08

Sep

The 12 Multi-Agent AI Frameworks I’d Actually Build On in 2026 (And 3 I’d Avoid)

Five agents coordinated by an orchestrator over a shared state bus, with one deprecated path drawn as a broken amber line

Why this one is here

Most of what I write on this blog is about strategy. This post gets closer to the workbench, so it’s worth saying why.

Building AI systems is my day job. At Omnilogic Labs we design and run AI automation for banks and enterprises, and we do it for clients on a regular basis. That repetition is the part that matters for this post. When you build the same class of system over and over, for companies with real deadlines and real money at stake, you find out fairly quickly which tools hold up and which ones only look good in a tutorial. You don’t learn that from documentation. You learn it on the third client project, when the thing you picked turns out to be the wrong choice and you have to explain why.

What follows is that accumulated view, written down.

First, what a multi-agent framework actually is

If you’ve used ChatGPT, you’ve used a single AI agent. You ask, it answers, the conversation ends. You are the one deciding what happens next.

A multi-agent system is what you get when you stop asking one AI to do everything and instead hand narrow jobs to several of them, letting them pass work to each other. Think of a small agency rather than a single freelancer. One agent researches. Another writes a draft. A third checks that draft against the research and sends it back if it doesn’t hold up. Something has to decide when the work is actually finished.

A framework is the plumbing that makes that possible. How do agents hand work between them? What happens when one of them fails halfway through a job? How does the system remember what it was doing? And when something goes wrong at 3 a.m., how do you see inside it?

That last question is where the real cost sits. Getting two AI agents to talk to each other is a weekend project. Getting them to keep doing it for months, unattended, without producing nonsense, is the actual work. The framework you choose decides how hard that is.

Which is why picking the right one matters, and why a lot of the advice out there is wrong.

The state of things, as of this month

Most of the framework roundups on this topic are quoting GitHub star counts that are three to five times too low, still recommend a project Microsoft moved into maintenance mode, and point people at a repository that’s been archived. I know, because this morning I pulled every number in this post from the GitHub API instead of copying it from somebody else’s article.

That sounds like a small thing. It isn’t. If you’re choosing what to build your company’s agent stack on, the difference between “actively developed” and “archived, community-managed, no new features” is the difference between a two-year investment and a rewrite.

So this is the honest version, checked on 8 September 2026 by someone who has to live with these decisions rather than just write about them.

How I picked these

I’ll be straight about my criteria, because most listicles won’t tell you theirs.

Is it alive? Not stars. Stars are a popularity lag indicator and they never go down, which makes them close to useless for judging whether a project is healthy. I checked the archived flag, the date of the last commit, and what each project’s own README says about its status. Two of the most-recommended frameworks failed this test.

Would I put it in front of a client? I’ve spent 20 years shipping software into large organizations. A framework that’s delightful in a notebook and undebuggable at 2 a.m. is not a framework I can recommend to somebody whose quarter depends on it.

Does it solve a real coordination problem? Plenty of projects are a thin wrapper on a model API with the word “agent” in the README. The ones below actually do something about state, handoffs, or failure.

I also want to be honest about my own position. My production fleet doesn’t run on any of these. It runs on Google’s A2A protocol with direct calls to the model APIs, because when I built it the framework options were less mature than they are now. Take that as context rather than advice: I’ve already paid for the parts they abstract away, so I know exactly what each one is saving you.

Bar chart of 15 multi-agent frameworks by GitHub stars, color-coded by project health: actively developed, maintenance mode, or archived

The twelve worth your time

1. LangGraph (41,226 stars)

The one I’d reach for when the work has to survive contact with reality. LangGraph models your system as a graph with explicit state, which sounds academic until the first time a run dies halfway through and you need to resume it without redoing the expensive half.

Its checkpointing is the feature nobody appreciates until they need it. Human-in-the-loop approval steps are a first-class concept rather than something you bolt on. The tradeoff is that you have to think about your state shape up front, and people coming from simpler tools find that annoying for about a week.

Best for: long-running workflows, approval gates, anything where a crash can’t mean starting over.

2. CrewAI (58,224 stars)

The fastest way to get from an idea to something running. You describe agents as roles with goals, hand them tasks, and it works. You can have a usable prototype running in a morning.

The role metaphor is also its ceiling. It maps beautifully onto “a researcher, a writer, and an editor” and it strains when your actual problem doesn’t decompose into job titles. Note that the repository moved to crewAIInc/crewAI, so older links redirect.

Best for: prototypes, and production systems whose shape really is a small team with clear jobs.

3. OpenAI Agents SDK (29,258 stars)

This is the real successor to Swarm, and if you read an article recommending Swarm, that article is out of date. Swarm was always labeled educational. This is the production version of the same ideas: handoffs between agents, guardrails, and tracing that actually helps.

If your stack is already committed to OpenAI models, starting anywhere else costs you integration work for benefits you may not need.

Best for: OpenAI-native builds that want handoffs and guardrails without much ceremony.

4. Microsoft Agent Framework (13,379 stars)

The lowest star count in this section and the one I’d bet on hardest if you’re an enterprise. This is where AutoGen went. Microsoft has stated that AutoGen is in maintenance mode and that new users should start here, with a documented migration path from AutoGen and a commitment to long-term support.

Thirteen thousand stars looks unimpressive next to AutoGen’s sixty thousand. Those sixty thousand stars are a monument to a project that stopped taking new features. Start where the work is happening.

Best for: Azure, .NET, and anywhere procurement asks who supports this.

5. Google ADK (21,451 stars)

Google’s Agent Development Kit is code-first, which I appreciate. It deploys to Agent Engine without you rewriting your agent for production, and Gemini and Vertex are wired in rather than adapted.

Same logic as the Microsoft entry. If your data already lives in Google Cloud, the framework that shortens the distance to your data usually wins over the one with a nicer API.

Best for: Google Cloud shops, and teams who want evaluation built in rather than added later.

6. Pydantic AI (19,781 stars)

The one I’d point a team to after they’ve been burned. Pydantic AI puts type validation at the boundary of every agent, which means malformed model output fails in your test suite instead of quietly corrupting something three steps downstream.

If your team already uses Pydantic, and in Python most do, the learning curve is close to zero. This is the least glamorous framework on the list and the one most likely to save you a bad week.

Best for: teams that want agents to fail loudly and early.

7. Agno (42,093 stars)

Agno has grown quickly by being fast and staying out of your way. It leans hard on performance and low overhead per agent, and it bundles memory and knowledge without requiring you to assemble four libraries first.

I’d call it the pragmatist’s pick. Less opinionated than LangGraph, more structured than rolling your own.

Best for: many lightweight agents where per-agent overhead actually shows up in your bill.

8. LlamaIndex Workflows (52,067 stars)

If your agents mostly answer questions about your own documents, start here rather than at a general orchestration framework. LlamaIndex was a retrieval system that grew agents. The ordering matters: retrieval is the hard part of that problem and it’s the part LlamaIndex has been solving longest.

Workflows added event-driven orchestration on top, so you’re no longer choosing between good retrieval and decent coordination.

Best for: document-heavy and knowledge-base agents.

9. Haystack (26,447 stars)

deepset’s framework, and the most production-minded of the retrieval-first options. Pipelines are explicit and inspectable, which makes Haystack pleasant to operate and slightly less pleasant to write. That’s a trade I’ll take on anything that has to run unattended.

Best for: search and question-answering systems that need to be auditable.

10. CAMEL-AI (17,683 stars)

Built to study how agents behave when they talk to each other, and very good at it. If you’re exploring negotiation, role-play, or emergent behavior between agents rather than shipping a product next quarter, this is a research instrument rather than a factory tool.

Best for: research, simulation, and agent-behavior work.

11. MetaGPT (70,262 stars)

The most-starred framework here, built on the idea of simulating a software company: a product manager agent, an architect, engineers. When the metaphor fits, the structured output is impressive.

One caution I’d give honestly: its last significant commit activity is older than most of this list. High stars, slowing pace. Watch it before you commit a roadmap to it.

Best for: code generation and structured multi-role output, with your eyes open about velocity.

12. AgentOps (5,811 stars)

Not an orchestration framework, and the one I’d argue hardest for you to install anyway. AgentOps is observability for agents: what each one did, what it cost, where it went wrong.

I put it last on the list and first in the build order. Every painful failure I’ve had in production was invisible before it was obvious.

Best for: every project on this page.

Decision guide matching eight common build situations to a recommended framework, with a note to add AgentOps on day one

Side by side

Framework Stars Status Learning curve Where it wins
MetaGPT 70,262 Active, slowing Medium Structured code generation
CrewAI 58,224 Active Low Speed to first prototype
LlamaIndex Workflows 52,067 Active Medium Retrieval-heavy agents
Agno 42,093 Active Low Low per-agent overhead
LangGraph 41,226 Active Medium-high Stateful, resumable workflows
OpenAI Agents SDK 29,258 Active Low OpenAI-native handoffs
Haystack 26,447 Active Medium Auditable search pipelines
Google ADK 21,451 Active Medium Google Cloud and Vertex
Pydantic AI 19,781 Active Low Type safety at the boundary
CAMEL-AI 17,683 Active Medium Agent-behavior research
Microsoft Agent Framework 13,379 Active Medium Enterprise and .NET support
AgentOps 5,811 Active Low Observability for all of the above

The three I’d avoid, and why

This is the section the other roundups don’t write, and it’s the one that will save you the most time.

Microsoft AutoGen (60,867 stars) is in maintenance mode. Its own README carries the notice: no new features, community-managed going forward, new users should start with Microsoft Agent Framework. Those sixty thousand stars will keep it at the top of search results and near the top of every listicle for another year. Don’t start a new project on it. If you already have one, Microsoft publishes a migration guide.

OpenAI Swarm (21,948 stars) was never meant for production. OpenAI describes it in its own words as an educational framework exploring lightweight multi-agent orchestration. It did its job, the ideas graduated into the Agents SDK, and that’s where you should be. Any 2026 article recommending Swarm for production was written by someone who didn’t check.

TaskWeaver (6,169 stars) is archived. The repository is flagged archived on GitHub, which means read-only, no fixes, no security patches. I still find it in “top frameworks for 2026” lists, sometimes recommended for data-intensive work. Building on an archived repository is taking on maintenance of a codebase you didn’t write and don’t understand.

Three things running these in production taught me

The framework you pick matters less than most people writing about frameworks want to admit. Here’s what actually decided whether my systems worked.

Your failure alarms matter more than your architecture. One of my pipelines stopped publishing for 47 days and I had no idea. A model I depended on had been quietly deprecated, my system kept calling something that no longer existed, and the alerting was pointed at a dead address. I found it by accident. An autonomous system without working alarms isn’t autonomous, it’s unsupervised, and no framework on this page fixes that for you.

Graceful degradation is often just shipping broken work quietly. My image pipeline was written to fall back through several providers and, if all of them failed, to publish anyway with a warning in the log. That’s textbook resilient design. In practice it meant articles going out with no image at all while the system reported success. Decide deliberately which failures should stop the line, because “keep going” is a choice with consequences.

A gate that fails open is not a gate. I had a duplicate-detection check that had been silently passing everything since the day it shipped, because of a bug in how it built its query. It ran, it logged, it reported clean, and it was doing nothing. It never crashed, so nothing ever told me. Now anything that guards quality gets a test that proves it can actually say no.

None of those three lessons are about LangGraph or CrewAI. They’re about operating autonomous systems, and they’ll be true whichever row of the table you pick.

Where this is going

The interesting movement in 2026 is happening underneath the frameworks, in the protocols. A2A for agent-to-agent communication and MCP for tool access are both being adopted across frameworks that otherwise compete, which points at a future where your orchestration choice stops being a lock-in decision.

That’s good news for anyone making this call today. Pick the one that fits the work in front of you, keep your business logic out of the framework’s abstractions where you can, and accept that you’ll probably migrate once. Everyone does.

Sources

Every star count and status in this post came from the GitHub REST API on 8 September 2026, using the stargazers_count and archived fields, plus each project’s own README for maintenance status.

Numbers move. If you’re reading this months from now, check them yourself before you decide anything. That’s the whole point of this post.

A note on how this gets made: I run my own content pipeline, the same kind of system I build for clients. It drafts, I edit and fact-check every line, and I sign it. Claiming expertise in production AI and then hiding that I use it would be a strange way to make the point.

— Juan

1 week ago Short URL Comments

Artificial Intelligence

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30

Sep

Balancing AI Regulation: A Path to Innovation

We’re at a pivotal moment. Decisions made today will shape our future with AI.

Artificial Intelligence is no longer a distant concept; it’s here, influencing everything from healthcare to finance. But with its rapid advancement comes a critical question: How do we harness its potential without unleashing unintended consequences?

Why is this crucial?

Because over-regulation could stifle innovation, preventing breakthroughs that could benefit society. Conversely, insufficient regulation might lead to ethical pitfalls, privacy breaches, and a loss of public trust in technology.

So, what’s the path forward?

A balanced approach to AI regulation.

Here’s how we can achieve it:

• Inclusive Dialogue: Engage technologists, policymakers, and the public in conversations about AI’s role.

• Adaptive Policies: Create flexible regulations that can evolve with technological advancements.

• Ethical Frameworks: Implement guidelines that prioritize transparency, fairness, and accountability.

• Investment in Education: Equip the workforce with the skills needed to thrive alongside AI.

By finding the middle ground, we can foster innovation while safeguarding societal values.

Let’s work together to shape an AI-powered future that’s both exciting and responsible.

What are your thoughts on achieving this balance?

2 years ago Short URL Comments

Artificial Intelligence/Innovation

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03

Dec

Impact of AI on jobs in the future

Currently and in the future, Artificial Intelligence (A.I.) is expected to affect the way we get the news, the ads we received, the things we buy, our transportation system, the jobs we have or may not have any more, our education system, our appliances, and our health. While many of these things are great news, however, we may have to be aware and ready for a rapidly changing world and job security.

flight technology tools astronaut

Photo by Pixabay on Pexels.com

Our news media rely heavily on a social network to distribute the news and also rely on automation of trends, market derivatives, algorithms, analytics to decide which news are presented in the network prime time hours. While we have always relied on data to make decisions, what if these data are manipulated or corrupted using pesky algorithms. Which was the case during the last U.S. election. Many data were manipulated using fake news websites and pesky algorithms. As this news with shocking headlines started to trend on social media, this disinformation would be picked for its popular response and end up on the major news network, and it would take these news network days before correcting them. As a result, studies showed that many voters were influenced or suppressed by fake news. Another trend of Artificial Intelligence is how advertisement is delivered to individuals using personal information collected by the browsers that you used and email accounts.

All the information or communications that you send or share using social media are collected to create smart ads. Eventually, all ads that anyone will see online on T.V. will be tied to their purchase and web activities. These ads will know the clothes that we want to buy and predict the trips or vacations that we want to take or direct us to our weekend activities.  These data used by smart ads are considered as predictors and are used in rudiment machine intelligence that can distribute contents online. On social media it can be the suggested friends, pages, news and other info; in the market, visual advertisement will showcase the foods that they think we want to buy and where they are located, GPS already pre-program your daily trips of the places we usually go or will want to go, the streets we drive on to go to work and school, or anywhere else.

Basically, what we human have been seeking are automations in our lives to make things easier, and once you tie these automations with machine who can do the actual work, that is even better. The first automations in our lives are currently seeing on the internet in the form of research results of our search engine, the GPS mapping, the algorithm results of our newsfeed or trends report. However, these automations will evolve into A.I. Through information gathering, how far are we in that evolution? How it had affected or will affect our jobs in healthcare industry, education, transportation, service industry and auto-industry based on data reviewed through multiple interviews by experts in this matter, university reports and other documents and articles. Some have a doom’s day prediction, and others believe there are the way to prevent a total takeover of A.I. in our lives, while others believe that it will be for the best. For example, according to a PWC study, 38% of U.S. jobs could be lost to automation in the next 15 years. However, the first solution is to know that A.I. is already there and we will get stronger going forward. The first step is to know which jobs will be affected by it and how. In addition, if you just look at what projects major companies are currently working on right now, we can have a clear picture of what jobs will be directly affected incrementally or immediately. These projects are: self-driving truck (Mercedes), self-driving cabs (Uber, Lyft). Ordering kiosk currently in use in some companies (Mc Donald, Burger King), robot servers (McDonald), Ordering and transaction cart (Publix, Walmart), mail delivery drones (Amazon), self-driving planes, drones (Boeing, Lockheed Marten), Automatic Teller system (Citibank, Bank of America, CHASE), Automated 3D Printers, Rudimentary Machine Intelligence (Facebook, Google), Deep Mind (Facebook, IBM, Google). These are just a few developments in the technological pipeline that would eventually make some current jobs more effective or obsolete; Our goals is to find out how.

Transportation

Artificial Intelligence projects that will impact our transportation system in the future are many: self-driving cars, self-driving trucks, GPS control system, and deepmind. One of the most advanced projects is the alliance of Mercedes with Udacity using their advanced A.I. software name nanoDegrees to create a self-driving truck. Udacity has also teamed up with Uber to come up with a self-driving taxi. Both projects are currently on trial stage. Upon full automation or development, these projects might affect 3.4 million jobs. The main purpose for these projects is the avoidance of human errors like truck drivers sleeping during a long trip and they expect the self-driving cars to be more effective on time and delivery. So far, all test show that they might need a driving monitor, especially in the truck trials (S1).  According to the U.S. Bureau of Labor Statistics, the jobs at risk are 2.4 million truck and delivery drivers, 180,000 taxi drivers, 160,000 Uber drivers, 500,000 school bus drivers, and 160,000 transit bus drivers. For this technology to function well it will need the assistance of other AI technology like the GPS, Deepmind, Robotic features, and Cognitive analysis. Currently, a car can self-park, summon, and stay on auto drive. The problems to resolve are test-driving car accident due to missing yellow light, resolving issues with aggressive drivers (S 7, 8). Other projects by other companies that are also in the trial stage:

  1. Self-driving cars Google (S 1,8) (Google car has driven on its own for 1,5 million miles, only 300 thousand miles were without an accident)
  2. Self-driving cars Apple (S 1,8)(no data)
  3. Self-driving cars Tesla (S 1,8) (no data)

The predictions and case study published by the White House in 2016 and conducted by the Council Economic Adviser (CEA) estimated that 2.2 to 3.1 million current part- and full-time U.S. jobs may be endangered or greatly changed by Automated Vehicle technology. Essentially, CEA also confirmed that this does not calculate the types of new jobs that may be created—but rather a calculation of existing jobs that are likely to be affected by AI-enabled AV technology. A second warning is that this technology may take years or decades to happen because there will be a delay especially for safety requirements between technological possibility and widespread adoption. There will also be a delay of adaptation or acceptance by the public.

However, as reported in the journal Artificial Intelligence, Employment and Income by Nils J. Nilsson, new infrastructures will also be needed in order to accommodate these transformative changes in the transportation industry, which in turn will result in creating new jobs.

Automobile Industry

There are two A.I. technologies who have already affected the auto industry, robotic technology, and 3D printing. From the first robot used by GM in 60’s, 70,000 robots are currently in use in the United States. Auto manufacturing jobs have gone from 1.1 million to 532,000 (S 13, 8). Another technology affecting the auto industry is 3D printing that is currently in use to print auto parts (S 14, 8), however, it is projected that by combining robotic technology with 3D printing the auto industry will be again completely transformed as far as the labor force is concerned.

Construction Industry

Another industry that is affected greatly by 3D printing is construction. Companies all over the world are already showcasing their construction work using 3D printing. Each company has developed its own technology: from WATG who used carbon fibers and plastic to construct a freeform structure, CO2NCRETE who collects carbon dioxide from the air and turn it into concrete print stock, and Emerging Object who 3D print bricks. These technologies are already in use. It is estimated that 12 million will be affected by these new technologies, jobs such as welder, masons, and construction materials shop and warehouses (S 15,8).

Healthcare

Healthcare workers will have many AI technologies in their crosshair either as tools or as replacement workers: 3D Printing/ bio-printing that are currently in use, robotic assistant currently in use, and deepmind already in use by many hospitals around the World.  36,000,000 can be affected by these new advancements.

3D printed implants and prosthetics are already in use and can be printed at the recovery center. It is not clear what jobs will be affected except the manufacturing companies who used to build these prosthetics and implants. However, manufacturing jobs will be created to build these printers. Bioprinting is currently under development and will be able to print organs for transplants on the spot at the clinic or hospitals.

Medical and pharmaceutical companies are using robotic technology as a mean for drug delivery, and operation. Robotics already created and sold robotic that can assist a doctor in operation rooms. Johnson and Johnson’s Sedasys system received FDA approval to provide anesthesia for standard procedures like colonoscopies. A doctor managing multiple machines at once can reduce the medical cost instead of having a dedicated human anesthesiologist. Many robots are in various stages of testing and approval for diagnosing disease. For example, IBM’s Watson demonstrated a higher rate of accuracy for diagnoses than human doctors.

When it comes to services all technologies are welcomed: Internet of Things (IOT) already in use, deepmind in use, algorithm, smart ads are been utilized by retail companies, finance and banking services. This industry employs more individuals than any other industry, 126,000,000 as of March 2017.  As a result, all tech companies that are involved in an A.I projects, Google, Facebook, IBM, Microsoft are very much vested in DeepMind and already in use. Currently, most phone calls inquiries made to any major companies are handled by artificial intelligence or automatic phone response system that is becoming more efficient by using voice responses instead of key responses. DigitalGenius, for example, has created an automated customer service which enables companies to automate basic questions and answers, and even chats with customers by harnessing natural language processing and machine learning to create reactions. Robots can now impersonate human speech patterns to provide service that is fast and easy to consumers, and very inexpensively for companies.

From Finance to shopping, the service industry is experiencing a rapidly increasing amount of data. Some financial services companies are turning to artificial intelligence to keep up with demand. Robots are using predictive systems and market data to forecast stock trends and manage finances. Financial advice is becoming automated, with a growing trend towards “roboadvisers”” that automatically dispense advice and suggestions to financial clients, especially those with relatively simple financial problems. Robots is using a variety of algorithms to provide recommendations that best meet clients’ spending, saving, and investment habits. Altogether, 174 million jobs have been or will be affected by A.I. technologies. That is more than half of the U.S. population.

Chart Analysis

Other projected data about the labor force were presented as charts by PWC, and outcome tables by the Executive Report by the White House in 2016. For example, the flip that will eventually occur from higher numbers having more workers at the high-value function than the low-value functions as it is now.

Graph1

The reason for this shift as shown in the second graphic by PWC, the labor tasks as A.I. evolves from assisted intelligence, that is now, to augmented intelligence, then to autonomous intelligence.

Graph2

The Executive reports provided by the White House and charts created by BLS, CEA, and PAAC seem to agree with the PWC forecast that highly educated or skills workers and high paying jobs will be the less vulnerable, and lowly educated workers and the least paying jobs will be the most vulnerable:

Graph3

Based on these charts provided by the Bureau of Labor Statistic, the less money a worker earns per hour, the higher the probability that their job will be replaced by automation. Similar effect will be felt by workers who have less than a high school diploma than a worker with a higher degree. However, the executive report proposed that the A.I. development and implementation be done incrementally to avoid irreversible negative impact to the labor market (S 6, 7, 8).

In conclusion, I believe that A.I., in general, should be part of every government local or national consolidated planning for the future that they put out every five to ten years. They cannot be caught off guard by new business models using A.I., and the private companies may have to also play a role in collaborating with these local businesses, national and local governments before launching projects that may have big economic impacts. Because we will not want in the future, Uber drivers attacking autonomous Uber vehicles.

Sources

  1. Nils J. Nilsson (Summer 1984) Artificial Intelligence Center SRI International Menlo Park, California 94025. ” Artificial Intelligence, Employment and Income (http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/General%20Essays/AIMag05-02-002.pdf )
  2. Report of the 2015 study panel ( September 2016) Stanford University” artificial intelligence and life in 2030 one hundred year study on artificial intelligence | (https://ai100.stanford.edu/2016-report)
  3. MICHAEL MILLS (November 3, 2015). What is artificial Intelligence (“AI”)? What is AI doing in law? Who is doing it? And where is it headed?” Artificial intelligence in law – the state of play in 2015?(https://www.legaltechnology.com/latest-news/artificial-intelligence-in-law-the-state-of-play-in-2015/)
  4. PWC A.I. Report (2017). This original research unpacks key ways AI may impact our world, delving into its implications for society, service, and management. (https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html)
  5. Cornell University A.I. Project Pipeline (2017) (http://www.cs.cornell.edu/courses/cs478/2001sp/mllinks/interesting_ai_demos_and_project.htm)
  6. Jason Furman John P. Holdren Chair, Council of Economic Advisers Director, Office of Science and Technology Policy, Cecilia Muñoz Megan Smith, Director, Domestic Policy Council U.S. Chief Technology Officer, Jeffrey Zients, Director, National Economic Council (2016). Artificial Intelligence Automation and the Economy, Executive Office of the President. (https://www.gpo.gov/fdsys/pkg/DCPD-2015CHECKLIST/pdf/DCPD-2015CHECKLIST.pdf)
  7. Cromwell Schubarth (Sep 13, 2016, Updated Sep 13, 2016) Silicon Valley Business Journal, “Udacity teams with Mercedes, others, to train selfdriving cars tech engineers” (https://www.bizjournals.com/sanjose/blog/techflash/2016/09/udacity-teams-with-mercedes-others-to-train-self.html)
  8. Bureau of Labor Statistic all labor data (bls.gov)
  9. Multiple Writer (July 1915–July 2015). The Monthly Labor Review through a century of economic transformation (https://www.bls.gov/opub/mlr/2016/article/pdf/the-monthly-laborreview-through-a-century-of-economic-pdf)
  10. James Brown (2012). The future of the economy is in STEM,” “Intro to tomorrow’s jobs” (https://www.bls.gov/careeroutlook/2014/spring/art01.pdf)
  11. Google DeepMind: What is it, how does it work and should you be scared? (http://www.techworld.com/personal–tech/google–deepmind–what–is–ithow–it–works–should–you–be–scared–3615354/)
  12. Carl Benedikt Frey and Michael A. Osborne ( September 17, 2013) THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO COMPUTERISATION? (http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_ of_Employment.pdf)
  13. Tom Ahlborn (2011). Industrial Robotics in the Automotive Industry https://www.bastiansolutions.com/blog/index.php/2015/09/17/industri al–robotics–automotive–industry/#.WRINukXyvcs
  14. Multiple authors (2017).  What is 3D printing? https://3dprinting.com/what–is–3d–printing/
  15. Nick Hall (2016. Top 10 3D printed construction innovations https://3dprintingindustry.com/news/top–10–3d–printed–constructioninnovations–83578/

References

  1. Chan Connie (2017). 5 Industries Being Most Affected By Artificial Intelligence. https://www.fowcommunity.com/blog/future–work/5industries–being–most–affected–artificial–intelligence
  2. Anna Sekaran (2016) IBM Media Relations. Industry Leaders Establish Partnership on AI Best Practices.  https://www–03.ibm.com/press/us/en/pressrelease/50668.wss
  3. John Ward (2012) International Trade Administration Journal. THE SERVICES         SECTOR:  HOW BEST TO MEASURE IT? http://trade.gov/publications/ita–newsletter/1010/services–sector–howbest–to–measure–it.asp
  4. Elizabeth Weise (2016). USA Today. Amazon just opened a grocery store without a checkout line. https://www.usatoday.com/story/tech/news/2016/12/05/amazon–gosupermarket–no–checkout–no–cashiers–artificial–intelligencesensors/94991612/ (not reviewed, not journal)
  5. AUTONOMOUS VEHICLES | SELF-DRIVING VEHICLES ENACTED LEGISLATION   (2017). http://www.ncsl.org/research/transportation/autonomous-vehicles-selfdriving-vehicles-enacted-legislation.aspx

by Schiller Ambroise

AN experienced professional with a demonstrated history of working in the non-profit industry. Skilled in Nonprofit Organizations’ capacity building, Entrepreneurship, Event Management, Public Speaking, Resources Development, and Marketing. Strong community and social services experiences with continuing studies in Criminal Justice/Law focused in Social Justice and crimes, International Law, Business Management, and Cyber Security from St. Thomas University.

Schiller Ambroise

Schiller Ambroise

You can find more about him on LinkedIn at https://www.linkedin.com/in/schiller-ambroise-98717b19/

Follow him on Twitter at https://twitter.com/aschiller2

8 years ago Short URL Comments

Innovation

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04

Sep

Strategic use of technology can grow your business; a look at the grocery retail industry

In the United States shopping for groceries (along with baseball and football) is a national pastime. Though the average household’s weekly spending on groceries continues to increase annually, these grocery dollars are now distributed amongst multiple shopping channels and the industry is highly fragmented. The problem of how to differentiate yourself from the competition and grow market share in a highly fragmented and competitive marketplace is not unique to the grocery retail industry but let me use it as an example here:

Limited-selection discount stores, Natural and Organic stores, Warehouse stores, Supercenters, Specialty stores and a growing number of Online retailers now compete with traditional supermarkets for every grocery dollar. Thus, the grocery retail industry is an extremely competitive marketplace. Price-conscientious U.S. consumers are increasingly prepared to shop multiple channels for their groceries. This multi-stop shopping should serve as a warning for the traditional grocery store operating using a traditional supermarket model and reluctant to change. Faced with the problem of growing competition, grocery retail outlets must seek out a competitive advantage to strategically differentiate themselves from the competition and make themselves relevant to the consumer.

grocery cart with item

Photo by Oleg Magni on Pexels.com

Competitive differentiation and increasing market share may be realized by employing a variety of strategic evidence-based initiatives that include, but is not limited to; strategic mergers and acquisitions to leverage economies of scale, strategic price reductions to compete on price with competitors, and the strategic use of smart technologies and automation to facilitate differentiation based on an improved customer experience.

I believe that a strategy to improve market share that is based on the strategic use of smart technologies to improve the customer experience, increase margins and drive sales may, in fact, be the most successful. The use of technology to improve the customer experience with an emphasis on convenience, personalization, transparency, and freshness may successfully differentiate your company from the competition.

We all know that shopping for groceries at the local supermarket can be a frustrating experience, but did you know that it can also be hazardous to your health? For some, supermarket shopping may be a significant cause of stress, hypertension, and cardiovascular disease. The problems, hazards, and frustrations associated with supermarket shopping, all contribute to a negative customer experience. Grocery retailers are therefore challenged to utilize technology to make the consumer experience more convenient, more personal, more enjoyable, less time consuming and even less hazardous!

What we are now observing in the grocery retail industry is a significant transformation with online giants like Amazon moving to establish a “bricks and mortar” foothold in the industry, and traditional stores such as Walmart, moving into the online digital e-commerce space to increase their market share. Though online grocery shopping still remains a small fraction of the industry, the transition to the online arena is increasing not surprisingly with both Amazon and Walmart leading the way in this online migration. Amazon has formed a strategic partnership with Google to increase their presence in the digital marketplace and is currently utilizing artificial intelligence to inform pricing and promotions and is experimenting with voice ordering technology. In this paradigm, the consumer has the option to avoid the grocery store altogether and perhaps engage in a more health-conscious activity. This alternative is currently most popular with millennials, though the convenience of online grocery shopping and associated home delivery or store pickup is also appealing to busy working parents.

Should the grocery shopper visit an actual store, then dramatic changes may be evident. Amazon recently announced that it is opening its second cashier-less Amazon Go store to the public shortly, again in Seattle, WA. This store will continue to utilize a combination of artificial intelligence and computer vision to enhance and personalize the customer experience. Amazon is currently employing the marketing mantra “No Lines, No Checkout (No, Seriously) to emphasize the convenience and stress-free nature of the checkout process and differentiate itself based on a superior consumer experience. The consumer experience is further enhanced by the widespread adoption of smart labels that satisfy the consumer’s need for transparency, whether this relates to ingredients, freshness, health, food safety or environmental responsibility and sustainability. In the background, Amazon Go’s utilization of machine learning will optimize fresh food replacement and pricing to further enhance the consumer experience (and maximize profit margins of course).

The grocery shopping experience continues to evolve as retailers embrace digital technology and social media to connect with consumers, increasing sales and growing market share. Stores fitted with Bluetooth-enabled beacons now send welcome messages, as well as targeted and personalized mobile coupons to consumers, significantly boosting in-store sales. In the future, grocery purchases made on mobile devices may be delivered by drone.

The role of the grocery retail workforce will also continue to evolve in the automation age, alongside these new smart technologies. Though in the short term, there may be some job losses a new breed of professional, capable of managing consumer experiences and technology will eventually evolve. Companies like Amazon will continue to invest in “people analytics” to optimize their workforce, maximize their staffing investments and reduce turnover. Store employees equipped with the latest digital tools will become experts, not only in product information but in providing nutritional advice, recipes and purchase recommendations, further improving the consumer experience.

Final thoughts

The use of technologies to improve the grocery shopping experience will only be possible in companies with leadership that has imagination and vision, combined with a willingness to challenge the status quo and view budgeting for technology as an investment rather than an expense. Though at present, technology implementation may only be feasible to the largest in the grocery retail hierarchy, if implemented, the transformative smart technologies used to enhance the consumer experience, may well serve as an effective solution to your problem of competitive differentiation in the highly fragment grocery retail industry moving forward.

 

By Graham Shaw, PhD

Dr Shaw is an experienced and innovative medical educator at Barry University’s School of Podiatric Medicine. He has extensive experience in medical education, pedagogy, and e-learning technologies. He has recently returned to school and is now an MBA student in the Andreas School of Business at Barry University.

Graham Shaw

Graham Shaw

You can find more about him on LinkedIn at https://www.linkedin.com/in/graham-shaw-3a615b40/

 

8 years ago Short URL Comments

Innovation/Retail

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16

Sep

Is the super hot Miami tech scene sustainable?

According to a recent article published by Fast Company, Florida ranks number one for Innovation and number three in Revenue per startup at $1.2 million.  There seems to be a consensus from the various media outlets that Miami’s high tech movement has legs.   I can attest to the veracity of the rankings and media buzz because almost every other day I learn about a new startup, technology event or product being launched either here in Miami or by a Miami based startup.

Brickell Ave

Brickell Ave

Unlike some hyper-local technology hubs, the theSouth Florida (SoFL) and in particular the Miami (MIA) tech hub has significant international influence and its ripples can be felt from as far as Buenos Aires (Argentina), Santiago (Chile),  Western Europe and of course San Francisco, California.  Much of the success can be attributed to the many local organizations doing a great job energizing and attracting talented individuals to the region.  Their hard work in conjunction with local and state leaders, educational institutions and local companies is paying off big.  I do want to mention some of the organization that should receive recognition, below in no particular order:

http://www.southfloridatech.org

http://venturehive.co/

http://refreshmiami.com/

http://sfima.com/

http://www.lift1428.com/projectlift

http://www.miamijobfest.co/

The good news is that all this attention is good for Miami.  The fact that media is labeling Miami’s Technology hub as “Silicon Beach” and social medial hash-tags like #SunshineEconomy are found frequently in twitter, has help attract some key technology events and conferences, like:

 http://tedxmiami.com/  

http://tfamericas.org/

http://fiba.net/

http://www.simemia.co/

http://webcongress.com/miami/

But I believe being able to attract great talent, foster collaboration and innovation, will only get you so far without a steady flow of capital.  To really make a significant and sustainable impact, investors must make money.  It seems traditional venture capital (VC) firms have just begun to expand their portfolios in SoFL, but most remain cautious and most continue to fund predominantly west coast startups based in “Silicon Valley”.

But not all the recent buzz has gone unnoticed, we have seen some organizations very active locally like the Knight Foundation and some non traditional organizations making inroads. Many agree that Miami already has the attention of the Latin American investors, or at least the local media is quick to point out in some of their recent publications like: “TechCrunch Disrupt Finale: Mark Zuckerberg, Marissa Mayer and .. The Latin Invasion” or “How Miami Is Filing The High-Tech Void In Latin America”.

And all this is beginning to sound very familiar, it it sounding like an excerpt from Andres Oppenheimer’s best seller book Cuentos Chinos, where he proposes a connection of between Miami’s success and the worsening economical and social situation in Latin-america.  I am not an economist, but one can easily extrapolate and conclude that if Miami’s high tech success is depending heavily in Latin-america, then the party will abruptly end by 2017 when Brazil economy decelerates and the rest of the region finds some form of equilibrium.

But independently of the outcome of Latin America and its impact on Miami, the region still must prove to the investors one thing and one thing only: it must show returns.  All this must produce success stories and demonstrate there are profits to be made.  If “Silicon Beach” is here to stay, it must produce a stable stream of IPOs (Initial Public Offerings), buyouts or any arrangement where institutional and private investors can see returns.

It takes a couple of years for the average startup to start seeing profits, this means if Miami’s high tech scene is going to be sustainable, we should be starting to hear the IPO chatter soon and hopefully thing getting ready to go no later than 2016.

So lets get the ball rolling, shall we?  What Miami based company do you think will IPO or be sold/bought first?

by Juan Meza

13 years ago Short URL 3 Comments

Innovation/Miami

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