and summarize the principal concepts discussed at the end of each section.

Tip #3: Practice meditation daily

Anxiety is the number one enemy of professional presenters. It slowly builds without you being aware of your doubts and can hinder your performance in multiple ways: making you feel paralyzed, fidgeting, making you forget language skills or concepts, affecting your health, etc.

Meditation is an ancient practice taken from Buddhist teachings that train your mind to be here in the present. We often see the concepts of meditation and mindfulness as synonyms, whereas you should be aware that meditation is a practice that sets the blocks to reach a state of mindfulness. For presenters, being in the here and now is essential to retain focus, but meditation techniques also teach us to control our breathing and be in touch with our body signals when stress builds up. 

The customary practice of meditation has an impact on imagination and creativity but also helps to build patience – a skill much needed for connecting with your audience in instructional presentations.

Having the proper set of presentation skills can be quite subjective. It goes beyond presentation tips and deepens into how flexible we can be in our ability to communicate ideas.

Different presentations and different audiences shape the outcome of our efforts. Therefore, having a basic understanding of how to connect, raise awareness, and empathize with people can be key ingredients for your career as a presenter. A word of advice: success doesn’t happen overnight. It takes dedication and patience to build communication skills . Don’t condition your work to believe you will be ready “someday”; it’s best to practice and experience failure as part of the learning process.

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What It Takes to Give a Great Presentation

• Carmine Gallo

Five tips to set yourself apart.

Never underestimate the power of great communication. It can help you land the job of your dreams, attract investors to back your idea, or elevate your stature within your organization. But while there are plenty of good speakers in the world, you can set yourself apart out by being the person who can deliver something great over and over. Here are a few tips for business professionals who want to move from being good speakers to great ones: be concise (the fewer words, the better); never use bullet points (photos and images paired together are more memorable); don’t underestimate the power of your voice (raise and lower it for emphasis); give your audience something extra (unexpected moments will grab their attention); rehearse (the best speakers are the best because they practice — a lot).

I was sitting across the table from a Silicon Valley CEO who had pioneered a technology that touches many of our lives — the flash memory that stores data on smartphones, digital cameras, and computers. He was a frequent guest on CNBC and had been delivering business presentations for at least 20 years before we met. And yet, the CEO wanted to sharpen his public speaking skills.

• Carmine Gallo is a Harvard University instructor, keynote speaker, and author of 10 books translated into 40 languages. Gallo is the author of The Bezos Blueprint: Communication Secrets of the World’s Greatest Salesman  (St. Martin’s Press).

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Effective Communication Skills

Aug 04, 2014

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Effective Communication Skills. Objectives. Define and understand communication and the communication process List and overcome the filters/barriers in a communication process Practice active listening Tips to improve verbal and non verbal communication.

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Objectives • Define and understand communication and the communication process • List and overcome the filters/barriers in a communication process • Practice active listening • Tips to improve verbal and non verbal communication

What are the most common ways we communicate? Visual Images Spoken Word Body Language Written Word

What is Communication? Communication is the transmission of an idea or feeling so that the sender and receiver share the same understanding. Communication is not a mysterious process.It takes place when the ideas from your mind are transferred to another’s and arrive intact, complete, and coherent.

The Communication Process Medium Barrier Barrier SENDER (encodes) RECEIVER (decodes) Feedback/Response

Types of Communication(One-way)

Types of Communication(Two-way)

Types of Communication(One to Many)

Components of Communication Verbal Communication Non-verbal communication Vocal communication

Features of Effective Communication • Active Listening • Eye contact • Posture • Simple language • Questioning skills

Benefits of effective communication • Quicker problem solving • Better decision making • Steady work flow • Strong business relations • Better professional image

Barriers to communication Cultural differences Noise Poor listening skills Inappropriate medium Assumptions/Misconceptions Use of jargon Emotions Distractions Language differences

Hearing Vs Listening Hearing – Physical process, natural, passive Listening – Physical as well as mental process, active, learned process, a skill Listening is hard. You must choose to participate in the process of listening.

Steps in the listening process You first respond emotionally, then intellectually. Then you decide how to respond. Your knowledge, attitudes, values, beliefs and self-concept influences your perception. Responding Your reaction to the message. It can be emotional and intellectual. Your own needs, interests, attitudes, and knowledge affects your choice to pay attention. Understanding Deciding what the message means to you. Not everyone hears the same way. Men actually prefer certain frequencies. Choosing The act of choosing to focus attention on the message. Hearing The reception of sound. Definition of listening: It is a physical and psychological process that involves choosing to listen, understanding, and respondingto symbolic messages from others.

BLOCKS TO LISTENING rehearsing being right mind reading judging comparing derailing identifying filtering sparring placating dreaming

Comparing constantly comparing yourself to the other person, checking to see if you measure up in terms of intelligence, wit, emotional stability, competence, or even level of suffering or children's achievements. • Mind Reading what you think someone "really means" (based primarily on your own feelings, assumptions, or hunches) than to what he or she is actually saying. • Rehearsing planning your response to what someone is saying to you while the other person is still speaking. • Filtering tuning out certain topics or you may hear only certain things and tune everything else out,any possible hint of unhappiness, no matter what he actually says.

Judging if you decide ahead of time that the other person is not worth hearing (because he or she is "stupid," "crazy,"'"hypocritical," or " immature"), and that you will therefore listen only in order to confirm your opinion. • Dreaming you pay only a fraction of your attention to the person talking; inside, your thoughts are wandering elsewhere. • identifying whatever you hear from the other person triggers memories of your own similar experiences, and you can't wait to.jump into your own story. • Advising Jumping in with advice when the other person has barely stopped talking (or before)

Sparring If you listen only long enough to find something to disagree with, and then assert your position-—regardless of what the other person says. • Being Right If you want to prove that you're right or to avoid the suggestion that you're wrong— including lying, shouting, twisting the facts, changing the subject, making excuses, and accusing • Derailing changing the subject or make a joke whenever you become bored or uncomfortable with the conversation. • Placating being so concerned with being nice, agreeable, or liked that without really listening you agree with everything being said.

Active Listening The process of recognizing, understanding, and accurately interpreting communicated messages and responding to spoken and/or nonverbal messages.

Tips to become a better listener • Don’t talk – listen. • Don’t jump to conclusions. • Listen between the lines. • Ask questions/paraphrase. • Don’t get distracted by the environment. • Keep an open mind. • Be willing to listen to someone else’s point of view and ideas. • Provide feedback. • Take advantage of your brain power.

Improving Verbal Communication-Tips • Eliminate Noise • Get Feedback – Verbal & Body Signals • Speak Slowly & Rephrase your sentence • Don’t Talk down to the other person • Listen Carefully & Patiently

Improving Body Language - Tips • Keep appropriate distance • Touch only when appropriate • Take care of your appearance • Be aware - people may give false cues • Maintain eye contact • Smile genuinely

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

• “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
• “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
• “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

McKinsey Live Event: Unlocking the full value of gen AI

Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

• data scientist:
• prompt engineering
• in-context learning
• bias detection
• pattern identification
• reinforcement learning from human feedback
• hyperparameter/large language model fine-tuning; transfer learning
• data engineer:
• data wrangling and data warehousing
• data pipeline construction
• multimodal processing
• vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

• Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
• Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
• Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

• Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
• Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
• Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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